eating, health behaviors and cognitive style by dr. lisa samuel 2010

Walden University

COLLEGE OF SOCIAL AND BEHAVIORAL SCIENCES

This is to certify that the doctoral dissertation by

Lisa Samuel

has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made.

Review Committee

Dr. Andrea Miller, Committee Chairperson, Psychology Faculty Dr. Tom Diebold, Committee Member, Psychology Faculty

Dr. Suzanne Manning, Committee Member, Psychology Faculty Dr. Peter Anderson, School Representative, Psychology Faculty

Chief Academic Officer

David Clinefelter, Ph.D.

Walden University 2010

ABSTRACT

Eating, Health Behaviors, and Cognitive Style

by

Lisa Kristine Samuel

M.B.A., University of Phoenix, 2005 B.A., Florida Metropolitan University, 1998

Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy Psychology

Walden University August 2010

ABSTRACT

Researchers have documented relationships between negative eating behaviors, such as

binge eating, and health related outcomes such as obesity. Obesity is a chronic illness

which increases the probability of developing high blood pressure, type 2 diabetes, and

heart disease. Even with increasing rates of obesity, research has remained focused upon

the treatment of obesity or behavioral weight-loss therapies rather than health behaviors

and decision making styles that may contribute to this epidemic. Using the Theory of

Planned Behavior Questionnaire, the Kirton Adaption-Innovation instrument, and the

Eating Disorders Questionnaire-6, the purpose of this study was to determine any

relationships between theory of planned behavior variables, adaption-innovation

variables, and body mass with eating behavior variables of dietary restraint (DR), eating

concern (EC), shape concern (SC), and weight concern (WC). The convenience sample

consisted of 137 participants without clinical health disorders ranging in ages 18 through

64. After first entering BMI into the model, hierarchical multiple regressions indicated

significant relationships between attitude towards overeating with DR, EC, SC, and WC;

perceived behavioral control with EC, SC, and WC; intention to manage eating behavior

with EC, SC, and WC; and BMI with SC and WC. The implications for positive social

change include a better understanding of how motivational influences can predict certain

behavioral features of eating habits and how this may have the potential to minimize the

consequences of negative eating behaviors, such as chronic diseases, that are associated

with the growing population of overweight and obese individuals in society.

Eating, Health Behaviors, and Cognitive Style

by

Lisa Kristine Samuel

M.B.A., University of Phoenix, 2005 B.A., Florida Metropolitan University, 1998

Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy Psychology

Walden University August 2010

UMI Number: 3411946

All rights reserved

INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.

In the unlikely event that the author did not send a complete manuscript

and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion.

UMI 3411946

Copyright 2010 by ProQuest LLC. All rights reserved. This edition of the work is protected against

unauthorized copying under Title 17, United States Code.

ProQuest LLC 789 East Eisenhower Parkway

P.O. Box 1346 Ann Arbor, MI 48106-1346

DEDICATION

This dissertation is dedicated to my family. To my husband and best friend, Phil,

you have inspired me, supported me, and encouraged me through this journey and I am

eternally grateful to have you in my life. To my children, Hagan and Ryland, your smiles

inspire me every day, and I hope that you will look at my journey as encouragement to

pursue and achieve your personal dreams.

ii

ACKNOWLEDGMENTS

The support, effort, and patience of many individuals have made this journey

possible. First, I would like to thank Dr. Andrea Miller, my dissertation chairperson. Her

guidance, positive support, wisdom, and clarity have been instrumental in this process

and I am sincerely grateful for the time and effort she put forth. Secondly, I would like to

thank my dissertation committee members Dr. Tom Diebold, who guided me through

every step of the methodology process with expertise and patience, Dr. Suzanne

Manning, who contributed to my doctoral learning experience with her insightful

comments on this dissertation, and Dr. Anderson, who provided clarity and precision

throughout this process. I am truly grateful to have had such a gifted team. Additionally, I

would like to thank Dr. M. J. Kirton for spending valuable time with me to discuss his

adaption-innovation theory.

I would also like to acknowledge my parents, Jack and Phyllis Finney. I would

like to thank Phyllis for her support, and without her I would not have been able to make

the many trips away from my home and my family to complete this dissertation. I also

want to thank my Dad for believing in me from the very beginning, for his affirmation

throughout this process, and for teaching me throughout my life the value of always

learning something new.

Finally, I would like to thank my husband, Dr. Philip Samuel. There are not words

to describe how much his unending support has meant to me. I thank him for the endless

hours of discussions, just listening to me, and for holding my hand through this process.

iii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. vi LIST OF FIGURES .......................................................................................................... vii CHAPTER 1: INTRODUCTION TO THE STUDY...........................................................1

Introduction ....................................................................................................................1 Summary of Literature ...................................................................................................2 Theory of Planned Behavior ..............................................................................2 Adaption-Innovation Theory ..............................................................................3 Motivational and Biological Applications to Eating Behaviors ........................4 Eating Behaviors and Binging ...........................................................................6 Problem Statement .........................................................................................................7 Nature of Study ..............................................................................................................8 Research Question .............................................................................................8 Hypotheses .........................................................................................................8 Purpose of Study ..........................................................................................................10 Operational Definitions ................................................................................................10 Assumptions, Limitations, Scope, and Delimitations of Project .................................13 Significance of Study ...................................................................................................13 Professional Application ..................................................................................14 Knowledge Generation ....................................................................................15 Positive Social Change Implications ...........................................................................16 Summary ......................................................................................................................18

CHAPTER 2: LITERATURE REVIEW ...........................................................................18

Introduction ..................................................................................................................19 Organization of Chapter ..................................................................................19 Strategy for Literature Review .........................................................................20 Content .............................................................................................................20 Qualtiative and Quantiative Methodologies ....................................................20 Binge Eating.................................................................................................................21 Eating Behaviors and Food Choices ...............................................................22 Food Selection and Binge Eating ....................................................................25 Psychological and Sociological Implications ..................................................27 Theory of Planned Behavior ........................................................................................30 Health Behaviors .............................................................................................34 Binge Behaviors ...............................................................................................35 Adaption-Innovation Theory of Problem Solving Style ..............................................36 Summary ......................................................................................................................42

CHAPTER 3: RESEARCH METHOD .............................................................................44 Organization of Chapter ...............................................................................................44

iv

Research Design and Approach ...................................................................................44 Setting and Sample ......................................................................................................45

Population and Sampling Method .................................................................. 45 Sample Size ..................................................................................................... 45 Participants and Characteristics .................................................................... 46

Instruments and Materials ............................................................................................46 Body Mass Index ............................................................................................. 47 Eating Disorder Examination Questionnaire, EDE-Q6 ................................. 48 Theory of Planned Behavior Questionnarie ................................................... 52 Kirton Adaption-Innovation Inventory ........................................................... 56 Background Data Questionnaire .....................................................................61

Data Collection and Analysis.......................................................................................61 Null Hypotheses (H0) ...................................................................................... 61 Nature of Scales .............................................................................................. 63 Protection of Participant’s Rights .................................................................. 63

Summary…… ..............................................................................................................64 CHAPTER 4: RESULTS ...................................................................................................65

Introduction ..................................................................................................................65 Data Screening and Cleaning .......................................................................................65 Assumptions and Pretest Analyses ..............................................................................66 Outliers ..................................................................................................................66 Multicollinearity, Normality, Linearity, and Homoscedasticity ............................67 Sample Characteristics .................................................................................................68 Data Analyses ..............................................................................................................69 Reliability Analysis ................................................................................................69 Descriptive Statistics ..............................................................................................70 Hierarchical Multiple Regression Analyses ..........................................................72 Primary Research Question and Hypotheses Evaluation .............................................79 Additional Findings and Observations ........................................................................81 Observed Consistencies and Inconsistencies .........................................................82 Summary ......................................................................................................................83

CHAPTER 5: DISCUSSION ............................................................................................84

Introduction and Overview of Study............................................................................84 Interpretation of Findings ............................................................................................85 Interpretation of Hierarchical Regression Analyses .............................................86 Theoretical Considerations ....................................................................................89 Implications for Positive Social Change ......................................................................93 Implications for Health Institutions .......................................................................96 Implications for Health Organizations ..................................................................97 Implications for Indviduals and Society ................................................................98 Recommendations for Action ....................................................................................100 Limitations and Recommendations for Future Study ................................................102

v

Clinically and Non-Clinically Significant Eating Behaviors ...............................103 Seasonal Eating Behaviors ..................................................................................104 Coping Strategies .................................................................................................106

Conclusion .................................................................................................................108 REFERENCES ................................................................................................................110

APPENDIX A: BODY MASS INDEX CALCULATION ..............................................128

APPENDIX B: EATING DISORDER EXAMINATION QUESTIONNAIRE .............129

APPENDIX C: THEORY OF PLANNED BEHAVIOR QUESTIONNAIRE ...............133

APPENDIX D: KIRTON ADAPTION-INNOVATION INVENTORY ........................135

APPENDIX E: BACKGROUND DATA QUESTIONNAIRE .......................................136

APPENDIX F: CONSENT FORM ..................................................................................137

CURRICULUM VITAE..……………………………………………………...…….…139

vi

LIST OF TABLES

Table 1. Correlations: IVs by IVs ..................................................................................... 67 Table 2. Demographic Characteristics of Study ............................................................... 68 Table 3. Descriptive Statistics for Variables ..................................................................... 71 Table 4. EDE-Q6 Percentile Ranks for EDE-Q6 Global and Subscale Scores ................ 72 Table 5. Summary of Hierarchical Regression Analyses for Variables Predicting Dietary Restraint ................................................................................................. 74 Table 6. Summary of Hierarchical Regression Analyses for Variables Predicting Eating Concern.................................................................................................... 75 Table 7. Summary of Hierarchical Regression Analyses for Variables Predicting Shape Concern .................................................................................................... 77 Table 8. Summary of Hierarchical Regression Analyses for Variables Predicting Weight Concern .................................................................................................. 78

vii

LIST OF FIGURES

Figure 1. Binge analysis .....................................................................................................24

Figure 2. Theory of planned behavior ................................................................................31

Figure 3. Cognitive schema ...............................................................................................37

Figure 4. Cognitive style distribution curve ......................................................................40

CHAPTER 1:

INTRODUCTION TO THE STUDY

Introduction

The process of eating food provides both biological and psychological feelings of

gratification; however, excessive eating or binge eating often results in the development

of obesity (Alonso-Alonso & Pascual-Leone, 2007). Obesity results in health disorders,

such as bulimia nervosa, anorexia nervosa, and body dysmorphia, as well as

psychological distresses (Fairburn & Brownell, 2002; Plowman, 2008). Research has

facilitated the development of treatment programs for obesity, and has assessed the

cognitive styles and personality characteristics associated with clinical eating disorders

(Kaye, Bastiani, & Moss, 1995; Treasure, Tchanturia, & Schmidt, 2005). Recently,

obesity has become the focus of research in the United States in areas such as

understanding compulsivity and impulsive behaviors with a greater focus on health

related disorders (Patte, 2006). For example, The American Obesity Association [AOA]

(2008) estimated that 64.5% of Americans are obese. This report also found obesity to be

a chronic illness which increases the probability of developing high blood pressure, type

2 diabetes, and additional heart diseases, and estimated that obesity will overtake

smoking as the leading cause of death due to health related disorders.

Cognitive processes such as thinking patterns and emotional responses have been

investigated for most clinical eating pathologies (Johansson, 2006). However, there is a

lack of research surrounding non-clinical eating disorders such as binge eating or over-

eating behaviors. Individuals overeat for a variety of reasons, such as not eating enough

2

during the day, overeating because of social situations, or eating simple carbohydrate-

laden foods as a reward or treat. These behaviors often results in obesity (Sysko, Devlin,

Walsh, Zimmerli, & Kissileff, 2007). In addition to the contributing factors of eating

excessively or improperly and having a sedentary lifestyle, factors such as a person’s

environment, individual behavior, culture, and socioeconomic status can contribute to

obesity (Center for Disease Control, 2009b). Clinical eating disorders—defined as

anorexia nervosa, bulimia nervosa, or eating disorders not otherwise specified—are

known to cause significant health problems (National Institute of Health, 2008).

However, non-clinical eating behaviors that affect average adults such as binge eating or

overeating also contribute to weight related health disorders. This study focused on a

planned eating behaviors and decision-making styles with regard to reported eating

behaviors.

Summary of Literature

A review of the literature is expanded on in chapter 2. This summary provides an

overview of the concepts of the theory of planned behavior, the adaption-innovation

theory, motivational applications to cognitive eating behaviors, and biological factors

associated with eating behaviors.

Theory of Planned Behavior

The theory of planned behavior (TPB) has been used in studies on eating

behaviors (e.g., Armitage, Conner, Loach, & Willetts, 1999). These studies have assessed

eating behaviors through self-reports and body mass indexes, and have revealed a

connection between individuals’ attitudes and classical conditioning, observational

3

learning, and social comparison (Baron, Byrne, & Branscombe, 2006). These studies

have also found a prevalence of ambivalent attitudes with regard to non-clinical eating

behaviors. Contributing to these findings, studies using the positive-incentive theory

suggest people eat because of the psychological response to feeling that food is

pleasurable (Baron et al., 2006; & Pinel, 2006). However, significant gaps exist in the

research on binge eating behaviors. Research surrounding non-purging binge eating has

focused on either treating obesity through behavioral weight-loss therapies, with little

attention on the cognitive factors associated with binge eating (DeAngelis, 2002). This

study will address this gap by using the TPB to verify applicability to eating behaviors. .

Adaption-Innovation Theory

The adaption-innovation theory, through the use of the Kirton Adaption-

Innovation inventory (KAI), measures cognitive style (Kirton, 1976). This psychometric

instrument has been developed and extensively tested and it demonstrates a relationship

between the cognitive styles of innovation and adaption (on a bipolar scale) and a

person’s preferred approach to problem solving (Hutchinson & Skinner, 2007). There are

three subscores in this psychometric instrument: sufficiency of originality, which

measures the manner in which a person generates ideas; efficiency, which measures the

concept of a person’s problem solving methods or processes; and rule/group conformity

which focuses on how style, being more or less adaptive or innovative, affects the

structures in which problem solving occurs. These subscores create an overall KAI score.

A person who scores as being more highly adaptive is more likely to make

decisions based on reliability, methodology, efficiency, and in a systematic method

4

(Kirton, 2003). A person who scores as being more highly innovative is more likely to

make decisions by addressing the situation from an undisciplined or unpredictable

manner and may make behavioral decisions differently or unexpectedly. This theory and

the associated psychometric instrument, KAI, have been applied in multiple dissertations

and research programs to measure the difference in personal style and behaviors (Kirton,

2003). For example, Saggin (1996) proposed a relationship between those suffering from

anorexia having a more adaptive cognitive decision making style and those suffering

from binge eating having a more innovative cognitive decision making style.

Understanding a person’s cognitive decision making style may help to understand

reactions to binge eating situations.

Motivational and Biological Applications to Eating Behaviors

Understanding motivation is an important part of the adaption-innovation theory

and the TPB. A person’s behavior towards eating and exercise is usually more the result

of internal belief systems rather than the influence of an environmental factor (Ajzen &

Holmes, 1974). Therefore, before a person takes on a behavioral modification program he

or she may benefit from being educated on psychological concepts of eating behavior

such as the positive-incentive theory, and should understand how this theory interacts

with motivation as well as biological responses to eating and expending energy (Fairburn

& Brownell, 2002).

Biologically, two theories explain the concept of weight gain, loss, and

maintenance: glucostatic and lipostatic theories. These theories both suggest that the

human body has a natural weight and glucose range, also referred to as a set-point. When

5

a person’s weight or glucose range varies, perhaps because of diet or exercise, the body

will eventually regulate to the original set point (Carrier, 1994). These theories state that

eating a meal or gaining and losing weight are all done with the effort of returning to a

homeostatic body state (Pittas et al., 2005). The glucostatic theory is based on the idea

that the body regulates itself for the short term by the blood glucose level and that as the

level of blood glucose is depleted a person psychologically and physically will begin to

prepare for his or her next meal, and on consuming the meal the body returns to its set

point (Panksepp, Tonge, & Oatley, 1972).

The lipostatic theory is based on a similar idea regarding regulation except this

theory is based on fat storage and long-term regulation. This theory suggests that eating

and metabolism are biologically activated if there is a deviation from the body’s weight

set-point (Baile et al., 2000). The lipostatic theory assumes that the relative stability of an

adult’s body weight is because leptin manages the stability of the body regardless of

short-term behavioral differences in food consumption or exercise behaviors (Baile,

Della-Fere, & Martin, 2000). This theory states that the body has its own place of

stability with regard to total weight and amount of fat, and that the body will eventually

return to that state regardless of environmental influences (Baile et al., 2000). If humans

all have a natural tendency to return to our set point then the obesity epidemic as well as

other eating disorders such as bulimia and anorexia, would not likely exist in such

extreme fashion.

Factors associated with hunger include the understanding of how eating behavior

is managed and maintaining a healthful eating lifestyle. Feelings of hunger can be driven

6

by motivational factors and cognitive decisions about eating patterns that are not

associated with the current level of homeostasis. For example, the eating experience itself

can be associated with grazing behaviors, binge eating, or gorging at one meal setting

(Oxford University Press, 2007). A person may cognitively be aware that he or she

should not overeat but some experience a momentary pleasure from the food and possibly

lose motivation to maintain a healthy eating regimen and continue to overeat (Oxford

University Press, 2008). The person may experience guilt and shame after the binge

eating comes to completion (Oxford University Press, 2008).

The idea of eating out and celebrating an occasion with a special meal is not a

new concept. However, many researchers note that these occasions are built on mannered

rituals, the bringing together of family and friends, and notably, structured meals often

results in binge eating (Chaney, 2002). A person may experience different social

pressures to lose weight or obtain a body image that is unrealistically thin and he or she

cognitively makes a decision to obsessively diet or restrict their next meal. This

motivation stems from social influences (Schnieder, Gruman, & Couts, 2005), and may

impact a person’s ability to restrict meals to avoid binge eating (Sirois, 2004).

Eating Behaviors and Binging

This study covers a wide range of eating behaviors in the general population.

This includes non-clinical disorders and binge eating is one such disorder that has been

associated with obesity. Reported binge eating disorder affects over 3% of the adult

population of the United States and 75% of individuals with obesity suffer from this

maladaptive eating behavior (Reuters, 2008). However, these data may not reflect the

7

true number of individuals who binge eat, as over 35 % of the population is currently

considered to be obese (CDC, 2009). Those who binge eat typically do not participate in

purging or excessive exercise to compensate for unusually high caloric intake; nor would

they consume laxatives—typically behaviors associated with bulimia nervosa (BEDA,

2009).

Binge eating can result in obesity, which is currently measured using the body

mass index. Body mass index, or BMI, is a standard measurement that is calculated using

a person’s height and weight, using the formula (weight (lb) / [height (in)]2) x 703, to

derive a body fat percentage to determine obesity (Hairon, 2006). The National Institute

of Health (2008) stated that a BMI of 30.0 or above is considered obese. The Center for

Disease Control (CDC) used the body mass index statistics in the United States and

concluded that obesity has risen from 15% to 33.9% in the last 24 years (2008c).

Researchers have shown that BMI has a relationship with obesity related health disorders.

For example, an increase in BMI has been associated with an increased risk for the

development of many chronic health conditions such as hypertension, coronary artery

disease, stroke, type 2 diabetes, and some cancers (Baum & Posluszny, 1999).

Problem Statement

Researchers have noted the importance of understanding cognitive processes

associated with eating behaviors and health outcomes (Johansson, 2006; Wethington,

2008). The TPB has been studied with a variety of health and eating behaviors (Armitage,

Conner, Loach, & Willetts, 1999) and the adaption-innovation theory has been applied to

understanding how cognitive style impacts personal decisions (Kirton, 2003). Yet these

8

theories have not been examined as they relate to non-clinical eating disorder

components. Therefore, the problem is that while the dangers of binge eating and

overeating are known, cognitive style and the cognitive processes associated with

planned behavior, as they apply to non-clinical eating behaviors, have not been

investigated. If a link between these variables and eating behaviors can be established,

health professionals will better understand how decisions regarding negative eating

behaviors occur.

Nature of Study

The nature of the study is described by defining specific research questions,

hypotheses, and the purpose of the study. Chapter 3 provides a detailed discussion of the

study design, hypothesis, variables, and methodology.

Research Question

This research study was quantitative and focused on understanding any

relationships between variables from the theory of planned behavior, the adaption-

innovation theory, and eating behaviors. The specific research question was if eating

behavior is affected by body mass, perceived behavioral control, attitude, subjective

norms, intentions, sufficiency of originality, efficiency, and rule/group conformity.

Hypotheses

In order to answer the research question the following hypotheses were tested in

this study:

9

Null Hypothesis (Ho):

Null 1: In a hierarchical multiple regression there will be no significant

relationship between the predictor variables (perceived behavioral control, attitude,

subjective norms, and intentions as measured by TPB, and sufficiency of originality,

efficiency, and rule/group conformity as measured by KAI, and BMI) and dietary

restraint as measured by EDE-Q6 (R = 0).




efficiency, and rule/group conformity as measured by KAI, and BMI) and eating concern

as measured by EDE-Q6 (R = 0).




efficiency, and rule/group conformity as measured by KAI, and BMI) and shape concern





efficiency, and rule/group conformity as measured by KAI, and BMI) and weight concern


10

Purpose of Study

The purpose of this study was to assess the combined effects of individual

problem solving styles (sufficiency of originality, efficiency, and rule/group conformity)

and planned behavior (attitudes, subjective norms, behavioral intentions, perceived

behavioral control), after first controlling for body mass, on eating behaviors. There may

be positive reinforcement from eating behaviors that derive from social influences as well

as psychological gratifications. The TPB posits a relationship between social influences,

personal control over behaviors, and belief systems. The adaption and innovation theory

draws connections with problem solving styles and cognitive processes. Binge eating

behaviors are associated with obesity and can lead to psychological and health related

disorders. However, there is no consistent research to assess how a person makes

problem solving decisions regarding eating behaviors.

Operational Definitions

Attitude: Attitude is a personal feeling about certain behavior that has been built

on throughout a person’s lifetime based on experiences, observations, and information

acquired about the behavior (Higgins & Marcum, 2005). Attitude will be measured using

the TPB questionnaire.

Binge eating disorder (BED): BED is the consumption of an objectively large

amount of food or eating in a mannerism that was not intended (APA, 2000). BED will

be assessed using the EDE-Q6.

Body Mass Index (BMI): BMI is a measurement that is calculated from a person’s

weight and height which can be used as an indicator for potential weight related health

11

disorders (Center for Disease Control, 2008a). BMI will be measured using a

mathematical calculation that creates a raw score.

Dietary restraint: This is a process in which a person severely restricts caloric

intake with the hope of achieving weight loss; however, this behavior often results in

binge eating during the refeeding period (Fairburn & Brownell, 2002). Dietary restraint

will be measured using the EDE-Q6.

Eating concern: This is a characteristic in which an individual is preoccupied with

thoughts about eating, weight, and eating around others (Fairburn & Brownell, 2002).

Eating concern will be measured using the EDE-Q6.

Efficiency: This is a cognitive style metric that measures the concept of a person’s

problem solving methods or processes (Kirton, 1999). Efficiency will be measured using

the KAI inventory.

Individual cognitive style: Individual cognitive style is the preferred manner in

which a person undertakes problem solving methods (Kirton, 2003). This will be

measured using the overall KAI inventory score.

Intentions: Intentions are how a person combines his or her attitudes and

subjective norms and thereby determines how to tackle a problem (Ziefelmann et al.,

2007). Intentions will be measured using the TPB Questionnaire.

Perceived behavioral control (PBC): PBC defines the ability for a person to feel

control over the ability to perform a specific behavior and follow through on achieving

goals (Ajzen, 2008). PBC will be measured using the TPB Questionnaire.

12

Restraint: This is a characteristic in which an individual is preoccupied with

thoughts about avoiding eating, avoiding food, having and empty stomach, and adhering

to self set dietary rules (Fairburn, 2008). Restraint will be measured using the EDE-Q6.

Rule/group conformity: This is a cognitive style metric that measures how style,

being more or less adaptive or innovative, affects the structures in which problem solving

occurs (Kirton, 1999). Rule/group conformity will be measured using the KAI Inventory.

Shape concern: This is a characteristic in which an individual is preoccupied with

thoughts about fear of weight gain, discomfort of seeing body, feelings of fatness, and

concern with overall shape (Fairburn, 2008). Shape concern will be measured using the

EDE-Q6.

Subjective norms: Subjective norms are beliefs about what other people in their

social circle such as spouses, neighbors, or peers, would have regarding any given

behavior such as dieting or achieving a thin ideal (Ajzen & Holmes, 1974). Subjective

norms will be measured using the TPB Questionnaire.

Sufficiency of originality: This is a cognitive style metric that measures the

manner in which a person generates ideas (Kirton, 1999). Sufficiency of originality will

be measured using the KAI Inventory.

Weight concern: This is a characteristic in which an individual is preoccupied

with thoughts about weight, the importance of weight, and desire to lose weight

(Fairburn, 2008). Weight concern will be measured using the EDE-Q6.

13

Assumptions, Limitations, Scope, and Delimitations of Project

One assumption of this study was that the general population to be surveyed does

not suffer from clinical eating disorders. This assumption could be challenged by non-

reported eating disorders from the surveyed population. Additionally, the survey design

relied on self-reporting which could have a potential bias to underreport binge eating

behavior. Further, the participants in the survey were from Colorado which has the lowest

rate of obesity (less than 20%) in the United States which may reduce the significance of

the results in comparison to other states (CDC, 2008c).

The participants were from the general population of Boulder County, Colorado.

Males and females ranging in ages from 18-65 who do not reside in a hospital or mental

health facility were asked to participate in the survey. The gender, ethnicity, and

educational levels reflect a random sample of the general population as described by the

U.S. Census Bureau for Boulder County (2008). The availability of the sampling frames

and potential respondents is reflected in the population group selection (Creswell, 2003).

The time of study for the survey may additionally affect the results as it was

conducted during a time period in the United States that included Hanukah, Christmas,

and New Year’s celebrations. This timing may influence the data as these celebrations, as

well as many not mentioned, are associated with social situations that include eating large

meals which may not otherwise occur during regular calendar dates (Brown, 2000).

Significance of Study

This study investigated the variables from KAI, TPB, and BMI with respect to

four eating behavioral components which are restraint, eating concern, shape concern,

14

and weight concern. This study adds to the body of knowledge regarding the mannerism

in which a person can engage in or maintain healthy behaviors, and in so doing,

contributes to improving strategies associated with avoiding negative eating behaviors.

Professional Application

Psychologists recognize that many current social issues are health related and may

be resolved with the application of research findings to behavior modification programs

(Roth & Armstrong, 1990). The social problem of unhealthy eating behavior in the

United States is a serious issue and is associated with high death rates as an estimated

280,000 to 325,000 adults in the United States die each year from causes related to

obesity (CDC, 2009). Obesity related diseases, physical and mental disabilities, and

increases in healthcare expenditures are additionally significant social concerns for

professionals (American Obesity Association, 2007). Medical research confirms that poor

diet contributes to obesity which is the second leading cause of death in the United States

(Mokdad et al., 2004).

Poor eating behaviors also result in greater social psychological disabilities such

as poor self image and self esteem, and psychopathologies such as social anxiety and

depression (Center for Disease Control, 2008b). Phares, Steinber, and Thompson (2004)

noted multiple cases of depression and low self-worth in young people that were directly

associated with dysfunctional body image perceptions associated with obesity and

explained that there was a high risk for these disorders to become lifelong dysfunctions.

Another reason that the social problem of unhealthy eating behavior must be addressed is

that medical spending in 2000 attributed to obesity and overweight related disorders was

15

approximately $117 billion according to the National Health Accounts (NHA) data and

this amount is increasing annually (Center for Disease Control, 2009). The application of

the research findings in this study contribute to the health psychology profession by

increasing the knowledge of how variables from cognitive-behavioral models are linked

to eating behaviors.

Knowledge Generation

People are constantly making dietary changes and long-term resolutions for their

eating patterns and habits each year all with the hopes of losing weight and improving

overall health (Costin, 1998). However, many people who do not have self-efficacy and

motivation regarding their ability to control their eating may find that they always fall

short of their goals. In fact, 95% of those who diet regain some of their lost weight within

five years (Costin, 1998). Unfortunately, these dieting failures are most often blamed on

the set-point theory as the dieters feel they have no control and are destined to return to

their body’s natural weight (Gabel & Lund, 2002).

However, the weight regain may be better explained by understanding motivation

and style. Motivation can be modeled using TPB variables and style can be modeled by

KAI variables. These theories propose to predict behavior with the knowledge and

understanding that each person is unique and therefore, has unique decision making

preferences, attitudes, subjective norms, perceived behavioral control, and intentions

(Ajzen & Fishbein, 1980; Kirton, 2009).

The TPB demonstrates that a person can take intention and act on it which thereby

results in a specific behavior or outcome (Ajzen & Fishbein, 1980). For example, this

16

behavior could suggest that a person’s eating and nutritional behavior is a result of their

intention to stay on a diet. Or, that if a person has an attitude that diets do not work and

subjective norms that it is normal to be somewhat overweight, it is unlikely that he or she

will successfully make a dietary lifestyle change (Armitage et al., 1999).

Similarly, using the adaption-innovation theory, a person with an adaptive style

may be more likely to follow a meticulous pattern of solving dietary problems and may

not find success with changing dietary lifestyle. A person with an innovative style may be

less careful about sticking to a diet. On the failure of a diet a person may likely look at

the outcome as reaffirmation that the set-point theory or the lipostatic theory is correct,

and that his or her weight is not something that can be changed or maintained (Matheson

& Crawford-Wright, 2000). This research contributes knowledge regarding how the

theory of planned behavior and the adaption-innovation theory can be used to better

understand behaviors and decisions that contribute to eating behaviors.

Positive Social Change Implications

This research has significant importance to the health conscious community and

health care systems overall as it can contribute to the future development of behavioral

modification programs to reduce weight-related health disorders. One side effect of

obesity is depression and poor quality of life (Daniels, 2006). This research can address

the obesity epidemic by contributing to the development of healthy eating behavioral

programs and research surrounding human behaviors that contributes to decreasing health

problems (Baum & Posluszny, 1999). When physicians or psychologists assess a

person’s overall health, existing eating behavior and decision making styles should be

17

taken into consideration as this can help determine the likelihood of success a person has

for changing eating behaviors (Carrier, 1994).

Positive social change results in positive transformations for humans and social

conditions that can come in the form of a change in family systems, the individual, and

the community. Research has demonstrated there are stereotypes surrounding binge

eating and associated obesity which include being lazy, stupid, incompetent, or that these

individuals should be avoided by members of society who believe obese people are of a

lower class (Klaczynski, Goold, & Mudry, 2004). With these negativities associated with

obesity it seems clear that those suffering with binge eating need support from their

community, even if it is only to help them develop skills and strategies to cope with the

negativity they could face in this society (Lindsay, Sussner, Kim, & Gortmaker, 2006).

The implications for positive social change from this research furthers research regarding

poor eating behaviors and includes the potential to minimize the negative influences and

contributors to obesity and harness the potential positives of understanding the

relationships of cognitive decision making and health behaviors. This research benefits

the health conscious community and health care systems with the contribution to the

future development of behavioral modification programs to reduce weight related health

disorders. There will be an improvement to the human condition by assisting

communities with managing the obesity epidemic by contributing to the development of

healthy eating behavioral programs. Additionally this research contributes to institutions

by potentially reducing secondary illnesses and health care costs associated with binge

eating and obesity.

18

Summary

To prevent diseases and psychological disorders, a better understanding is needed

on the relationships between binge eating, obesity, and overeating eating behaviors. The

key point of this study was to identify any relationships or non-relationships with how a

person internally and externally makes decisions and acts on those decisions with regard

to eating behaviors. Chapter 2 defines the approach to the literature review and the

theories described prior are investigated. The lack of research surrounding binge eating

disorders and eating behaviors in association with psychological decision making

behaviors is addressed. Additionally, chapter 3 defines the methods and strategy

associated with the data collection process and statistical design. A new approach to

measuring why individuals make decisions regarding eating behaviors, including the

personal decisions associated with the intentions and problem solving styles, is

investigated and described. Chapter 4 describes the findings of the study and chapter 5

discusses the applications of this research including how it contributes to the

implementation of positive social change in the area of prevention-related health

behavioral programs.

CHAPTER 2: LITERATURE REVIEW

Introduction

Although research has established a link between binge eating behaviors and

obesity or weight-related health disorders, little attention has been devoted to the

relationship between eating behaviors and problem-solving styles and planned behavior.

Binge eating disorders are traditionally investigated from a clinical standpoint in which

the behavior has resulted in bulimia nervosa (Fingeret, Warren, Cepeda-Benito, &

Gleaves, 1996), or by assessing dietary management, treatment programs, psychosocial

interactions, physical risks, medication, and clinician skill in the treatment process

(Treasure, Tchanturia, & Schmidt, 2005). The purpose of this chapter is to discuss the

roles of decision making and planned behaviors on eating behaviors. These assessments

underscore the need to expand research in this area to promote alternative means to

resolve binge eating behaviors and associated health-related disorders.

Organization of Chapter

This review defines current literature regarding binge eating behavior, the TPB,

and the adaption-innovation theory. The chapter begins with a broad overview of how

binge eating has been researched and includes research surrounding how social

influences affect eating behavioral decision-making strategies and body mass. Next, the

TPB is discussed with a focus on current research surrounding eating behaviors. The

adaption-innovation theory is then examined to address previous research on the

relationship between cognitive decision making style and behavior. This chapter builds

on the literature to demonstrate the need to conduct research to understand how eating

20

behavior in the adult population may be associated with internal and external decision-

making processes.

Strategy for Literature Review

Information in this review was obtained from a multitude of scholarly journals

and primary author books. The majority of the literature was obtained from EBSCOhost

databases which include Mental Measurements Yearbook, PsycARTICLES, SocINDEX,

Health Source: Nursing/ Academic Edition, Academic Search Premier, and CINAHL

Plus. The review focuses on articles published in the last ten years but does include

several later references from the original authors of the TPB and the adaption-innovation

theory. Search terms include, but are not limited to the following: binge eating,

overeating, TPB, cognitive style, adaption and innovation, body mass index, problem

solving, obesity, food addictions, food behavior, social eating, dietary restraint, diets,

food intake, health attitudes, hunger, health behavior, eating disorders, body image, and

decision making.

Content

The scope of this literature review includes binge eating and general eating

behaviors as they are associated with psychological decision making processes, and

behaviors regarding food selection processes for binge eating and obesity issues,

psychological and social implications, the TPB, health behaviors, binge behaviors, and

adaption-innovation theory of problem solving style. Some clinical eating disorders and

general obesity related disorders are out of the scope of this research including general

knowledge regarding overeating behaviors, caloric intake, exercise behaviors, and

21

biological and genetic related eating disorders or obesity in addition to others are

excluded from review. Additionally, cognitive behavioral treatments and

psychopharmacological treatments are out of scope of this research.

Quantitative and Qualitative Methodologies

Literature related to the use of differing methodologies to investigate the

outcomes of interest has been reviewed. Qualitative research is quite prevalent in the area

of addressing eating behaviors as many researchers use ethnographical techniques or one

on one interview techniques. For example, clinician based interviews have been used to

assess psychopathology in eating disorders (First, Spitzer, Gibbon, & Williams, 1997).

Additionally, binge eating behaviors have been qualitatively documented during

diagnostic interviews (Mitchell & Peterson, 2008). Medical practitioners have also

incorporated qualitative analyses into identifying any potential barriers for obesity

focused assessments (Fairburn & Brownell, 2002). For the purpose of this literature

review the quantitative methodology was focused on although multiple qualitative studies

have been cited. This is based on the selected predictor and criterion variables which are

quantitative in nature.

Binge Eating

Eating disorders have been popularized with the increase in television and media

exposure surrounding young females with body image disorders and anorexia and

bulimia are now household terms (Serdar, 2005). For most individuals the more

prominent eating disorder is that of overeating or deviating from a normal eating pattern

which can lead to obesity. Fairburn (2008) defines regular eating as consisting of a

22

pattern of consuming breakfast, a small midday snack, lunch, a small afternoon snack,

dinner, and a small evening snack. Binge eating, in contrast, is defined as an episode of

uncontrolled eating that is usually triggered by an event, a mood, or by breaking a dietary

rule. Binge eating often results in feelings of uncomfortable fullness after eating, shame,

and guilt. Those who binge may feel uncomfortable fullness after consumptions. Binge

eating disorder was introduced in 1992 and has been used to describe excessive eating

without purging the food to lose weight, often resulting in obesity (Academy for Eating

Disorders, 2008). Yet binge eating has not been officially recognized as an eating

disorder in the American Psychiatric Association’s (2000) Diagnostic and Statistical

Manual (DSM- IV-TR), as it is considered to be in the category of Eating Disorder Not

Otherwise Specified (EDNOS). Although binge eating is more common among women

than men, it is a challenge that affects Hispanics Americans, African Americans, and

European Americans fairly equally (Regan & Cachelin, 2006).

Eating Behaviors and Food Choices

A binge eating episode is defined as having a sense of lack of control over a

period of eating which includes a consumption of food that is traditionally larger than

what would be considered to be normal by others in the same situation (Fairburn, 1995).

Binge eating can also represent the deviation from an eating plan or program associated

with health requirements such as avoiding simple carbohydrates when diabetic or

avoiding salt with hypertension diseases (Sohn, 2008). Foods that are most often

consumed in a binge eating episode noted in those with clinical disorders include but are

not limited to ice cream, popcorn and salty foods, cheese, cereal, candy, and donuts; the

23

range of caloric intake can vary from 1,200 to 11,500 over a period of 15 minutes to 8

hours (Mitchell, Pyle, & Eckert, 1981). Although these boundaries have been specified,

less research is available for those suffering with overweight-related binge eating who eat

a variety of foods in a rapid manner after they have skipped meals or avoided specific

foods for a period of time due to dieting.

Binge eating has also been associated with behaviors such as breaking a dietary

rule. These behaviors could include eating something considered to be fattening or salty,

eating alone, having premenstrual tension, drinking alcohol, or having a lack of a dietary

routine (Abraham & Beumont, 1982; see Figure 1). Body image dissatisfaction is also

associated with having higher incidences of dieting, unhealthy eating behaviors, and

binge eating (Neumark-Sztainer, Paxton, Hannan, Haines, & Story, 2006). Women with

binge eating disorder rate body image dissatisfaction as higher influences on their

behaviors than do men; however, men also rate body image dissatisfaction as

contributing factors to binge eating disorder behind depression and self-esteem (Grilo &

Masheb, 2005; Grilo et al., 2005). Age of onset of binge eating or individual age does not

seem to be a predictor of binge eating disorder or adult obesity (Masheb & Grilo, 2008).

Yet, there are biological and social reasons associated with eating behaviors that may

develop in early childhood that contribute to lifelong decisions about eating.

24

Figure 1. Note. From “Cognitive behavior therapy and eating disorders (p. 140) by C. G. Fairburn, 2008, New York, NY: The Guilford Press. Copyright 2008 by Fairburn. Reprinted with permission. Food Selection and Binge Eating

Food selection and availability also play a role in understanding eating behaviors.

Humans are no longer dependent on eating readily and obsessively when food is made

available to us in an effort to survive thanks to mass agriculture, so it is important to be

aware of consumption behaviors. One particular less formal type of dining, often called a

buffet, offers a different view of the implications eating behaviors and decision-making

processes. Dietary diversity traditionally is looked on as a benefit to diet maintenance and

overall nutritional health (Toray & Cooley, 1997). However, there are also negative

implications to diet diversity. For example, if people are presented with a wide variety of

high-caloric foods low in nutritional value they will eat more than they normally would if

they were only presented with one option (Kennedy, 2004). The same concept applies

from a positive-incentive perspective because the desirability to eat one food decreases

Binge Analysis • Breaking a dietary rule • Being disinhibited (e.g. alcohol) • Under eating for a period of time • Adverse event or mood

BINGE EATING

25

upon consumption. However, when presented with a wide variety of food options, such

as a cafeteria, the positive-incentive desire to indulge in the rest of the foods is not as

strong as with the first item, but it still exists and contributes to overeating (Nayga, 2000).

The pleasure of each food and the positive incentive of the value of taste for each new

food will decrease (Pinel, 2006). Although the human stomach can hold one liter of food

comfortably, in many situations, such as being presented with a variety of food options, it

can be pushed to hold two liters even though there are chemical and stretch receptors that

are signaled when overeating occurs (Toray & Cooley, 1997).

Biological factors also contribute to binge eating. The brain codes food choices in

the orbitofrontal cortex and assigns a value with the level of reward a person experiences

when consuming a specific food (Zald, 2008). This area of the brain responds to tastes,

the visual appearance of food, aromas, and texture and makes decisions regarding food

selection. Recent research demonstrated that binge eating occurred in patients after self-

reported satiety; these patients had various levels of abnormal functioning in their

orbitofrontal cortex (Woolley et al., 2007). These findings suggest this area of the brain

places greater value on the immediate reward of certain food selections over long-term

rewards such as long-term health and weight management (Zald, 2008).

Social psychologists have noted non-biological instances that contribute to binge

eating behaviors. For example, the positive incentive theory suggests that individuals eat

out of habits, social stimuli, the physical appearance and smell of food, and other reasons

unrelated to biologically-induced hunger (Pinel, 2006). Food selections in binge eating

26

behaviors seem consistent with this theory. Eating patterns are often based on

consumption habits and are not always based upon eating behavioral goals.

Many individuals are not aware of the external factors that mold their eating

habits. For example, in a study conducted by the University of Toronto, 120 female

college students were observed eating either alone or with friends (Liebman, 1995). The

students who ate alone consumed 375 calories, whereas the students who ate with friends

consumed over 700 calories, suggesting that social factors influence how much someone

eats and the social influence usually results in increased consumption. In an additional

study, a group of college students was given unlimited access to mini pizzas and they

were allowed to consume as many of the pizzas as they wanted in the group setting while

they watched television together (Herman et al., 2005). The results showed that members

of the group ate similar amounts of pizza during the timeframe, again suggesting that the

group environment dictated the eating behavior.

Social influences can also positively influence eating behaviors. Neumark-

Sztainer, Wall, Story, and Fulkerson (2004) used logistical regression in a population of

4746 ethnically diverse adolescent females and noted that 18.1% of those females who

ate 1-2 meals per week with their family reported eating disorders whereas only 8.8% of

females who ate 3-4 meals with their family reported similar behaviors. Additionally, in a

study by Vartanian, Herman, and Wansink (2008) two groups participated in a study

which measured their awareness of the influences that dictated their food selection

process. Variables such as eating partners, hunger, taste, satiety, free will and behavior of

co-eater were taken into consideration to assess the determinants of food consumption for

27

each individual. The results suggested that although the individuals were able to

determine what factors contributed to their partner’s eating behavior they were unable to

recognize these behaviors in their own eating behaviors. This suggests that subconscious

social cues may also play a role in eating behaviors.

Psychological and Sociological Implications

Fairburn (2008) noted that many individuals who participate in a binge do not

necessarily eat an extreme amount of food nor do they always experience guilt. Rather,

they may feel an overwhelming awareness of their body image as a result which could

result in excessive temporary dieting which increases the risk for repeat binge episodes or

they could be disinhibited, such as being under the influence of alcohol, which

contributes to the episode. A person is considered to have binge eating disorder if quality

of life is affected but, this can also be caused by emotional issues that initiate a binge

eating period.

For example, Chua, Touyz, and Hill (2004) demonstrated that the induction of a

negative mood after viewing a sad film did promote overeating in 40 obese female

participants by assessing hunger motivation, dietary restraint, and food intake. In

comparison with individuals with normal body mass indexes, overweight binge eaters

had great concern with body image but had a tendency to over eat when they were in a

negative mood (Eldredge & Agras, 1994). Negative affect has further been demonstrated

to contribute to binge eating and abnormal eating behaviors (Lyubomirsky, Casper, &

Sousa, 2001).

28

In addition to the external social environment, social groups have proven to be

significant influences on whether or not a person binges eats. Using two sorority groups

Crandall (1988) found that the members of the sorority binged in equal frequencies and

amounts compared to the mean of the other members of the group. Although this study

noted the influence of social norms on eating behaviors, it did not address any cognitive

decision-making styles for the individuals. Decision-making processes and social

influences that do contribute to binge eating include a person’s role in the immediate

family, cultural influences, early life experiences with in the family, community, and

social class (Wethington, 2008).

Although social situations can influence eating behaviors, many individuals feel

that binge eating behavior is a result of poor self-esteem or depression (Mond & Hay,

2008). Obese individuals self-report that binge eating behaviors are often a result of an

inability to manage the social pressures in society to be thin (Sorbara & Geliebter, 2001).

These stereotypes and social pressures can be damaging and can encourage additional

episodes of binge eating that contribute not only to the negative psychological health of

the individual but also impacts negative physical health.

Much research has been dedicated to understanding how binge eating is related to

overall health, obesity, and body mass index (BMI). The cycle of binge eating results in

challenges maintaining a healthy BMI, risks to being obese, challenges losing weight,

and weight regain (Elfhag & Rössner, 2004) although binge eating has been reported

approximately equally in women at all levels of the body mass index scale (Shisslak et

al., 2006). Weight maintenance, which means a person does not regain weight that was

29

successfully lost prior, is influenced by many variables such as social factors, personal

motivation, realistic goal measurement, eating restraint, and binge eating. Binge eating

specifically has been found to be related to weight regain over a five year period in

patients who have had multiple forms of obesity related surgery (Pekkarinen, Koskela,

Huikuri, & Mustajoki, 1994).

All of these health implications can be related to motivation and problem solving

styles. However, limited research has been conducted in these areas including the theory

that assesses planned behavior and relationships with motivation. Rather, the majority of

research focuses on the implications of cognitive behavioral therapy and

psychopharmacological solutions (Fairburn, 2008; Grilo, Masheb, & Wilson, 2006; Kaye,

Bastiani, & Moss, 1995). Guided self-help programs using cognitive behavioral therapies

have demonstrated success for treating BED but have not proven successful as a first step

for individuals with BED who are considered obese (Grilo & Masheb, 2005).

Cognitive behavioral therapies such as self-help programs and motivational

interviewing are considered to be the treatments of choice for BED according to a study

by Dunn, Neighbors, and Larimer (2006). In this study 90 undergraduate college

students received either motivational enhancement therapy or a self-help manual to

promote their readiness to change eating behaviors. Using repeated measures ANOVA

there was an increase in both groups for being able to abstain from binge eating episodes

temporarily. However, this does not contribute to understanding the intentions of the

individuals who abstained or did not abstain nor do these studies contribute to

understanding how people make decisions about eating behaviors. With all of the focus

30

on therapeutic interventions and lack of research in surrounding prevention, it is

important to understand underlying theories of overall health behaviors.

Theory of Planned Behavior

Factors that have been noted to prevent eating disordered behaviors include

general knowledge about nutrition, an understanding of eating pathology, dieting

behaviors, thin-ideal internalization, and body dissatisfaction (Fingeret, Warren, Cepada-

Benito, & Gleaves, 2006). However, understanding how a person’s internal cognitive

decision making process with regard to how it applies to poor eating behaviors has had

limited discussion. The TPB is one theory that can be used to investigate a person’s

intention and perceived behavioral control when applied to health behaviors.

The TPB is an extension of a model called the theory of reasoned action which

was developed by Fishbein and Ajzen (1975). The original theory of reasoned action

suggested that individuals systematically assessed a variety of inputs before making a

decision whether or not to act on or avoid acting on a certain behavior. These inputs

include individual beliefs, social influence, attitude towards a behavior, importance of

attitude and subjective norms, and the person’s overall intention for the attitude. This

theory was extended by Ajzen with the addition of the concept of perceived behavioral

control (Ajzen, 1988). The addition of perceived behavioral control as a component can

measure the effect a person’s experience with acting on a specific behavior has upon the

current ability to perform the behavior (see Figure 2).

31

Figure 2. Note. From “Constructing a theory of planned behavior questionnaire” by I.

Ajzen, 2009, TPB Model, Retrieved February 15, 2009 from: www.people.umass.edu.

Copyright 2006 by I. Ajzen. Reprinted with permission.

Armitage et al. (1999) noted that the TPB has been studied in relationship to a

variety of social psychology issues including eating behaviors and binge drinking.

Psychologically this theory is similar to understanding how a person measures locus of

control; however, it also measures a person’s feeling of control over a behavior rather

than just the internal control of events. Even with compelling evidence regarding the

dangers of unhealthy eating behaviors many individuals still demonstrate ambivalence

regarding changing their eating behavior and as this is often a result of personal decisions

and social influences (Snow, 2000).

The TPB was created to understand the interactions of beliefs, attitudes, and

social influences on a person’s final behavior with regard to personal intentions (Ajzen,

2008). The model has three tiers. The first tier is that a person will have behavioral

32

beliefs surrounding whether or not a specific behavior will result in an outcome which

impacts personal attitudes towards a behavior (Armitage et al., 1999). The second tier

addresses normative beliefs (which are perceived behavioral expectations of individuals

the person feels is important) and subjective norms (which are the perceived social

pressure to perform the specific behavior) as they apply to an initial behavioral belief

(Ajzen, 2008). The third tier consists of control beliefs and perceived behavioral control

which is a person’s internal and external feeling regarding the ability to execute a specific

behavior (Armitage et al., 1999). This theory has been popularized with the use in a

variety of social issues that are related to personal behavior such as understanding the

spread of HIV, measuring health behaviors for those with chronic illnesses, and

understanding goal directed behaviors for drug abuse recovery treatments (Young, 1991).

The TPB can be applied to personality and attitudes regarding healthy eating

behavior which is often formed through the media’s usage of agenda setting or the ability

to frame the issue with a specific angle to influence public opinion to achieve a certain

body image (Halliwell & Harvey, 2006). Although neuropsychological and satiety issues

are associated with eating behavior, personalities and attitudes toward poor eating

behaviors have been changed through educational programs and behavioral modification

(Ozelli, 2007). One such example was demonstrated by Carpenter, Finely, and Barlow

(2004) in a pilot study in which three groups of individuals were compared. Individuals

had poor eating behaviors according to the USDA’s Health Eating Index and either

received weekly nutritional educational and training, internet based nutritional

educational training, or no educational training. The results demonstrated a significant

33

improvement in eating behavior and a change in the associated attitude towards changing

their behavior in the group that received weekly nutritional education and training

(Carpenter et al., 2004). Although measuring behavior such as in the Carpenter, Finely,

and Barlow (2004) study can reflect a change in attitude, the measurement of an attitude

before a specific treatment, such as nutritional education or behavioral modification, the

TPB is often not applied in binge eating or obesity related studies (Reid, 2006).

Conner, Povey, Sparks, James, and Shepard (2003) used the TPB to assess

attitudinal ambivalence with regard to maintaining eating behaviors. They used an

increase in ambivalence towards healthy eating behaviors as the dependent variable and

attitudes and intentions, attitudes and behavior, and perceived behavioral control as

independent variables. By performing correlation studies based on results from two TPB

designed Likert scales, the study found that those participants who demonstrated higher

ambivalence with their healthy eating behaviors were more likely to have weaker

relationships between the independent variables and the outcome of healthy eating

behavior (Conner et al., 2003).

The TPB has been used to predict a wide variety of behaviors such as exercise

intentions (Shen, McCaughtry, & Martin, 2008), sexual behaviors (Myklestad & Rise,

2008), vegetable consumption in children (Pawlak & Malinauskas, 2008), smoking

behaviors (Nehl et al., 2009), and intentions for healthy eating (Tiejian et al., 2009).

The TPB has also demonstrated results in areas such as predicting the consumption of

dairy products by the elderly using attitudes, subjective norms, and perceived behavioral

control as variables (Kyungwon, Reicks, & Sjoberg, 2003) as well as addressing a variety

34

of health behaviors associated with exercise and dietary change. Additionally, behaviors

regarding the participation in physical activities have noted the influence of attitudes on

participation (Kiviniemi, Voss-Humke, & Seifert, 2007). However, attitudes do not

always demonstrate the willingness or intentions to eat in a healthful manner (Fila &

Smith, 2006). This is often caused by the lack of understanding how individuals make

decisions in specific situations. The TPB has not assessed the cognitive decision making

styles associated with how a person internally feels regarding health behaviors such as

binge eating.

Health Behaviors

When a person feels that a health threat exists, which is considered to be a

vulnerability to the consequences of a health related action, behavior is often modified to

avoid the perceived consequence (Brannon & Feist, 2004). This can be a reaction to a

combination of feelings of self-efficacy regarding the ability to change behavior as well

as a combination of internalizing the cost versus gain benefit a person believes will

equate to the change in behavior. The TPB incorporates internal decision making issues

regarding health behaviors such as what a person feels is a predictor of health, what a

person can to do pursue tasks associated with obtaining good health, social learning

theories that reinforce behaviors, personal intentions, internal perceptions of the future

consequences of continued behaviors as well as what a person feels can be achieved

giving existing capabilities (Sirois, 2004).

Conner, Norman, and Bell (2002) looked to examine the power of the TPB as it

applies to healthy eating. In their longitudinal study using questionnaires they looked to

35

understand the intentions associated with eating a healthy diet. The results demonstrated

that the TPB was predictive of healthy eating intentions for time periods up to 6 years.

This suggests stability in this instrument and can be beneficial for understanding

intentions and actual behavior.

Binge Behaviors

Non-eating binge behaviors have been examined, using the TPB, in this area.

Stewart, Brown, Devoulyte, Theakston, and Larsen (2006) noted that self-reporting binge

drinkers who drank for emotional relief rather than social pressures were also more likely

to have binge eating behaviors and they found that the root cause of the binge eating and

drinking were similar in nature. Additionally, poor internally reported self-control and

self-efficacy issues often lead to binge drinking and binge eating (Williams &

Ricciardelli, 2003).

Collins and Carey (2007) used longitudinal models to examine how the TPB

could predict drinking behaviors in college students. They noted that intentions should

predict behavior and, in their study, attitudes were a consistent predictor for binge

drinking. Additionally, a study using correlation and regression tested associations

between attitudes, perceived behavioral control, subjective norms, and beliefs and

perceived behavioral control reached significance for binge drinking behaviors. (Norman

et al., 1998).

As of June, 2010, this literature review found over 3,100 journal articles that have

cited the TPB. However, the quantities of research articles that include references to

binge eating behavior are significantly limited. Rather, the instances in which binge

36

eating behaviors or obesity issues are referenced in articles it is in the form of an

independent variable rather contributing to the outcome of the TPB. This study is

looking to demonstrate any relationship between the contributions of the TPB on eating

behaviors.

Adapted motivational interviewing and other therapeutic techniques have been

proven to demonstrate some success with assisting individuals with binge eating disorder

in the process of restraining from binge eating and improved the ability to feel control

over their decision making process (Cassin, von Ranson, Heng, Brar, & Wojtowicz,

2008). Yet, there is limited research available that demonstrates any relationship between

problem solving style and a person’s ability to change their eating behavioral styles. In

order to understand how attitudes and intentions contribute to actual eating behaviors it is

important to factor in the role of cognitive decision making style. Unfortunately, limited

research has examined the TPB with obesity disorders, dieting, or weight loss (Gardner &

Hausenblas, 2002).

Adaption-Innovation Theory of Problem Solving Style

The adaption-innovation theory of problem solving style assesses the relationship

between problem solving and creativity using a cognitive function schema (Kirton,

2003). This theory focuses on understanding the relationship between cognitive function,

which includes cognitive resource (knowledge, skills, and experience) and cognitive

affect (needs, values, and beliefs) in conjunction with cognitive effect which is the level

that a person is born with (such as intelligence) and preferred decision making style

(which is either more or less adaptive or innovative). These major theories are

37

additionally influenced by a person’s preferred style and coping behaviors and the social

effect of their culture and opportunities (Figure 3). These elements contribute to the

manner in which a person makes decisions which may influence eating behaviors.

Figure 3. Note. From “Certification Course (p. 27) by M. J. Kirton, 2008, Occupational

Research Centre: Pennsylvania State University. Copyright 2008 by Kirton. Reprinted

with permission.

The adaption-innovation theory of problem solving style uses the KAI inventory

to measure a person’s style, which is a part of the cognitive effect that all individuals are

born with and does not change throughout the lifespan (Kirton, 2008). Each individual

has a specific score which is a measure of the manner in which the diversity of problem

solving and managing changes can be incorporated into both a person’s lifestyle as well

as in a group context (Kirton, 2008). These scores range from 32, being the most highly

adaptive, to 160, being the most highly innovative (Figure 4). Cognitive style, which is

38

determined using this measure, affects how a person learns and solves problems in a

creative manner.

All individuals problem solve and the manner in which they do such may affect

the success they have with managing change, such as a new eating style, as well as

resulting in a person’s need to cope with a change that does not fit within the style. If a

person must behave in a manner that is not consistent with preferred style, then a person

must perform a behavior that is considered to be a coping skill. For example, resistance to

an idea that is not within a person’s preferred style may be met with objections or

resistance (Kirton, 2008).

Coping behavior can be evaluated by assessing how much effort it takes to

execute a behavior based on how close or far the behavior is related to a person’s

cognitive style. Coping occurs when it is necessary to perform a behavior that is outside

of a person’s preferred style (Kirton, 1995). Individuals will do the minimum amount of

coping as possible because there is a negative psychological cost associated with coping.

Binge eating has been associated with depressive symptomology in women due to the

repetitive coping skills required when a person has an eating disorder (Harrell & Jackson,

2008). Therefore, understanding a person’s preferred problem solving style may be

associated with how comfortable the person is with managing a dietary program or

refraining from binge eating behaviors.

When individuals have to conform to a certain behavioral style of eating, such as

maintaining a strict diet, it may result in feelings of having to cope. This could influence

the success or failure of a healthy eating program as a person who is highly innovative

39

may find it harder to maintain a strict diet that has many rules or they may sense a feeling

of boredom with a very rigorous diet. Alternatively, a person who is highly adaptive may

not be comfortable with a dietary style that is very flexible and does not have clearly

defined parameters (Kirton, 2008).

There is not a better or worse style; rather it is a measure of how comfortable a

person feels with change. A person who is considered to be more innovative is more

likely to be seen by others as being unconventional in thinking style, undisciplined,

nonconforming, bold, risk seekers, flexible, abrasive, and often impractical (Bagozzi &

Foxall, 1995). Alternatively, a person who is considered to be more adaptive is more

likely to be seen by others as being more sensitive to risky ideas, focused on doing things

better rather than differently, prudent, conforming, methodological, disciplined, and

perform better in situations surrounded with structure (Bagozzi & Foxall, 1995, Figure

4).

40

Figure 4. Note. From “Certification Course (p. 76) by M. J. Kirton, 2008, Occupational

Research Centre: Pennsylvania State University. Copyright 2008 by Kirton. Reprinted

with permission.

Many studies have applied the adaption-innovation theory over the years in a

variety of practices resulting in mean scores for various occupations (Kirton, 1996). For

example, bank branch managers, civil servants, plant managers, cost accountants,

programmers, and maintenance engineers have mean scores ranging from 80-90 which

places them on the more adaptive spectrum of the scale. On the more innovative side of

the KAI scale, engineers, research and development managers, and fashion buyers have

mean scores ranging from 101-110. This demonstrates that cognitive style is associated

with work preferences.

41

Additionally, the impact of problem solving style has been investigated in nursing

programs (Adams, 1993), marketing and intelligence planning (Bhate, 1999), musical

compositional development styles of students (Brinkman, 1999), managerial skill

assessments (Buttner, Gryskiewicz, & Hidor, 1999), and problem solving within the

health services (Flanagan, 2007). All of these studies have confirmed that preferred

decision making style is a critical component of personal performance in the workplace

environment.

However, this theory not only applies to preferred working environments, it is

equally applicable in understanding personal behavioral styles. In a study by Hutchinson

and Skinner (2007) the relationship between self-awareness, self-consciousness and

cognitive style was investigating using a population of 55 undergraduate students. Using

multiple regression analyses, students who scored more highly innovative on the KAI

inventory demonstrated lower levels of social anxiety and self-monitoring whereas the

students who scored more highly adaptive demonstrated increased public self-

consciousness and higher private self-consciousness. This is significant in that it

suggests that preferred style is associated with internal decision-making processes.

Cognitive behavioral analyses have noted an association between those who have

binge eating disorder and an analysis of being driven towards perfectionism, self-imposed

standards, and extreme self-evaluative view points (Dunkley, Blankstein, Masheb, &

Grilo, 2003). This is consistent with research using the Adaption-Innovation theory

which demonstrates that cognitive style has a relationship with maladaptive eating

behaviors (Saggin, 1996). Specifically, in a study by Saggin (1996), it was noted that

42

anorexic patients would rigorously adhere to a diet regimen even if it meant risking life

and health. Conversely, binge eaters were less likely to adhere to a dietary program and

would often lapse from their diet for a lengthy time period. Saggin (1996) divided

patients into three groups which were anorexic (n = 8), bulimic (n = 9), and binge eaters

(n = 19). Upon administering the KAI inventory which measures adaptive and innovative

style, the results demonstrated that the anorexic group had a mean KAI score of 76.75,

bulimics had a mean score of 102.66, and binge eaters had a mean score of 111.11. What

this study demonstrated was that anorexic patients had scores that were significantly

more adaptive than the mean score for the general female population (M = 91) and binge

eating patients scored significantly more innovative. This suggests that there is an

opportunity to understand preferred eating behaviors once the preferred problem solving

style is determined. However, to the knowledge of the researcher and based on the

literature review, there has never been an investigation regarding potential relationships

between the innovation-adaption theory with regard to non-clinical eating behaviors

which makes this research pertinent.

Summary

There is significant evidence linking the relationships between self-efficacy,

dieting cycles, body image, and binge eating (Cain, Bardone-Cone, Abramson, Vohs, &

Joiner, 2008). However, there is a lack of understanding associated with the cyclical

behavior of motivation, control, and psychological influences results in negative eating

behaviors such as binges (McDonald, 2003). Although the social influences associated

with binge eating behaviors have been defined, there is a stigma associated with binge

43

eating that results low self-esteem and depression. Additionally, psychologically-related

eating disorders of this nature have been associated with high rates of mortality due to the

obesity related diseases. These findings underscore the need for this research (Newman et

al., 1996). Further investigations to measure perceived behavioral control, attitude,

subjective norms, intention, sufficiency of originality, efficiency, rule/group conformity,

BMI, dietary restraint, eating concern, shape concern, and weight concern will result in

positive social change. The next chapter delineates the proposed research design to assess

these factors.

CHAPTER 3: RESEARCH METHOD

Organization of Chapter

This chapter presents the research design, the setting and sample, and the three

instruments for data collection: the Eating Disorder Examination Questionnaire (EDE-

Q6), TPB Questionnaire, and KAI Inventory. It also outlines the other supplemental

materials including height, weight, and a background data questionnaire used in the

research. Each instrument is discussed in terms of the type of instrument, the concepts

measured by instrument, how scores are calculated and their meaning, the assessment of

reliability and validity of instrument, the process needed to complete instrument by

participants, where raw data will be stored, and a detailed description of data that

comprise each variable in the study. Lastly, the data collection and analysis process is

discussed including an explanation of descriptive analyses used in the study, the nature of

scale for each variable, the statements of hypotheses related to each research question, a

description of analytical tools used, a description of data collection process, and the

protection of human subjects.

Research Design and Approach

This study employed a quantitative design, and used criterion measures and

predictor variables obtained at a single point in time. Due to the sensitive nature of

measuring eating behaviors, it was not ethically justifiable to manipulate health

behaviors, psychological intention, or the natural decision-making processes of human

participants. Therefore, correlation studies were used in the design instead of treatment or

experimental design. An advantage of the use of a correlation design using self-reported

45

surveys is that multiple factors that have not been previously investigated can be assessed

with relative efficiency. A disadvantage of this design is that causality cannot be

determined.

Setting and Sample

Population and Sampling Method

The sample was recruited via convenience sampling techniques (Creswell, 2003).

The population of interest included men and women between 18 and 65 years of age who

were not residing in a hospital or mental health facility, and who volunteered to be

surveyed. No participants were excluded based on gender, ethnicity, occupation, or

education level. The method of sampling was one of convenience (Creswell, 2003) using

available populations from universities, grocery stores, churches, local businesses, or

mailed forms in the greater Boulder, Colorado area. Interested individuals were provided

contact details to participate in the study.

Sample Size

In this study, multiple variables from the general population were investigated so

a non-random sample of convenience was employed. For this study, the alpha level (α)

was set to .05 and the power level was .80. Effect sizes were determined using Cohen’s

(1992) criteria where f2 = 0.02 (small effect), f2 = 0.15 (medium effect), and f2 = 0.35

(large effect). The effect size was set at a medium effect (f2 = 0.15) based on a literature

review using similar KAI and TPB models (Hutchinson & Skinner, 2007; Goldsmith &

Matherly, 1987; Ajzen, 2006). Additionally, a small to medium effect size has been

recommended in a meta-analysis performed by Lipsey and Wilson (1993) in the areas of

46

psychological, educational, and behavioral research. There were 8 predictor variables

which are BMI, perceived behavioral control, attitude, subjective norms, intention,

sufficiency of originality, efficiency, and rule/group conformity. Therefore, to have

adequate power to reach statistical significance for the combined effect of 8 predictors,

the recommend sample size was 108 participants who fully completed the survey. A total

of 140 participants fully completed the survey.

Participants and Characteristics

The eligibility criteria for study participants were that they were not receiving

medical treatment for eating disorders and that they were willing to participate on an

anonymous and voluntary basis. The characteristics of the selected sample were that

volunteers were interested in participating in a study that examines eating behaviors, and

intend to either change or remain in their specific eating behavioral style. The participants

ranged in ages from 18 through 65 and did not report suffering from any terminal

illnesses nor residing in a hospital or mental health facility.

Instruments and Materials

Three instruments were used in this study in the form of surveys in addition to

height, weight, and other background data. The three instruments were the Eating

Disorder Examination Questionnaire, the TPB Questionnaire, and the KAI Inventory, and

a background data questionnaire are described in detail below. Body mass index was

calculated from height and weight dimensions. All surveys were conducted using pen and

paper and were formatted in a self-report design.

47

Body Mass Index

A currently accepted measure to assess a person’s body fatness is the body mass

index referred to as BMI (CDC, 2008). This type of instrument is a tool that is used to

screen for possible weight problems for adults using standard weight categories for

adults: underweight, normal, overweight, and obese (Mei et al., 2002).

BMI scores were calculated using height and weight measurements. The

calculation for pounds and inches measures is: (weight (lb) / [height (in)]2) * 703. Often

the raw score is classified in four categories which are underweight (BMI = less than

18.5), normal (BMI = 18.5 to 24.9), overweight (BMI = 25.0 – 29.9) and obese (BMI =

30.0 or greater).

In empirical research, correlation coefficients for height and weight using the

BMI have been 0.99 and 0.96 (p = 0.0001) (Nakamura, Hoshino, Kodama, & Yamamoto,

1999); in addition, the Center for Disease Control noted that calculating BMI as a

screening tool is one of the best methods to assess the general public to determine obesity

or being overweight (2009). The BMI measurement concluded that using 95%

confidence intervals demonstrated a higher risk for health issues such as coronary heart

disease (Willet et al., 1995). Although research demonstrates the reliability and validity

of the BMI, there are still challenges with the fact that a person with a BMI over 25

would be considered obese, a category which would inadvertently include healthy

athletes. Despite these limitations in measurement, athletes were not the focused

population of this study.

48

The process needed to complete the instrument by participants was contained in

two questions requesting the height in inches and the weight in pounds using paper and

pencil. The variables of height and weight were entered into a SPSS datafile and the

participants’ names were coded numerically. The raw data were presented in tables and

maintained by the researcher in a secure locked location in the research lab to be

available on request only to qualified professionals.

Eating Disorder Examination Questionnaire, EDE-Q6

The Eating Disorder Examination Questionnaire, referred to as the EDE-Q6, is a

self-reported version of the original Eating Disorder Examination Edition 16.0D. The

EDE-Q6 is scored in the same manner but allows for a similar assessment without the

longer qualitative interview process and interpretation (Fairburn, 2008). This type of

instrument is quantitative and focused on self reported behaviors that have occurred

within the last four weeks.

The concepts measured by the EDE-Q6 are based on subscales which were

specified in the categories of restraint, eating concern, shape concern, and weight

concern. These four subscales are the criterion variables. The EDE-Q6 also has a

category that measures the frequency of occurrence. These questions, which are items

13-18, are not necessary to calculate a global EDE-Q6 score and thus were not included

within the four subscale criterion variables (Fairburn, 2008).

The restraint category measures the variables of empty stomach, dietary rules,

restraint, avoidance of eating, and food avoidance. The scale of restraint consists of five

items and the instrument item numbers for this subscale are 1, 2, 3, 4, and 5. Some

49

example items include in the past 28 days “have you had a definite desire to have an

empty stomach with the aim of influencing your shape or weight” and “have you gone for

long periods of time (8 waking hours or more) without eating anything in order to

influence your shape or weight”? The range of the score is 0-6. The possible response

options are 0 days (score = 0), 1-5 days (score = 1), 6-12 days (score = 2), 13-15 days

(score = 3), 16-22 days (score = 4), 23-27 days (score = 5), or 28 days (score = 6). This

subscale is specifically calculated by adding each score together and then the sum is

divided by the total number of items forming the subscore. The community norm for this

subscale is M = 1.251, SD = 1.323 (Fairburn, 2008). A lower score would imply a less

symptomatic focus on eating restraint where as a higher score would imply a greater

symptomatic focus on eating restraint. The Cronbach’s alpha value for this subscale is .84

(Luce & Crowther, 1999).

The eating concern category measures guilt about eating, fear of losing control

over eating, social eating, preoccupation regarding eating, and secretive eating. The scale

of eating concern consists of five items and the instrument item numbers for this subscale

are 7, 9, 19, 20, and 21. Some example items include in the past 28 days “has thinking

about food, eating, or calories made it very difficult to concentrate on things you are

interested in (for example, reading, working, following a conversation” and “have you

had a definite fear of losing control over eating”? The range of the score is 0-6. The

possible response options are 0 days (score = 0), 1-5 days (score = 1), 6-12 days (score =

2), 13-15 days (score = 3), 16-22 days (score = 4), 23-27 days (score = 5), or 28 days

(score = 6). This subscale is specifically calculated by adding each score together and

50

then the sum is divided by the total number of items forming the subscore. The

community norm for this subscale is M = 0.624, SD = 0.859. A lower score would imply

a less symptomatic focus on eating concern whereas a higher score would imply a greater

symptomatic focus on eating concern. The Cronbach’s alpha value for this subscale is .78

(Luce & Crowther, 1999).

The shape concern category measures feelings of fatness, flat stomach,

preoccupation with shape, importance of shape, fear of weight gain, discomfort of

visualization of body, and avoidance of body exposure. The scale of shape concern

consists of eight items and the instrument item numbers for this subscale are 6, 8, 10, 11,

23, 26, 27, and 28. Some example items include in the past 28 days “have you had a

desire to have a totally flat stomach” and “has your shape influenced how you think

(judge) yourself as a person”? The range of the score is 0-6. The possible response

options are 0 days (score = 0), 1-5 days (score = 1), 6-12 days (score = 2), 13-15 days

(score = 3), 16-22 days (score = 4), 23-27 days (score = 5), or 28 days (score = 6). The

range of the score is 0-6. This subscale is specifically calculated by adding each score

together and then the sum is divided by the total number of items forming the subscore.

The community norm for this subscale is 2.149 (SD = 1.602) (Fairburn, 2008). A lower

score would imply a less symptomatic focus on shape concern where as a higher score

would imply a greater symptomatic focus on shape concern. The Cronbach’s alpha value

for this subscale is .93 (Luce & Crowther, 1999).

The weight concern category includes the importance of weight, the desire to lose

weight, dissatisfaction with current weight, reaction to recommended weight loss advice,

51

and preoccupation with weight. The scale of weight concern consists of five items and

the instrument item numbers for this subscale are 8, 12, 22, 24, and 25. Some example

items include in the past 28 days “has your weight influenced how you think about

(judge) yourself as a person” and “have you had a strong desire to lose weight”? The

range of the score is 0-6. The possible response options are listed on a scale of 0-6 and

the participant selects the number in accordance to not at all (score = 0), slightly (score =

2), moderately (score = 4), or markedly (score = 6) going from left to right. This subscale

is specifically calculated by adding each score together and then the sum is divided by the

total number of items forming the subscore. The community norm for this subscale is

1.587 (SD = 1.369) (Fairburn, 2008). A lower score would imply a less symptomatic

focus on shape concern where as a higher score would imply a greater symptomatic focus

on shape concern. The Cronbach’s alpha value for this subscale is .89 (Luce & Crowther,

1999).

The process for assessment of reliability and validity of the EDE-Q6 has been

obtained by a literature review of over 40 publications from the Centre for Research on

Eating Disorders at Oxford (2009). Specifically, Luce and Crowther (1999) investigated

the internal consistency and the test-retest reliability of the EDE-Q including the overall

score and the subscales. Using Pearson correlation coefficients the researchers

determined that all of the correlations measuring behavioral features, such as binge

eating, were statistically significant. Additionally, Cronbach alphas were used to

investigate the internal consistency of the four subscales and the exceeded recommended

levels while Pearson correlation demonstrated statistical significance when investigating

52

the stability of the results over time. The EDE-Q has also been demonstrated using a

general population of women with ages ranging from 18-45 in a test-retest interval of 315

days (Mond, Hay, Rodgers, Owen, & Beaumont, 2004). This study demonstrated the

instrument had Pearson correlations, when assessing attitudinal features, of 0.57 for the

restraint subscale and 0.77 for the eating concern subscale. Additionally, the instrument

had a high internal consistency with a Cronbach’s alpha coefficient of 0.93 for the global

scale.

The process needed to complete the instrument by participants was a pen and

paper and the data were entered into a SPSS datafile. The participants’ names were

coded numerically. The raw data were presented in tables and maintained by the

researcher in a secure locked location in the research lab to be available on request to

qualified professionals.

Theory of Planned Behavior Questionnaire

The TPB questionnaire measures relationships between attitude, subjective norms,

perceived behavioral control and intention (Ajzen 2006). This instrument is a self-report

survey design that uses a Likert-scale measurement system to predict health behaviors.

Specifically, the total measurement addresses whether or not a person can perform a

specific health behavior. For the purpose of this research four predictor variables from the

TPB will be measured. They are attitude, which measures how much the person is in

favor of performing the behavior, subjective norms, which measures the social pressure a

person feels to perform a behavior, perceived behavioral control, which measures the

53

internal control a person believes exists over the behavior and intention, which measures

the likelihood a person will demonstrate the specific behavior.

Attitudes measure a person’s overall evaluation of the behavior of binging, or

overeating (Francis et al., 2004). The attitude subscale consists of 3 items and the

instrument item numbers for this subscale are 1, 2, and 3. The questions are arranged in a

possible response option, ranging from left to right, describing how participant’s attitude

ranges on a scale of 1-7. This subscale is specifically calculated by adding each score

together to form an overall attitude sum composite subscore. The participant will place

an X on one of the 7 dots. The range of the sum composite subscore is 3-21. Some

example items include on a scale of 1-7, with a 1 being extremely worthless and a 7 being

extremely useful, “healthy eating on a regular basis is”, and on a scale of 1-7, with a 1

being not important at all and a 7 being very important, “maintaining a healthy diet is”. A

lower score would imply a poor attitude towards healthy eating and a higher score would

imply a positive attitude towards not overeating. The Cronbach’s alpha value for this

subscale is .83 (Conner & Norman, 2002; Francis et al., 2004).

Subjective norms measure a person’s own estimate of social pressure to overeat or

abstain from eating (Francis et al., 2004). The subjective norms subscale consists of 3

items and the instrument item numbers for this subscale are 4, 5, and 6. The questions are

arranged in a possible response option, ranging from left to right, describing how

participant’s attitude ranges on a scale of 1-7. The participant will place an X on one of

the 7 dots. This subscale is specifically calculated by adding each score together to form

an overall subjective norm sum composite subscore. The range of the sum composite

54

subscore is 3-21. Some example items include on a scale of 1-7, with a 1 being not

important and a 7 being very important, “people that are important to me think that

keeping a healthy weight is”, and on a scale of 1-7, with a 1 being not important at all and

a 7 being very important, “what my doctor or health care provider thinks I should do to

eat healthy is”. A lower score would imply a low social pressure towards healthy eating

and a higher score would imply higher social pressure towards not overeating. The

Cronbach’s alpha value for this subscale is .84 (Conner & Norman, 2002; Francis et al.,

2004).

Perceived behavioral control measures the extent in which a person has a feeling

of being able to control how much they eat (Francis et al., 2004). The perceived

behavioral control subscale consists of 3 items and the instrument item numbers for this

subscale are 7, 8, and 9. The questions are arranged in a possible response option, ranging

from left to right, describing how participant’s attitude ranges on a scale of 1-7. The

participant will place an X on one of the 7 dots. This subscale is specifically calculated

by adding each score together to form an overall perceived behavioral control sum

composite subscore. The range of the sub composite subscore is 3-21. Some example

items include on a scale of 1-7, with a 1 being strongly agree and a 7 being strongly

disagree, “my weight or shape is in my control”, and on a scale of 1-7, with a 1 being

strongly agree and a 7 being strongly disagree, “the decision to stick to a diet program is

beyond my control”. A lower score would imply a person feels unable to manage control

of their weight whereas a higher score would imply a person feels has the internal control

55

to manage weight. The Cronbach’s alpha value for this subscale is .74 (Conner &

Norman, 2002; Francis et al., 2004).

Intention is a proximal measure of behavior towards a person’s eating behavior

(Francis et al., 2004). The intention subscale consists of 3 items and the instrument item

numbers for this subscale are 10, 11, and 12. The questions are arranged in a possible

response option, ranging from left to right, describing how participant’s attitude ranges

on a scale of 1-7. This subscale is specifically calculated by adding each score together

to form an overall intentions sum composite subscore. The participant will place an X on

one of the 7 dots. The range of the sub composite subscore is 3-21. Some example items

include on a scale of 1-7, with a 1 being extremely difficult and a 7 being extremely easy,

“for me, intending to eat healthy on a daily basis is”, and on a scale of 1-7, with a 1 being

strongly disagree and a 7 being strongly agree, “I intend to maintain healthy eating

behaviors on a daily basis”. A lower score would imply a person does not intend to

manage eating behaviors whereas a higher score would imply a person does intend to

manage eating behaviors. The Cronbach’s alpha value for this subscale is .82 (Conner &

Norman, 2002; Francis et al., 2004).

A brief form of the questionnaire was used for the purpose of this research as the

goal is an analysis to predict variance in behavioral intentions. Therefore, this format

resulted in a questionnaire that has three questions for each of the four predictor variable

items as recommended by the Constructing Theory of Planned Behavior Questionnaires

Manual (2004).

56

The process needed to complete instrument by participants was a pen and paper

responses to a total of 12 questions. The hard copy raw data were calculated using SPSS

and the participants names were coded numerically. The raw data were presented in

tables and maintained in a secure locked location in the research lab to be available on

request to qualified professionals.

Kirton Adaption-Innovation Inventory

The KAI is an instrument that is a self-report questionnaire that asks the

participant to rate on a bipolar scale how easy or difficult it is to present oneself

consistently over a long period of time with a specific style of behavior. This type of

instrument is quantitative and requires certification to administer which is obtained by

attending a week long training session as well as passing a certification test. Each

questionnaire is numerically identified and registered by the Occupational Research

Center in the United Kingdom and may not be administered in electronic format. The

concepts measured by this instrument are focused on the manner in which individuals use

creativity to solve problems and manage change (Kirton, 1999).

Scores and their meaning are calculated through a scoring method that is based on

three sub scores which, when totaled result in one final score for the inventory. The

possible range of scores for this inventory is between 32, being highly adaptive, through

160, being highly innovative. The population mean is 96 with male scores being normally

distributed at 91 and female scores being normally distributed at 98 (Kirton, 1999).

Research has also demonstrated relationships with professionals and their mean scores.

For example, in the area of marketing, finance, or fashion buyers have a mean score

57

ranging from 104-110 where as accountants, programmers, and plant managers have a

mean score ranging from 80-90 (Kirton, 1999).

The KAI has been studied in multiple populations to confirm its construct validity

(Goldsmith, 1985). Bagozzi and Foxall (1995) performed a confirmatory factor analysis

which demonstrated satisfactory levels of reliability as well as strong evidence for

convergent and discriminate validity using postgraduate students in the United Kingdom,

Australia, and the United States.

There are three subscores that compile the overall KAI score which are

sufficiency of originality, efficiency, and rule/group conformity (Kirton, 1976, 1999,

2003). The first concept is sufficiency of originality (SO). SO measures the manner in

which a person generates ideas. Innovators have a tendency to generate large amounts of

ideas in comparison with those who are more adaptive. These ideas are often paradigm

breaking and may result in problem solving solutions that may not be readily accepted by

others, may seem unsound, bizarre, or even outside of the scope of the problem entirely.

Additionally, those who are more innovative tolerate a higher failure rate of their ideas

although the quantity of their ideas is large. Adaptors solve problems differently. Their

level of SO reflects idea generation approach that is focused more on improvements to

the current problem rather than the out of the paradigm idea generation style of

innovators. Although adaptors generate fewer ideas than innovators, they expect a higher

success rate from their ideas.

The SO subscale consists of 13 items and the instrument item numbers for this

subscale are 3, 5, 11, 12, 13, 16, 18, 19, 21, 23, 24, 26, and 31. The questions are

58

arranged in a possible response option, ranging from left to right, from very hard, hard,

easy, and very easy on a 17 point dotted scale. The participant will place an X on any

location of the continuum scale that contains 17 dots. Each X can be converted to a raw

score between 1-5. SO scores range from 13 through 65 (M = 41, SD= 9). This subscale

is specifically calculated by adding each score together to form an overall SO sum

composite subscore. Some example items include how easy or difficult do you find it to

present yourself, consistently, over a long period of time as “a person who when stuck

will always think of something” or “a person who has fresh perspectives on old

problems”. A lower score would imply a more adaptive style of ideas created inside the

paradigm within a consensually agreed structure. A higher score would imply a more

innovative style of ideas formed, usually outside the paradigm, with less regard for

consensually agreed structure. The Cronbach’s alpha value for this subscale is .81

(Kirton, 2009).

The second concept measured by KAI is efficiency (E). Not to be confused with

SO which measures the style of idea generation, E measures the concept of a person’s

problem solving methods or processes. The efficiency concept helps clarify the manner in

which a person problem solves with those being more innovative likely be less

methodological and to pay less attention to the detail of solving the problem and accept a

higher level of risk with the proposed solution. Adaptors prefer to work closely with the

existing system that surrounds the problem in a rigorous and methodological way to

improve the current structure while tolerating much less risk in their solutions.

59

The E subscale consists of 7 items and the instrument item numbers for this

subscale are 4, 14, 15, 17, 22, 25, and 28. The questions are arranged in a possible

response option, ranging from left to right, from very hard, hard, easy, and very easy on a

17 point dotted scale. The participant will place an X on any location of the continuum

scale that contains 17 dots. Each X can be converted to a raw score between 1-5. E

scores range from 7 through 35 (M = 19, SD = 6). This subscale is specifically calculated

by adding each score together to form an overall E sum composite subscore. Some

example items include how easy or difficult do you find it to present yourself,

consistently, over a long period of time as “a person who enjoys detailed work” or “a

person who is methodological and systematic”. A lower score would imply a more

adaptive style of working within an existing system to solve a problem whereas a higher

score would imply a more innovative style of looking outside of a system to solve a

problem. The Cronbach’s alpha value for this subscale is .76 (Kirton, 2009).

The third concept measured by KAI is rule/group conformity (R). This concept

focuses on how style, being more or less adaptive or innovative, affects the structures in

which problem solving occurs. Adaptors are more likely to accept group conformity and

look for collaboration in problem solving processes. They prefer rules and guidelines for

solving problems. Innovators are more likely to have less regard for rules, guidelines, or

structure when solving problems. They may be more comfortable bending or breaking

rules in order to solve a problem or make a decision.

The R subscale consists of 12 items and the instrument item numbers for this

subscale are 2, 6, 7, 8, 9, 10, 20, 27, 29, 30, 32, and 33. The questions are arranged in a

60

possible response option, ranging from left to right, from very hard, hard, easy, and very

easy on a 17 point dotted scale. The participant will place an X on any location of the

continuum scale that contains 17 dots. Each X can be converted to a raw score between

1-5. R scores range from 12 through 60 (M = 36, SD = 9). This subscale is specifically

calculated by adding each score together to form an overall R sum composite subscore.

Some example items include how easy or difficult do you find it to present yourself,

consistently, over a long period of time as “a person who conforms” or “a person who

holds back ideas until they are obviously needed”. A lower score would imply a more

adaptive style in which a person prefers to work within a group and have group cohesion

whereas a higher score would imply a more innovative style in which a person prefers to

initiate changes that may result in going outside of the rule/group structure. The

Cronbach’s alpha value for this subscale is .82 (Kirton, 2009).

The process needed to complete instrument by participants was a pen and paper

answer to a total of 33 questions, one which is not graded, resulting in a final total of 32

questions. These responses were anchored to a carbon copy that compiles them into a

score of 1 through 5. The instrument takes approximately 15 minutes to complete

(Kirton, 1999). The data from each KAI inventory was entered into a SPSS datafile and

the participants names were coded numerically and maintained by the researcher in a

secure location in the research lab in a double locked environment to be available on

request to qualified professionals.

61

Background Data Questionnaire

A general demographics survey was also administered in addition to the consent

form. This instrument was comprised of fill in the blank questions to gather information

to assist in the data collection process. The questionnaire gathered information regarding

age, gender, and ethnic group. The process needed to complete instrument by participants

was a pen and paper answer to a total of three questions. The data from the questionnaire

was entered into a SPSS datafile and the participants names were coded numerically and

maintained by the researcher in a secure locked location in the research lab to be

available on request to qualified professionals.

Data Collection and Analysis

The specific research question was if each of four components of eating behavior

are affected by the variables of BMI, perceived behavioral control, attitude, subjective

norms, intentions, sufficiency of originality, efficiency, and rule/group conformity. In

order to answer this research questions the following hypotheses were tested.

Null Hypotheses (Ho)

Null 1: In a hierarchical multiple regression there will be no significant relationship

between the predictor variables (perceived behavioral control, attitude, subjective norms,

and intentions as measured by TPB, and sufficiency of originality, efficiency, and

rule/group conformity as measured by KAI, and BMI) and dietary restraint as measured

by EDE-Q6 (R = 0).



62


rule/group conformity as measured by KAI, and BMI) and eating concern as measured by

EDE-Q6 (R = 0).




rule/group conformity as measured by KAI, and BMI) and shape concern as measured by

EDE-Q6 (R = 0).




rule/group conformity as measured by KAI, and BMI) and weight concern as measured

by EDE-Q6 (R = 0).

For the purpose of this study a hierarchical multiple regression model was

performed. As demonstrated by prior research, hierarchical regression was appropriate

for this type of research since there are more than two variables that are going to be

measured to obtain predictions regarding eating behaviors (Ajzen, 2008; Hair, Anderson,

Tatham, & Black, 1998; Francis et al., 2004; Gravetter & Wallnau, 2007; & Hutchinson

& Skinner, 2007). A hierarchical regression assesses multiple predictor variables that

may or may not generate a model that demonstrates a best fitting equation for a criterion

variable. The coefficient of determination (R), obtained from the analysis, can provide an

explanation for the proportion of variability in criterion variables accounted by the

63

variability in predictor variables. Hierarchical regression adds terms to the regression

model in stages. At each stage, an additional term or terms are added to the model and the

change in R is calculated. A hypothesis test is done to test whether the change in R is

significantly different from zero. For the present study, BMI was first entered into the

model and then TPB and KAI were entered into the model as a set.

Nature of Scales

The eight predictor variables included in this study were BMI, perceived

behavioral control, attitude, subjective norms, intention, sufficiency of originality,

efficiency, and rule/group conformity. All predictor variables are expressed in ordinal

scales of measurement. Each variable from the TPB (perceived behavioral control,

attitude, subjective norms, and intention) can take a value from 3 through 21. The SO

variable from the KAI inventory can take a value of 13 through 65. The E variable from

the KAI inventory can take a value of 7 through 35. The R variable from the KAI

inventory can take a value of 12 through 60. The criterion variables EDE-Q6 also form

ordinal measures of scale. Each of the variables, namely dietary restraint, eating concern,

shape concern, and weight concern, can take an ordinal value from 0 through 6.

Protection of Participant’s Rights

The protections of participants’ rights were a vital part of this research study.

Participants completed an informed consent form prior to the administration of the

surveys and their identity was protected as they were numerically identified. The data

were collected and maintained in a private research location that has a lock on the file

cabinet as well as the main entrance which is only accessible to the researcher. As the

64

participation in this study was voluntary and the participants were not associated with the

researcher on any personal or professional manner the protection of the participants’

rights were, and are, maintained. Although no unforeseeable psychological distresses

arose, if they do the participants will be provided with a list of local medical facilities as

well eating disorder treatment resources. Participants were able to withdraw from the

study at any time with no penalty.

Summary

It has been demonstrated that a great deal of research has focused on cognitive-

behavioral therapy for the treatment of binge eating disorders; however, that effort has

not completely solved the challenge of understanding how binge eating behaviors occur

(Wilfey et al., 2008). Normal individuals who demonstrate some levels of binge eating

disorders have higher lifetime rates of social maladjustment, anxiety, and mood disorders

(Stice et al., 2000). The following two chapters discuss the findings of the research as

they are related to the hypotheses and research question, the overall data analysis process,

and the outcomes. Interpretations of the findings, recommendations for further studies,

and how they relate to positive social change are also addressed.

CHAPTER 4: RESULTS

Introduction

This chapter presents the process of data screening, the demographic data for the

participants, and the results of the hierarchical regression analyses. The purpose of this

study was to assess the combined effects of individual problem solving styles (sufficiency

of originality, efficiency, and rule/group conformity) and planned behavior (attitudes

towards overeating, subjective norms, behavioral intentions to manage eating behavior,

perceived behavioral control), after first controlling for body mass index, on eating

behaviors. This study proposes a relationship between the predictor variables (perceived

behavioral control, attitude towards overeating, subjective norms, and intentions to

manage eating behavior as measured by TPB, and sufficiency of originality, efficiency,

and rule/group conformity as measured by KAI, and BMI) and eating behaviors as

measured by EDE-Q6. In order to investigate this relationship, the specific research

question was presented: Is eating behavior affected by body mass index, perceived

behavioral control, attitude towards overeating, subjective norms, intention to manage

eating behavior, sufficiency of originality, efficiency, and rule/group conformity?

Data Screening and Cleaning

The data collection instruments and the process of data collection followed all the

guidelines described in chapter 3. A total of 145 participants responded to the

questionnaire employed in this study. After data collection, the data were visually

screened and five surveys were excluded from the sample. Three of these surveys were

missing responses on an entire page of the survey. Kirton (1999) explained that

66

participants who frequently answer the KAI instrument with ten or more 3s (the median

point on the response scale) often are unwilling to commit to honestly disclosing

cognitive style and thus, according to Condition 3 of scoring the KAI instrument, results

in a score that is unreliable and must be dismissed (Kirton, 2008). Two completed

surveys had ten or more 3s, including 1 or 2 omitted responses, in the KAI section of the

questionnaire and were therefore dismissed.

The data from the remaining 140 questionnaires were entered into an SPSS

version 15.0 data set document. Male participants were coded using the value “1” and

female participants were coded using the value “2”. Participants’ ethnicities were coded

as European American = 1, African American = 2, Hispanic American = 3, Asian

American = 4, and Native American = 5.

Assumptions and Pretest Analyses

Prior to accepting the results of a multiple regression analysis a number of

assumptions regarding the data collected should be checked. These considerations

include the following: outliers, multicollinearity, normality of residuals, homoscedasticity

of residuals, and reliability analyses.

Outliers

To determine whether the remaining data included any outliers, a regression

analysis was conducted that involved all variables. The procedure produced the

maximum value of 40.592 for the Mahalanobis distance. This distance is evaluated

against chi-square at a p value of .001 for a degree of freedom equal to the number of

variables. In this case there were a total of nine variables and the critical value calculated

67

was 27.88. Therefore, any case with a value greater than 27.88 was considered to be a

multivariate outlier. Three cases in data rows 69, 98, and 105 fell into this category and

thus reduced the sample size to 137.

Multicollinearity, Normality, Linearity, and Homoscedasticity

An analysis of the relationships among the independent variables is required when

using multiple regression modeling so correlations were checked between the eight

independent variables. Pearson correlations between the IVs ranged from r(137) = .005,

p = .479 to r(137) = .650, p < .001. Table 1 contains additional relevant correlation

statistics.

Table 1

Correlations: IVs by IVs Variables BMI A SN PBC I SO E R BMI

1.000

A

-.021 1.000

SN

-.018 .448* 1.000

PBC

-.240* .154* .315* 1.000

I

-.169* .404* .323* .650* 1.000

SO

-.020 -.027 .031 .334* .221* 1.000

E

-.023 -.010 .005 .139 .167* .313* 1.000

R -.052 -.011 .029 .201* .081 .366* .502* 1.000 Note. N= 137. BMI = Body Mass Index; A = Attitude; SN = Subjective Norm; PBC = Perceived Behavioral Control; I = Intentions; SO = Sufficiency of Originality; E = Efficiency; and R = Rule/Group Conformity. * p <.05.

68

The Variance Inflation Factor (VIF) values were all under 5, making the possibility of

collinearity between independent variables unlikely. Further, a review of Scatterplots of

the Standardized Residuals by the Regression Standardized Predicted Values revealed

randomly scattered residuals around the horizontal line which demonstrated relatively

homogenous distributions for all variables.

Sample Characteristics

The final sample contained 137 participants; 55.5% were female and 44.5% were

male. The ages ranged from 18 years old to 64 years old (median = 39). The majority of

respondents were European American (85.4%) followed by Hispanic Americans (8.0%)

and included African Americans (2.9%), Asian Americans (2.9%), and a Native

American (0.7%). Table 2 provides additional sample characteristics.

Table 2

Demographic Characteristics of Study Sample (N = 137) Characteristic N Percent Gender Male 61 44.5Female 76 55.5Total

137 100.0

Ethnicity European American 117 85.4African American 4 2.9Hispanic American 11 8.0Asian American 4 2.9Native American 1 .7Total 137 100.0

69

Data Analyses

Reliability Analysis

Cronbach’s alpha coefficient for internal consistency reliability should be

calculated for any scales or subscales one may be using (Gliem & Gliem, 2003).

Therefore, Cronbach’s alpha coefficient for internal consistency and reliability for all

subscales in this study were calculated using SPSS.

The EDE-Q6 contains four subscales. The dietary restraint subscale consisted of 5

items (α = .775), the eating concern subscale consisted of 5 items (α = .867), the shape

concern subscale consisted of 8 items (α = .919), and the weight concern subscale

consisted of 5 items (α =.790). The Cronbach’s alphas for these subscales are within

acceptable range.

The TPB questionnaire contains four subscales. The attitude subscale consisted of

3 items (α = .764), the subjective norm subscale consisted of 3 items (α = .842), the

perceived behavioral control subscale consisted of 3 items (α = .829), and the intention

subscale consisted of 3 items (α =.765). The Cronbach’s alphas for these subscales are

within acceptable range.

Lastly, the KAI inventory contains three subscales. The rule/group conformity

subscale consisted of 12 items (α = .732), the efficiency subscale consisted of 7 items (α

= .824), and the sufficiency of originality subscale consisted of 13 items (α =.717). The

Cronbach’s alphas for these subscales are within acceptable range.

70

Descriptive Statistics

Descriptive statistics for the variables are depicted in Table 3. The total sample of

the predictor variables reported a mean BMI score of 26.02 (SD = 5.17) with the potential

range of scores of less than 18.5 being underweight and greater than 30 being obese ,

mean Attitude score of 16.84 (SD = 3.52) with the range of scores being 3 to 21, mean

Subjective Norm score of 15.62 (SD = 3.97) with the range of scores being 3 to 21, mean

Perceived Behavioral Control score of 15.09 (SD = 4.17) with the range of scores being 3

to 21, mean Intention score of 13.48 (SD = 3.79) with the range of scores being 3 to 21,

mean Sufficiency of Originality score of 42.70 (SD = 7.02) with the range of scores being

13 through 65, mean Efficiency score of 20.48 (SD = 6.13) with the range of scores being

7 through 35, and mean Rule/Group score of 35.43 (SD = 7.23) with the range of scores

being 12 through 60.

The total sample of the criterion variables reported mean Dietary Restraint score

of 1.55 (SD = 1.42) with the range of scores being 0 through 6, mean Eating Concern

score of .741 (SD = 1.07) with the range of scores being 0 through 6, mean Shape

Concern score of 2.23 (SD = 1.71) with the range of scores being 0 through 6, and mean

Weight Concern score of 1.74 (SD = 1.45) with the range of scores being 0 through 6.

71

Table 3

Descriptive Statistics for Variables N Minimum Maximum Mean SD Body Mass Index 137 17.37 50.07 26.02 5.178Restraint 137 0.00 5.00 1.55 1.422Eating Concern 137 0.00 4.80 .74 1.079Shape Concern 137 0.00 6.00 2.23 1.711Weight Concern 137 0.00 5.40 1.74 1.452Attitude 137 3.00 21.00 16.84 3.524Subjective Norms 137 3.00 21.00 15.62 3.971Perceived Behavioral Control 137 4.00 21.00 15.09 4.173Intention 137 3.00 21.00 13.48 3.793Sufficiency of Originality 137 25.00 58.00 42.70 7.023Efficiency 137 8.00 33.00 20.48 6.135Rule/Group Conformity 137 17.00 52.00 35.43 7.238

The EDE-Q6 contains the four criterion variables, described prior in the study,

and also calculates an overall mean global score by adding the subscore totals together

and then dividing by four. In this research, the total sample reported a mean EDE-Q6

global score of 1.56 (N = 137, SD = 1.21). Table 4 presents descriptive data and

percentile ranks for the EDE-Q6 global score and four subscale scores for this research

sample.

72

Table 4

EDE-Q6 Percentile Ranks for EDE-Q6 Global and Subscale Scores (N = 137) GS R EC SC WC

Percentile rank 5 0.02 0.00 0.00 0.00 0.00 10 0.12 0.00 0.00 0.12 0.00 15 0.29 0.00 0.00 0.25 0.20 20 0.43 0.00 0.00 0.50 0.40 25 0.58 0.20 0.20 0.75 0.40 30 0.71 0.40 0.20 0.87 0.60 35 0.82 0.60 0.20 1.25 0.80 40 0.99 0.80 0.20 1.62 1.04 45 1.30 1.00 0.20 1.87 1.22 50 1.31 1.20 0.40 2.00 1.60 55 1.50 1.40 0.40 2.11 1.60 60 1.69 1.96 0.40 2.25 1.80 65 1.86 2.00 0.60 2.87 2.00 70 2.03 2.40 0.60 3.12 2.40 75 2.25 2.40 0.80 3.50 2.80 80 2.57 3.00 1.08 3.75 3.20 85 3.08 3.32 1.40 4.50 3.40 90 3.38 3.64 1.84 5.15 4.00 95 4.06 4.20 3.80 5.37 4.60

Note. GS = mean global score, R = dietary restraint subscale, EC = eating concern subscale, SC = shape concern subscale, WC = weight concern subscale.

Hierarchical Multiple Regression Analyses

A hierarchical multiple regression analysis was performed on each of the four

criterion variables to test the hypothesis. To determine the relative relationship between

the predictor variables and eating behavior, variables were entered using a hierarchical

block approach. Body mass index was entered first to account for as much variance as

possible in the criterion variable. Subsequently, the remaining predictor variables were

entered to account for any remaining variance.

73

The first component of eating behavior this study examined was dietary restraint.

In the first regression model body mass index was not found to be statistically significant,

R2 = .015, F(1, 135) = 2.007, p = .159. When perceived behavioral control, attitude,

subjective norms, intentions, and sufficiency of originality, efficiency, and rule/group

conformity were entered into the equation, the change in variance accounted for a

significant proportion of the dietary restraint variance after controlling for the effects of

body mass index, R2 change = .148, F(7, 128) = 3.102, p = .003. Table 5 provides the

regression summary.

74

Table 5

Summary of Hierarchical Regression Analysis for Variables Predicting Dietary Restraint (N = 137)

Variable

B

SEB

β

Sig.

Step 1

Body Mass Index

.033 .023 .121 .159

Step 2

Body Mass Index

.021 .023 .077 .361

Attitude

.118 .040 .292 .003

Subjective Norm

.051 .034 .142 .134

Perceived Behavioral Control

-.069 .040 -.201 .089

Intention

-.014 .044 -.038 .750

Sufficiency of Originality

.010 .019 .048 .608

Efficiency

-.023 .022 -.100 .301

Rule/Group Conformity

.012 .019 .062 .528

Note. R2 = .015 for Step 1; ΔR2 = .148 for Step 2 ( p < .05). In the second model it was found that attitude towards overeating significantly predicted

dietary restraint (β = .292, p =. 003).

The second component of eating behavior this study examined was eating

concern. In the first regression model body mass index accounted for 5.2% of the eating

concern variability, R2 = .052, F(1, 135) = 7.340, p = .008. When perceived

behavioral control, attitude, subjective norms, intentions, and sufficiency of originality,

efficiency, and rule/group conformity were entered into the equation, the change in

75

variance accounted for a significant proportion of the eating concern variance after

controlling for the effects of body mass index, R2 change = .253, F(7, 128) = 6.993, p

< .001. Table 6 provides the regression summary.

Table 6

Summary of Hierarchical Regression Analysis for Variables Predicting Eating Concern (N = 137)

Variable

B

SEB

β

Sig.

Step 1

Body Mass Index

.047 .017 .227 .008

Step 2

Body Mass Index

.022 .016 .106 .168

Attitude

.060 .027 .197 .029

Subjective Norm

.025 .023 .091 .292


-.083 .028 -.319 .003

Intention

-.084 .031 -.296 .007


.003 .013 .018 .835

Efficiency

.019 .015 .107 .225


-.010 .013 -.064 .473

Note. R2 = .052 for Step 1; ΔR2 = .253 for Step 2 ( p < .05).

In the second model it was found that attitude towards overeating significantly predicted

eating concern (β = .197, p =. 029), as did perceived behavioral control (β = -.319, p =

.003), and intention to manage eating behavior (β = -.296, p = .007).

76

The third component of eating behavior this study examined was shape concern.

In the first regression model body mass index accounted for 8.8% of the shape concern

variability, R2 = .088, F(1, 135) = 13.023, p < .001. When perceived behavioral

control, attitude, subjective norms, intentions, and sufficiency of originality, efficiency,

and rule/group conformity were entered into the equation, the change in variance

accounted for a significant proportion of the shape concern variance after controlling for

the effects of body mass index, R2 change = .315, F(7, 128) = 10.882, p < .001. Table

7 provides the regression summary.

77

Table 7

Summary of Hierarchical Regression Analysis for Variables Predicting Shape Concern (N = 137)

Variable

B

SEB

β

Sig.

Step 1

Body Mass Index

.098 .027 .297 .000

Step 2

Body Mass Index

.060 .023 .183 .011

Attitude

.169 .040 .349 .000

Subjective Norm

.039 .034 .090 .261


-.129 .041 -.315 .002

Intention

-.124 .045 -.276 .007


-.010 .019 -.041 .597

Efficiency

-.022 .023 -.077 .343


.011 .020 .045 .586

Note. R2 = .088 for Step 1; ΔR2 = . 315 for Step 2 ( p < .05).

In the second model it was found that body mass index significantly predicted shape

concern (β = .183, p = .011), as did attitude towards overeating (β = .349, p < .001),

perceived behavioral control (β = -.315, p = .002), and intention to manage eating

behavior (β = -.276, p = .007).

The fourth and final component of eating behavior this study examined was

weight concern. In the first regression model body mass index accounted for 12.3% of the

78

weight concern variability, R2 = .123, F(1, 135) = 18.866, p < .001. When perceived

behavioral control, attitude, subjective norms, intentions, and sufficiency of originality,

efficiency, and rule/group conformity were entered into the equation, the change in

variance accounted for a significant proportion of the weight concern variance after

controlling for the effects of body mass index, R2 change = .366, F(7, 128) = 13.095, p

< .001. Table 8 provides the regression summary.

Table 8

Summary of Hierarchical Regression Analysis for Variables Predicting Weight Concern (N = 137)

Variable

B

SEB

β

Sig.

Step 1

Body Mass Index

.098 .023 .350 .000

Step 2

Body Mass Index

.062 .018 .222 .001

Attitude

.155 .032 .377 .000

Subjective Norm

.010 .034 .090 .261


-.105 .032 -.302 .001

Intention

-.135 .035 -.353 .000


-.005 .015 .022 .761

Efficiency

-.019 .018 -.078 .299


-.011 .015 -.055 .473

Note. R2 = .123 for Step 1; ΔR2 = . 366 for Step 2 ( p < .05).

79

In the second model it was found that body mass index significantly predicted weight

concern (β = .222, p = .001), as did attitude towards overeating (β = .377, p < .001),

perceived behavioral control (β = -.302, p = .001), and intention to manage eating

behavior (β = -.353, p < .001).

Primary Research Question and Hypotheses Evaluation

This research addressed the following primary question: Are each of four

components of eating behavior affected by the variables of BMI, perceived behavioral

control, attitude towards overeating, subjective norms, intention towards eating behavior,

sufficiency of originality, efficiency, and rule/group conformity. Based on the

presumption that eating behaviors are affected by cognitive style and motivation, four

hypotheses were formulated and their corresponding null forms are presented below.

Null Hypothesis (Ho):




efficiency, and rule/group conformity as measured by KAI, and BMI) and dietary

restraint as measured by EDE-Q6 (R = 0).

The results of the hierarchical regression showed that the combined effects of the

eight predictor variables did significantly predict dietary restraint and therefore the null

hypothesis is rejected.



80


efficiency, and rule/group conformity as measured by KAI, and BMI) and eating concern



eight predictor variables did significantly predict eating concern and therefore the null





efficiency, and rule/group conformity as measured by KAI, and BMI) and shape concern



eight predictor variables did significantly predict shape concern and therefore the null





efficiency, and rule/group conformity as measured by KAI, and BMI) and weight concern



eight predictor variables did significantly predict weight concern and therefore the null


81

Therefore, to address the research question, attitude towards overeating affects the

eating components of dietary restraint, eating concern, shape concern and weight

concern; perceived behavioral control affects the eating components of eating concern,

shape concern and weight concern; intention towards eating behavior affects the eating

components of eating concern, shape concern, and weight concern; and, BMI affects the

eating components of shape concern and weight concern.

Additional Findings and Observations

Additional findings and observations of the data related to the results that should

be discussed is the overall results of the EDE-Q6 as they relate to the clinically

significant range of eating disorders. A clinically significant eating disorder score or

negative eating behavior pattern can be determined by a total score that is greater than or

equal to 4.0 on the dietary restraint subscale, eating concern subscale, shape concern

subscale, weight concern subscale, or the mean global score (Fairburn & Cooper, 1993;

Fairburn, Cooper, Doll, & Davies, 2005; Luce, Crowther, & Pole, 2008).

Using the cut-off value of ≥ 4.0 for clinical significance, 8% of the sample (n =

11) scored in clinical significance range on dietary restraint, 3% of the sample (n = 5)

scored in clinical significance range on eating concern, 17% of the sample (n = 24)

scored in clinical significance range on shape concern, 11% of the sample (n = 16) scored

in clinical significance range on weight concern, and 5% of the sample (n = 7) scored in

clinical significance range on the global scale. The total sample that reported one or more

subscale scores in clinical significance range was 20% (n = 28).

82

Observed Consistencies and Inconsistencies

Several aspects of the findings relate to observed consistencies and

inconsistencies among the individual participant survey responses. One such observation

was noted with male respondents. If an individual reports a score of zero on any of the

EDE-Q6 subscales it is interpreted as an absence of the eating behavior feature, and a

score of 1 is interpreted as a feature almost, but not quite, absent (Fairburn, 2008). On

reviewing the raw data, it was noted that 13 male participants reported individual answers

on the EDE-Q6 of all zeros with a few reported scores of 1. Further, five male

participants had overall EDE-Q6 global mean scores of 0.00. Alternatively, the top 20

highest EDE-Q6 global mean scores were reported by female participants. Only one

female participant in the sample population reported individual answers on all the EDE-

Q6 subscales of all zeros. All the remaining female participants in the sample reported at

least one individual score as a 2 or higher. This could be explained in that female

participants in this sample have more negative eating behaviors or an increased

awareness regarding their eating behaviors, whereas male participants have less negative

eating behaviors or less awareness regarding their eating behaviors. Or this could be

alternatively interpreted that women are more comfortable disclosing any issues or

concerns they may have regarding eating behaviors, whereas men are less likely to

disclose any eating concerns or behaviors. Research using the Eating Disorders

Examination has noted that women are more likely than men to report negative eating

behaviors associated with emotional responses (Tanofsky, Wilfley, Spurrell, Welch, &

Brownell, 1998). However, there are relatively few studies in this research area and

83

therefore these aspects of this research must be considered merely observations and not

findings.

Summary

This chapter described data screening, assumptions and pretest analyses, sample

characteristics, and reported the demographic statistics for the survey participants.

Additionally a description of the data analyses and the results of the hierarchical

regression analyses were presented. These results were used to answer the research

question through the study hypotheses that attitude towards overeating affects the eating

components of dietary restraint, eating concern, shape concern and weight concern;

perceived behavioral control affects the eating components of eating concern, shape

concern and weight concern; intention towards eating behavior affects the eating

components of eating concern, shape concern, and weight concern; and, BMI affects the

eating components of shape concern and weight concern.

Lastly, additional findings and observations from the research were addressed.

Chapter 5 summarizes the study, discusses the conclusions and implications, addresses

the positive social change implications of the study, and presents recommendations for

future action and further study.

CHAPTER 5: DISCUSSION

Introduction and Overview of Study

This chapter begins with a brief overview of why and how the study was

conducted. It further provides an interpretation of the findings and how they relate to the

theoretical framework and implications for positive social change. Lastly, the chapter

discusses limitations of the present research and provides recommendations for action

and further research.

The CDC estimates that in 2009 health care costs associated with obesity had

risen to over $147 billion per year based on research comparing normal weight

individuals and obese individuals’ inpatient, non-inpatient, and prescription drug

spending (Finkelstein, Trogdon, Cohen, & Dietz, 2009). More Americans than ever are

considered to be obese and the health problems associated with this are evident (Baskin,

Ard, Franklin, & Allison, 2005). Although there are medical, genetic, and physiological

reasons for obesity, psychologists are interested in investigating the cognitive factors

associated with eating behaviors which contribute to this epidemic. However, there has

not been consistent research to assess how a person incorporates problem solving

decisions and planned behavior components with eating behaviors. While many

researchers have looked at how people become obese, little is known about what might

motivate a person’s eating behaviors. The purpose of this study was to assess the

combined effects of individual problem solving styles (sufficiency of originality,

efficiency, and rule/group conformity) and planned behavior (attitudes, subjective norms,

behavioral intentions, perceived behavioral control), after first controlling for body mass,

85

on eating behaviors. The specific research question was if eating behavior is affected by

body mass, perceived behavioral control, attitude, subjective norms, intention, sufficiency

of originality, efficiency, and rule/group conformity.

In order to fulfill the objective of answering this question, a final sample of 137

participants from Colorado, ranging in ages 18-65, fully completed the voluntary surveys.

The surveys contained three instruments which were the EDE-Q6, the TPB

Questionnaire, the KAI Inventory, as well as a Background Data Questionnaire and BMI

questionnaire. Four hypothesis were investigated using hierarchical multiple regression

analyses. The null hypotheses were that there would be no significant relationships



rule/group conformity as measured by KAI, and BMI) and the criterion variables of

dietary restraint, eating concern, shape concern, and weight concern, as measured by

EDE-Q6.

Interpretation of Findings

As described in chapter 4, all four hierarchical multiple regressions were

statistically significant and the null hypotheses were rejected. Therefore, it was found that

attitude towards overeating affects the eating components of dietary restraint, eating

concern, shape concern and weight concern; perceived behavioral control affects the

eating components of eating concern, shape concern and weight concern; intention

towards eating behavior affects the eating components of eating concern, shape concern,

86

and weight concern; and, BMI affects the eating components of shape concern and

weight concern.

Interpretation of Hierarchical Regression Analyses

The first hierarchical regression was performed on the eating behavioral

component of dietary restraint. Dietary restraint is a process that addresses a person’s

restriction of calories with the goal of losing weight, or maintaining a current weight

(Fairburn & Brownell, 2002). In this analysis, all the combined predictor variables

accounted for a significant proportion of the dietary restraint variance after first

controlling for the effects of body mass index. The predictor variable of attitude toward

overeating significantly predicted dietary restraint. An increase in positive attitude

towards eating healthy and not overeating was associated with an increase in concern

with dietary restraint. Dietary restraint is associated with a person having rigid food rules,

eating specific food items, and dietary imperatives with an overall goal of restricting

weight gain but this behavior does not necessarily indicate a reduction in binge eating or

weight gain (White, Masheb, & Grilo, 2009). Having a negative attitude towards eating

healthy does imply that a person places less restriction on their dietary intake which could

contribute to negative eating behaviors. For example, if a person does overeat, an

increase in dietary restraint can contribute to an increase in binge eating behaviors (Lowe,

Thomas, Safer, & Butryn, 2007), whereas a positive attitude towards not overeating

results in a higher awareness of personal dietary restraint.

The second hierarchical regression was performed on the eating behavioral

component of eating concern. Eating concern is a characteristic in which a person is

87

preoccupied with the process of eating and food consumption (Fairburn & Brownell,

2002). In this analysis, all the combined predictor variables accounted for a significant

proportion of the eating concern variance after first controlling for the effects of body

mass index. The predictor variables of attitude towards overeating, perceived behavioral

control, and intention to manage eating behaviors significantly predicted eating concern.

An increase in positive attitude towards not overeating was associated with an increase in

eating concern. An increase in perceived behavioral control of eating behavior was

associated with a decrease in eating concern, and an increase in intention to manage

eating behavior was associated with a decrease in eating concern. Obese individuals or

those who report binge eating behaviors traditionally have higher levels of eating concern

(Darby, Hay, Mond, Rodgers, & Owen, 2007). This research suggests that if an

individual does not feel they have control over eating, and does not have the intention to

eat healthy, they are more likely to display negative eating behaviors by not

demonstrating concern over what foods are consumed.

The third hierarchical regression was performed on the eating behavior

component of shape concern. Shape concern is a characteristic that reflects a person’s

preoccupation with his or her bodily shape, fear of weight gain that will impact shape, or

feelings of fatness (Fairburn & Brownell, 2002). In this analysis, all the combined

predictor variables accounted for a significant proportion of the shape concern variance

after first controlling for the effects of body mass index. The predictor variables of body

mass index, attitude towards overeating, perceived behavioral control, and intention to

manage eating behavior significantly predicted shape concern. An increase in a person’s

88

body mass index was associated with an increase in concern with shape. An increase in

positive attitude towards not overeating was associated with an increase in concern with

shape. An increase in perceived behavioral control of eating behavior was associated with

a decrease in shape concern, and, an increase in intention to manage eating behavior was

associated with a decrease in shape concern. Individuals with binge eating disorder and

those who are considered to be morbidly obese have reported higher than average

concerns with shape while reporting a sense of loss of control, or a lack of self-efficacy,

over their eating behaviors and intentions to manage satiety control (Hsu et al., 2002)

which is consistent with these research findings.

The fourth hierarchical regression was performed on the eating behavior

component of weight concern. Weight concern is a characteristic in which a person is

preoccupied with the importance of his or her weight and desires to lose weight (Fairburn

& Brownell, 2002). In this analysis, all the combined predictor variables accounted for a

significant proportion of the weight concern variance after first controlling for the effects

of body mass index. Again, the predictor variables of body mass index, attitude towards

overeating, perceived behavioral control, and intention to manage eating behavior

significantly predicted weight concern. An increase in a person’s body mass index was

associated with an increase in concern over weight. An increase in positive attitude

towards not overeating was associated with an increase in concern with weight. An

increase in perceived behavioral control of eating behavior was associated with a

decrease concern with weight, and an increase in intention manage eating behavior was

associated with a decrease in concern with weight. Higher levels of weight concern have

89

been noted in a variety of studies with binge eating disordered participants who report a

sense of loss of control with the ability to manage weight; this behavior is also consistent

with the research regarding weight concern (Hsu et al., 2002).

Theoretical Considerations

The cognitive style subscales of sufficiency of originality, efficiency, and

rule/group conformity were not independently detected as significant predictors in this

study. Perhaps the sample size precluded finding meaningful comparisons of weak verses

strong predictors. Alternatively, as Kirton (2008) suggests, individual cognitive style may

not be a determinant with eating behaviors as individuals often use coping skills to

manage decisions that are uncomfortable by nature. This theory will be addressed in

further detail in the limitations and recommendations for future study section.

In this research study, an individual’s body mass index was significant in

predicting shape and weight concern. A higher body mass index produced a higher shape

and weight concern. The research results in this study demonstrate consistency with

additional research regarding body mass index and shape and weight concerns. Watkins,

Christie, and Chally (2008) found that BMI was significantly correlated with negative

body image and, specifically, significant differences were found with weight and shape

concern. This is consistent with this research study’s findings.

The overall results of this research study are consistent with the conceptual and

theoretical framework of the Theory of Planned Behavior. The TPB makes an attempt to

understand how individuals internalize behaviors and then act on them (Christian &

Armitage, 2002) and the TPB is considered to be the leading model in the field of health

90

psychology for investigating health behavioral relationships (Armitage & Conner, 2000).

Using this model, it is possible to predict behaviors and ultimately design intervention

strategies that can either reinforce positive behaviors or modify existing behaviors to

enhance a person’s overall health.

The predictor variable of attitude toward overeating plays an important role as

individuals who positively evaluate eating healthy, or avoid overeating behaviors, were

more likely to demonstrate concern regarding current eating behavior. Therefore, a

person is more likely to perform positive health and eating behaviors when there is an

internal belief system that affects the behavioral decision making process to personally

achieve the desired outcome, or positive eating behavior (Bandura, 1997; Fishbein &

Ajzen, 1975). Attitudes toward overeating consistently had a positive correlation with all

of the components of the eating behaviors as measured by the EDE-Q6, which implies

that individuals who had a more positive belief about the consequences of overeating

demonstrated higher levels of concern when they reflected on their past eating behavior.

Conversely, individuals who had an attitude reflecting an indifferent or lack of concern

about the consequences of negative eating behaviors also demonstrated a lack of concern

about their personal eating behaviors. Individuals with an attitude that overeating and

binge eating may have negative consequences demonstrated there may be a connection

between attitude and a personal awareness and concern of body shape, body weight,

awareness of the need to monitor dietary restraint, and a concern of the impact of

unrestrained eating. This connection implies that a positive attitude is associated with an

awareness of overall eating behavior in a healthful way.

91

Intentions are also important factors and are often tied with attitudes when

investigating a person’s motivation to perform a particular behavior by addressing how

much effort and time a person is willing to commit to that behavior (Rivas & Sheeran,

2004). In this research intention was negatively correlated with eating concern behaviors

and this suggests that the greater the person’s internal motivation to avoid binge eating,

the less concern he or she had with this eating behavior, perhaps because the person felt a

greater sense of self-efficacy with eating behaviors. Additionally, an increase in intention

to manage eating behavior was associated with a decrease in shape and weight concern,

which may be related to a person’s ability and intentions to set and operationalize goals

for specific behavioral performance regarding both shape and weight (Gollwitzer &

Schall, 1998). These findings suggest that such individuals may have fewer concerns with

these eating behavioral characteristics (Bagozzi, 1992).

Perceived behavioral control is often the most significant predictor in a variety of

research studies described prior (Conner, Norman, & Bell, 2002; Conner et al., 2003;

Gardner & Hausenblas, 2001; Rivas & Sheeran, 2004). In this study, perceived

behavioral control was negatively correlated with eating behaviors. Perceived behavioral

control is a measure of the power that an individual believes he or she has over a

behavior. This finding implies that the greater internal control a person has over his or her

eating behavior, the less concern the individual has with how he or she eats and his or her

weight or shape.

Although the subjective norm subscale was not independently detected as a

significant predictor in this study, this finding is consistent with multiple studies of the

92

TPB (Armitage & Conner, 2001; Ajzen, 1988; Christian & Armitage, 2002; & Conner,

Norman, & Bell, 2002). This finding implies that social groups and peer groups alone

may not provide enough support or influence for an individual to make effective eating

behavior decisions. Additionally, research using the Eating Disorder Examination

Questionnaire has noted that women with scores in the clinically significant range did not

have any difference in socioeconomic status, cultural affiliation, education level, or

satisfaction levels with their social and family groups compared with those who had

scores that were not in the clinically significant range (Soh et al., 2007). Personal

motivation to perform a behavior and the ability to have a positive attitude, or an ability

to gain a positive attitude towards a behavior, has a greater contribution to actually

performing a specific health behavior (Conner & Norman, 1998).

In conclusion, the theory of planned behavior suggests that individuals are likely

to perform a particular health action if they believe the behavior will lead to outcomes

that they value. This study demonstrated consistencies with current conceptual and

theoretical frameworks in the context of how it applies to eating behaviors and addressed

and answered the research question, rejecting the null hypotheses, that attitude towards

overeating affects the eating components of dietary restraint, eating concern, shape

concern and weight concern; perceived behavioral control affects the eating components

of eating concern, shape concern and weight concern; intention towards eating behavior

affects the eating components of eating concern, shape concern, and weight concern; and,

BMI affects the eating components of shape concern and weight concern. This

understanding, when applied with the theories described prior, is useful to enable

93

researchers, health professionals, and individuals to develop interventions and cognitive

strategies to alter underlying unhealthy behaviors as they relate to overeating and binge

eating (Armitage & Conner, 2001).

Implications for Positive Social Change

This research study, as described in chapter 1, was motivated by several

opportunities for positive social change to minimize the negative influences and

contributors to obesity. By understanding the relationships of the cognitive factors

associated with eating behaviors described in chapter 4, this research has the potential to

contribute to tangible improvements in the psychological health and well being for

individuals and families suffering with obesity, contribute to the development of obesity

related programs in health institutions, increase health promotion in public health

organizations, and potentially decrease health problems by reducing secondary illnesses

related to binge eating and obesity.

The implications for positive social change specifically include having a better

understanding that attitudes, perceived behavioral control, body mass index, and

intentions can predict the certain behavioral features of eating habits and may have the

potential to minimize the consequences of negative eating behaviors, such as chronic

diseases, that are associated with the growing population of overweight and obese

individuals in society.

In order facilitate positive social change with the obesity epidemic it is critical to

understand the reasons why the rate of individuals who are overweight or obese is

steadily rising. The findings from this research are noteworthy in that the sample

94

population for this research study came from the greater Boulder, Colorado area and this

region is noted for being one of the leanest in the nation. However, the obesity rate in

Colorado is growing faster than the U.S. average obesity growth rate, respectively rising

89% from 1995 to 2008 compared with 67% nationwide (Colorado Department of Public

Health and Environment, 2010). This discouraging news may be better understood by

noting the trend of misperception of what is considered a healthy weight and body mass

index status. For example, in this research study, the mean body mass index score of the

population studied was 26, which is in the overweight category. However, the mean

scores for all the behavioral features of eating habits that contribute to negative eating

behavior were consistently low, implying that the participants did not perceive that there

was a need or reason for personal dietary restraint, concern with weight or shape, or

concern specifically with personal eating behaviors. This finding is consistent with

research that demonstrates that there are increasing numbers of overweight individuals

who fail to recognize that their weight or eating behaviors may be a cause for concern

(Johnson, Cooke, Croker, & Wardle, 2008).

Perhaps this can be attributed to the growing misperception of what classifies an

individual as being overweight or obese, and therefore at risk for multiple chronic

diseases such as cardiovascular diseases, type 2 diabetes, stroke, and hypertension. Miller

et al. (2008) noted that individuals who consider themselves to be active or self-reporting

themselves as normal weight, although their BMI classifies them as being either

overweight or obese, misperceive their risk for chronic diseases and strokes.

Additionally, those who fall into the overweight status tend to perceive themselves as

95

being in the normal range or in a healthy weight status and are unlikely to change eating

behaviors that may contribute to additional weight gain (Johnson-Taylor, Fisher,

Hubbard, Starke-Reed, & Eggers, 2008). An increase in the societal levels of being

overweight, which currently constitutes over 66% of the population of the United States

and encompasses multiple ages and ethnic groups (Kolodinsky & Reynolds, 2009;

Masheb & Grilo, 2001; Wang, Liang, & Chen, 2009), leads to an increased acceptance of

higher weight and body fat due to social comparison (Francis, Ventura, Marini, & Birch,

2007; Johnson et al., 2008). If an individual who is overweight or obese has low eating,

shape, and weight concern that individual may be unlikely to see overall personal eating

habits as a concern which could eventually lead to health problems and the individual

may not be motivated to make a behavioral change which could prevent such obesity and

overweight related illnesses.

This current research study contributes to positive social change as it could assist

with the reduction in the proportion of individuals in higher risk body mass indices (BMI

greater than 25) who could significantly benefit from moderate weight loss that will

reduce their risk for weight related diseases (Miller et al., 2008) by raising awareness of

the eating habits of overweight individuals. However, overweight individuals are unlikely

to make changes if they do not feel they are at risk or are not motivated to make a

behavioral change (Caperchione, Duncan, Mummery, Steele, & Schofield, 2008).

Therefore, understanding a person’s attitude, perceived behavioral control, and intentions

towards eating behavior may better assist with the facilitation of the changes necessary to

reduce an individual’s risk for certain negative eating habits. And, given the current

96

focus on strategies for reducing overweight and obesity related disorders in this country,

an understanding of psychologically-related barriers to positive eating behavior may aid

in the development of future interventions and health related social marketing programs

that specifically target awareness for the overweight and obese populations.

Implications for Health Institutions

Health care systems and the manner in which medical care is provided are

undergoing significant transformations. Medical treatment traditionally has been

provided using evidence based diagnoses once symptoms present themselves; however,

understanding how the role individual human behavior contributes to healthcare is often

not incorporated into the overall health care system (Oldham, 2009). This research

demonstrates that although eating behaviors are complex, understanding the role that

attitudes, intentions, and self-efficacy/ perceived behavioral control have with individual

behaviors and health actions could contributes to a broader understanding that there are

multiple variables that influence eating behavior above and beyond an individual’s body

weight. This concept is valuable to both health psychologists and public health

department professionals as it may introduce the understanding that obesity and binge

eating behaviors that contribute to this epidemic are not limited to physiological

components alone. Discussions regarding these concepts may offer many opportunities

for health care systems and policy makers to collaborate with psychologists to uncover

individuals’ motivations and cognitive styles when developing holistic health care

programs with the goal to reduce the obesity epidemic. If a patient with negative eating

behaviors approaches a physician or psychologist for a radical procedure, such as gastric

97

bypass surgery, or is looking to undertake a significant dietary change program, it may be

beneficial for the medical or psychological professional to consider the patients’ existing

eating behaviors and health decision making styles to determine the likelihood of success

a person has for changing eating behaviors before a behavioral change strategy is

recommended. This positive social change could be accomplished by investigating the

motivational variables described in this study.

Additionally, there are psychological interventions that can benefit from

understanding the relationships between attitudes toward eating, perceived behavioral

control, and intentions to manage eating behavior. For example, many psychologists

assess overeating and binge eating behaviors by addressing the specific types of food and

amounts of food consumed, eating patterns, weight history, and body image satisfaction

or dissatisfaction (Mitchell & Peterson, 2008) or they suggest psychoeducational

programs that focus on body weight regulation, coping with urges to binge or overeat,

education programs regarding nutrition, or psychological factors such as family dynamics

that may contribute to underlying causes of overeating (Fairburn, 1995). Although these

techniques have had success, incorporating the role that attitudes, intentions, and self-

efficacy/ perceived behavioral control have with individual behaviors could be beneficial

with existing dietary behavioral therapeutic interventions.

Implications for Health Organizations

The results of this research has implications for positive social change for the

health conscious community and health care organizations by contributing to the

development of behavioral modification programs to reduce weight related health

98

disorders. This can be achieved by incorporating an understanding of how cognitive

health behaviors and decision-making processes are related to eating behaviors. Many

formal weight loss programs measure the success of their participants by looking at

metrics such as overall weight loss; however, measuring factors that contribute to

individual motivation to change eating behaviors often is not addressed (Adams, 2008).

Additionally, by increasing the awareness of the motivational factors that contribute to

eating behaviors, health promotion, which is the process of enabling people to have an

increased level of control over their individual health and health outcomes (World Health

Organization, 2010), can be improved and can allow for a greater success rate in many

formal and informal health conscious organizations and communities. The outcomes of

this research demonstrate that although the body mass index of an individual contributes

to increased concerns about personal shape and weight, psychological components such

as attitude towards eating, perceived behavioral control towards eating behavior, and

intention to manage eating behaviors are substantial and should not be overlooked.

Incorporating a shortened version of the Theory of Planned Behavior questionnaire into

many existing eating behavioral modification programs could provide insight into the

existing motivation a person has to perform a certain behavior, such as changing negative

eating patterns (Armitage & Christian, 2004).

Implications for Individuals and Society

Individuals who suffer from non-clinical eating disorders such as binge eating,

overeating, and obesity often experience negative stereotypes such as not having self-

control, being lazy or unwilling to change, having a lack of education, or having general

99

incompetence in comparison to non obese individuals (Klaczynski, Goold, & Mudry,

2004). This research has identified several factors that contribute to eating behaviors that

allow an individual suffering from these challenges to have greater insight into how

personal decisions and intentions contribute to eating behaviors.

Personal motivation, which can be derived from attitudes, self-efficacy, and

intentions towards a behavior, are contributors to a person’s eating behaviors and overall

concern about how eating affects them personally. The results of this research can

provide a practical application of health promotion at the individual level if personal

motivation is used to predict negative eating behaviors, such as binging and overeating

with an understanding of an individual’s psychological control mechanisms (Conner &

Sparks, 1998). If a person, a family unit, and a community group can recognize the

complexities associated with eating behaviors and work to first understand the

contributing factors to negative eating behaviors, changes in perceptions and stereotypes

regarding obesity and overeating behaviors may be changed one small step, and family

unit, at a time. If an individual or a social group, such as a family unit or local

community, are able to understand the underlying contributors of a negative attitude

toward eating healthy, why there is a low sense of perceived behavioral control to

manage eating behaviors, and how this impacts a lower intention to change eating

behaviors prior to initiating a health behavioral change program, the potential for new

positive eating behaviors may increase. Accomplishing change is possible at both the

individual and group level through volunteer community outreach and educational

programs, through local school systems, and through church groups as well as other

100

social networking forums. This change in perception could result in the positive social

change needed to adjust the social stigma that the obesity epidemic is limited to excessive

caloric consumption and lack of exercise alone.

Recommendations for Action

Addressing the challenge of obesity related health disorders is enormous. In order

for a positive social change to occur, the issue needs to be addressed at both the macro

level, such as our physical and mental healthcare systems, at the corporate at government

levels, and in our media. Additionally, at the micro level, the problem needs to be

addressed in local communities, with local health care providers, local education centers,

and with individual neighborhoods and families. Although these changes require a large

effort by a variety of professionals and community members, individual actions can start

to make a difference.

Even though the physical and psychological dangers of binge eating, overeating,

and obesity have been researched substantially, cognitive style and the cognitive

processes associated with planned behavior, as they apply to non-clinical eating

behaviors, are not systemically being incorporated into weight loss and weight

maintenance programs or health educational and preventative programs. As a link

between these variables and eating behaviors was established in this research, health

professionals have an opportunity to incorporate these findings to gain a better

understanding of how decisions regarding negative eating behaviors manifest at the

individual level. Health practitioners can investigate the predictor variables in this study

to assess individuals' attitudes, beliefs, decision making styles, and expectations when

101

formulating a diet or a health behavioral modification program. This could be

accomplished by specifically identifying an individual’s attitudes towards overeating,

perceived behavioral control over the influence eating behavior, and intentions to manage

eating behaviors factors that to first address the cognitive attributes that can hinder or

help an eating behavioral change program. If actions can be taken to change an

individual’s outlook and self-efficacy regarding ability to manage eating behavior prior to

a systematic dietary change, the weight loss and health strategies may be more

successful.

One recommendation for action is for these results to be disseminated to a variety

of sources that may assist in elevating the need to understand the cognitive factors

associated with overeating and binge eating into their existing efforts to reduce the

impact of obesity on society. Individuals and groups that need to pay attention to the

results of this study include health psychologists, social psychologists, clinicians,

researchers, and physicians. These results also should be disseminated to special interest

groups, such as the National Weight Control Registry, CDC's Division of Nutrition,

Physical Activity, and Obesity (DNPAO), and the Colorado Health Foundation and

Colorado Department of Public Health and Environment, both of which are funding

through Live Well Colorado and the DNPAO. The authors of the EDE-Q6, the TPB, and

the KAI should additionally be informed of the results of this study. This researcher will

contribute to the dissemination of these results by sending electronic copies of this

research to the above mentioned parties as well as working to publicize these results in

journal articles and local community publications.

102

Upon the dissemination of this research, an additional call to action would be for

health organizations that may have traditionally relied on weight and body mass index

and metrics for weight loss/control strategies to reassess their evaluation and treatment

methods. For example, it is challenging to identify any single approach in formal weight

loss programs that claims to be effective in maintaining long-term weight control

(Adams, 2008). Future interventions and health programs aimed at obesity related

conditions should incorporate weight loss and maintenance strategies that address the

attitudes, intentions, and perceived behavioral control measurements for individuals in an

effort to understand the personal decision making styles that contribute to eating

behaviors. Additional recommendations for action are included in the following section.

Limitations and Recommendations for Future Study

Research studies are often limited by various restrictions such as time limitations,

available sample, and financial capabilities of the researcher. One major limitation to this

study was the available sample, which was limited to Boulder, Colorado. Although the

demographics of the sample were reflective of the overall population, the study was

limited from an ethnic standpoint as the majority of the respondents were European-

American. Colorado is also the only state in the United States that, according to the

Center for Disease Control (2009), has less than 20% of the population that is considered

to be obese. This limits the generalizability of the study. It may be assumed that eating

behaviors are impacted by the predictor variables in this study in most adults; however,

this assumption needs to be validated. Future research could expand on this study into

more diverse ethnic regions of the United States.

103

Further, cognitive level was not incorporated into this research design. Cognitive

level can be defined as the cognitive resources and potential cognitive capacity that an

individual has with regard to eating behaviors (Kirton, 2008). For example, measuring

the participants’ education level, personal knowledge about nutrition, health behaviors, or

the physical, mental, and social effects of obesity or binge eating were not incorporated

into this study.

Additionally, as the KAI inventory is not in the public domain, costs associated

with the inventories limited the sample size which may have resulted in an inability to

discriminate significance with the predictor variables of sufficiency of originality,

efficiency, and rule/group conformity. As the sample size in this research was modest,

increasing the sample size may provide an opportunity to further research specific to the

KAI inventory. Lastly, there are three specific areas that generate a new round of

questions which are clinically and non-clinically significant eating behaviors, seasonal

eating behaviors and coping strategies associated with eating behaviors.

Clinically and Non-Clinically Significant Eating Behaviors

In this research study, the availability of two independent samples for eating

behaviors in the clinically significant and non-clinically significant range could introduce

the following research question. Is there a difference in the population means of BMI,

attitude, subjective norm, perceived behavioral control, intention, sufficiency of

originality, efficiency, and rule/group conformity between individuals with clinically

significant eating behaviors and individuals with non-clinically significant eating

behaviors? On completing the data analyses in this research sample, 109 participants

104

reported no clinically significant scores and 28 participants reported clinically significant

scores in this study. An independent two samples t-test for equality of means was

performed to determine any differences between the participants with EDE-Q6 scores

that had scores in the clinical range and non-clinical range. Reporting equal variances

not assumed, the results indicate a significant difference in means for body mass index, t

(135) = -2.40, p = .021; attitude, t (135) = -2.95, p = .004; perceived behavioral control, t

(135) = 3.85, p ≤ .001; and intention, t (135) = 2.52, p = .016.

Although the EDE-Q6 is considered to be the standard assessment instrument for

binge eating and overeating behaviors (Grilo, Masheb, & Wilson, 2001), it is still

difficult to precisely identify the specific negative eating behaviors that occur as the

results are based on self-reporting historical eating behaviors. However, as Colorado is

considered to be one of the healthiest states and has the one of the lowest levels (20% or

less of the population) of obesity in the country (Centers for Disease Control and

Prevention, 2010), it is interesting to note that 20% of the total sample also reported an

eating behavior that was in the clinical significant range of the EDE-Q6. Although this

information is an observation and cannot be supported with in this current study, it is an

area of interest worthy of further study.

Seasonal Eating Behaviors

A holiday can be defined as a day in which someone celebrates a religious event,

a day free from work, or a commemoration of an event (Merriam-Webster, 2010).

Although holidays occur throughout the entire year, there are certain seasons in the

United States in which multiple holidays are celebrated, such as the months of November

105

through January, in which Thanksgiving, Hanukkah, Christmas, New Year’s Day and

many other celebrations occur. As these dates occurred near or during the data collection

period of this research, it is important to investigate the relationship between eating

behaviors and holidays and how this may or may not have impacted the implications of

this research. The holiday season is often social in nature and can present situations in

which individuals are faced with multiple poor food choices, such as dishes that are

higher in calories and fats compared with what a person would normally eat during a

non-holiday period. When faced with a multitude of poor food choices, individuals who

have a history of binge eating and overeating behaviors often have a higher level of

personal observation with the amount of food they consume and have a heightened

awareness of a need to control eating behaviors, often directly after an excessive holiday

eating experience or binge eating experience (Phelan et al., 2008). This is often

demonstrated with increased dietary restraint after the eating experience. Alternatively, a

decrease in awareness of eating behaviors during holiday periods is associated with

greater weight gain (Phelen et al., 2008).

Multiple research studies have reported mild body weight changes that occurred

over the entire holiday season (Hull, Radley, Dinger, & Fields, 2006; Ma et al., 2006; &

Yanovski et al., 2000). The typical weight gain reported during the holiday period is

approximately less than 1 pound, or 0.4 kilograms (Yanovski et al., 2000). Although this

is not a significant amount of weight, individuals who are already reporting obese body

mass indices tend to keep the weight gain throughout the year and this may contribute to

long term weight gain (Watras, Bucholz, Close, Zhang, & Schoeller, 2007). Future

106

research could be expanded to address the implications of eating behaviors before,

during, and after the holiday season to investigate any differences or changes in self-

reporting on the EDE-Q6. Additionally, an increase in weight gain is often associated

with stress, availability of excessive food in social situations, and perceived needs that

surround the eating occasion (Vue, Degeneffe, & Reicks, 2008). Investigating the

relationships between stress, social eating and the variables of the Theory of Planned

Behavior (attitude, subjective norm, intention, and perceived behavioral control) offers a

venue for future research.

Coping Strategies

Successful weight loss losers, who are defined by the National Weight Control

Registry (2010) as persons over the age of 18 who have maintained a 30 pound weight

loss for one year or longer, use different strategies that enable them to keep weight off in

comparison to normal weight individuals or those who are not able to keep weight off.

For example, Phelan et al. (2008) demonstrated in a study that individuals who have

successfully lost weight and do not regain weight over the holidays use strategies such as

increased physical activity, increased awareness and control over eating, and were more

likely to be strict in maintaining their dietary routines before, after, and during what they

considered to be high risk periods of eating. Kirton refers to this type of behavior, as

applied to the adaption-innovation theory, as a coping strategy (1994).

A coping strategy is a behavior that requires an individual to perform in a manner

that is not necessarily natural, or comfortable, with their preferred cognitive style. The

adaption-innovation theory notes that an individual’s preferred style is extremely hard to

107

change but performing a behavior can be flexible (Kirton, 2008). When a person must

participate in a behavior, such as self-monitoring eating behavior or planning a long-term

eating behavioral change, and the behavior is not consistent with a person’s preferred

style, the person experiences stress and inefficiency when performing the behavior

(Stum, 2009).

Coping behaviors, such as self-monitoring eating behaviors, are learned, and self-

monitoring of eating and increased awareness of eating behaviors is critical to successful

long-term weight loss (Wing, Tate, Gorin, Raynor, & Fava, 2006). Coping behaviors may

play a significant role in the success of long term eating behaviors. Although the KAI

predictor variables were included in this study, independent statistical significance was

not detected. However, future research needs to ask different questions about self-

monitoring eating behaviors by incorporation the adaption-innovation theory to

understand if a person with an adaptive style may be more likely to follow a meticulous

pattern of solving dietary problems compared with a person who has a more innovative

style who may be less careful about maintaining a dietary lifestyle change. On first

understanding a person’s motivations and intentions towards an eating behavioral change

program, research addressing the specific area of cognitive style could contribute to

improved success rates of obesity prevention, succession of binge eating behaviors, and

long term dietary improvements. Future research in this area should systematically

explore the relationships between the adaption-innovation theory and specific styles of

dietary restraint and self-monitoring eating behaviors.

108

Conclusion

This study contributes to the literature by being the first to focus on the

relationship between the variables of BMI, perceived behavioral control, attitude,

subjective norms, intentions, sufficiency of originality, efficiency, and rule/group

conformity and eating variables of dietary restraint, eating concern, shape concern and

weight concern. The predictor variables of attitude towards overeating, perceived

behavioral control, and intention to manage eating behaviors indicate that a relationship

exists between cognitive and motivational behaviors with factors that contribute to eating

behaviors, such as overeating and binge eating. This research provides a concrete step

towards looking at cognitive factors that contribute to negative eating behaviors.

Approximately 280,000 to 325,000 adults in the United States die each year from

causes related to obesity (CDC, 2009). Experts do not expect these numbers to decrease;

rather they expect them to continue to increase along with a rise in secondary illnesses

such as heart disease, diabetes, body dissatisfaction and depression symptoms (Goldfield

et al., 2010). This research study is beneficial for clinicians and researchers in

interpreting the relationship between eating behaviors and a person’s motivations and

cognitive decision-making processes. The findings that there are relationships between

attitude towards overeating with dietary restraint, eating concern, shape concern and

weight concern; perceived behavioral control with eating concern, shape concern and

weight concern; intentions to manage eating behavior with eating concern, shape

concern, and weight concern; and, BMI with shape concern and weight concern

109

are important for health professionals and society as a whole. Further insights are likely

to be produced from this research to understand what cognitive and health behavioral

factors contribute to eating behaviors. Understanding that it is not just genetics or lack of

self control that contributes to overeating behaviors, but that attitude, intentions, and

perceived behavioral control play an important role in overeating behaviors opens many

possibilities for positive social change to occur. By managing negative eating behaviors

not only from a physiological perspective, but also from a psychological perspective, an

understanding of the cognitive mechanism that underlie overeating and binge eating

behaviors can contribute to the reduction of obesity rates with the goal that the secondary

illnesses associated with obesity will subside.

REFERENCES

Abraham, S. F., & Beumont, P.J.V. (1982). How patients describe bulimia or binge eating. Psychological Medicine, 12, 625-635.

Academy for Eating Disorders. (2008). Binge eating. Diagnoses of Eating Disorders.

Retrieved January 3, 2008, from: http://www.aedweb.org/ Adams, C. E. (1993). Innovative behavior in nursing executives. Nursing Management,

25(5), 44-50. Adams, M. (2008). Taking charge of one’s life: A model for weight management success.

The Qualitative Report, 13(1), 30-52. Ahern, A. L., & Hetherington, M. M. (2006). The thin ideal and body image: An experimental study of implicit attitudes. Psychology of Addiction, 20(3), 338-342. Ajzen, I. (2008). Theory of planned behavior. TPB Model. Retrieved November 6, 2008,

from: www.people.umass.edu. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Pearson Education, Inc. Boston. Ajzen, I., & Holmes, W. H. (1974). Uniqueness of behavioral effects in causal

attribution. Department of Psychology, University of Massachusetts, Amherst. American Obesity Association. (2008). What is obesity? Education. Retrieved June 25,

2008 from www.obesity1.com. American Psychological Association. (2000). DSM-IV-TR: Diagnostic and statistical

manual of mental disorders (4th ed.). Arlington, VA: American Psychiatric Publishing.

APA. (2000). Diagnostic and statistical manual of mental disorders (4th ed.). Arlington,

VA: American Psychiatric Association. Armitage, C. J., & Conner, M. (2000). Social cognition models and health behavior: A

structured review. Psychology and Health, 15, 173-189. Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behavior: A

meta-analytic review. British Journal of Social Psychology, 40, 471-499.

111

Armitage, C. J., Conner, M., Loach, J., & Willetts, D. (1999). Different perceptions of control: Applying an extended theory of planned behavior to legal and illegal drug use. Basic and Applied Social Psychology, 21(4), 301-316.

Armitage, C. J., & Christian, J., (2004). Planned behavior: The relationship between

human thought and action. New Brunswick, NJ: Transaction Publishers. Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago, IL: Dorsey Press. Ajzen, I. (2009). Constructing a theory of planned behavior questionnaire. TPB Model.

Retrieved February 15, 2009 from: www.people.umass.edu. Alonso-Alonso, M., & Pascual-Leone, A. (2007). The right brain hypothesis for obesity.

JAMA, 297, 1819-1822. Bagozzi, R., & Foxall, G. (1995). Construct validity and generalizability of the kirton

adaption-innovation inventory. European Journal of Personality, 9(3), 185-206. Baile, C. A., Della-Fera, M. A., & Martin, R. J. (2000). Regulations of metabolism and body fat mass by leptin. Annual Review of Nutrition, 20. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Baron, R., Byrne, D., & Branscombe, N. (2006). Social psychology (11th ed.).

Boston, MA: Pearson Education, Inc. Baskin, M., Ard, J., Franklin F., & Allison, D. (2005). Prevalence of obesity in the united

states. Obesity Reviews, 6(1), 5-7. Baum, A. & Posluszny, D. M. (1999). Health psychology: Mapping biobehavioral contributions to health and illness. Annual Review of Psychology, 1999. Bagozzi, R. P. (1992). The self-regulation of attitudes, intentions and behavior. Social

Psychology Quarterly, 55, 178-204. Bagozzi, R., & Foxall, G. (1995). Construct validity and generalizability of the kirton adaption-innovation inventory. European Journal of Personality, 9(3), 185-206. BEDA (2009). About binge eating disorder. Binge Eating Disorder Association.

Retrieved May 11, 2009 from: www.bedaonline.com. Bhate, S. (1999). Cognitive style differences and their impact on responses to message

sources. Marketing Intelligence and Planning, 17(6), 280-287.

112

Brannon, L. & Feist, J. (2004). Health psychology: An introduction to behavior and health (5th Ed.). CA: Wadsworth/Thomson Learning.

Brinkman, D. J. (1999). Problem finding, creativity style and the musical compositions of

high school students. Journal of Creative Behaviour, 33(1), 62-68. Brown, L. (2000). ‘Tis the (overeating) season. Better Nutrition, 62(12), 26-28. Buttner, E. H., Gryskiewicz, N., & Hidor, C. S. (1999). The relationship between styles

of creativity and managerial skills assessment. British Journal of Management, 10, 228-238.

Cain, A. S., Bardone-Cone, A. M., Abramson, L. Y, Vohs, K. D., & Joiner, T. E. (2008).

Refining the relationships of perfectionism, self-efficacy, and stress to dieting and binge eating: Examining the appearance, interpersonal, and academic domains. International Journal of Eating Disorders. 41(8), 713-722.

Caperchione, C. M., Duncan, M. J., Mummery, K., Steele, R., & Schofield, G. (2008).

Mediating relationship between body mass index and the direct measures of the theory of planned behavior on physical activity intention. Psychology, Health, & Medicine, 13(2), 168-179.

Carpenter, R. A., Finley, C., & Barlow, C. E. (2004). Pilot test of a behavioral skill

building intervention to improve overall diet quality. Journal of Nutritional Educational Behavior, 36(2), 20-26.

Carrier, K. M. (1994). Rethinking traditional weight management programs: A 3-year follow-up evaluation of a new approach. Journal of Psychology, 128(5), 517. Casanovas, C., Fernandez-Aranda, F., Granero R., Krug, I. Jimenez-Murcia, S., Bulik, C.

M., & Vallejo-Ruiloba, J. (2007). Motivation to change in eating disorders: Clinical and therapeutic implications. European Eating Disorders Review, 15, 449-456.

Cassin, S. E., von Ranson, K. M., Heng, K., Brar, J., & Wojtowicz, A. E. (2008). Adapted motivational interviewing for women with binge eating disorder: A randomized controlled trial. Psychology of Addictive Behaviors, 22(3), 417-431.

CDC. (2009). About bmi for adults. Centers for Disease Control and Prevention.

Retrieved April 25, 2009 from www.cdc.gov/healthyweight/assessing/bmi/adult. CDC. (2009b). About bmi for adults. Healthy Weight - It’s Not a Diet, It’s a Lifestyle.

Retrieved January 13, 2009 from www.cdc.gov.

113

CDC. (2009c). Overweight and obesity. Retrieved May 4, 2009 from: http://www.cdc.gov/nccdphp/dnpa/obesity/.

Celio, A. A., Wilfley, D. E., Crow, S. J., Mitchell, J., & Walsch, B. T. (2004). A comparison of the binge eating scale, questionnaire for eating and weight patterns-revised, and eating disorder examination questionnaire with instructions with the eating disorder examination in the assessment of binge eating disorder. Journal of Eating Disorders, 434-444.

Center for Disease Control. (2008a). Body Mass Index. Healthy weight: It’s not a diet, it’s a lifestyle. Retrieved November 8, 2008 from: www.cdc.gov.

Center for Disease Control. (2008b). Introduction. Overweight and Obesity. Retrieved

January 11, 2008 from: www.cdc.gov. Center for Disease Control. (2008c). Overweight and Obesity. U.S. Obesity Trends,

1985–2007. Retrieved November 10, 2008 from: www.cdc.gov. Center for Disease Control. (2009). Trends by State 1985–2008. Obesity Trends.

Retrieved April 4, 2010 from: www.cdc.gov. Center for Disease Control. (2009). Preventing Chronic Disease: Prevalence of

overweight, obesity, and comorbid conditions among u.s. and kentucky adults, 2000–2002. Retrieved July 1, 2009 from: www.cdc.gov.

Centers for Disease Control and Prevention. (2010). Estimated county-level prevalence of

diabetes and obesity: United states. JAMA, 303(10), 933-935. Chaney, D. (2002). Eating out: Social differentiation, consumption and pleasure. Canadian Journal of Sociology, 26(3). Chua, J.L., Touyz, S., & Hill, A. J. (2004). Negative mood-induced overeating in obese

binge eaters: An experimental study. International Journal of Obesity, 28, 606-610.

Christian, J., & Armitage, C.J. (2002). Attitudes and intentions of homeless persons

towards service provisions in south wales. British Journal of Social Psychology, 41, 219-231.

Clapp, R. (1993). The stability of cognitive style in adults: A longitudinal study of the kai. Psychological Reports, 73, 1235-1245. Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.

114

Collins, S. E., & Carey, K. B. (2007). The theory of planned behavior as a model of heavy episodic drinking among college students. Psychology of Addictive Behaviors, 21(4), 498-507.

Colorado Department of Public Health and Environment. (February 24, 2010).

Colorado’s obesity rate grew faster than the country’s. Press Release. Retrieved from www.cdphe.state.co.us.

Conner, M., Povey, R., Sparks, P., James, R., & Shepard, R. (2003). Moderating the role of attitudinal ambivalence within the theory of planned behavior. British Journal of Social Psychology, 42(1), 75-94.

Conner, M., and Norman, P. (1998). Predicting Health Behavior. Philadelphia: Open University Press.

Conner, M., Norman, P., & Bell, R. (2002). The theory of planned behavior and healthy

eating. Health Psychology, 21(2), 194-201.

Conner, M., & Sparks, P. in Conner, M., and Norman, P. (1998). Predicting Health Behavior. Philadelphia: Open University Press.

Costin, C. (1998). Overt behaviors. National Eating Disorders Association. Retrieved March 15, 2009 from www.NEDA.com.

Creswell, J. W. (2003). Research design: Qualitative, quantitative and mixed methods

approaches (2nd ed.). Thousand Oaks, CA: Sage Publications, Inc. Daniels, S. R. (2006). The consequences of childhood overweight and obesity. The

Future of Children, 16(1), 47-52. Darby, A., Hay, P., Mond, J., Rodgers, B., & Owen, C. (2007). Disordered eating

behaviors and cognitions in young women with obesity: Relationship with psychological status. International Journal of Obesity, 31, 876-882.

DeAngelis, T. (2002). Binge-eating disorder: What’s the best treatment? Researchers

debate whether to tackle psychological or weight issues first. Monitor on Psychology, 33,(3), 1-6.

DeLauzon, B., Romon, M., Deschamps, V., Lafay, L., Borys, J., Karlsson, J.,

Ducimetiere, P., & Charles, M. A. (2004). The three-factor eating questionnaire-r18 is able to distinguish among different eating patterns in a general population. The American Society for Nutritional Sciences Journal of Nutrition, 134, 2372-2380.

115

Dunkley, D. M., Blankstein, K. R., Masheb, R. M., & Grilo, C. M. (2003). Personal standards and evaluative concerns dimensions of “clinical” perfectionism: A reply to Shafran et al. (2002, 2003) and Hewitt et al. (2003). Behaviour Research & Therapy, 44(1), 62-85.

Eldredge, K. L., & Agras, W. S. (1994). Weight and shape overconcern and emotional

eating in binge eating disorder. International Journal of Eating Disorders, 19(1), 73-82.

Elfhag, K., & Rössner, S. (2004). Who succeeds in maintaining weight loss? A

conceptual review of factors associated with weight loss maintenance and weight regain. The International Association for the Study of Obesity Reviews, 6, 67-85.

Engelsen, B. K., & Hagtvet, K. A. (1999). The dimensionality of the 12-item version of

the eating attitudes test: Confirmatory factor analyses. Scandinavian Journal of Psychology, 40, 293-300.

Fairburn, C. G. (2008). Cognitive behavior therapy and eating disorders. New York, NY: The Guilford Press.

Fairburn, C. G. (1995). Overcoming binge eating. New York, NY: The Guildford Press. Fairburn, C. G., & Brownell. (2002). Eating disorders and obesity: A comprehensive

handbook (2nd ed.). New York, NY: The Guilford Press. Fairburn, C. G., Cooper, Z., in Fairburn CG, Wilson GT (1993). Binge eating: nature,

assessment and treatment. The Eating Disorder Examination. New York: Guilford Press.

Fairburn, C. G., Cooper, Z., Doll, H. A., & Davies, B. (2005). Identifying dieters who

will develop an eating disorder: A prospective, population-based study. American Journal of Psychiatry, 162, 2249-2255.

Fila, S. A., & Smith, C. (2006). Applying the theory of planned behavior to healthy

eating behaviors in urban native american youth. International Journal of Behavioral Nutrition and Physical Activity, 3(11), 5868-5979.

Fingeret, M.C., Warren, C. S., Cepeda-Benito, A., & Gleaves, D. H. (1996). Eating

disorder prevention research: A meta-analysis. Eating Disorders, 14, 191-213. Finkelstein, E. A., Trogdon, J. G., Cohen, J. W., & Dietz, W. (2009). Annual medical

spending attributable to obesity: Payer-and service-specific estimates. Health Affairs, 28, 822-831.

116

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, M.A.: Addison-Wesley.

First, M.B., Spitzer, R. L., Gibbon, M., & Williams, J. B. (1997). Structured Clinical

Review for DSM-IV Personality Disorders. Washington, D.C.: American Psychiatric Press.

Flanagan, H. (2007). Review of adaptors and innovators: Styles of creativity and

problem-solving. Health Services Management Research, 20(4), 279-280. Fontaine, K. R. (1997). Prediction treatment attendance and weight loss: Assessing the psychometric properties and predictive validity of the dieting readiness test. Journal of Personality Assessment, 68. Franklin, A., Germov, J., & Williams, L. (2001). A sociology of food and nutrition: The social appetite. Journal of Sociology, 37(1). Francis, J. J., Eccles, M. P., Johnstson, M., Walker, A., Grimshaw, J., Foy, R., Kaner, E.

F. S., Smith, L., & Bonetti, D. (2004). Contructing questionnaires based on the theory of planned behavior: A manuel for health service researchers. Centre for Health Services Research, University of Newcastle. ISBN: 0-9540161-5-7.

Francis, L. A., Ventura, A. K., Marini, M., & Birch, L. L. (2007). Parent overweight

predicts daughters’ increase in bmi and disinhibited overeating from 5 to 13 years. Obesity: National Institutes of Health, 15(6), 1544-1553.

Gabel, K. A., & Lund, R. J. (2002). Weight loss at a cost: Implications of high-protein, low-carbohydrate diets. JOPERD--The Journal of Physical Education, Recreation & Dance, 73(2), 18-30. Gardner, R. E., & Hausenblas, H. A. (2001). The theory of planned behavior: A critical

review of its application to exercise and diet behaviors. Journal of Applied Social Psychology, 10, 441-457.

Goldfield, G. S., Moore, C., Henderson, K., Buchholz, A., Obeid, N., & Flament, M. F.

(2010). Body dissastisfaction, dietary restraint, depression, and weight status in adolescents. Journal of School Health, 80(4), 186-7.

Goldsmith, R. (1985). Adaption-innovation and cognitive complexity. Journal of Psychology, 119(5), 461. Goldsmith, R. W., & Matherly, T. A. (1987). Adaption-innovation and creativity: A replication and extension. British Journal of Social Psychology, 26, 79-82.

117

Gollwitzer, P. M., & Schall, B. (1998). Metacognition in action: The importance of implementation intentions. Personality and Social Psychology Review, 2, 124-136.

Gliem, J. A., & Gliem, R. R. (2003). Calculating, interpreting, and reporting

cronbach’s alpha reliability coefficient for likert-type scales. 2003 Midwest Research to Practice Conference in Adult, Continuing, and Community Education. 82-88.

Gravetter, F. J., & Wallnau, L. B. (2007). Statistics for the behavioral sciences (7th ed.). Stamford, CT: Wadsworth. Grilo, C. M., & Masheb, R. M. (2005a). A randomized controlled comparison of guided

self-help cognitive behavioral therapy and behavioral weight loss for binge eating disorder. Behavioral Research & Therapy, 43(11), 1509-1526.

Grilo, C. M., & Masheb, R. M. (2005b). Correlates of body image dissatisfaction in

treatment-seeking men and women with binge eating disorder. International Journal of Eating Disorders, 38(2), 162-167.

Grilo, C. M., Masheb, R. M., & Wilson, G. T. (2006). Rapid response to treatment for

binge eating disorder. Journal of Consulting & Clinical Psychology, 74(3), 602-614.

Grilo, C. M., Masheb, R. M., & Wilson, G. T. (2006). A comparison of different methods

for assessing the features of eating disorders in patients with binge eating disorder. Journal of Consulting & Clinical Psychology, 69(2), 317-322.

Grilo, C. M., Masheb, R. M., Brody, M., Burke-Martindale, C. H., & Rothschild, B. S.

(2005). Binge eating and self-esteem predict body image dissatisfaction among obese men and women seeking bariatric surgery. International Journal of Eating Disorders, 37(4), 347-352).

Hairon, N. (2006). Gps pressed to screen all adults for obesity. Pulse, 66(19), 6-7. Halliwell, E. & Harvey, M. (2006). Examination of a sociocultural model of disordered

eating among male and female adolescents. British Journal of Health Psychology, 11(2), 235-248.

Hair, J. E., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NY: Prentice-Hall, Inc.

118

Harrell, Z. A. T., & Jackson, B. (2008). Thinking fat and feeling blue: Eating behaviors, ruminative coping, and depressive symptoms in college women. Sex Roles, 58(658-668).

Herman, C. P., Koenig-Nobert, S., Peterson, J. B., & Roth, D. A. (2003). Is the effect of

a social model on eating attenuated by satiety? Psychological Bulletin, 129, 873-886.

Higgins, G. E. & Marcum, C. D. (2005). Can the theory of planned behavior mediate the

effects of low self-control on alcohol use? College Student Journal, 39(1), 90-111.

Hill, R. F. (1992). The impact of problem-solving styles on problem solving behavior, interpersonal relationships, and job satisfaction in small workgroups. M.Sc. Thesis: University of Calgary. Hull, H. R., Radley, D., Dinger, M. K., & Fields, D. A. (2006). The effect of the

thanksgiving holiday on weight gain. Nutrition Journal, 2006, 5-29. Hutchinson, L. R., & Skinner, N. F. (2007). Self-awareness and cognitive style:

Relationships among adaption-innovation, self-monitoring, and self-consciousness. Social Behavior and Personality, 355(4), 551-560.

Hsu, L. K. G., Mulliken, B., McDonagh, B., Krupa Das, S., Rand, W. Fairburn, C. G.,

Rolls, B., McCrory, M. A., Saltzman, E., Shikora, S., Dwyer, J., & Roberts, S. (2002). Binge eating disorder in extreme obesity. International Journal of Obesity, 26, 1398-1403.

Johansson, L. (2006). The role of cognitive processes in eating pathology. ProQuest

Information and Learning Company, ISBN: 1652-9030. Johnson, F., Cooke, L., Croker, H., & Wardle, J. (2008). Changing perceptions of weight

gain in Great Britian: Comparison of two populations surveys. British Medical Journal, 337-494.

Johnson-Taylor, W. L., Fisher, R. A., Hubbard, V. S., Starke-Reed, P., & Eggers, P. S.

(2008). The change in weight perception of weight status among the overweight: Comparison of nhanes III (1988-1994) and 1999-2004 nhanes. International Journal of Behavioral Nutrition and Physical Activity, 5(9), 5868-5874.

Kansi, J., Wichstrom, L., & Bergman, L. R. (2005). Eating problems and their risk factors: A 7-year longitudinal study of a population sample of norwegian adolescent girls. Journal of Youth and Adolescence, 34.

119

Kaye, W. H., Bastiani, A. M., & Moss, H. (1995). Cognitive style of patients with anorexia nervosa and bulimia nervosa. International Journal of Eating Disorders, 18(3), 287-290.

Kennedy, E. (2004). Dietary diversity, diet quality, and body weight regulation. Nutritional Reviews, 62(7), 78-81. Kirton, M. J. (1994). Adaptors and innovators: Styles of creativity and problem solving.

Routledge, London.

Kirton, M. J. (1976). Adaptors and innovators: A description and measure. Journal of Applied Psychology, 5, 622-629. Kirton, M.J. (2008). Certification course notes. Certification Course. Occupational

Research Centre: Pennsylvania State University. Kirton, M. J. (1999). Kirton adaption-innovation inventory: Manuel (3rd ed.).

Occupational Research Centre: Suffolk, UK. Kirton, M. J. (2003). Adaption-innovation in the context of diversity and change. East

Sussex, GB: Routledge. Kiviniemi, M. T., Voss-Humke, A. M., & Seifert, A. L. (2007). How do i feel about the

behavior? The interplay of affective associations with behaviors and cognitive beliefs as influences on physical activity behavior. Health Psychology, 26(2), 152-158.

Klaczynski, P. A., Goold, K. W., & Mudry, J. J. (2004). Culture, obesity stereotypes,

self-esteem, and the "thin ideal": A social identity perspective. Journal of Youth and Adolescence, 33(4), 307-315.

Kolodinsky, J., & Reynolds, T. (2009). Segmentation of overweight Americans and

opportunities for social marketing. International Journal of Behavioral Nutrition and Physical Activity, 6(13), 1-11.

Kyungwon, K., Reicks, M., & Sjoberg, S. (2003). Applying the theory of planned

behavior to predict dairy product consumption by older adults. Journal of Nutritional Educational Behavior, 35, 294-301.

Liebman, B. (1995). More friends, more food. Nutrition Action Health Letter, 22(4).

Lindsay, A. C., Sussner, K. M., Kim, J., & Gortmaker, S. (2006). The role of parents in preventing childhood obesity. The Future of Children, 16(1), 169-179.

120

Lipsey, M.W., & Wilson, D.B. (1993). The efficacy of psychological, educational, and behavioral treatment: Confirmation from meta-analysis. American Psychologist, 48(12), 1181-1209. Lowe, M. R., Thomas, J. G., Safer, D. L., & Butryn, M. L. (2007). The relationship of

weight suppression and dietary restraint to binge eating in bulimia nervosa. International Journal of Eating Disorders, 40, 640-644.

Long, J. D. & Stevens, R. K. (2004) Using technology to promote self-efficacy for

healthy eating in adolescents. Journal of Nursing Scholarship 36(2), 134–139.

Luce, K., & Crowther, J. (1999, April). The reliability of the eating disorder examination -- self-report questionnaire version (EDE-Q). International Journal of Eating Disorders, 25(3), 349-351.

Luce, K. H., Crowther, J. H., & Pole, M. (2008). Eating disorder examination questionnaire (ede-q): Norms for undergraduate women. International Journal of Eating Disorders, 41(3), 273-276.

Lyubomirsky, S., Casper, R. C., & Sousa, L. (2001). What triggers abnormal eating in bulimic and nonbulimic women? Psychology of Women Quarterly, 25, 223-232.

Masheb, R. M., & Grilo, C. M.. (2001). On the relation of flexible and rigid control of

eating to body mass index and overeating in patients with binge eating disorder. Department of Psychiatry, Yale University School of Medicine, 82-91.

Masheb, R. M., & Grilo, C. M. (2008). Examination of predictors and moderators for

self-help treatments of binge-eating disorder. Journal of Consulting and Clinical Psychology, 76(5), 900-912.

Matheson, H., & Crawford-Wright, A. (2000). An examination of eating disorder profiles

in student obligatory and non-obligatory exercisers. Journal of Sport Behavior, 23(1), 42.

Ma, Y., Olendzkim B. C., Li, W., Hafner, A. R., Chiriboga, D., Hebert, J. R., Campbell,

M., Sarniem M., & Ockene, I. S., (2006). Seasonal variation in food intake, physical activity, and body weight in a predominately overweight population. European Journal of Clinical Nutrition, 60(4), 519-528.

Mayr, S., Erdfelder, E., Buchner, A., & Faul, F. (2007). A short tutorial of GPower.

Tutorials in Quantitative Methods for Psychology, 3(2), 51-59.

121

McDonald, F. (2003). A prospective investigation of repeat dieting using self-efficacy and attribution theories to explain the influence of weight control behaviors. Australian Journal of Psychology, 55, 7-7.

Mei. Z., Grummer-Strawn, L.M., Pietrobelli, A., Goulding, A., Goran, M.I., & Dietz,

W.H. (2002). Validity of body mass index compared with other body-composition screening indexes for the assessment of body fatness in children and adolescents. American Journal of Clinical Nutrition, 7597–7985.

Merriam-Webster. (2010). Holiday. Merriam-Webster Online Dictionary. Retrieved

March 25, 2010, from http://www.merriam-webster.com/dictionary/holiday. Miller, E. C., Schultz, M. R., Bibeau, D. L., Galka, A. M., LaPronda, I. S., Martin L. B.,

Aronson, R. E., and Chase, C. M. (2008). Factors associated with misperception of weight in the stroke belt. Journal of General Internal Medicine, 23(3), 323-328.

Mitchell, J. E., & Peterson, C. B. (2008). Assessment of eating disorders. New York,

NY: The Guilford Press. Mitchell, J.E., Pyle, R.L., & Eckert, E. D. (1981). Frequency and duration of binge-eating

episodes in patients with bulimia. American Journal of Psychiatry, 138, 835-836. Mokdad, A. H., Marks, J. S., Stroup, D. F., & Gerberding, J. L. (2004). Actual causes of

death in the united states. Journal of American Medical Association, 291, 1238-1245.

Mond, J. M., & Hay, P. J. (2008). Public perception of binge eating and its treatment.

International Journal of Eating Disorders, 41(5), 419-427. Mond, J. M., Hay, P. J., Rodgers, B., Owen, C., & Beaumont, P. J. V. (2004). Temporal

stability of the eating disorder examination questionnaire. International Journal of Eating Disorders, 36(2), 195-204.

Myklestad, I., & Rise, Jostein. (2008). Predicting intentions to perform protective sexual

behaviours among norwegian adolescents. Sex Education: Sexuality, Society and Learning, 8(1), 107-125.

Nakamura, K., Hoshino, Y., Kodama, K., & Yamamoto, M. (1999). Reliability of self-

reported body height and weight of adult japanese women. Journal of Biosocial Science, 31, 555-558.

National Institute of Health. (2008). Eating behaviors, health, and wellness. Retrieved

July 12, 2008 from: http://health.nih.gov/.

122

National Weight Control Registry. (2010). Research Findings. Retrieved March 30, 2010

from http://www.nwcr.ws/. Nayga, J. (2000). Nutrition Knowledge, Gender and Food Label Use. Journal of

Consumer Affairs, 34(1), 97. Nehl, E. J., Blanchard, C. M., Peng, C, J., Rhodes, R. E, Kupperman, J., Sparling, P.B., &

Courneya, F., B. (2009). Understanding nonsmoking in african american and caucasion college students: An application of the theory of planned behavior. Behavioral Medicine, 35(1), 341-347.

Newman, D. L., Moffitt, T. E., Caspi, A., Magdol, L., Silva, P. A., & Stanton, W. (1996).

Psychiatric disorder in a birth cohort of young adults: Prevalence, comorbidity, clinical significance, and new case incidence from ages 11-21. Journal of Consulting & Clinical Psychology, 64, 552-562.

Neumark-Sztainer, D., Paxton, S. J., Hannan, P. J., Haines, J., Story, M. (2006). Does

body satisfaction matter? Five-year longitudinal associations between body satisfaction and health behaviors in adolescent females and males. Journal of Adolescent Health, 39(2), 244-252.

Neumark-Sztainier, D., Wall, M., Story, M., & Fulkerson, J. A. (2004). Are family meal

patterns associated with disordered eating behaviors among adolescents? Journal of Adolescent Health, 35(5), 350-360.

Norman, P., Bennett, P., & Lewis, H. (1998). Understanding binge drinking among

young people: An application of the theory of planned behavior. Health Education Research, 13, 163-169.

Ode, J., Pivarnik, J., Reeves, M., & Knous, J. (2007). Body mass index as a predictor of

percent fat in college athletes and nonathletes. Medicine & Science in Sports & Exercise, 39(3), 403-409.

Oldham, J. (2009). Achieving large system change in health care. JAMA. 301(9), 965-

966. Oxford University Press. (2008). Understanding your patient’s eating behavior. The

Cognitive Model of Overeating. Retrieved June 20, 2008 from: www.ox.ac.uk. Ozelli, K. L. (2007). This is your brain on food. Scientific America, 297(3), 84-85.

Panksepp, J., Tonge, D., & Oatley, K. (1972). Insulin and glucostatic control of feeding. Journal of Comparative and Physiological Psychology, 78(2), 226-32.

123

Patte, K. A. (2006). Overeating and obesity: The role of impulsivity. ProQuest

Information and Learning Company, ISBN: 978-0-494-29602-8. Pawlak, R., & Malinauskas, B. (2008). Predictors of intention to eat 2.5 cups of

vegetables among ninth-grade students attending public high schools in eastern north carolina. Journal of Nutrition Education and Behavior, 40(6), 392-398.

Pekkarinen, T., Koskela, K., Huikuri, K., & Mustajoki, P. (1994). Long-term results of

gastroplasty for morbid obesity: Binge-eating as a predictor of poor outcome. Obesity Surgery, 4, 248-255.

Phelan, S., Wing, R. R., Raynor, H. A., Dibello, J., Nedeau, K., & Peng, W. (2005).

Holiday weight management by successful weight losers and normal weight individuals. Journal of Consulting and Clinical Psychology, 76(3), 442-448.

Pinel, John P. J. (2006). Biopsychology with “beyond the brain and behavior” (6th ed.). Boston: Allyn and Bacon. Pinel, J. P. J. & Lehman, D. R. (1998). Personal theories of hunger and eating. Journal of

Applied Social Psychology, 28(11). Pittas, A. G., Hariharan, R., Stark, P. C., Hajduk, C. L., Greenberg, A. S., & Roberts, S.

B. (2005). Interstitial glucose level is a significant predictor of energy intake in free-living women with healthy body weight. The Journal of Nutrition, 135.

Plowman, S. F. (2008). Self-esteem as a predictor of treatment outcome among women

with eating disorder. ProQuest Information and Learning Company, UMI 3293713.

Reuters. (2008). New association serves binge eating disorder. Retrieved October 6, 2008

from: http://www.reuters.com/article/pressRelease. Regan, P. C., & Cachelin, F. M. (2006). Binge eating and purging in a multi-ethnic

community sample. International Journal of Eating Disorders, 39(6), 523-526. Reeve, J. (2005). Understanding motivation and emotion (4th ed.). Thousand Oaks, CA:

Wiley. Reid, N. (2006). Thoughts on attitude measurement. Research in Science &

Technological Education, 24(1), 3-27.

124

Rivas, A., & Sheeran, P., in Armitage, C. J., & Christian, J., (2004). Planned behavior: The relationship between human thought and action. New Brunswick, NJ: Transaction Publishers.

Roth, D. M. & Armstrong, J. G. (1990). Perceptions of control over eating disorder and

social behaviors. International Journal of Eating Disorders, 10(3), 265-271. Saggin, A. (1996) in Kirton, M. J. (2003). Adaption-innovation in the context of diversity

and change. New York, NY: Routledge. Saling, M., Ricciardelli, L. A., & Mccabe, M. P. (2005). A prospective study of

individual factors in the development of weight and muscle concerns among preadolescent children. Journal of Youth and Adolescence, 34(6), 651-662.

Schneider, F., Gruman, J., & Coutts, L. (Eds.). (2005). Applied social psychology.

London: Sage Publications, Inc. Segelken, R. (2005). Researchers view obesity from a life course perspective: By looking

at the course of individuals' and groups' lives over time, researchers can see early warning signs of obesity and determine opportune times for intervention. Human Ecology, 33(3), 14-19.

Serdar, K. (2005). Female body image and the mass media: Perspective on how women

internalize the ideal beauty standard. The Myriad: Westminster College Undergraduate Academic Journal, 2(1).

Shannon, B., Bagby, R., Qi Wang, M., & Trenkner, L. (2001). Self-efficacy: A

contributor to the explanation of eating behavior. Health Education Research, 5(4), 395-407.

Shen, B., McCaughtry, N., & Martin, J.(2008). Urban adolescents’ exercise intentions

and behaviors: An exploratory study of a trans-contextual model. Contemporary Educational Psychology, 33(4), 841-849.

Shisslak, C. M., Mays, M. Z., Crago, M., Jirsak, J. K., Taitano, K., & Cagno, C. (2006).

Eating and weight control behaviors among middle school girls in relationship to body weight and ethnicity. Journal of Adolescent Health, 38(5), 631-634.

Sirois, F. M. (2004). Procrastination and intentions to perform health behaviors: The role

of self-efficacy and the consideration of future consequences. Personality and Individual Differences, 37(1), 115-128.

Soh, N. L., Touyz, S., Dobbins, L. J., Surgenor, S. C., Kohn, M. R., Lee, E. L., Leow, V.,

Rieger, E., Eng Khean Ung, K., & Walter, G. (2007). Restraint and eating concern

125

in north European and east asian women with and without eating disorders in australia and singapore. Australian and New Zealand Journal of Psychiatry, 41, 536-545.

Sohn, E. (2008). Making the salt-blood pressure link: Health. Los Angeles Times.

Retrieved October 27, 2008 from: latimes.com. Sorbara, M., & Geliebter, A. (2001). Body image disturbance in obese outpatients before

and after weight loss in relation to race, gender, binge eating, and age of onset of obesity. International Journal of Eating Disorders, 31, 416-423.

Stewart, S. H., Brown, C. G., Devoulyte, K., Theakston, J., & Larsen, S. E. (2006). Why

do women with alcohol problems binge eat? Exploring connections between binge eating and heavy drinking in women receiving treatment for alcohol problems. Journal of Health Psychology, 11(3), 409-426.

Stice, E., Agras, W. S., Telch, C. F., Halmi, K. A., Mitchell, J. E., & Wilson, T. (2000).

Subtyping binge eating-disordered women along dietary and negative affect dimensions. International Journal of Eating Disorders, 30, 11-27.

Stum, J. (2009). Kirton’s Adaption-innovation theory: Managing cognitive styles in times

of diversity and change. Emerging Leadership Journeys, 2(1), 66-78. Sysko, R., Devlin, M. J., Walsh, B. T., Zimmerli, E., & Kissileff, H. R. (2007). Satiety

and test meal intake among women with binge eating disorder. International Journal of Eating Disorders 40(6), 554–561.

Tanofsky, M. B., Wilfley, D. E., Spurrell, E. B., Welch, R., & Brownell, K. D. (1998).

Comparison of men and women with binge eating disorder. International Journal of Eating Disorders, 21(1), 49-54.

Tiejan, W., Snider, J. B., Floyd, M. R., Florence, J. E., Stoots, J. M., & Makamey, M. I.

(2009). Intention for healthy eating among southern appalachian teens. Journal of Health Behavior, 33(2), 115-125.

Toray, T., & Cooley, E. (1997). Weight fluctuation, bulimic symptoms, and self-efficacy

for control of eating. Journal of Psychology, 131(4), 383. Treasure, J., Tchanturia, K., & Schmidt, U. (2005). Developing a model of the treatment

for eating disorder: Using neuroscience research to examine the how rather than the what of change. Counselling & Psychotherapy Research, 5(3), 191-202.

University of Oxford. (2009). Assessment measures. Department of Psychology.

Retrieved January 16, 2009 from: http://www.psychiatry.ox.ac.uk

126

U. S. Census Bureau. (2008). State & county quick facts: Colorado. Retrieved July 13, 2008 from: http://quickfacts.census.gov/qfd/states/08000.html Wang, Y., Liang, H., & Chen, X. (2009). Measured body mass index, body weight

perception, dissatisfaction and control practices in urban, low-income African American adolescents. BMC Public Health, 9(183), 1-12.

Watkins, J. A., Christie, C., & Chally, P. (2008). Relationship between body image and

body mass index in college men. Journal of American College Health, 57(1), 95-100.

Watras, A.C., Buchholz, A. C., Close, R. N., Zhang, Z., & Schoeller, D. A. (2007). The

role of conjugated linoleic acid in reducing body fat and preventing holiday weight gain. International Journal of Obesity, 31, 481-487.

Wethington, E. (2008). Contributions of the life-course perspective to research on family

decision-making: Social constraints and individual agency. Decision Making in Eating Behavior: Perspectives from the Individual, Family, and Environment. National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Health, Lecture Notes, April 14-15, 2008.

White, M. A., Masheb, R. M., & Grilo, C. M. (2009). Regimented and lifestyle restraint

in binge eating disorder. International Journal of Eating Disorders, 42, 326-331. Wilfley, D. E., Welch, R., Robinson, S., Richard, I., Spurrell, E. B., Cohen, L. R., Brian,

E. Douchis, J. Z., Frank, M. A., Wiseman C. V., & Matt, G. E. (2008). A randomized comparison of group cognitive-behavioral therapy and group interpersonal psychotherapy for the treatment of overweight individuals with binge-eating disorders. Archives of General Psychiatry, 59(8), 713-801.

Willett, W. C., Manson, J. E., Stampfer, G. A., Colditz, B., Rosner, F. E., & Speizer, C.

H. (1995). Weight, weight change, and coronary heart disease in women: Risk within the ‘normal’ weight range. Journal of the American Medical Association, 273(6), 1098-1104.

Wing, R. R., Tate, D. F., Gorin, A., Raynor, H. A., & Fava, J. L. (2006). A self-regulation

program for maintenance of weight loss. New England Journal of Medicine, 346, 393-403.

Woolley, J. D., Gorno-Tempini, M. L., Seeley, W. W., Ranking, K., Lee, S. S.,

Matthews, B. R., & Miller, B. L. (2007). Binge eating is associated with right orbitofrontal-insular-striatal atrophy in frontotemporal dementia. Neurology, 69(14), 1424-1434.

127

World Health Organization. (2010). Health promotion. Health Topics. Retrieved April 2,

2010 from: http://www.who.int/topics/health_promotion/en/. Yanovski J. A., Yanovski, S. Z., Sovik, K. N., Nguyen, T. T., O’Neil, P. M., & Sebring,

N. G. (2000). A prospective study of holiday weight gain. New England Journal of Medicine, 342(12), 861-867.

Young, H. M. (1991). Operationalizing the theory of planned behavior. Research in

Nursing & Health, 14(2), 137-44. Zald, D. H. (2008). Orbitofrontal cortex contributions to food selection and decision

making. Decision Making in Eating Behavior: Perspectives from the Individual, Family, and Environment. National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Health, Lecture Notes, April 14-15, 2008.

Ziefelmann, J. P., Luszczynska, A., Lippke, S., & Schwarzer, R. (2007). Are goal intentions or implementation intentions better predictors of health behavior? A longitudinal study in orthopedic rehabilitation. Rehabilitation Psychology, 52(1), 97-102.

APPENDIX A:

BODY MASS INDEX CALCULATION

Public Domain

What is your weight at present? (Please give your best estimate.) ____________

What is your height? (Please give your best estimate.) ____________

The BMI is considered public domain, and can therefore be used for educational

purposes without permission from its author.

APPENDIX B:

EATING DISORDER EXAMINATION QUESTIONNAIRE VERSION 6

Christopher G. Fairburn and Sarah Beglin

Eating Questionnaire

Instructions: The following questions are concerned with the past four weeks (28 days) only. Please read each question carefully. Please answer all the questions. Thank you. Questions 1 to 12: Please circle the appropriate number on the right. Remember that the questions only refer to the past four weeks (28 days) only.

On how many of the past 28 days…

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

1 Have you been deliberately trying

to limit the amount of food

you eat to influence your

shape or weight (whether or not

you have succeeded)?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

2 Have you gone for long periods of time (8 waking hours or more) without eating

anything at all in order to influence

your shape or weight?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

3 Have you tried to exclude from your diet any foods that you like in order to influence your shape or weight (whether or not

you have

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

130

succeeded)? 4 Have you tried to

follow definite rules regarding your eating (for

example, a calorie limit) in order to influence your

shape or weight (whether or not

you have succeeded)?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

5 Have you had a definite desire to have an empty

stomach with the aim of influencing

your shape or weight?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

6 Have you had a definite desire to have a totally flat

stomach?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

7 Has thinking about food, eating, or calories made it very difficult to concentrate on things you are

interested in (for example, working,

following a conversation, or

reading)?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

8 Has thinking about shape or weight made it very difficult to concentrate on things your are

interested in (for example working,

following a conversation, or

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

131

reading)? 9 Have you had a

definite fear of losing control over eating?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

10 Have you had a definite fear that you might gain

weight?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

11 Have you felt fat? No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

12 Have you had a strong desire to

lose weight?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

19 On how many days have you eaten in secret?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

20 On what proportion of the times that you have eaten have you felt guilty because of the effect on your shape or weight?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

21 How concerned have you been about other people seeing you eat?

No days

1-5 days

6-12 days

13-15 days

16-22 days

23-27 days

Every day

Questions 22-28: Please circle the appropriate number on the right. Remember that the questions only refer to the past four weeks (28 days).

Over the past 28 days…

Not at

all,

1 Slightly

3 Moderate

5 Marked

22 Has your weight influenced how you

think (judge yourself) as a

0 1 2 3 4 5 6

132

person? 23 Has your shape

influenced how you think about (judge) yourself as a person

0 1 2 3 4 5 6

24 How much would it upset you if you had

been asked to weight yourself once a week (no

more, or less, often) for the next four

weeks

0 1 2 3 4 5 6

25 How dissatisfied have you been with

your weight?

0 1 2 3 4 5 6

26 How dissatisfied have you been with

your shape?

0 1 2 3 4 5 6

27 How uncomfortable have you felt seeing

your body (for example, seeing

your shape in the mirror, in a shop

window reflection, while undressing or

taking a bath or shower)?

0 1 2 3 4 5 6

28 How uncomfortable have you felt about others seeing your

shape or figure

0 1 2 3 4 5 6

The EDE-Q6 is considered public domain, and can therefore be used for educational purposes without permission from its author. Permission for use is granted from the University of Oxford Department of Psychology: http://www.psychiatry.ox.ac.uk/research/researchunits/credo/assessment-measures-pdf-files/EDE-Q6.pdf.

APPENDIX C:

THEORY OF PLANNED BEHAVIOR QUESTIONNAIRE

I. AJZEN

Theory of Planned Behavior: Eating Behavioral Questionnaire

Please answer each of the following questions by circling the number that best describes

your opinion. Some of the questions may appear to be similar, but they do address

somewhat different issues. Please read each question carefully.

Predictor Variables: Attitudes (direct measures of attitude * 2 outcome expectations) 1. For me eating healthy and not overeating on a regular basis is

Worthless :___1__:___2__:___3__:___4__:___5__:___6__:___7__: Useful

2. Overeating impacts my feelings about myself in a

Positive way :___1__:___2__:___3__:___4__:___5__:___6__:___7__: Negative way

3. Maintaining a healthy diet is

Not important :___1__:___2__:___3__:___4__:___5__:___6__:___7__: Important

Subjective Norms (normative beliefs * 2 motivation to comply) 4. People that are important to me think that keeping a healthy weight is

Not important:___1__:___2__:___3__:___4__:___5__:___6__:___7__: Important

5. People that are important to me think that overeating or binge eating is

:___1__:___2__:___3__:___4__:___5__:___6__:___7__:

Normal in some situations Harmful

6. What my doctor or health care provider thinks I should do to eat healthy is

Not important:___1__:___2__:___3__:___4__:___5__:___6__:___7__: Important

134

Perceived Behavioral Control (self-efficacy * 2 influence of controllability) 7. For me controlling my eating behavior is

:___1__:___2__:___3__:___4__:___5__:___6__:___7__:

Extremely difficult Extremely easy

8. The decision to stick to a diet program is beyond my control

:___1__:___2__:___3__:___4__:___5__:___6__:___7__:

Strongly agree Strongly disagree

9. My weight or shape is in my control

:___1__:___2__:___3__:___4__:___5__:___6__:___7__:

Strongly disagree Strongly agree

Behavioral Intentions (intention performance, general intention, intention simulation) 10. For me intending to eat healthy on a daily basis is

:___1__:___2__:___3__:___4__:___5__:___6__:___7__:

Extremely difficult Extremely easy

11. I intend to maintain healthy eating behaviors on a daily basis

:___1__:___2__:___3__:___4__:___5__:___6__:___7__:

Strongly disagree Strongly agree

12. If I intend to maintain a consistent diet that results in overall health I find it

Extremely hard :___1__:___2__:___3__:___4__:___5__:___6__:___7__:Extremely easy

The TpB questionnaire is considered public domain, and can therefore be used

for educational purposes without permission from its author. This instrument is published

with permission from University of Massachusetts:

http://www.people.umass.edu/aizen/pdf/tpb.questionnaire.pdf

APPENDIX D:

KIRTON ADAPTION-INNOVATION INVENTORY

M. J. Kirton

Due to copyright laws the Kirton Adaption-Innovation Inventory may not be

published for the general public and requires certification for administration which has

been completed by this researcher.

APPENDIX E:

BACKGROUND DATA QUESTIONNAIRE

L. Samuel

What is your age? __________________

What is your gender? ______________________

What is your ethnicity? ____________________

APPENDIX F:

CONSENT FORM

Walden University, L. Samuel

You are invited to take part in a research study that investigates non-clinical eating behaviors with personal eating decision making styles. You were chosen for the study because you reside in the state of Colorado. This form is part of a process called “informed consent” to allow you to understand this study before deciding whether to take part. This study is being conducted by a researcher named Lisa K. Samuel, who is a doctoral student at Walden University. Background Information: The purpose of this study is to learn more about how individuals make everyday decisions regarding eating choices so that health psychologists can assist in creating positive wellness plans. Procedures: If you agree to be in this study, you will be asked to:

• Complete the surveys enclosed • Return them within three days to the researcher

Voluntary Nature of the Study: Your participation in this study is voluntary. This means that everyone will respect your decision of whether or not you want to be in the study. As this is confidential and participants are selected randomly, no one will treat you differently if you decide not to be in the study. If you decide to join the study now, you can still change your mind during the study. If you feel stressed during the study you may stop at any time and request assistance from the researcher to find assistance. You may skip any questions that you feel are too personal. Risks and Benefits of Being in the Study: The risks of being in this study include the uncovering of personal decision making styles that may contradict personal beliefs. However, the benefits of this study include understanding whether or not personal eating behaviors may or may not contribute to health disorders, understanding what your personal cognitive decision making style is as it contributes to multiple factions in life, and how you manage desires versus intentions which can assist in long term goal development.

138

Compensation: The compensation for this study is that you will receive personalized feedback regarding your preferred cognitive style, body mass index, and intentions regarding eating behaviors. However, this information can only be returned to you if you choose to provide your address or an e-mail address. This information is typically provided for a fee and the feedback is for information purposes only. The researcher is not soliciting any subsequent enrollment in any fee-based services and the feedback is not intended for diagnosis or treatment. Confidentiality: Any information you provide will be kept confidential. The researcher will not use your information for any purposes outside of this research project. Also, the researcher will not include your name or anything else that could identify you in any reports of the study. Contacts and Questions: You may ask any questions you have now. Or if you have questions later, you may contact the researcher via 303-604-6080 or [email protected]. If you want to talk privately about your rights as a participant, you can call Dr. Leilani Endicott. She is the Walden University representative who can discuss this with you. Her phone number is 1-800-925-3368, extension 1210. Walden University’s approval number for this study is 11-30-09-0342645 and it expires on November 29, 2010. The researcher will give you a copy of this form to keep. Statement of Consent: I have read the above information and I feel I understand the study well enough to make a decision about my involvement. By signing below, I am agreeing to the terms described above.

Printed Name of Participant

Date of consent

CURRICULUM VITAE

LISA K. SAMUEL EDUCATION Ph.D. Candidate, Psychology, 2006- Present Walden University, Denver, CO Masters Degree in Business Administration, September, 2005 University of Phoenix, Phoenix, AZ

Bachelors Degree in Business Administration, July, 1998 Florida Metropolitan University, Clearwater, FL

Associates Degree in Medical Science, July, 1995 Webster College, New Port Richey, FL EXPERIENCE

Ph.D. Candidate, Walden University, 4.O GPA The Health Psychology Ph.D. specialization focuses on the complex relationships among psychological, social, and biological factors implicated in health, illness, and well being.

SIX SIGMA ACADEMY, Scottsdale, AZ, October 1999- August 2005

Director, Operations, August 2003 to July 2005

Researched market trends, e-business and new market opportunities and managed market research teams, sales research process, resource and consulting services management, training material development and distribution teams. Managed new product development teams with multiple training curriculums utilizing adult learning theory. Allocated resources and provided recommendations on performance reviews. Developed deployment best practices, managed intellectual property and project databases including client specific examples, case studies, material translations, tools and templates. Provided deployment initialization support and coordinated operation support activities for major corporations throughout their Six Sigma deployments including Merrill Lynch, Visteon, Air Liquide, Rhodia P.I., DuPont, IndyMac Bank, Ford Motor Company, Grupo Antolin, Tyco International, IKON Office Supplies, Westinghouse, Albertsons, Johnson Controls, and Anthem.

New Market Development / Research Manager, October 1999 – August 2003 Identified and analyzed market opportunities for building new business models. Performed detailed research on prospective clients, analyzed and designed deployments and pricing structures, performed market research and financial

140

analysis of potential business opportunities, plans and proposals. Developed and executed competitive intelligence process, designed client centric sales presentations and proposals. Managed and developed a case study library and client sensitive documentation. Developed sales contact list database and standardized corporate pricing model. Created a knowledge management structure for the organization. BAYONET POINT FOOT HEALTH CENTERS, Port Richey, FL Podiatric Surgical Nurse, 1995-1999 Office manager for three podiatric surgeons and three surgical facilities responsible for a team of 28 nurses. Lead surgical nurse for all podiatric surgeries. Additional certifications included Hazardous Material Manager, Registered Phlebotomist, Registered X-ray Technician, Registered Podiatric Surgical Assistant, ICD9 Certified Insurance Code Specialist and Certified Medical Transcriptionist. UNITED STATES NAVY, Barbers Point, HI Maintenance and Ground Support Operations, 1992 – 1995 Maintained pre and post flight operations for a squadron of P3 Orion aircraft both domestically and internationally. Received Southwest Asia Service medal, National Defense Service Medal, Overseas Duty Ribbon, and Honorable Discharge.

PROFESSIONAL CERTIFICATIONS, MEMBERSHIPS AND ACHIEVEMENTS

Walden International Corps – Social Changers Without Borders, 2010 Psi Chi, The International Honor Society in Psychology, 2010 KAI Certification Course (registered), Kirton Adaption-Innovation

Inventory, Dr. Kirton, Penn State University, December 11, 2008 Protecting Human Research Participants Certification (45805), National

Institutes of Health (NIH), January, 2008 American Psychological Association (APA), 2006 Association for Psychological Science (APS), 2006 Master Black Belt Certification, Six Sigma Academy International,

February, 2005 Supply-Chain Operations Reference Model v.6.0 certification, Supply

Chain Council, May 28, 2004 C3 (Creating a Customer Centered Culture) Certification, IMTC3, April 6,

2004 Achieving Performance Excellence and Metric Development Certification,

American Productivity and Quality Center (APQC), October 2003 Knowledge Management Certification, American Productivity and Quality

Center (APQC), October 2003

141

Theory of Inventive Problem Solving (TRIZ) Certification, PQR Group, February 20, 2003

Certified Six Sigma Academy Black Belt, May 18, 2001

PAPERS and PRESENTATIONS (Samuel, L. K. is the researcher’s name and Modesitt, L.K. is the researcher’s prior name)

Samuel, L. K. (2010). Good Psychology. Website and Blogsite. www.goodpsych.com.

Samuel, L. K. (2009). Eating, Health Behaviors, and Cognitive Style. Poster Presentation, Walden Residency. Sheraton Hotel, January 22, 2009.

Modesitt, L. K. & Samuel, P. (2007). Linking Lean Six Sigma. The African Business Review, September-October, 4-6.

Modesitt, L. K. & Samuel, P. (2005). Linking Six Sigma Projects to Strategic Imperatives. European Business Review, May-June, 30-33. Samuel, P., Modesitt, L.K., & Finney, J. E. (2004). Creating New Markets Using Six Sigma. G100 Insights, Fall 2004, 20-25. Modesitt, L. K., & Samuel, P. (2004). Next Generation Six Sigma: A New Age in Process Improvement Strategies? Pharmaceutical Manufacturing and Packaging Sourcer, Autumn 2004, 18-22. Samuel, P., & Modesitt, L. K. (2004). Executing for Corporate Breakthrough Success: The Evolution of Six Sigma. Proceedings of the First International Conference on Six Sigma, 48-62. Samuel, P., Modesitt, L. K., & Sollenberger, D. (2004). Next Generation Six Sigma for Corporate Breakthrough Success. European CEO, September- October, 20-22. Samuel, P., & Modesitt, L. K. (2004). Six Sigma for Corporate Revenue Growth. The Growing Business Handbook: Kogan Page Limited, 405-409.

eating, health behaviors and cognitive style by dr. lisa samuel 2010

Documents