Sekaran Modified

11

Chapter 1

Introduction to Research

© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran

Definition of Business Research

• Business research: an organized, systematic, data-based, critical, objective, scientific inquiry or investigation into a specific problem, undertaken with the purpose of finding answers or solutions to it.

2© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran

Applied versus Basic Research

• Basic research: generates a body of knowledge by trying to comprehend how certain problems that occur in organizations can be solved.

• Applied research: solves a current problem faced by the manager in the work setting, demanding a timely solution.


Examples Applied Research• Apple’s iPod fueled the company’s success in recent

years, helping to increase sales from $5 billion in 2001 to $32 billion in the fiscal year 2008. Growth for the music player averaged more than 200% in 2006 and 2007, before falling to 6% in 2008. Some analysts believe that the number of iPods sold will drop 12% in 2009. “The reality is there’s a limited group of people who want an iPod or any other portable media player,” one analyst says. “So the question becomes, what will Apple do about it?”

• The existing machinery in the production department has had so many breakdowns that production has suffered. Machinery has to be replaced. Because of heavy investment costs, a careful recommendation as to whether it is more beneficial to buy the equipment or to lease it is needed.


Why managers should know about research

• Being knowledgeable about research and research methods helps professional managers to: – Identify and effectively solve minor problems in the work setting. – Know how to discriminate good from bad research. – Appreciate the multiple influences and effects of factors impinging on

a situation. – Take calculated risks in decision making. – Prevent possible vested interests from exercising their influence in a

situation. – Relate to hired researchers and consultants more effectively. – Combine experience with scientific knowledge while making decisions.


66

Chapter 2

Scientific Investigation


Hallmarks of Scientific Research:

• Hallmarks or main distinguishing characteristics of scientific research: – Purposiveness – Rigor – Testability – Replicability – Precision and Confidence – Objectivity – Generalizability – Parsimony


Hypothetico-Deductive Research

• The Seven-Step Process in the Hypothetico-Deductive Method – Identify a broad problem area– Define the problem statement – Develop hypotheses– Determine measures – Data collection – Data analysis – Interpretation of data


Deduction and Induction

• Deductive reasoning: application of a general theory to a specific case. – Hypothesis testing

• Inductive reasoning: a process where we observe specific phenomena and on this basis arrive at general conclusions. – Counting white swans

• Both inductive and deductive processes are often used in research.


1010

Chapter 3

The Research Process - The Broad Problem Area and Defining the

Problem Statement


The Broad Problem Area

• Examples of broad problem areas that a manager could observe at the workplace: – Training programs are not as effective as

anticipated.

– The newly installed information system is not being used by the managers for whom it was primarily designed.

– The introduction of flexible work hours has created more problems than it has solved in many companies.


Literature Review

• A good literature survey: – Ensures that important variables are not left out

of the study. – Helps the development of the theoretical

framework and hypotheses for testing. – Ensures that the problem statement is precise and

clear. – Reduces the risk of “reinventing the wheel”. – Confirms that the problem is perceived as relevant

and significant.


Data sources

• Textbooks• Academic and professional journals• Theses• Conference proceedings • Unpublished manuscripts• Reports of government departments and

corporations• Newspapers • The Internet


Searching for Literature

• Most libraries have the following electronic resources at their disposal:– Electronic journals– Full-text databases– Bibliographic databases– Abstract databases


The Problem Statement

• Examples of Well-Defined Problem Statements– To what extent has the new advertising campaign been successful in

creating the high-quality, customer-centered corporate image that it was intended to produce?

– How has the new packaging affected the sales of the product?

– What are the effects of downsizing on the long-range growth patterns of companies?


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Chapter 4

The Research Process - Theoretical Framework & Hypothesis

Development


Theoretical Framework

• A theoretical framework represents your beliefs on how certain phenomena (or variables or concepts) are related to each other (a model) and an explanation on why you believe that these variables are associated to each other (a theory).


Theoretical Framework

• Basic steps:– Identify and label the variables correctly

– State the relationships among the variables: formulate hypotheses

– Explain how or why you expect these relationships


Variable

• Any concept or construct that varies or changes in value

• Main types of variables:– Dependent variable– Independent variable– Moderating variable – Mediating /intervening variable


(In)dependent Variables

• Dependent variable (DV)– Is of primary interest to the researcher. The goal of

the research project is to understand, predict or explain the variability of this variable.

• Independent variable (IV)– Influences the DV in either positive or negative

way. The variance in the DV is accounted for by the IV.


Example


Moderators

• Moderating variable Moderator is qualitative (e.g., gender, race, class) or quantitative (e.g., level of reward) variable that affects the direction and/or strength of relation between independent and dependent variable.


Mediating/intervening Variable

• Mediating variable

– surfaces between the time the independent variables start operating to influence the dependent variable and the time their impact is felt on it.


Hypothesis• A proposition that is empirically testable

(falsifiable). It is an empirical statement concerned with the relationship among variables.

• Good hypothesis:– Must be adequate for its purpose– Must be testable– Must be better than its rivals

• Can be:– Directional– Non-directional


Exercise

26

Give the hypotheses for the following framework:

Service quality Customer switching

Switching cost


Exercise

27

Give the hypotheses for the following framework:

Customer satisfactionService quality Customer switching


Argumentation

• The expected relationships / hypotheses are an integration of:

– Exploratory research– Common sense and logical reasoning


2929

Chapter 5

The Research Process – Elements of Research Design


30

Research Design


Purpose of the Study

• Exploration• Description• Hypothesis Testing



• Exploratory study:– is undertaken when not much is known about the

situation at hand, or no information is available on how similar problems or research issues have been solved in the past.

• Example:– A service provider wants to know why his

customers are switching to other service providers



• Descriptive study:– is undertaken in order to ascertain and be able to describe the

characteristics of the variables of interest in a situation.

• Example:– A bank manager wants to have a profile of the individuals who have

loan payments outstanding for 6 months and more. It would include details of their average age, earnings, nature of occupation, full-time/part-time employment status, and the like. This might help him to elicit further information or decide right away on the types of individuals who should be made ineligible for loans in the future.



• Hypothesis testing:– Studies that engage in hypotheses testing usually

explain the nature of certain relationships, or establish the differences among groups or the independence of two or more factors in a situation.

• Example:– A marketing manager wants to know if the sales of

the company will increase if he doubles the advertising dollars.


Type of Investigation

• Causal Study– it is necessary to establish a definitive cause-and-

effect relationship. • Correlational study

– identification of the important factors “associated with” the problem.


Study Setting

• Contrived: artificial setting

• Non-contrived: the natural environment where work proceeds normally


Population to be Studied

• Unit of analysis:– Individuals– Dyads– Groups– Organizations– Cultures


Time Horizon

• Cross-sectional studies– Snapshot of constructs at a single point in time– Use of representative sample

• Multiple cross-sectional studies– Constructs measured at multiple points in time– Use of different sample

• Longitudinal studies– Constructs measured at multiple points in time– Use of same sample = a true panel


3939

Chapter 6

Measurement of Variables: Operational Definition


Measurement

• Measurement: the assignment of numbers or other symbols to characteristics (or attributes) of objects according to a pre-specified set of rules.


(Characteristics of) ObjectsObjects Characteristicspersons achievement

motivationstrategic business units ethnic diversitycompanies organizational

effectivenessshampoo effectsyogurt Taste

Types of Variables

• Two types of variables: – One lends itself to objective and precise

measurement;– The other is more nebulous and does not lend

itself to accurate measurement because of its abstract and/or subjective nature.


Operationalizing Concepts

• Operationalizing concepts: reduction of abstract concepts to render them measurable in a tangible way.

• Operationalizing is done by looking at the behavioral dimensions, facets, or properties denoted by the concept.


Example


4545

Chapter 7

Measurement of Variables: Scaling, Reliability, Validity


Scale

• Scale: tool or mechanism by which individuals are distinguished as to how they differ from one another on the variables of interest to our study.


Nominal Scale• A nominal scale is one that allows the researcher to assign subjects to

certain categories or groups.

• What is your department?O Marketing O Maintenance O Finance O Production O Servicing O Personnel O Sales O Public Relations O Accounting

• What is your gender?O MaleO Female


Ordinal Scale• Ordinal scale: not only categorizes variables in such a way as

to denote differences among various categories, it also rank-orders categories in some meaningful way.

• What is the highest level of education you have completed?O Less than High School O High School/GED Equivalent O College Degree O Masters Degree O Doctoral Degree


Interval Scale

• Interval scale: whereas the nominal scale allows us only to qualitatively distinguish groups by categorizing them into mutually exclusive and collectively exhaustive sets, and the ordinal scale to rank-order the preferences, the interval scale lets us measure the distance between any two points on the scale.


Interval scale• Circle the number that represents your feelings at this particular moment best.

There are no right or wrong answers. Please answer every question.

1. I invest more in my work than I get out of it

I disagree completely 1 2 3 4 5 I agree completely

2. I exert myself too much considering what I get back in return


3. For the efforts I put into the organization, I get much in return



Ratio Scale

• Ratio scale: overcomes the disadvantage of the arbitrary origin point of the interval scale, in that it has an absolute (in contrast to an arbitrary) zero point, which is a meaningful measurement point.

• What is your age?


Properties of the Four Scales


Goodness of Measures


Validity


Reliability

• Reliability of measure indicates extent to which it is without bias and hence ensures consistent measurement across time (stability) and across the various items in the instrument (internal consistency).


Stability

• Stability: ability of a measure to remain the same over time, despite uncontrollable testing conditions or the state of the respondents themselves.– Test–Retest Reliability: The reliability coefficient

obtained with a repetition of the same measure on a second occasion.

– Parallel-Form Reliability: Responses on two comparable sets of measures tapping the same construct are highly correlated.


Internal Consistency

• Internal Consistency of Measures is indicative of the homogeneity of the items in the measure that tap the construct. – Interitem Consistency Reliability: This is a test of the

consistency of respondents’ answers to all the items in a measure. The most popular test of interitem consistency reliability is the Cronbach’s coefficient alpha.

– Split-Half Reliability: Split-half reliability reflects the correlations between two halves of an instrument.


5858

Chapter 8

Data Collection Methods


Sources of Data • Primary data: information obtained firsthand by the

researcher on the variables of interest for the specific purpose of the study.

• Examples: individuals, focus groups, panels

• Secondary data: information gathered from sources already existing.

• Examples: company records or archives, government publications, industry analyses offered by the media, web sites, the Internet, and so on.


Personal Interview• Advantages

– Can clarify doubts about questionnaire– Can pick up non-verbal cues– Relatively high response/cooperation– Special visual aids and scoring devises can be used

• Disadvantages– High costs and time intensive– Geographical limitations– Response bias / Confidentiality difficult to be assured– Some respondents are unwilling to talk to strangers– Trained interviewers


Telephone Interview

• Advantages– Discomfort of face to face is avoided– Faster / Number of calls per day could be high– Lower cost

• Disadvantages– Interview length must be limited– Low response rate– No facial expressions


Self-administered

• Advantages– Lowest cost option– Expanded geographical coverage– Requires minimal staff– Perceived as more anonymous

• Disadvantages– Low response rate in some modes– No interviewer intervention possible for clarification– Cannot be too long or complex– Incomplete surveys


Principles of Questionnaire Design.


Questionnaire Design • Definition

A questionnaire is a pre-formulated, written set of questions to which the respondent records his answers

• Steps1. Determine the content of the questionnaire2. Determine the form of response3. Determine the wording of the questions4. Determine the question sequence5. Write cover letter


1. Questionnaire content

• FrameworkNeed information for all constructs in framework

• Measurement: Operationalizing– Objective construct:

• 1 element/itemsoften 1 question

– Subjective construct: • multiple elements/items

multiple questions


2. Response format

• Closed vs. Open-ended questions– Closed questions

• Helps respondents to make quick decisions• Helps researchers to code

– Open-ended question• First: unbiased point of view• Final: additional insights• Complementary to closed question: for

interpretation purpose


3. Question Wording• Avoid double-barreled questions

• Avoid ambiguous questions and words

• Use of ordinary words

• Avoid leading or biasing questions

• Social desirability

• Avoid recall depended questions


3. Question Wording

• Use positive and negative statements – Dresdner delivers high quality banking service

Dresdner has poor customer operational support– Avoid double negatives

• Limit the length of the questionsRules of thumb: – < 20 words – < one full line in print


4. Question Sequence

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Personal and sensitive data at the end© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran

5. Cover Letter

• The cover letter is the introductory page of the questionnaire

• It includes:– Identification of the researcher– Motivation for respondents to fill it in– Confidentiality– Thanking of the respondent


Structured Observations

• Recording prespecified behavioral patterns of people, objects and events in a systematic manner.

• Quantitative in nature

• Different types– Personal observation

(e.g., mystery shopper, pantry audit)

– Electronic observation (e.g., scanner data, people meter, eye tracking)


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Chapter 10

Sampling


Sampling

• Sampling: the process of selecting a sufficient number of elements from the population, so that results from analyzing the sample are generalizable to the population.


Relevant Terms

• Population refers to the entire group of people, events, or things of interest that the researcher wishes to investigate.

• Sampling unit: the element or set of elements that is available for selection in some stage of the sampling process.

• A sample is a subset of the population. It comprises some members selected from it.


Relevant Terms - 3

• The characteristics of the population such as µ (the population mean), σ (the population standard deviation), and σ2 (the population variance) are referred to as its parameters. The central tendencies, the dispersions, and other statistics in the sample of interest to the research are treated as approximations of the central tendencies, dispersions, and other parameters of the population.


Statistics versus Parameters


Advantages of Sampling

• Less costs• Less errors due to less fatigue• Less time• Avoiding the destruction of elements


The Sampling Process

• Major steps in sampling:– Define the population.– Determine the sample frame – Determine the sampling design – Determine the appropriate sample size– Execute the sampling process


Sampling Techniques

• Probability versus nonprobability sampling

• Probability sampling: elements in the population have a known and non-zero chance of being chosen


Sampling Techniques

• Probability Sampling– Simple Random Sampling– Systematic Sampling– Stratified Random Sampling– Cluster Sampling

• Nonprobability Sampling– Convenience Sampling– Judgment Sampling– Quota Sampling


Simple Random Sampling

• Procedure– Each element has a known and equal chance of being selected

• Characteristics– Highly generalizable– Easily understood– Reliable population frame necessary


Systematic Sampling

• Procedure– Each nth element, starting with random choice of an element

between 1 and n

• Characteristics– Similar to simple random sampling– Systematic biases when elements are not randomly listed


Cluster Sampling• Procedure

– Divide of population in clusters– Random selection of clusters– Include all elements from selected clusters

• Characteristics– Intercluster homogeneity– Intracluster heterogeneity– Easy and cost efficient– Low correspondence with reality


Stratified Sampling

• Procedure– Divide of population in strata– Include all strata– Random selection of elements from strata

• Proportionate• Disproportionate

• Characteristics– Interstrata heterogeneity– Intrastratum homogeneity– Includes all relevant subpopulations


(Dis)proportionate Stratified Sampling

• Number of subjects in total sample is allocated among the strata (dis)proportional to the relative number of elements in each stratum in the population

• Disproportionate case:– strata exhibiting more variability are sampled more than proportional

to their relative size– requires more knowledge of the population, not just relative sizes of

strata


Example


87

Overview


88

Overview


89

Overview


90

Choice Points in Sampling Design


Tradeoff between precision and confidence

91

We can increase both confidence and precision by increasing the sample size


Sample size: guidelines

• In general: 30 < n < 500

• Categories: 30 per subcategory

• Multivariate: 10 x number of var’s

• Experiments: 15 to 20 per condition


Sample Size for a Given Population Size


Sample Size for a Given


9595

Chapter 11

Quantitative Data Analysis


Getting the Data Ready for Analysis

• Data coding: assigning a number to the participants’ responses so they can be entered into a database.

• Data Entry: after responses have been coded, they can be entered into a database. Raw data can be entered through any software program (e.g., SPSS)


Editing Data

• An example of an illogical response is an outlier response. An outlier is an observation that is substantially different from the other observations.

• Inconsistent responses are responses that are not in harmony with other information.

• Illegal codes are values that are not specified in the coding instructions.


Transforming Data


Getting a Feel for the Data


Frequencies


Descriptive Statistics: Central Tendencies and Dispersions


Reliability Analysis


103103

Chapter 12

Quantitative Data Analysis: Hypothesis Testing


Type I Errors, Type II Errors and Statistical Power

• Type I error (): the probability of rejecting the null hypothesis when it is actually true; accepting the alternate hypothesis when in fact it is false. For example; Concluding that two variables are related when in fact they are not.

• Type II error (): the probability of accepting the null hypothesis given that the alternative hypothesis is actually true; rejecting the alternate hypothesis when in fact it is true. For example; Concluding that two variables are not related when in fact they are.

• Statistical power (1 - ): the probability of correctly rejecting the null hypothesis.


Choosing the Appropriate Statistical Technique

105


Testing Hypotheses on a Single Mean

• One sample t-test: statistical technique that is used to test the hypothesis that the mean of the population from which a sample is drawn is equal to a comparison standard.


Testing Hypotheses about Two Related Means

• Paired samples t-test: examines differences in same group before and after a treatment.

• The Wilcoxon signed-rank test: a non-parametric test for examining significant differences between two related samples or repeated measurements on a single sample. Used as an alternative for a paired samples t-test when the population cannot be assumed to be normally distributed.


Testing Hypotheses about Two Related Means - 2

• McNemar's test: non-parametric method used on nominal data. It assesses the significance of the difference between two dependent samples when the variable of interest is dichotomous. It is used primarily in before-after studies to test for an experimental effect.


Testing Hypotheses about Two Unrelated Means

• Independent samples t-test: is done to see if there are any significant differences in the means for two groups in the variable of interest.


Testing Hypotheses about Several Means

• ANalysis Of VAriance (ANOVA) helps to examine the significant mean differences among more than two groups on an interval or ratio-scaled dependent variable.


Regression Analysis

• Simple regression analysis is used in a situation where one metric independent variable is hypothesized to affect one metric dependent variable.


Scatter plot

112

30 40 50 60 70 80 90

PHYS_ATTR

20

40

60

80

100

LKLH

D_D

ATE


Simple Linear Regression

113

Y

X

0̂0̂0̂0̂0̂0̂ `0?

0̂

iii XY 10

1̂1


Ordinary Least Squares Estimation

114

Yi

Xi

Yiei

n

1i

2i Minimize e

ˆ


SPSSAnalyze Regression Linear

115

Model Summary

.841 .707 .704 5.919Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

ANOVA

8195.319 1 8195.319 233.901 .000

3398.640 97 35.038

11593.960 98

Regression

Residual

Total

Model1

Sum ofSquares df Mean Square F Sig.


SPSS cont’d

116

Coefficients

34.738 2.065 16.822 .000

.520 .034 .841 15.294 .000

(Constant)

PHYS_ATTR

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.


Model validation

1. Face validity: signs and magnitudes make sense2. Statistical validity:

– Model fit: R2

– Model significance: F-test– Parameter significance: t-test– Strength of effects: beta-coefficients– Discussion of multicollinearity: correlation matrix

3. Predictive validity: how well the model predicts– Out-of-sample forecast errors


SPSS

118

Model Summary

.841 .707 .704 5.919Model1




Measure of Overall Fit: R2

• R2 measures the proportion of the variation in y that is explained by the variation in x.

• R2 = total variation – unexplained variation total variation

• R2 takes on any value between zero and one:– R2 = 1: Perfect match between the line and the data points.– R2 = 0: There is no linear relationship between x and y.


SPSS

120

Model Summary

.841 .707 .704 5.919Model1



= r(Likelihood to Date, Physical Attractiveness)


Model Significance

• H0: 0 = 1 = ... = m = 0 (all parameters are zero)

H1: Not H0


Model Significance

• H0: 0 = 1 = ... = m = 0 (all parameters are zero)

H1: Not H0

• Test statistic (k = # of variables excl. intercept)

F = (SSReg/k) ~ Fk, n-1-k

(SSe/(n – 1 – k)

SSReg = explained variation by regression

SSe = unexplained variation by regression


SPSS

123

ANOVA

8195.319 1 8195.319 233.901 .000

3398.640 97 35.038

11593.960 98

Regression

Residual

Total

Model1



Parameter significance

• Testing that a specific parameter is significant (i.e., j 0)

• H0: j = 0

H1: j 0

• Test-statistic: t = bj/SEj ~ tn-k-1

with bj = the estimated coefficient for j SEj = the standard error of bj


SPSS cont’d

125

Coefficients

34.738 2.065 16.822 .000

.520 .034 .841 15.294 .000

(Constant)

PHYS_ATTR

Model1

B Std. Error


Beta


t Sig.


Conceptual Model

126

Physical Attractiveness

Likelihood to Date

+


Multiple Regression Analysis

• We use more than one (metric or non-metric) independent variable to explain variance in a (metric) dependent variable.


Conceptual Model

128

Perceived Intelligence


+

+Likelihood

to Date


Model Summary

.844 .712 .706 5.895Model1



ANOVA

8257.731 2 4128.866 118.808 .000

3336.228 96 34.752

11593.960 98

Regression

Residual

Total

Model1


Coefficients

31.575 3.130 10.088 .000

.050 .037 .074 1.340 .183

.523 .034 .846 15.413 .000

(Constant)

PERC_INTGCE

PHYS_ATTR

Model1

B Std. Error


Beta


t Sig.

© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran 129

Conceptual Model

130



Likelihood to Date

Gender

+ +

+


Moderators• Moderator is qualitative (e.g., gender, race, class) or quantitative

(e.g., level of reward) that affects the direction and/or strength of the relation between dependent and independent variable

• Analytical representation

Y = ß0 + ß1X1 + ß2X2 + ß3X1X2

with Y = DVX1 = IVX2 = Moderator


Model Summary

.910 .828 .821 4.601Model1



ANOVA

9603.938 4 2400.984 113.412 .000

1990.022 94 21.170

11593.960 98

Regression

Residual

Total

Model1



Moderators

Coefficients

32.603 3.163 10.306 .000

.000 .043 .000 .004 .997

.496 .027 .802 18.540 .000

-.420 3.624 -.019 -.116 .908

.127 .058 .369 2.177 .032

(Constant)

PERC_INTGCE

PHYS_ATTR

GENDER

PI_GENDER

Model1

B Std. Error


Beta


t Sig.

interaction significant effect on dep. var.


Moderators

Conceptual Model

134



Communality of Interests

Likelihood to Date

Gender

Perceived Fit

+ +

+

+

+


Mediating/intervening variable• Accounts for the relation between the independent and

dependent variable

• Analytical representation1. Y = ß0 + ß1X

=> ß1 is significant

2. M = ß2 + ß3X=> ß3 is significant

3. Y = ß4 + ß5X + ß6M => ß5 is not significant => ß6 is significant

135

With Y = DVX = IVM = mediator© 2009 John Wiley & Sons Ltd.

www.wileyeurope.com/college/sekaran

Step 1

136

Mode l Summary

.963 .927 .923 3.020Model1



ANOVA

10745.603 5 2149.121 235.595 .000

848.357 93 9.122

11593.960 98

Regression

Residual

Total

Model1



Step 1 cont’d

137

Coefficients

17.094 2.497 6.846 .000

.030 .029 .044 1.039 .301

.517 .018 .836 29.269 .000

-.783 2.379 -.036 -.329 .743

.122 .038 .356 3.201 .002

.212 .019 .319 11.187 .000

(Constant)

PERC_INTGCE

PHYS_ATTR

GENDER

PI_GENDER

COMM_INTER

Model1

B Std. Error


Beta


t Sig.

significant effect on dep. var.


Step 2

138

Mode l Summary

.977 .955 .955 2.927Model1



ANOVA

17720.881 1 17720.881 2068.307 .000

831.079 97 8.568

18551.960 98

Regression

Residual

Total

Model1



Step 2 cont’d

139

Coefficients

8.474 1.132 7.484 .000

.820 .018 .977 45.479 .000

(Constant)

COMM_INTER

Model1

B Std. Error


Beta


t Sig.

significant effect on mediator


Step 3

140

Mode l Summary

.966 .934 .930 2.885Model1



ANOVA

10828.336 6 1804.723 216.862 .000

765.624 92 8.322

11593.960 98

Regression

Residual

Total

Model1



Step 3 cont’d

141

Coefficients

14.969 2.478 6.041 .000

.019 .028 .028 .688 .493

.518 .017 .839 30.733 .000

-2.040 2.307 -.094 -.884 .379

.142 .037 .412 3.825 .000

-.051 .085 -.077 -.596 .553

.320 .102 .405 3.153 .002

(Constant)

PERC_INTGCE

PHYS_ATTR

GENDER

PI_GENDER

COMM_INTER

PERC_FIT

Model1

B Std. Error


Beta


t Sig.

significant effect of mediator on dep. var.insignificant effect of indep. var on dep. Var.


142142

Chapter 13

Qualitative Data Analysis


Qualitative Data

• Qualitative data: data in the form of words.

• Examples: interview notes, transcripts of focus groups, answers to open-ended questions, transcription of video recordings, accounts of experiences with a product on the internet, news articles, and the like.


Analysis of Qualitative Data

• The analysis of qualitative data is aimed at making valid inferences from the often overwhelming amount of collected data.

• Steps:– data reduction – data display – drawing and verifying conclusions


Data Reduction

• Coding: the analytic process through which the qualitative data that you have gathered are reduced, rearranged, and integrated to form theory.

• Categorization: is the process of organizing, arranging, and classifying coding units.


Data Display

• Data display: taking your reduced data and displaying them in an organized, condensed manner.

• Examples: charts, matrices, diagrams, graphs, frequently mentioned phrases, and/or drawings.


Drawing Conclusions

• At this point where you answer your research questions by determining what identified themes stand for, by thinking about explanations for observed patterns and relationships, or by making contrasts and comparisons.


Reliability in Qualitative Research

• Category reliability “depends on the analyst’s ability to formulate categories and present to competent judges definitions of the categories so they will agree on which items of a certain population belong in a category and which do not.” (Kassarjian, 1977, p. 14).

• Interjudge reliability can be defined degree of consistency between coders processing the same data (Kassarjian 1977).


Validity in Qualitative Research

• Validity refers to the extent to which the qualitative research results:– accurately represent the collected data (internal

validity) – can be generalized or transferred to other

contexts or settings (external validity).


150150

Chapter 14

The Research Report


Presentation of Results

• Results of the study and recommendations to solve the problem have to be effectively communicated to the sponsor, so that suggestions made are accepted and implemented.

• Contents and organization of written report and oral presentation depend on the purpose of the research study, and the audience to which it is targeted.


The Written Report

• Important to identify the purpose of the report, so that it can be tailored accordingly.

• Examples– Simple descriptive report– Comprehensive report, offering alternative

solutions


Characteristics of a Well-Written Report

• Clarity• Conciseness• Coherence• The right emphasis on important aspects• Meaningful organization of paragraphs• Smooth transition from one topic to the next• Apt choice of words• Specificity


Contents of Research Report

• Title • Executive summary or a synopsis • Table of contents• The research proposal

– Purpose of the study– Background – Problem statement

• Framework of the study & hypotheses • Method • Data analysis • Conclusions and recommendations


Oral Presentation• Deciding on the Content

• Visual Aids – For instance graphs, charts, tables

• The presenter

• The presentation

• Handling questions


Sekaran Modified

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Transcript of Sekaran Modified