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Running head: CONSCIENTIOUSNESS AS A PREDICTOR FOR REDUCING TURNOVER 1

Adopting Conscientiousness as a Predictor for Reducing High Turnover Rates in Customer

Service Positions at an Electrical Company

Steven Matthew Brown

Valdosta State University

CONSCIENTIOUSNESS AS A PREDICTOR FOR REDUCING TURNOVER 2

Abstract

This research report focuses on an electrical company that would like to incorporate measuring

conscientiousness as part of their selection criteria for new customer service employees.

Conscientiousness is known to be an accurate predictor of work performance in the workplace

and the electrical company is interested in knowing if it is a feasible and usable measure to

reduce high turnover rates and accurately measure job performance. This report utilizes and

interprets many statistical methods such as reliability, exploratory factor analyses, correlation

matrices, regression matrices, and descriptive statistics to determine the usefulness of the new

construct. This report also explicitly details the content validity, factorial validity, construct

validity, and criterion-related validity of the company’s current selection criteria and the criteria

when adding conscientiousness. A recommendation is then provided as to whether or not the

company should adopt the new conscientiousness measure.


Section 1: Content Analysis

Conscientiousness has been a thoroughly researched topic that ultimately has become one

of the best predictors of performance in the workplace (Salgado, 1997). Based on current

research by John & Srivastava (1999), a working definition of conscientiousness is the

propensity to follow socially prescribed norms and rules, to be goal-directed, planful, able to

delay gratification, and to control impulses. According to Orvis et al. (2008), because

conscientious individuals are cautious and planful and are more willing to delay gratification of

their needs when they are faced with breach, they should be less likely to generate withdrawal

cognitions. Orvis et al. (2008) continues by stating that if conscientious employees perceive a

breach and experience immediate feelings of anger and thoughts of turnover, such employees are

likely to control and moderate these thoughts, because turnover is viewed as a rash action in

response to breach.

I have determined that a working of definition of conscientiousness is the extent to which

an individual is hard-working, plays by the rules of the organization, and is meticulous in pre-

planning and carrying through with task or projects (Barrick & Mount, 1991). In addition,

conscientious individuals relate to one another and are attentive to each other’s needs, because

they are focused on a common goal and want the project completed successfully (Orvis et al.,

2008). Being conscientious requires attitudinal components because actions from an individual

are caused by internal thought processes, such as being 1) detailed, 2) goal-oriented, 3) hard-

working, 4) compliant, 5) attentive, 6) ethical, and 7) meticulous. Therefore, these seven items

are my identifiable content domains for the measure.

The conscientiousness measure provided by the electrical company appears to be valid

based on observing the content domain of the questions. It is unwise to base the validity on this


assumption because face validity is not a good measure for assessing whether a measure is valid.

Face validity incorporates subjects’ input on scale items and is a measure of how representative a

research project is at face value, and whether it appears to be a good project (Gatewood, et al.,

2008). It is also important to observe the content validity ratio of the scale. According to Lawshe

(1975), to determine content validity, a panel of subject matter experts will examine a set of

items indicating whether the items are essential, useful, or not necessary. The CVR is calculated

to indicate whether the item is pertinent to the content validity. This ratio determines how much

raters agree that the items were important and relevant to the domain. The ratio calculated was .2

and was based on 5 panelists rating each scale item. This is in fact a low ratio, but it could be

skewed by the low number of SME’s used to determine the content validity. It is best that the

content validity ratio to be as close to +1 as possible because it shows little variance between the

raters and demonstrates high rater agreement (Lawshe, 1975). Based on this information, I have

determined that the content validity of this scale to be very poor.

I did notice some possible contaminations in the conscientiousness scale. For example, “I

strive for recognition when completing a task” could be used to measure another content domain

such as narcissism and based on previous research, narcissism may not have anything to do with

conscientious individuals since conscientious individuals are more goal-oriented and are not as

concerned about rewards. Another possible contamination is “I can control my impulses”, where

this could also measure a different content domain such as thrill-seeking behavior in addition to

conscientiousness. There are also some deficiencies based on the provided conscientiousness

scale. The scale items do not mention anything about conscientious individuals and how they

treat, act, and behave around other people. Based on research and my definition of

conscientiousness, conscientious individuals typically relate to one another and are attentive to


each other’s needs to reach a common goal. Item 12 uses the word “hate” which is a strong word

that could lead to researcher bias. I would recommend changing “hate” to “dislike”, preventing

any persuasion of the respondents’ answers. Another deficiency is located between items 10, 11,

and 12. I believe the measurement scale is asking the same thing about someone becoming

annoyed to disorganization and instead I would recommend choosing only one. Finally, I would

recommend removing at least one scale item from questions 9-12 because they all pertain to

some aspect of being organized. It may be better to replace one or two of the questions with

another aspect of conscientiousness, such as being attentive, ethical, or compliant.

There were numerous errors that I noticed in the conscientiousness scale. I noticed that

item 24, “When given a task, I always complete the task in an efficient and precise manner rather

than procrastinating or pushing the task off to another individual”, is double-barreled in the

question content. This indicates an issue because we are unsure what exactly the item is

measuring, what the respondent is basing his answer off of in the question, and the item touches

upon more than one issue that allows for only one answer. Item 12’s, “I hate when people are

organized”, use of the word “hate” may be an issue because it may be too sternly worded or

misleading such that a respondent hesitates to answer accordingly. Item 14, “I always have a

plan”, appears to be too vague in the wording because it is uncertain what kind of “plan” is

indicated in the question and could lead to inaccurate responses. In item 6, “I feel accomplished

when I conquer my daily task list”, I believe instead of using “conquer” the scale item should

have used “finish” or “complete” because the word “conquer” has other connotations that does

not necessarily apply to our content domain. Items 9-12 are asking about organization and I am

concerned that they may all appear to be measuring very similar content and may have high cross

loadings on the pattern matrix from the resulting EFA. They may also have high inter-item


correlations as well so it is important that I monitor these items. Items 10, “I become annoyed

when things around me are disorganized”, and 12, “I hate when people are unorganized”, also

appear to be measuring very similar content domains with disliking or “hating” a lack of

organization. This may also lead to some high cross loadings on the pattern matrix. Finally, I

made a correction by adding number 19 from the data in D2L because it was missing in the

rubric given to us. The item was “Rules are made to be followed”. There is one aspect of this

scale that I like. I like that there are reverse scored items in the test because it allows us to see if

people have malicious intent or are actually paying attention to the questions as they answer

them. Although, it is important that researchers are aware of any reverse scored items so they can

make the necessary adjustments to their data to properly run and analyze the data.

Section 2: Factorial Validity

Based on my definition of conscientiousness in Section 1, I expect to see 7 factors within

the construct. Before running any analyses through SPSS, I cleaned the data of respondents and

scale items. I first deleted Case 25 because of the lack of demographics reported. I then deleted

Case 24 because of the lack of data reported, whereas the respondent only reported

demographics. I decided to delete the variable “SAT” because I was uncertain as to whether it

was inputted correctly out of the new score of 1600 versus the old score of 2400. I also don’t

believe it has enough of a relevance to conscientiousness to keep in the study. I also decided to

delete the variable “ACT” because only about half of the respondents reported it and therefore

will not help demographic data to only have half the scores represented.

After the data cleanup, I focused my attention on any missing data. There were numerous

instances of missing data and I decided to use data imputation to correct these missing fields. I


replaced missing values in Consc 1-25 using linear interpolation method. My syntax is as

follows:

DATASET ACTIVATE DataSet8.RMV /Consc1_1=SMEAN(Consc1) /Consc2_1=SMEAN(Consc2) /Consc3_1=SMEAN(Consc3) /Consc4_1=SMEAN(Consc4) /Consc5_1=SMEAN(Consc5) /Consc6_1=SMEAN(Consc6) /Consc7_1=SMEAN(Consc7) /Consc8_1=SMEAN(Consc8) /Consc9_1=SMEAN(Consc9) /Consc10_1=SMEAN(Consc10) /Consc11_1=SMEAN(Consc11) /Consc12_1=SMEAN(Consc12) /Consc13_1=SMEAN(Consc13) /Consc14_1=SMEAN(Consc14) /Consc15_1=SMEAN(Consc15) /Consc16_1=SMEAN(Consc16) /Consc17_1=SMEAN(Consc17) /Consc18_1=SMEAN(Consc18) /Consc19_1=SMEAN(Consc19) /Consc20_1=SMEAN(Consc20) /Consc21_1=SMEAN(Consc21) /Consc22_1=SMEAN(Consc22) /Consc23_1=SMEAN(Consc23) /Consc24_1=SMEAN(Consc24) /Consc25_1=SMEAN(Consc25).

I chose to use linear interpolation because it accounts for the immediate preceding and

immediate anteceding valid values in the data to replace the missing value. I believe this method

is most useful because the scale items were organized into groups of questions that asked the

same content (i.e. items 9-12 asking about organization). With this is mind, I wanted values to be

replaced based on this method hoping to receive higher predictive values of what respondent

would have put. After running the syntax, I replaced the new variable names with the

conscientiousness questions and deleted the old conscientiousness questions with missing

variables.

My next step was to focus on reliability estimates to determine if the scale had high

enough reliability to use. I used the following syntax:

RELIABILITY /VARIABLES=Consc1_1 Consc2_1 Consc3_1 Consc4_1 Consc5_1 Consc6_1 Consc7_1 Consc8_1 Consc9_1 Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc14_1 Consc15_1 Consc16_1 Consc17_1 Consc18_1 Consc19_1


Consc20_1 Consc21_1 Consc22_1 Consc23_1 Consc24_1 Consc25_1 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE CORR /SUMMARY=TOTAL.

Reliability StatisticsCronbach's Alpha Cronbach's Alpha

Based on Standardized Items

N of Items

.803 .816 25

The reliability estimate shows a Cronbach’s alpha of.803, which is a strong indicator of

reliability. The data shows only two items in the Cronbach’s Alpha if deleted column, meaning

they would improve Cronbach’s Alpha if removed. I removed question 20 based on this table

because the alpha would be raised to at least .826, which means the scale item was potentially

skewing the data. In addition, it was a lengthy question that could have been phrased in a

different manner.

Item-Total StatisticsScale

Mean if Item

Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if

Item Deleted

I practice self-discipline in my work and personal life.

89.95 72.337 .404 .409 .794

I often work after hours to make sure I complete a project on time.

90.51 69.939 .329 .280 .797

I can control my impulses. 90.45 69.541 .481 .363 .789I don't work as hard as the people around me.

92.35 79.632 -.250 .281 .821

I strive for recognition when completing a task.

91.32 72.761 .180 .108 .805


I feel accomplished when I conquer my daily task list.

89.86 71.630 .444 .431 .793

In my free time, I am constantly looking for things to do to challenge myself.

91.05 70.904 .294 .340 .798

I am always stricing to better myself.

90.07 70.856 .471 .417 .791

I prefer organization in my life. 90.00 68.397 .606 .627 .784I become annoyed when things around me are disorganized.

90.29 68.406 .491 .550 .788

I like to keep my surroundings organized and neat.

90.28 68.490 .498 .610 .788

I hate when people are unorganized.

90.61 69.569 .373 .494 .794

I plan tasks according to importance.

90.08 71.814 .378 .324 .795

I always have a plan. 90.66 68.965 .403 .336 .792I carefully evaluate a situation before I take action.

90.33 70.037 .490 .487 .789

I think before I speak. 90.55 70.450 .371 .415 .794I believe it is important to pay close attention to details.

90.06 71.013 .494 .434 .791

It is not okay to break company rules.

90.43 70.168 .331 .438 .797

Rules are made to be followed. 90.41 70.036 .450 .574 .791When the deadline is coming close and I am running behind, I feel it's ookay to go around the rules if no harm is done.

91.78 80.899 -.320 .339 .826

I do what I think is right in the workplace.

90.11 72.981 .330 .363 .797

I double check tasks for correctness.

90.17 72.405 .367 .424 .795

I am more likely to go to a pre-planned event than a last minute event.

90.56 72.193 .222 .303 .802


When given a task, I always complete the task in an efficient and precise manner rather than procrastinating or pushing the task off to another individual.

90.89 68.350 .406 .318 .792

It is better to make sure something is done correctly than quickly.

90.08 70.498 .516 .493 .789

I also noticed that item 4, if removed, would increase Cronbach’s Alpha to .821. Upon

closer evaluation of Item 4 though, I discovered that Item 4 of the Conscientiousness scale was

reverse scored, so I used the following syntax to reverse score that item:

RECODE Consc4_1 (1=5) (2=4) (3=3) (4=2) (5=1) INTO Consc4_1R.EXECUTE.

I did this because an item that is reverse scored is not measuring conscientiousness but

rather the lack of. This can also decrease the reliability of the measure as well. Therefore, I

reverse coded the item to show the measuring of conscientiousness. Then I ran another reliability

analysis excluding item 20 and including the reverse coded item 4.

RELIABILITY /VARIABLES=Consc1_1 Consc2_1 Consc3_1 Consc4_1R Consc5_1 Consc6_1 Consc7_1 Consc8_1 Consc9_1 Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc14_1 Consc15_1 Consc16_1 Consc17_1 Consc18_1 Consc19_1 Consc21_1 Consc22_1 Consc23_1 Consc24_1 Consc25_1 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE CORR /SUMMARY=TOTAL.




N of Items

.844 .857 24

This raised my Cronbach’s Alpha to .844 which helped to improve the reliability of the

measure even more. Looking at the Item-Total Statistics table again, I did not have any scale

items that if removed, would increase Cronbach’s Alpha. Next, to further clean my data, I

inserted a screening variable to remove respondents who I believe would skew any results. I

created a nominal dummy code variable of “Screening”, where I coded the following: 1 = Keep,

2 = Remove. I based my screening variable off of tenure in months and occupation of the

respondent. I deleted cases based on the following criteria: less than 6 months of tenure if student

assistant or graduate assistant, any occupation that is just “student”, less than 10 months of

tenure with no occupation listed, or less than 3 months of tenure if not a student occupation. I

made this decision because our sample for this study needs to be representative of that of an

electrical company customer service employee. Most students have not had the necessary work

experience for us to measure conscientiousness in a working environment. As far as the tenure, it

was important that a respondent demonstrated time and experience in the workforce. My goal

and hopes is that conscientiousness will be reflected more in these respondents. Below is my

syntax for my screening variable:

DATASET ACTIVATE DataSet1.USE ALL.COMPUTE filter_$=(Screening = 1).VARIABLE LABELS filter_$ 'Screening = 1 (FILTER)'.VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.FORMATS filter_$ (f1.0).FILTER BY filter_$.


EXECUTE.

After selecting the cases, I deleted the cases that were screened to make my data more

comprehensible. I decided to run another reliability estimate and my Cronbach’s Alpha was

increased to .862 which considerably helped improve the reliability of the measure. The result of

the deleted screened cases yielded 104 cases left to analyze. With the low number of cases left

over, I needed to make sure that reducing the number of respondents from 167 to 104 was

appropriate for my analysis. According to MacCallum et al. (1999), even though adequate

sample size is a relatively complex issue, they recommended that no sample should be less than

100. Based on this, I decided that my sample size was adequate and continued with my analysis.



N of Items

.862 .875 24

My next step involved conducting an Exploratory Factor Analysis (EFA) for

conscientiousness. This statistical method is used to uncover the underlying structure of a

relatively large set of variables. My goal here is to identify any underlying relationships between

the conscientiousness questions and determine that latent construct (Fabrigar et al. 1999). I

selected my remaining conscientiousness variables that were left over after screening to use in

my EFA. To set up my EFA, I selected Maximum Likelihood and Promax rotation. I decided to

use Maximum Likelihood because maximum likelihood measures which parameters makes the

observed data most likely to occur (Field, 2009). I used promax rotation because according to

Gorsuch (1983), oblique rotation methods, in contrast to orthogonal rotation methods, assume


that the factors are correlated. In this measurement, I assume the factors are correlated in some

way and therefore chose to go with promax rotation. Below is my syntax for the EFA:

/VARIABLES Consc1_1 Consc2_1 Consc3_1 Consc4_1R Consc5_1 Consc6_1 Consc7_1 Consc8_1 Consc9_1

Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc14_1 Consc15_1 Consc16_1 Consc17_1 Consc18_1 Consc19_1

Consc21_1 Consc22_1 Consc23_1 Consc24_1 Consc25_1 /MISSING PAIRWISE /ANALYSIS Consc1_1 Consc2_1 Consc3_1 Consc4_1R Consc5_1 Consc6_1

Consc7_1 Consc8_1 Consc9_1 Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc14_1 Consc15_1 Consc16_1

Consc17_1 Consc18_1 Consc19_1 Consc21_1 Consc22_1 Consc23_1 Consc24_1 Consc25_1 /PRINT UNIVARIATE INITIAL CORRELATION SIG EXTRACTION ROTATION /FORMAT SORT BLANK(.20) /PLOT EIGEN /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION ML /CRITERIA ITERATE(25) /ROTATION PROMAX(4).

Total Variance ExplainedFactor Initial Eigenvalues

Total % of Variance

Cumulative %

1 6.529 27.203 27.2032 2.285 9.521 36.7243 1.779 7.413 44.1364 1.453 6.054 50.1915 1.312 5.468 55.6586 1.187 4.946 60.6057 1.021 4.254 64.8598 .928 3.867 68.7269 .891 3.713 72.43910 .877 3.654 76.09311 .786 3.277 79.37012 .661 2.756 82.12513 .606 2.526 84.65214 .527 2.198 86.84915 .509 2.121 88.97116 .414 1.727 90.697


17 .382 1.591 92.28818 .364 1.518 93.80519 .343 1.431 95.23620 .324 1.350 96.58621 .263 1.097 97.68222 .238 .993 98.67623 .173 .719 99.39524 .145 .605 100.000Extraction Method: Maximum Likelihood.

By observing the eigenvalues, I can see how much percentage variance the factor can

explain for the measure. My goal is to surmise how many factors demonstrate the most

accounted variance in the measure. Originally, I surmised that there would be 7 factors that

would attribute to my definition of conscientiousness and the scale. Based on the eigenvalues

and observing the scree plot, I decided to adjust my number of factors to 4. I chose to extract a

maximum of 4 factors because this allows all the variance of the items to be distributed among

the 4 factors I chose to extract. I did not want to run the risk of over-factoring which could

spread the variance too thin over an unnecessary amount of factors. This would make it more

difficult for me to determine how strongly an item is loading onto a factor because that relevant

variance goes elsewhere. Using the scree plot, I made a subjective decision of where the “break”

or “elbow” was in the line graph and also observed how much variance was being explained by

the 4 factors. Therefore, I used the following syntax to extract a maximum of 4 factors which

allows the variance of the other extracted factors to distribute among the 4 I chose to extract:

FACTOR /VARIABLES Consc1_1 Consc2_1 Consc3_1 Consc4_1R Consc5_1 Consc6_1 Consc7_1 Consc8_1 Consc9_1 Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc14_1 Consc15_1 Consc16_1 Consc17_1 Consc18_1 Consc19_1 Consc21_1 Consc22_1 Consc23_1 Consc24_1 Consc25_1 /MISSING PAIRWISE


/ANALYSIS Consc1_1 Consc2_1 Consc3_1 Consc4_1R Consc5_1 Consc6_1 Consc7_1 Consc8_1 Consc9_1 Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc14_1 Consc15_1 Consc16_1 Consc17_1 Consc18_1 Consc19_1 Consc21_1 Consc22_1 Consc23_1 Consc24_1 Consc25_1 /PRINT UNIVARIATE INITIAL CORRELATION SIG EXTRACTION ROTATION FSCORE /FORMAT SORT BLANK(.20) /PLOT EIGEN /CRITERIA FACTORS(4) ITERATE(25) /EXTRACTION ML /CRITERIA ITERATE(25) /ROTATION PROMAX(4).

Pattern Matrixa

Factor1 2 3 4

I feel accomplished when I conquer my daily task list. .787I practice self-discipline in my work and personal life. .624I don't work as hard as the people around me. .591 -.224I am always stricing to better myself. .566I believe it is important to pay close attention to details. .467 .276In my free time, I am constantly looking for things to do to challenge myself.

.464

I do what I think is right in the workplace. .350 .347I often work after hours to make sure I complete a project on time. .323I plan tasks according to importance. .304I like to keep my surroundings organized and neat. .819I become annoyed when things around me are disorganized. .806I hate when people are unorganized. .755I prefer organization in my life. .218 .578I always have a plan. .350 .315Rules are made to be followed. .876It is not okay to break company rules. .875I double check tasks for correctness. .309 .426I am more likely to go to a pre-planned event than a last minute event.

-.304 .414 .248

It is better to make sure something is done correctly than quickly. .248 .382I can control my impulses. .219 .278 .252When given a task, I always complete the task in an efficient and precise manner rather than procrastinating or pushing the task off to another individual.

.273 .274


I strive for recognition when completing a task. .237I think before I speak. .970I carefully evaluate a situation before I take action. .577Extraction Method: Maximum Likelihood. Rotation Method: Promax with Kaiser Normalization.a. Rotation converged in 6 iterations.

My next step was to evaluate the pattern matrix to determine if there were any cross

loadings or no strong loadings on any factors. Since I am using oblique rotation for my EFA,

SPSS gives me a pattern matrix which presents pattern loadings. These pattern loadings are

regression coefficients of the variable on each of the factors (Rietveld & Van Hout, 1993). I

decided to use the cut-off score of .30 to make a decision on the strength of the scale item

loadings on each factor. The first question I decided to eliminate was “I do what I think is right

in the workplace”. The question cross loaded strongly on factor 1 and factor 2, meaning it did not


load strongly on a single factor. I base the cross loading on the primary principle of simple

structure: we want each item to load strongly on one factor and one factor only. My next

question or item to eliminate was “I always have a plan”. Again, there is strong cross loading on

factor 2 and factor 4. The next question eliminated was “I am more likely to go to a pre-planned

event than a last minute event”. There were cross loadings on factor 1 and factor 2. The next

eliminated was “I can control my impulses”. This was chosen because there were no strong

loadings on either of the factors. Next to be eliminated was “When given a task, I always

complete the task in an efficient and precise manner rather than procrastinating or pushing the

task off to another individual”. This was because there were no strong loadings on any factors.

The final question to be eliminated was “I strive for recognition when completing a task”. Again,

there were no strong loadings on any factors. I eliminated a total of 6 factors in total due to the

primary principle of simple structure. I conducted another EFA with the remaining scale items

which yielded the following syntax and results:

FACTOR /VARIABLES Consc1_1 Consc2_1 Consc4_1R Consc6_1 Consc7_1 Consc8_1 Consc9_1 Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc15_1 Consc16_1 Consc17_1 Consc18_1 Consc19_1 Consc22_1 Consc25_1 /MISSING PAIRWISE /ANALYSIS Consc1_1 Consc2_1 Consc4_1R Consc6_1 Consc7_1 Consc8_1 Consc9_1 Consc10_1 Consc11_1 Consc12_1 Consc13_1 Consc15_1 Consc16_1 Consc17_1 Consc18_1 Consc19_1 Consc22_1 Consc25_1 /PRINT UNIVARIATE INITIAL CORRELATION SIG EXTRACTION ROTATION FSCORE /FORMAT SORT BLANK(.20) /PLOT EIGEN /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION ML /CRITERIA ITERATE(25) /ROTATION PROMAX(4)


Pattern Matrixa

Factor1 2 3 4

I become annoyed when things around me are disorganized. .909I like to keep my surroundings organized and neat. .791I hate when people are unorganized. .694I prefer organization in my life. .643I feel accomplished when I conquer my daily task list. .854I practice self-discipline in my work and personal life. .664I don't work as hard as the people around me. .608 -.261I am always stricing to better myself. .536In my free time, I am constantly looking for things to do to challenge myself.

.454

I believe it is important to pay close attention to details. .395 .304I plan tasks according to importance. .341I often work after hours to make sure I complete a project on time. .278It is not okay to break company rules. .956Rules are made to be followed. .811I double check tasks for correctness. .252 .406It is better to make sure something is done correctly than quickly. .228 .382I think before I speak. .962I carefully evaluate a situation before I take action. .642Extraction Method: Maximum Likelihood. Rotation Method: Promax with Kaiser Normalization.a. Rotation converged in 5 iterations.

The removal of the testing items yielded very favorable results. My two hesitations were

the items “I believe it is important to pay close attention to details” and “I often work after hours

to make sure I complete a project on time”. After reviewing the testing construct and my

definition of conscientiousness, I decided to leave the questions in believing that it was pertinent

enough to measure the construct. To help me in determining the definition of the four factors that

define conscientiousness based on the remaining scale items, I organized the remaining scale

items to their respective factors.


F1 F2 F3 F4I become annoyed when things around me are disorganized.

I feel accomplished when I conquer my daily task list.

It is not okay to break company rules.

I think before I speak.

I like to keep my surroundings organized and neat.

I practice self-discipline in my work and personal life.

Rules are made to be followed.

I carefully evaluate a situation before I take action.

I hate when people are unorganized.

I don't work as hard as the people around me.

I double check tasks for correctness.

I prefer organization in my life.

I am always striving to better myself.

It is better to make sure something is done correctly than quickly.

In my free time, I am constantly looking for things to do to challenge myself.I believe it is important to pay close attention to details.I plan tasks according to importance.I often work after hours to make sure I complete a project on time.

After reviewing the table above, I made a decision to define and label each of the factors: Factor

1 is Orderliness; Factor 2 is Diligence; Factor 3 is Procedural Compliance; and Factor 4 is Self-

Control.

Factor Correlation MatrixFactor 1 2 3 41 1.000 .577 .423 .2572 .577 1.000 .507 .4483 .423 .507 1.000 .4924 .257 .448 .492 1.000Extraction Method: Maximum Likelihood. Rotation Method: Promax with Kaiser Normalization.


As seen above in the factor correlation matrix, there is some homogeneity in the scale.

Factors 1 through 4 are positively correlated with each other, but the correlations are low enough

meaning that the factors are heterogeneous enough to be measuring separate aspects, but all the

factors together have an underlying construct. In this case, I expect that underlying construct to

be conscientiousness. The overlapping correlation between Factor 1 and 2 is a bit high, but that is

to be expected when measuring determining factors that measure the same construct. A

correlation of 0 would indicate an absolute absence of relationship between the two factors. The

closer the correlation value comes to +1 or -1, the stronger the relationship is for those two

factors. My cutoff score for the correlations in this matrix is .6 because at this value I believe the

scale can still have some homogeneity while still being heterogeneous.

From the EFA, I would say this measure has moderate factorial validity. Even though

current research and I have defined conscientiousness as having more than four factors in the

content domain, the four extracted here do have high item factor loadings to assert that the

provided scale items measure aspects of conscientiousness, just not necessarily the construct as a

whole. I would recommend that anyone attempting to use this scale look closely to ensure that

the factors extracted here are the ones they need from test-takers in order to use this measure

appropriately for their purposes.

Section 3: Construct Validity

Conscientiousness is a comprehensive and broad topic that covers a wide array of

constructs. This requires researchers to search for convergent and divergent validity in the

measure and to look for any evidence of contamination based on the correlations of the different

constructs. The strength of the relationship between two variables is usually expressed by a

correlation coefficient. This can range from +1 to -1 depending on the relationship. The closer


the relationship is to +1 or -1, the more of a relationship the variables have. In the provided

correlation matrix, conscientiousness should be moderately related to general mental, or

cognitive, ability (GMA) because researchers can infer that dedicated and hardworking

individuals should have cognitive and mental ability to carry out their work. The interview

should be moderately correlated with conscientiousness because hopefully a qualified panel of

interviewers could determine whether or not a potential candidate shows good levels of

conscientiousness and who would be a good fit for the organization. I expect conscientiousness

to be related to integrity; because those with integrity do what they think is right and is

committed to moral values and obligations (McFall, 1987). Conscientiousness is known to be a

good predictor of task performance, and the correlation matrix confirms a moderate relationship

between the two. It is possible that conscientiousness is moderately related to organizational

citizenship behavior (OCB) because my definition of conscientiousness from the literature

included an aspect where those who are conscientiousness make an effort to help others complete

a task to reach a common goal. Conscientiousness should be negatively correlated with

counterproductive work behaviors (CWB) because a conscientious individual would not do

anything to harm or delay the successful outcome of a goal. Conscientiousness should have a

very low correlation with mechanical ability because having the ability to use machinery and

tools does not relate to my research and the literature’s research of what it means to be

conscientiousness. Lastly, conscientiousness should have some moderate relationship with

turnover intentions. Highly conscientious individuals, according to the literature and my

definition, are less likely to leave an organization than low conscientious individuals because

conscientiousness employees who perceive a breach and experience immediate feelings of anger

and thoughts of turnover are likely to control and moderate these thoughts, because turnover is


viewed as a rash action in response to breach. Therefore, the correlation between

conscientiousness and turnover intentions should be negative.

Based on the correlation matrix, there are some incongruities between what is expected

and what is actually observed. GMA’s correlation with conscientiousness is positive, which is

what I expect, but it is a fairly weak correlation for two constructs that should have some

overlap. Integrity is fairly high and is positive, which is a good result. The structured interview

has a very weak correlation with conscientiousness, which may or may not be a problem with the

scale. It may be a sign that the interview itself is not measuring what we think it should measure

or the interview is not being conducted properly. Without knowing how the interview is prepared

and handled, it is difficult to say why there is a weak correlation. Another slight problem in the

correlation matrix occurs where the task performance construct has a slightly weak correlation

with conscientiousness. It is known that conscientiousness is a good predictor for task

performance and having an r = .26 should be much higher based on research. The correlation

with OCB may be lower than expected, but that may be due to the conscientiousness scale

questions. The scale fails to ask whether or not respondents have an inclination to help others to

achieve a goal. The correlation between CWB and conscientiousness is very concerning. What

should be a moderately high negative correlation is moderately positive instead. This is

extremely troubling because conscientiousness individuals would not engage in CWB because it

would be detrimental to the organization and overall goal achievement. Another concerning

correlation is between conscientiousness and mechanical work ability. There should be a very

low correlation between the two yet the correlation is r = .70. This greatly reduces the validity of

the measure because mechanical ability has very little to do with conscientiousness. The last

concern is the correlation between conscientiousness and turnover intentions. As stated in


Section 1, conscience individuals will not quit as easily as those with low conscientiousness.

This correlation should be weak, if not negative and instead is positive and strong. The higher an

individual scores on conscientiousness, the more likely they will demonstrate turnover

intentions. This is very concerning because the basic purpose of this study was to see if adding

conscientiousness would reduce turnover intentions, whereas if this conscientiousness scale is

added, it will increase turnover intentions. Including this scale would affect the electrical

company very negatively because this scale would increase turnover intentions instead of

reducing them. This scale would allow individuals to be hired who possess work behaviors that

would be counter-productive and cost the company time and money. It is unclear why there are

discrepancies in the provided scale and further research would need to be conducted to reach a

conclusion, but there is undoubtedly an issue between task performance, CWB, mechanical

ability, and turnover intentions.

Some of the concerns with mechanical ability may be attributed to an invalid measure of

mechanical ability. It is also strongly correlated with GMA and weak with all other constructs.

Furthermore, the turnover intentions construct is strongly correlated with task performance, and

has no correlation with OCB where it should be somewhat negative. This brings up some

suspicion in the quality of the turnover intentions scale as well. On the other hand, the CWB

construct appears to have some validity as it is negatively correlated with integrity.

Unfortunately, it also has almost no correlation with OCB, although this relationship should be at

least moderately negative.

I have some concerns about the conscientiousness scale after having looked at the

convergent and divergent validity through the provided correlation matrix. There are constructs

that should have had strong positive correlations that were instead weak, some that should have


been negative and were instead positive and slightly strong, and some that should have had no

correlation and instead were strong. Some of these concerns can be attributed to the validity of

the measurements of the other constructs or the length of the conscientiousness scale, but there

are problems with the scale regardless. I will address these concerns in my final suggestion as to

whether or not to include this scale. It would be useful and beneficial if I could assess all the

constructs used and determine where the inconsistencies are stemming from. That would require

additional time, resources, and finances; something the electrical company does not have at the

moment. Reassessing the constructs would be something to consider for future research projects.

Section 4: Criterion-Related Validity

From the provided regression tables, I can see how the independent variables of general

mental ability (GMA), integrity, interview, and conscientiousness regress onto the dependent

variables of job performance and turnover intentions. B-values are unstandardized coefficients

that tell me how to predict the dependent variables based on the independent variable. For

example, with every one unit increase in GMA, the table shows me that there will be a .43 unit

increase in job performance. The B-value does not allow me to compare across the independent

variables to determine influence on the dependent variable, so I need to utilize the beta values.

The beta values in the job performance regression table are problematic in terms of the

conscientiousness scale. Surprisingly, of the four variables, job performance is least affect by

conscientiousness. It is generally understood that conscientiousness should at least be moderately

related to job performance. This creates more concern of the validity of the conscientiousness

scale in this regard. Based on the F-value in this table, the four variables together appear to

reliably predict job performance. The F-value is used to determine if the variances between the

means of two populations (within group means and between group means) are significantly


different. There is a significant effect of GMA, integrity, interview, and conscientiousness

combined on job performance (F = 32.5, p < .05). The R2 value informs me the amount of

variance in the dependent variable that can be explained by the independent variables. In this

case, 45% (R2 = .45) of the variance in job performance can be attributed to GMA, integrity,

interview, and conscientiousness. Even though the percent of variance predicted in job

performance is almost half, I would like to see more variance accounted for, especially more of

conscientiousness, in this regression table.

The second table demonstrates conscientiousness unfavorably in terms of turnover

intentions. The table actually shows conscientiousness as the highest predictor by B-values. For

every unit increase in conscientiousness, turnover intentions increase by .42. This is not ideal at

all, because all the independent variables featured in this table should be negative in terms of

turnover intentions. Instead, the independent variables are positive and have weak to moderate

effects. Again, there is a significant effect of GMA, integrity, interview, and conscientiousness

on turnover intentions (F = 30.3, p < .05). From the provided table, 60% (R2 = .60) of the

variance in turnover intentions can be attributed to these four variables. At least in this table the

independent variables are positively related and they account for over half of the variance in

turnover intentions.

Multicollinearity is my highest concern in any scale measurement because I do not want

two or more predictor variables in the regression model to be highly correlated. Multicollinearity

misleadingly inflates the standard errors which make some variables statistically insignificant

while they should be otherwise significant. This leads to an undesirable situation where the

correlations among independent variables are strong. There is some possibility of collinearity

between the independent variables. When comparing between the independent variables


resulting from the regression table, the regression table shows that the concerns of collinearity

are between GMA and the interview for job performance regressed onto the selection battery and

integrity and conscientiousness for turnover intentions regressed onto the selection battery. As

mentioned earlier in Section 3, conscientiousness and integrity have some overlap because those

who are conscientiousness will have some form of integrity when completing tasks

appropriately. GMA and the interview show some overlap and may be because during the

interview, interviewers can detect GMA without having to administer a test. In this situation,

multicollinearity may have some overlapping variance in the explanation of the dependent

variables of turnover intent and job performance. The demonstration of multicollinearity could

also be a result of the correlation matrix in Section 3 which demonstrated counter intuitive

positive correlations, especially between mechanical ability and turnover intentions. The unusual

correlations could have had an effect on the regression table in such a way that skewed the B-

values unfavorably on both job performance and turnover intentions. As a result, researchers will

need to make a decision whether or not the two regression tables either explain or do not explain

as much variance as previously claimed and if there is more unexplained variance in the

measurement scale.

The evidence presented in the two regression tables continues to lower my trust in the

conscientious measure. Not only is conscientiousness the lowest and least useful independent

variable in job performance, it is the strongest and most positively correlated independent

variable with turnover intentions where it should be moderately negative. The issue here is that

the scale cannot be utilized in evaluating conscientiousness appropriately. This is especially

apparent with the relationship to the two dependent variables, job performance and turnover

intentions, that the electric company requires in order to adequately select worthwhile applicants.


Section 5: Recommendation

I recommend that this measure of conscientiousness not to be used as part of the selection

criteria for customer service positions at the electric company. In the beginning, the face validity

of the measure appeared to be valid to the construct definition. The other validity measures,

unfortunately, clearly demonstrate that this measure of conscientiousness is not measuring what

it should and therefore, it cannot appropriately be used to assess conscientious as necessary for

the organization. The content validity calculation was very low, even though I did attribute some

of it to lack of knowledge and a very small number of raters. The Cronbach’s alpha coefficient

did yield high results; however, reliability is not an indicator of validity. In fact, what this is

explaining is that the measure has a high probability of returning the same or similar scores for

individuals continuously, but not what is actually being measured by the scale. The factorial

analysis only yielded four out of the original seven content domains from my previous definition,

so it is probable that there is some contamination in what is being measured, as well as some

deficiency in the scale’s item content.

The construct validity assessment exposed further problems with the scale. Not only is

conscientiousness not correlated strongly enough to constructs that are similar, but it is strongly

correlate to highly unrelated constructs. Further, it is strongly positively correlated with some

constructs that it should be negatively related to, some variables that are essential for the

organization and thus present bigger underlying issues. The construct validity of

conscientiousness is most concerning to me. It is easy to attribute the content validity and

factorial analysis issues to a small scale or inexperienced raters. There are clearly other factors at

play, such as the sample size and type of respondents, which reduce the content validity.

However, the low construct validity is an indication that the scale is not measuring what the


organization needs to measure, and may damage the selection process to include individuals who

are not actually conscientious. In reality, it has the potential of increasing turnover rates and

counterproductive work behavior.

The criterion-related validity assessment furthers the problem by demonstrating that

conscientiousness is potentially the worst variable of the four independent variables used to

measure job performance and turnover intentions. Conscientiousness is the least correlated with

job performance and the highest in turnover intentions. This result is counterintuitive to the

definition presented earlier. Based on research, conscientiousness should have high correlation

with job performance and very low correlation with turnover intentions. These incongruent

results affirm my decision that this scale should not be used by the company.

Fortunately, there is one last area to that can show me if the scale is useful in any way.

This can be done by looking the provided tables which provide adverse impact data and baserate

ratios. Adverse impact is an issue in many organizations where the selection process

inadvertently results in preference to a majority group of individuals. It is a result of Title VII of

the Civil Rights Act of 1964, which is a federal law that prohibits discrimination in employment

on the basis of sex, race, color, national origin, and religion. The calculation of current and

predicted adverse impact tables may indicate some benefits of the conscientiousness scale if

added to the selection criteria. Primarily, there is a change of baserates in the addition of the

scale. Currently, the ratio of people that are successful on the job compared to total available

applicants is at .7, which increases to .9 with the addition of the conscientiousness scale.

Unfortunately, this is the only benefit of adding conscientiousness to the selection criteria.

In terms of adverse impact, the addition of conscientiousness actually diminishes the

equality of minority groups in selection for the customer service positions. Currently, adverse


impact is present in the African-American minority group in both external and internal hires.

Males also experience this as a minority group compared to the female group. In the predicted

adverse impact statistics, the males receive a small increase in adverse impact ratios while the

females experiences adverse impact in internal hire. The biggest concern, though, is in the

African American group. In both internal and external hires, the African American group has a

significant drop in both the number of hires and adverse impact ratio from the current to

predicted calculations. From a .3 hire ratio and a .6 adverse impact ratio, the internal hires drop

to .125 and an adverse impact ratio of .25. This is not just a concern for the hiring criteria for the

organization’s job performance and turnover. This would place the electric company in even

worse jeopardy if they were ever to be audited and their adverse impact ratios were calculated.

Any organization with such a low hire ratio for minority groups is a legal threat to the

organization. Future lawsuits could cost the company dearly financially to settle lawsuits in court

or to justify having included such an invalid selection measure of conscientiousness.

Finally, including the legal aspects of selection measures for this electrical company, I

unequivocally reject the idea of including this conscientiousness scale in the organization’s

selection process. There would be an incredible negative impact, financially and practically, to

adding this measure. Long-term costs resulting from the scale could put the company at great

risk of losing money, which does not even begin to cover how damaging the scale could be to the

company’s employee workforce. Not to mention if the company was to ever be audited and come

to find out adverse impact was a rampant problem. I am not concerned with respondent’s faking

this scale because I will not allow the company to include this measure with the intention of

selecting appropriate candidates for their customer service positions. The measure is useless,

unsuitable, and could result in hefty legal fines for the company. It is recommended that the


electric company continue using their current selection criteria and focus their limited financials

on another project.

Section 6: Raw Data Interpretation

I computed the means for the three scales as follows: conscientiousness, Protestant work

ethic, and turnover intentions. The syntax for each is as follows:

*Compute means for Protestant work ethic.DATASET ACTIVATE DataSet1.COMPUTE PW=MEAN(PW6R,PW1,PW2,PW3,PW4,PW5,PW7,PW8).VARIABLE LABELS PW 'Protestant Work Ethic'.EXECUTE.

*Compute means for turnover intent.COMPUTE TI=MEAN(TI1,TI2,TI3).VARIABLE LABELS TI 'Turnover Intent'.EXECUTE.

*Compute means for conscientiousness.COMPUTE Consc=MEAN(Consc1_1,Consc2_1,Consc4_1R,Consc6_1,Consc7_1,Consc8_1,Consc9_1,Consc10_1, Consc11_1,Consc12_1, Consc13_1,Consc15_1,Consc16_1,Consc17_1,Consc18_1,Consc19_1,Consc22_1, Consc25_1).VARIABLE LABELS Consc 'Conscientiousness'.EXECUTE.

After executing the mean computation above, I execute the syntax below to create a

correlation matrix where I can compare the three variables:

*Correlation matrix for Consc, TI, and PW.CORRELATIONS /VARIABLES=PW TI Consc /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE.


CorrelationsProtestant

Work EthicTurnover

IntentConscientiou

sness

Protestant Work Ethic

Pearson Correlation

1 .061 .438**

Sig. (2-tailed) .538 .000N 104 104 104

Turnover Intent

Pearson Correlation

.061 1 .016

Sig. (2-tailed) .538 .873N 104 104 104

Conscientiousness

Pearson Correlation

.438** .016 1

Sig. (2-tailed) .000 .873N 104 104 104

**. Correlation is significant at the 0.01 level (2-tailed).

The results from the correlation table are very favorable and the correlations were very

much expected. Conscientiousness and Protestant work ethic should be highly correlated because

both emphasize hard work and diligence when completing tasks. The same is expected when

comparing Protestant work ethic and conscientiousness to turnover intent because we do not

expect them be correlated since quitting is often not an option when completing a task proves

incredibly difficult.

Below is the syntax for the descriptives for the scale items:

*Descriptives for scale items.DATASET ACTIVATE DataSet1.DESCRIPTIVES VARIABLES=HighGPA CurrentGPA CollegeStatus TenureMonths /STATISTICS=MEAN STDDEV MIN MAX.


Descriptive StatisticsN Minimum Maximum Mean Std.

DeviationHighGPA 104 1 4 3.24 .830CurrentGPA 98 1 4 2.78 1.041CollegeStatus 102 1 5 3.85 1.038TenureMonths 104 3 318 48.82 65.394Valid N (listwise)

97

The table above details the education and job status of the sample respondents. The mean

of the high school GPA is close to a B+ with a small standard deviation. This is concerning

because that is a high mean with little variance, which I believe will not generalize to a larger

population. The college status is very high because the majority of the respondents are still

pursuing their bachelor’s degree, which makes it extremely difficult to properly generalize to the

customer service population. On O*Net online, the average customer service individual (16%)

has obtained a bachelor’s degree, where our sample average has our respondents almost

completing a Bachelor’s degree. I do like the tenure in months which places the average of the

respondents having at least four years of experience in their respective fields. The standard

deviation is a bit high, but that is to be expected when the minimum and maximum range is very

broad for the respondents.

Below is the syntax for the frequencies for the nominal items:

*Frequencies for nominal items.FREQUENCIES VARIABLES=Ethnicity Major Married Position /STATISTICS=STDDEV MINIMUM MAXIMUM SKEWNESS SESKEW /ORDER=ANALYSIS.


EthnicityFrequency Percent Valid

PercentCumulative

Percent

Valid

Caucasian 68 65.4 65.4 65.4African American

30 28.8 28.8 94.2

Latino/Hispanic 3 2.9 2.9 97.1Other 3 2.9 2.9 100.0Total 104 100.0 100.0

MajorFrequency Percent Valid

PercentCumulative

Percent

Valid

Psychology 54 51.9 52.4 52.4Speech Communications

1 1.0 1.0 53.4

Other 48 46.2 46.6 100.0Total 103 99.0 100.0

Missing System 1 1.0Total 104 100.0

MarriedFrequency Percent Valid

PercentCumulative

Percent

Valid

Single 77 74.0 74.0 74.0Married 24 23.1 23.1 97.1Divorced 3 2.9 2.9 100.0Total 104 100.0 100.0


PositionFrequency Percent Valid

PercentCumulative

Percent

Valid

Entry-level

44 42.3 42.3 42.3

Supervisor 10 9.6 9.6 51.9Manager 9 8.7 8.7 60.6Other 41 39.4 39.4 100.0Total 104 100.0 100.0

The tables above demonstrate that the typical respondent to this conscientiousness scale

is Caucasian (65.4%), a psychology major (51.9%), single (74%), and holds an entry-level job

position (42.3%). Obviously, this is not descriptive of a majority of the customer service

population. I would need to see the demographics of the electrical company to make an accurate

analysis to see if my study results are generalizable. With the many ethnic groups in America

today, I refuse to believe that the electric company customer service position is solely comprised

of Caucasians and African-Americans. I estimate that Caucasians and African-Americans are

closer to 50% and another ethnicity would have a higher percentage. Another problem I have is

the response of Psychology majors in this study. Even though Psychology majors make up over

half of the respondents, it would have been beneficial to give respondents the option to write in

their degree rather than limiting them to four options of “Psychology”, “Speech

Communications”, “Political Science”, and “Other”. It would have been immensely helpful to

know each individual degree so that I could better make generalizations of the study. Based on

the marital status I would have to assume that our average age of respondents was fairly young,

something I will touch on momentarily. Customer service positions comprise of a wide array of

ages and I do not believe the majority are young. In addition, the majority of the respondents

hold entry-level jobs which further increase my suspicions of a young sample. I would say this


sample is not adequately representative of the population of customer service professionals,

although I do believe it can still be applicable to a portion at least.

The methodology of the study also makes it difficult to generalize the study to the

population. Surprisingly, the study did not measure the gender of the respondents. I believe this

is a necessary and vital demographic to include in any study because it can tell us a lot of

information pertaining to any respondent sample. It can provide us with more insight to who the

respondents are and allow us to make better interpretations of the data. Another concern of the

methodology is the way it was possibly administered that resulted in a small sample size. The

sample size could have been corrected by conducting the survey numerous times or using

different methods of exposing the survey to as many people as possible.

Overall, I would not be comfortable at all attempting to generalize the collected sample

data to any population outside of an educational institution, and even less so with a customer

service population that has much more demographic variance than what has been collected in the

study.


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