Post on 28-Nov-2014
transcript
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.
3© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
4© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
5© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
66
Chapter 2
Scientific Investigation
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Hallmarks of Scientific Research:
• Hallmarks or main distinguishing characteristics of scientific research: – Purposiveness – Rigor – Testability – Replicability – Precision and Confidence – Objectivity – Generalizability – Parsimony
7© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
8© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
9© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
1010
Chapter 3
The Research Process - The Broad Problem Area and Defining the
Problem Statement
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
FIGURE 4.1Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
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.
12© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
13© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Data sources
• Textbooks• Academic and professional journals• Theses• Conference proceedings • Unpublished manuscripts• Reports of government departments and
corporations• Newspapers • The Internet
14© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Searching for Literature
• Most libraries have the following electronic resources at their disposal:– Electronic journals– Full-text databases– Bibliographic databases– Abstract databases
15© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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?
16© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
1717
Chapter 4
The Research Process - Theoretical Framework & Hypothesis
Development
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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).
18© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
19© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Variable
• Any concept or construct that varies or changes in value
• Main types of variables:– Dependent variable– Independent variable– Moderating variable – Mediating /intervening variable
20© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
(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.
21© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Example
22© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
23© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
24© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
25© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Exercise
26
Give the hypotheses for the following framework:
Service quality Customer switching
Switching cost
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Exercise
27
Give the hypotheses for the following framework:
Customer satisfactionService quality Customer switching
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Argumentation
• The expected relationships / hypotheses are an integration of:
– Exploratory research– Common sense and logical reasoning
28© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
2929
Chapter 5
The Research Process – Elements of Research Design
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
30
Research Design
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Purpose of the Study
• Exploration• Description• Hypothesis Testing
31© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Purpose of the Study
• 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
32© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Purpose of the Study
• 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.
33© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Purpose of the Study
• 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.
34© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
35© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Study Setting
• Contrived: artificial setting
• Non-contrived: the natural environment where work proceeds normally
36© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Population to be Studied
• Unit of analysis:– Individuals– Dyads– Groups– Organizations– Cultures
37© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
38© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
3939
Chapter 6
Measurement of Variables: Operational Definition
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Measurement
• Measurement: the assignment of numbers or other symbols to characteristics (or attributes) of objects according to a pre-specified set of rules.
40© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
(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.
42© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
43© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Example
44© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
4545
Chapter 7
Measurement of Variables: Scaling, Reliability, Validity
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
46© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
47© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
48© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
49© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
I disagree completely 1 2 3 4 5 I agree completely
3. For the efforts I put into the organization, I get much in return
I disagree completely 1 2 3 4 5 I agree completely
50© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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?
51© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Properties of the Four Scales
52© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Goodness of Measures
53© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Validity
54© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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).
55© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
56© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
57© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
5858
Chapter 8
Data Collection Methods
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
59© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
60© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
61© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
62© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Principles of Questionnaire Design.
63© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
64© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
65© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
66© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
67© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
68© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
4. Question Sequence
69
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
70© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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)
71© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
7272
Chapter 10
Sampling
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
73© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
74© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
75© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Statistics versus Parameters
76© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Advantages of Sampling
• Less costs• Less errors due to less fatigue• Less time• Avoiding the destruction of elements
77© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
78© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Sampling Techniques
• Probability versus nonprobability sampling
• Probability sampling: elements in the population have a known and non-zero chance of being chosen
79© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Sampling Techniques
• Probability Sampling– Simple Random Sampling– Systematic Sampling– Stratified Random Sampling– Cluster Sampling
• Nonprobability Sampling– Convenience Sampling– Judgment Sampling– Quota Sampling
80© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Simple Random Sampling
• Procedure– Each element has a known and equal chance of being selected
• Characteristics– Highly generalizable– Easily understood– Reliable population frame necessary
81© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
82© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
83© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
84© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
(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
85© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Example
86© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
87
Overview
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
88
Overview
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
89
Overview
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
90
Choice Points in Sampling Design
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Tradeoff between precision and confidence
91
We can increase both confidence and precision by increasing the sample size
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
92© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Sample Size for a Given Population Size
93© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Sample Size for a Given
94© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
9595
Chapter 11
Quantitative Data Analysis
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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)
96© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
97© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Transforming Data
98© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Getting a Feel for the Data
99© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Frequencies
100© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Descriptive Statistics: Central Tendencies and Dispersions
101© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Reliability Analysis
102© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
103103
Chapter 12
Quantitative Data Analysis: Hypothesis Testing
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
104© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Choosing the Appropriate Statistical Technique
105
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
106© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
107© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
108© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
109© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
110© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Regression Analysis
• Simple regression analysis is used in a situation where one metric independent variable is hypothesized to affect one metric dependent variable.
111© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Scatter plot
112
30 40 50 60 70 80 90
PHYS_ATTR
20
40
60
80
100
LKLH
D_D
ATE
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Simple Linear Regression
113
Y
X
0̂0̂0̂0̂0̂0̂ `0?
0̂
iii XY 10
1̂1
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Ordinary Least Squares Estimation
114
Yi
Xi
Yiei
n
1i
2i Minimize e
ˆ
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
117© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
SPSS
118
Model Summary
.841 .707 .704 5.919Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
119© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
SPSS
120
Model Summary
.841 .707 .704 5.919Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
= r(Likelihood to Date, Physical Attractiveness)
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Model Significance
• H0: 0 = 1 = ... = m = 0 (all parameters are zero)
H1: Not H0
121© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
122© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
SPSS
123
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.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
124© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Conceptual Model
126
Physical Attractiveness
Likelihood to Date
+
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Multiple Regression Analysis
• We use more than one (metric or non-metric) independent variable to explain variance in a (metric) dependent variable.
127© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Conceptual Model
128
Perceived Intelligence
Physical Attractiveness
+
+Likelihood
to Date
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Model Summary
.844 .712 .706 5.895Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
ANOVA
8257.731 2 4128.866 118.808 .000
3336.228 96 34.752
11593.960 98
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran 129
Conceptual Model
130
Perceived Intelligence
Physical Attractiveness
Likelihood to Date
Gender
+ +
+
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
131© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Model Summary
.910 .828 .821 4.601Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
ANOVA
9603.938 4 2400.984 113.412 .000
1990.022 94 21.170
11593.960 98
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran 132
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
interaction significant effect on dep. var.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran 133
Moderators
Conceptual Model
134
Perceived Intelligence
Physical Attractiveness
Communality of Interests
Likelihood to Date
Gender
Perceived Fit
+ +
+
+
+
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
ANOVA
10745.603 5 2149.121 235.595 .000
848.357 93 9.122
11593.960 98
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
significant effect on dep. var.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Step 2
138
Mode l Summary
.977 .955 .955 2.927Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
ANOVA
17720.881 1 17720.881 2068.307 .000
831.079 97 8.568
18551.960 98
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
significant effect on mediator
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Step 3
140
Mode l Summary
.966 .934 .930 2.885Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
ANOVA
10828.336 6 1804.723 216.862 .000
765.624 92 8.322
11593.960 98
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
significant effect of mediator on dep. var.insignificant effect of indep. var on dep. Var.
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
142142
Chapter 13
Qualitative Data Analysis
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
143© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
144© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
145© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
146© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
147© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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).
148© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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).
149© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
150150
Chapter 14
The Research Report
© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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.
151© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
152© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
153© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
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
154© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Oral Presentation• Deciding on the Content
• Visual Aids – For instance graphs, charts, tables
• The presenter
• The presentation
• Handling questions
155© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran