face to face 2

FACE TO FACE 2

FEM 3002 RESEARCH METHODOLOGY

rozumah baharudin, pem 3001 fem 3002, first semester 0405 2

VARIABLE AND MEASUREMENT


VARIABLES

Measurable characteristics or properties of people or things that can take on different values.

Vary + Able


Variable

• Variables are what is studied by researchers.

• It have several types: 2 IMPORTANT TYPES

• Dependent• Independent


Dependent Variables

Indicates whether the treatment or manipulation of the independent variable had an effect – Synonym

• Outcome variable• Result variable• Effect • Criterion variable


Independent Variables

A variable that is manipulated to examine it’s impact on a dependent variable or outcome variableTreatmentSynonym

•Factor •Predictor

• The dependent variable is placed on the y-axis

• The independent variable is placed on the x-axis.

Antecedent variable

• An antecedent variable is a variable that occurs before the independent variable and the dependent variable.

http://en.wikipedia.org/wiki/Variable_(mathematics)

http://en.wikipedia.org/wiki/Independent_variable

http://en.wikipedia.org/wiki/Dependent_variable

Academic achievement

ParentingBehavior

Parental Characteristics• Age• Education • Self-efficacy

Family Contexts• # of children• Family income• Parental Marital Q

Quality

Child Characteristics• Age• Sex• Aspiration

Conceptual Framework for a study on “Predictors of Parenting Behavior and Child Academic Achievement

AV

IV DV

Types of variables based on Adjectives

• Quantitative Variables– Discrete Variables– Continuous Variables

• Qualitative or Categorical Variables

• A variable that can be measured numerically is called a quantitative variable. The data collected on a quantitative variable are called quantitative data.

• A variable whose values are countable is called a discrete variable. In other words, a discrete variable can assume only certain values with no intermediate values.

• Example: A household could have:– three children or six children, but not 4.53

children. – two or three cars, but not 2.5 cars.

• A variable that can assume any numerical value over a certain interval or intervals is called a continuous variable.

• Example: A person can be:– 5.7 inches tall, & 80.1 kg in weight

• A variable that cannot assume a numerical value but can be classified into two or more nonnumeric categories is called a qualitative or categorical variable. The data collected on such a variable are called qualitative data.

Variable

Quantitative Qualitative orcategorical (e.g.,

make of a computer,hair color, gender)

Continuous(e.g., length,age, height,weight, time)

Discrete (e.g.,number of

houses, cars,accidents)


Definition of Variable:

• Conceptual• Operational


Conceptual definition

• Definition that explain the idea/concept the variable is trying to capture.


Operational definition

• Definition of how the variable will be measured in practice.


e.g.,

Variable = academic achievement Conceptual = performance of student in all the

courses taken since enrolled. Operational = The accumulative great point

average of the student.


MEASUREMENT

• Procedure for assigning symbols, letters, or numbers to empirical properties of variables according to rules.


• Difficulty in measuring concepts directly (e.g., academic achievement)

• Usually measure indicators of concepts (e.g., CGPA)


• Level of measurement determines the type of statistical analysis.

• The level of measurement you use depends on how you want to measure an outcome.


Levels of Measurement

• Nominal• Ordinal• Interval • Ratio


Nominal• Latin word nomin (name)• Variable categorical in nature• Differ in quality not quantity (numbers have no meaning only

label)

• Characterizes observation into one (and only one) category mutually exclusive

• Solely qualitative• No obsolute zero (0)• Matematical operation not possible.


Ordinal• Describes variables that can be ordered along some

type of continuum.• Not only categories, order as well.• Ranking according to various outcomes, e.g., big &

little.• No obsolute ‘0’, only relative position; e.g., Zul is

taller than Sheereen and Sheereen is taller than Rozumah (no information on how much taller).

• Matematical operation not possible.


Interval• Latin word intervalum (spaces between walls).• Describes variables that have equal intervals btw them.• Allow us to determine the difference btw points along the

same type of continuum (e.g., the difference btw 300 and 400 is the same as the difference btw 700 and 800; i.e., 100).

• 0 is arbitrary (subjective, temporary).• Simple matematical operation.• More precise & convey > info than nominal & ordinal; but

must be cautious in interpreting.


Ratio

• Latin word ratio (calculation).• Describes variables that have equal intervals

btw them & have absolute 0.• Most precise.• Complex matematical operation.• Highest level of measurement.


Summary

• Nominal level variables are categorical in nature (lowest level)

• Ordinal -- are ranked.• Interval -- have equidistant points along some

underlying continuum.• Ratio -- have a true zero (highest level).


What is the level of these measurements of height?

• Precise height measured on a scale with true zero.• Tall and Short (have some meaning, but nature of

difference is not known).• A is 5 inches taller than B (know precise

difference).• Categorize people into A and B (people different

in height).


RELIABILITY & VALIDITY OF

MEASUREMENT


Reliability and validity are the hallmarks of good measurement.

Assessments tools must be reliable and valid, otherwise the research hypothesis may be incorrectly rejected.


• Reliability is a practical measure of how consistent and stable a measurement instrument or a test might be.

• Reliability is often measured using the correlation coefficient.


Synonyms for Reliability

• Dependency• Consistency• Stablility• Trustworthiness• Predictability• Faithfulness


Types of Reliability

1. Test-retest2. Parallel forms3. Inter-rater4. Internal consistency


1. Test-retest:

• A measure of stability.• Examines consistency over time.• Administer the same test/measure at two

different times to the same group of participants.

• Coefficient: rtest1.test2


2. Parallel Forms

• A measure of equivalence.• Examines consistency between forms.• Administer different forms of the same test to

the same group of participants.• Coefficient: rform1.form2


3. Inter-rater

• A measure of agreement.• Examines consistency across raters.• Have two raters, rate behaviors and determine

the amount of agreement between them.• Coefficient: % of agreement.


4. Internal consistency

• A measure of consistently each item measures the same underlying construct.

• Examines reliability within a particular set of item.• Correlate performance on each item with overall

performance across participants.• Coefficient: Chronbach’s alpha


Validity

• Is the quality of a test doing what it is designed to do.

• The test or instrument you are using actually measures what you need to have measured.


Synonyms for Validity

• Truthfulness,• Accuracy• Authenticity• Genuineness• soundness


Types of Validity

1. Content2. Criterion

i. Concurrentii. Predictive

3. Construct


Content Validity

• A measure of how well the items represent the entire universe of items

• Established by asking expert if the items assess what you want them to.

• History test = test items ask questions on history not Science.


Criterion Validity

i. Concurrent validity A measure of how well a test estimates a

criterion.

Established by selecting a criterion and correlate scores on the test with scores on ther criterion in the present.

Good student = test result + reports by lecturers.


ii. Predictive Validity A measure of how well a test predicts a criterion.

Select a criterion and correlates scores on the test with scores on the criterion in the future.

High merit on STPM/Diploma = Score high CGPA.

Pass driving test = Good driver.


Construct Validity A measure of how well a test assesses some underlying

construct.

Assess the underlying construct on which the test is based and correlate these scores with the scores.

Theoretically and practically sound.

Intelligence test actually measures intelligence.


Relationship between Reliability and Validity

• A test can be reliable without being valid but the reverse is not true.A test can be reliable, but not valid, but a test cannot be

valid without first being reliable.Reliablity is a necessary, but not sufficient, condition of validity.You are answering questions on simple addition, but we called it

spelling test! Obviously it is not a test on spelling lack of validity, does not affect reliability.


POPULATION AND SAMPLE


POPULATION

• Definition

A group of potential participants to whom you want to generalize the results of a study.


Generalize : The key to a successful study; because it is only the results that can be generalized from a sample to a population; that research results have meaning beyond the limited setting.


Not generalize : The sample selected is not an accurate representation of the population.


Important Research Terms:


Population vs. Census

• Population the a group of people or things you are interested in.

• Census is a measurement of all the units in the population


Population Parameter vs. Statistic

• PP = number that results from measuring all the units in the population.

• Statistic = number that results from measuring all the units in the sample; statistics from samples are used to estimate PP.


Sampling Frame vs Unit of Analysis

• SF = specific data from which sample is drawn, e.g., a phone book.

• UA = type of object of interest, e.g., arsons, fire departments, firefighters.


Sampling Frame

• Is a list or quasi list of the members of a population. • Resource used in the selection of a sample.• A sample’s representativeness depends directly on

the extent to which a sampling frame contains all the members of the total population that the sample is intented to represent.


e.g., Sampling Frame

• The data for this research were obtained from a random sample of parents of children in the third grade in government primary schools in Selangor.


SAMPLES

Definition :Sample is a subset of the population.

– Good sampling : include maximizing the degree to which this selected group represent the population.


POPULATION

Sample

Sample


WHY SAMPLE?


Types of sampling

1. Probability sampling2. Non probability sampling


Probability sampling• Allows use of statistics, tests hypotheses.• Can estimate population parameter.• Eliminates bias.• Must have random selections of units.


Non-probability sampling

• Exploratory research, generates hypotheses.• Population parameters not of interests.• Adequacy of sample unknown.• Cheaper, easier, quicker to carry out.• Cant generalized findings.• Non-representative.


PROBABILITY SAMPLING

• A type of sampling where the likelihood of any one member of the population being selected is known.

• Commonly used because the selection of participants is determined by chance.


–e.g., if there are 4,500 students in the Faculty of Human Ecology, and if there are 1,000 seniors, the odds of selecting one senior as part of the sample is 1000:4,500 or 0.22.


NON-PROBABILITY

• Where the likelihood of selecting any one member from the population or where the probability of selecting a single individual is not known.


–e.g., if you do not know how many seniors in the Faculty of Human Ecology, the likelihood of anyone being selected cannot be computed.


TYPES OF PROBABILITY SAMPLING

1. Simple Random Sampling2. Systematic Sampling3. Stratified Random Sampling4. Cluster Sampling


1. Simple Random Sampling When the population’s members are similar to one another.


Adv: • Ensures a high degree of

representativenessDisadv:

• Time consuming and tedious


Using Table of Random Numbers

• See Handout


2. Systematic Sampling

When the population’s members are similar to one another.


Adv : • Ensures a high degree of

representativeness; no need to use a table of random numbers.

Disadv : • Less truly random than simple

random sampling


3. Stratified Random Sampling

When the population is heterogeneous in nature and contains several different groups.


Adv : • Ensures a high degree of

representativeness of all the strata in the population.

Disadv : • Time consuming and tedious


Two Types of Stratified Random Sampling (SRM)

• Proportionate SRM• Non-Proportionate SRM


Proportionate SRM

• Sampel selected is in proportion to the size of each stratum in the population


example: PSRM

• Population = 100• Layer 1 = 40 males• Layer 2 = 60 females• For a sample size of 10, you will take 4 males +

6 females.


Non-proportionate SRM

• Selection of sample is not according to size of stratum in the population


e.g., NPSRM

• Population = 100• Layer 1 = 40 males• Layer 2 = 60 females• For a sample size of 10, you will take 5

males + 5 females.


4. Cluster SamplingWhen the population consist of units rather than individuals.


Adv : • Easy and convenient

Disadv : • Possibility that members of units are

different from one another, decreasing the sampling’s effectiveness


TYPES OF NON-PROBABILITY SAMPLING

1. Convenience Sampling2. Quota sampling3. Purposive Sampling4. Snowball sampling



1. Convenience SamplingWhen the sample is captive.– Adv :

• convenient and inexpensive– Disadv :

• results in questionable representativeness.


2. Quota sampling

When strata are present, and stratified, sampling is not possible– Adv :

• Ensures some degree of representativeness of all the strata in the population

– Disadv : • Results in questionable representativeness



3. Purposive Sampling

• Researcher uses own judgement in the selection of sample members

• Sometimes called a judgmental sample.


4. Snowball sampling

A technique often used in rare populations; each subject interviewed is asked to identify others.


SAMPLING ERROR

• Lack of fit between the sample and the population.

• The difference between the characteristics of the sample and the characteristics of the population from which the sample was selected.


• Reducing sampling error is the major goal of any selection technique.

• Larger sample, lower sampling error.


SAMPLE SIZE

• How big?• Depends on type of research design.• Desired confidence level of results.• Amount of accuracy wanted.• Characteristics of population of interest.


SAMPLE SIZE

• Big enough to answer research question.• But not so big that the process of sampling

becomes uneconomical.

• Heterogeneous sample = bigger size• Homogeneous sample = smaller size


• General Rule of Thumb

30 participants/ respondents in each group.


General guide for sample size:

1. Larger sample, smaller sampling error, better representativeness.

2. If using several subgroups, starts with large enough subjects to account for the eventual breaking down of subject groups.


3. If mailing out surveys or questionnaires, increase sample size by 40-50% to account for lost mails or uncooperative subjects.

4. Big is good, but appropriate is better.


DATA COLLECTION

• Gathering information about a situation, problem or phenomenon.


Methods in Information Gathering:

1. Secondary Data Information required is already available &

need only be extracted.2. Primary Data

Information must be collected.


Secondary sources

• DocumentsGovernment publicationsEarlier researchCensusPersonal records


Primary Sources

1. Observation Participant Non-participant

2. Interviewing Structured Unstructured

3. Questionnaire Mailed questionnaire Collective questionnaire


OBSERVATION

• Is a purposeful, systematic, and selective way of watching and listening to an interaction or phenomenon as it takes place.

• Appropriate in situations where full and/or accurate information cannot be elicited by questioning.


Types of observation

1. Participant observation2. Non-participant observation


Participant observation

• Researcher participates in the activities of the group being observed in the same manner as its members, with or without knowing that they are being observed.


Non-participant observation

• Researcher does not get involved in the activities of the group but remains a passive observer, watching, & listening to its activities and drawing conclusions from this.


Problems with using observation:

• Respondent may be aware & change behavior.• Observer bias.• Interpretation btw observer inconsistent.• Possibility of incomplete observation and/or

recording.


Situations for observation

1. Natural Does not intervene.

2. Controlled Introduce stimulus to observe reactions.


Methods of recording observations:

• Narrative• Scales • Categorical recording• Recording on mechanical devices


Narrative

• Take brief notes first• Soon after makes detailed notes• Adv: provides deep insight into the

interaction.• Disadv: observer bias & incomplete recording.


Scales

• Develop scale to rate interactions or phenomenon.

• Adv: quick, easy to record.• Disadv: does not provide in-depth information

about interaction.


Categorical Recording

• Depend on classification develop by researcher; e.g. passive/active, etc.

• Adv: quick, easy to record.• Disadv: does not provide in-depth information

about interaction.


Recording on Mechanical Devices:

• Observation recorded on a video tape and then analyzed.

• Adv: can watched it many times b4 making conclusion; can invite expert to view to make right conclusion.

• Disadv: respondent uncomfortable, or behave differently.


INTERVIEW

• Person-to-person interaction with specific purpose.

• Most common method.• 2 types:

1. STRUCTURED 2. UNSTRUCTURED


Unstructured Interview

• Known as in-depth interview.• Use interview guide/framework; no specific

set questions.• + spontaneous questions.• Can be conducted in …….

1. One-to-one2. Group interview (focused group)


• Use for in-depth information.• Or when lack of information.• Flexibility on what to ask of a respondent;

elicit rich information.• Thus, sometimes used to contruct structured

instrument.


• Disadv.: – No specific set question, comparability difficult.– Questions may keep changing; info at beginning

may be different from later.– Freedom may lead to interviewer bias.– More skill needed to use interview guide than

structured interview.


Structured interview• Pre-determined set questions in interview schedule:

– Same wording– Same order of questions

• Interview schedule/research instrument: – Written list of questions– Open-ended/ closed– For use by interviewer– In person-to-person interaction (face-to-face, by

telephone, or by other electronic means)


• Adv: provides uniform info, which ensures comparability of data.

• Required fewer interviewing skills than unstructured interviewing.


QUESTIONNAIRE

• Is a written list of questions; answer recorded by respondents.

• Respondent read the questions, interpret & write down answers him/herself.

• Different from interview, where interviewer asks qn & write respondents replies on interview schedule.


Rules for questionnaire:• Questions must be clear & easy to understand.• Layout is easy to read, pleasant to the eye,

sequence of qn easy to follow.• Interactive style – as if someone talking to

respondent.• Sensitive qn – prefaced with statement of

explanation (use different font for preface to distinguish them from acual question).


Choose Interview Schedule or Questionnaire?

Depends on:• Nature of investigation

– Sensitive questions, questionnaire better.• Geographical distribution of study population

– Respondents scattered, use questionnaire – cheaper.• Type of study

– Illiterate, very young or very old, or handicapped – use interview schedule.


Ways of Administering a Questionnaire

1. Mailed questionnaire• Send out to prospective rspdnt• Must have addresses• Prepaid self-address envelope• With covering letter (brief explanation of

study, indicate confidentiality & participation is voluntary, + other impt qn).

• A Major problem --- low response rate.


2. Collective questionnaire• Captive audience (e.g., students in lecture

hall)• High response rate coz few will refuse.• Can explain purpose & importance of study

face-to-face + can clarify qn.• Quickest was of collecting data• Save money


3. Administration in public place•Approach & request participation of potential rspdnt

•More time consuming•Adv same as collective qnn.


Questionnaire or Interview?

• Adv & Disadv of Questionnaire• Adv & Disadv of Interview


Adv & Disadv of Questionnaire• Adv:

Less expensiveGreater anonymity

• Disadv: Limited application (only for those who can read & write) Low response rate if mailed. Self-selecting bias (only those with good attitudes or

motivations will response; may not be representative of study population).


Spontaneous response not allowed for.Response to a question may be influenced by

response to other questions.Possible to consult others.A response cannot be supplemented with other

information.


Adv & Disadv of Interview

Adv:• More appropriate for complex situations.• Useful for collecting in-depth information.• Information can be supplemented (from

observations of non-verbal reactions).• Questions can be explained.• Interviewing has a wider application.

– Any type of population – children, illiterate, young & old.


Disadv:• Time-consuming & expensive.• Quality of interaction can influence quality of

data.• Quality of interviewer can influence quality of

data.• Quality of data vary when many interviewers are

used.• Researcher may introduce his/her bias (e.g., in

framing the question).• Interviewer may be biased (e.g., in the way of

questioning).


Forms of Questions

• Form & wording of questions may affect type & quality of information obtained.

• Types of question:

Open-endedClose-ender


Open-ended Question

• Possible responses are not given.• Respondent writes the answer (for

questionnaire)• Interviewer record the respondents’ answers

(verbatim or summary)• Useful for seeking opinions, attitudes or

perceptions.


Closed-ended Question

• Possible answers given.• Respondent or interviewer tick the answer.• Useful for eliciting factual information


Adv & Disadv of Open-ended Question

Adv:• Provide in-depth & wealth of info.• Provide opportunity for respondent to express

their opinion, resulting in more variety of info.• Allow respondents to express themselves

freely; eliminate the possibility of investigator bias.


Disadv:• Analysis more difficult (must do content

analysis in order to classify the data).• Some respondents may not be able to

express themselves, so information may be lost.

• Greater chance of interviewer bias.


Adv & Disadv of Closed-ended Question

Adv:• Ready-made categories; help ensure info

needed is obtained.• Easy to analyse.Disadv:• Info lacks depth & variety.• Investigator bias – may list answer he/she is

interested in.


• Given response could condition thinking of respondents

• May create tendency among respondents and interviewers to tick a category/ries without thinking through the issue.


Considerations in formulating questions:

• Always use simple & everyday language.• Do not use ambiguous questions.• Do not ask double-barrelled questions.• Do not ask leading questions.• Do not ask questions that are based on

presumptions.


FORMULATING QUESTIONS

• Is anyone in your family having ‘H1N1”?• Is difficult for you to be a father and a

husband?• Are you happy with your cafeteria?• How often and how much time do you spend

visiting your lecturer?• In your opinion, eating lemang with rendang

or peanut sauce is nice?


• Smoking is bad, isn’t it?• ‘Ponteng kuliah’ is bad, isn’t it?• How many cigarettes do you smoke in a day?• What contraceptives do you use?


COLLECTING DATA USING SECONDARY SOURCES

Sources of Data:• Government or semi-government publications• Earlier research• Personal records• Mass-media


Problems using secondary data

• Validity & reliability• Personal bias• Availability of data• Format


DATA ANALYSIS


DATA ANALYSIS

• Ways to use/organize/manipulate data in order to reach research conclusions.


DATA ANALYSIS

1. UNIVARIATE ANALYSIS2. BIVARIATE ANALYSIS3. MULTIVARIATE ANALYSIS


UNIVARIATE ANALYSIS

• Is the examination of the distribution of cases on only one variable at a time.– Distributions– Central tendency– Dispersion


• The full original data usually difficult to interpret.

• Data reduction is the process of summarizing the original data to make them more manageable; while maintaning the original data as much as possible.


PROCESSING DATA

1. EDITING DATA2. CODING DATA3. DEVELOPING A FRAME OF ANALYSIS4. ANALYSING DATA


Editing Data

• Data Cleaning• Checking the completed instruments; to

identify and minimise errors incompleteness inconsistencies misclassification etc. (illegible writing)


Coding Data

2 Considerations for Coding:– Measurement of a variable (scale?, structure –

open/closed ended?).

– Communication of findings about a variable (measurement scale?, type of statisitical procedures?) (e.g., Ratio scale – mean, mode, median)


Process for coding:

• For analysis using computer, data must be coded in numerical values.

• The coding of raw data involves 4 steps:– Developing a code book (master-code book)– Pre-testing the book– Coding the data; and – Verifying the coded data.


Developing A Frame of Analysis

• Develop from beginning of research and evolve continuously to end.

• Frame of analysis:– Identify variable to analyse– Determine method to analyse– Determine cross-tabulations needed – Determine which variable to combine for constructing

major concepts or develop indices – Identify which variable for which statistical procedures


Analysing Data

1. UNIVARIATE ANALYSIS2. BIVARIATE ANALYSIS3. MULTIVARIATE ANALYSIS


UNIVARIATE ANALYSIS

• Is the examination of the distribution of cases on only one variable at a time.

• Distributions• Central tendency• Dispersion

• Can be generated thro’ Descriptive statistics in the SPSS.

• Purpose of univariate analysis is purely descriptive.


Distributions

• Attribute of each each case under study in terms of the variable in question.

• Reporting marginals• E.g., how many respondents, what % of them

fall under a certain variable.500 of 1000 FEM students have CGPA = 3.5 &

above.50% of 1000 FEM students.


Frequency Distribution

• Shows the number of cases having each of the attributes of a given variable.


Central Tendency

• Reporting summary• In term of averages

– Mode (most frequent attribute)– Mean (arithmetic mean)– Median (middle attribute)


Which measure of Central Tendency to use?

Measure Level of Measurement

Examples

Mode Nominal Eye color, party affiliation

Median Ordinal Rank in class, birth order

Mean Interval & ratio Speed of response, age in years


Dispersion

• Spread of raw data/info of a variable.• Detailed information of distribution of a

variable.Range (simplest measure)PercentileStandard deviation (more sophisticated)


• Range: distance separating the highest from the lowest value.

(e.g., the respondents mean age is 22.75 with a range from 20 to 26).


Percentile

• A number or score indicating rank by telling what percentage of those being measured fell below that particular score.

• e.g., scored 75th percentile, means 75% of the other people scored below your score and 25% scored at or above your score.


Standard Deviation

• Is a measure of the average amount the scores in a distribution deviate from average (mean) of the distribution.

• Observation near mean, small SD. Observation far from mean, large SD.


BIVARIATE ANALYSIS

• Focuses on the relationships/association between two variables.

• Among the many measures of bivariate association are eta, gamma, lambda, Pearson’s r, Kendall’s tau, and Spearman’s rho.


MULTIVARIATE ANALYSIS

• Is a method of analyzing the simultaneous relationships among several variables and may be used to understand the relationship between two variables more fully.

• e.g., multiple regression, factor analysis, path analysis, discriminant analysis.


STATISTICAL ANALYSIS

1) Descriptive Statistics2) Inferential Statistics


What is Descriptive Statistics?

• A medium in describing data in manageable forms (dealing with collection, tabulation, and summarization of data so as to present meaningful information).

• Quantitative descriptions• Describe single variables• Describe the associations that connect one

variable with another


Descriptive Statistics

1. Data Reduction2. Measures of Association3. Regression Analysis


Descriptive Stat:Data Reduction

• Reduction of data from unmanageable details to manageable summaries.

• e.g., for 100 respondents you may get data on 100 different ages; these data can be summarize to manageable form by coding it into 3-4 categories.


Descriptive Stat:Measures of Association

• Provides information on the nature and extent of the relationship between any two variables.


• Measures of association for two nominal variables = Lambda,

• For two ordinal variables = Gamma,

• For two interval or ratio variables = Pearson’s product-moment correlation (r).


The Value of r

• 0.0 = no linear relationship btw the 2 variables• + 1.0 = Strong positive linear relationship; as X

increases in value, Y also increases and vice versa.• - 1.0 = Strong inverse linear relationship; as X

increases in value, Y decreases in value; as X decreases in value, Y increases in value.


Positive or negative r?


Descriptive Stat: Regression Analysis

• Represents the relationships between variables in the form of equations, which can be used to predict the values of a dependent variable on the basis of values of one or more independent variables.


• The basic regression equation – for a simple linear regression:

Y = a + bx + e

• Y = value estimated of the dependent variable• a = constant variable / alpha or intercept• b = slope, numerical value (multiplied by X, the

value of the independent variable)(beta coefficient).

• e = error


• Simple linear regression model does not sufficiently represent the complexity of social life.


Multiple Regression

• A social phenomenon (DV) is normally affected simultaneously by several IVs.


• Multiple regression equation:

Y = a + b1x1 + b2x2 + bi xi + e

• Y = value estimated of the DV• a = constant variable• X1 to Xi = predictors• b = slope (beta coefficient) for X• e = residual (error)


What is Inferential Statistics?

• Typically it involves drawing conclusions about a population from the study of a sample drawn from it.

• i.e., Generalizing your findings to a broader population group.


POPULATION

Sample

Infer from sample (statistic) to population (parameter)


• Techniques that allow us to determine if hypothesis is supported, while considering sampling error hypothesis testing.


• Inferential statistics can help us estimate or predict population parameter from sample statistics.

• Population value = parameter• Sample value = statistics


Inferential Statistics

• Inferential statistics are based on the assumption that population distributions of variables from which samples are selected are normal in shape (Normal Curve/Distribution).


Normal Curve

• Represents how variables are distributed.• Characteristics: Bell-shaped; unimodal,

symmmetric and asymptotic.


Characteristics of Normal Curve:

• Unimodal = mean, median & mode same value.• Symmetrical = left & right halves of curve are

mirror images.• Asymptotic = tails of curve get closer to X axis,

but never touch it.


• See diagram on normal curve.

• The area under the curve is very important in inferential statistics.


• Accuracy of inference depends on representativeness of sample from population.

• Random selection– Equal chance for anyone to be selected makes

sample more representative


• Inferential statistics help researchers test hypotheses and answer research questions, and derive meaning from the results.

A result found to be statistically significant by testing the sample is assumed to also hold for the population from which the sample was drawn.

The ability to make such an inference is based on the principle of probability.


– Researchers set the significance level of each statistical test they conduct.

– By using probability theory as a basis for their tests, researchers can assess how likely it is that the difference they find is real and not due to chance


Test of Significance

• What inferential statistics does best is allow decisions to be made about populations based on the information about samples.

• One of the most useful tools for doing this is a test of statistical significance


• Inferential statistics test the likelihood that the alternative (research) hypothesis (H1) is true and the null hypothesis (H0) is not.


– In testing differences, the H1 would predict that differences would be found, while the H0

would predict no differences.

– By setting the significance level (generally at .05), the researcher has a criterion for making the following decision:


• If the .05 level is achieved (p is equal to or less than .05), then a researcher rejects the H0 and accepts the H1.

• If the .05 significance level is not achieved, then the H0 is retained.


Convention for significance levels (alpha levels)

.05

.01.001


Alpha levels are often written as the “p-value”.

e.g., p =.05; p < .05; (p less than .05)p < .05 (p equal to or less than) (the chance of making 5 in 100 or 1 in 20 of

making an error)


Variance of statistical test = Degrees of Freedom

• Df are the way in which the scientific tradition accounts for variation due to error.– It specifies how many values vary within a

statistical test.


– It specifies how many values vary within a statistical test

• Scientists recognizes that collecting data can never be error-free

• Each piece of data collected can vary, or carry error that we cannot account for

• By including df in statistical computations, scientists help to account for this error


Testing Hypothesis

• If reject H0 and conclude groups are really different, it doesn’t mean they’re different for the reason you hypothesized

• May be other reason


• Since H0 is based on sample means, not population means, there is a possibility of making an error or wrong decision in rejecting or failing to reject H0

• Type I error• Type II error


• Type I error – rejecting H0 when it was true (it sound have been accepted)

– If alpha = .05, then there’s a 5% chance of Type 1 error.


• Type II error – accepting H0 when it should have been rejected

– If increase alpha, you will decrease the chance of Type II error


Choosing Appropriate Statistical Test of Difference

• One variable One-way chi-square• Two variables

( 1 IV with 2 levels; 1 DV) t-test• Two variables

( 1 IV with 2+ levels; 1 DV) ANOVA


• Three or more variables ANOVA

• See handouts for more other examples of inferential statistics


WRITING QUANTITATIVE REPORTS

Using the APA Style


9 Major Components

1. Title Page2. Abstract3. Introduction (Chapter 1)4. Review of the Literature (Chapter 2)5. Method (Chapter 3)6. Results (Chapter 4)7. Discussion (or Summary, Conclusion, &

Implications) (Chapter 5)8. Bibliography9. Apendices (letters, instruments)


1. Title Page

• Summarize the main topic• About 10 -12 words

PREDICTORS OF MATERNAL BEHAVIOR AND THEIR EFFECTS ON THE ACHIEVEMENT OF CHLDREN

Write in Top Heavy style

PREDICTORS OF MATERNAL BEHAVIOR AND THEIR EFFECTS ON THE ACHIEVEMENT OF CHLDREN

Bottom Heavy


2. Abstract

• Comprehensive summary• About 120 words• For manuscript submitted for review, typed on

separate page.


3. Introduction

• Begin with current scenario, country data / statistics, what are the symptoms in the society that make you want to study the problem. Place the problem in the context of other research literature

• Statement of the Problem• Purpose of the Study (May incorporate under

Statement of Problem, check with your supervisor)• Research Objectives


• Theoretical Framework• Conceptual Model• Conceptual and Operational Definitions• Rationale for the Present Study (May include

under Statement of Problem, check with your supervisor)


4. Review of Literature

i. Inform reader about previous research conducted on the topic being research.

ii. Also reflect how knowledgeable writer is on the topic.iii. Review studies which have focused on the DV.iv. Indicates the theory (if any) on which the study is based;

critique and weigh studies as theory is built.v. Identify knowledge gap.


i. Present review in logical and comprehensive manner. Organize with reference to the objectives of the study.

ii. Write a summary paragraph which identifies all the major variables found to influence or related to the DV. Add a statement to show how your research topic flows from or adds to the research reviewed.


3. Method

• Location of Study• Sampel (number, selection, characteristics)• Measures (Instrumentation)• Procedure / Data Collection


4. Results

• Results of data analysis and statistical significance testing

• Include tables and figures.


5. Discussion

• Interpret and evaluate your results• State whether hypotheses were supported.• Answer basic questions

– what your study contribute?– how study helped to solve study problem?– what conclusion and theoretical implications can

be drawn from your study?)

face to face 2

Documents

variable variables

definition of variable

measurementrozumah baharudin

independent variables

categorical variables

qualitative data

quantitative data

certain values