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Research Methods for BusinessMBA course - ESLSCA
Dr. Ashraf Elsaftyashraf@ashrafelsafty.com
0100 146 3 111
1st Reference Book:
Research methods for business: a skill building approach, 5th Edition, Uma Sekaran and Roger Bougie.
2nd Reference Book:
Business research methods, 8th Edition, Donald Cooper and Pamela Shindler
22
Chapter 1
Introduction to Research
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.
3
A process of determining, acquiring, analyzing, synthesizing, and disseminating relevant business data, information, and insights to decision makers in ways thatmobilize the organization to take appropriate business actions that, in turn, maximize business performance
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.
4
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.
5
More Examples of Research Areas in Business
Absenteeism Communication Motivation Consumer decision making Customer satisfaction Budget allocations Accounting procedures
6
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.
7
The Manager–Researcher Relationship
Each should know his/her role Trust levels Value system Acceptance of findings and implementation Issues of inside versus outside
researchers/consultants
8
1-9
What’s Changing in Business that Influences Research
Critical Scrutiny of Business
Computing Power &
Speed
Computing Power &
Speed
Battle for Analytical
Talent
Battle for Analytical
Talent
FactorsFactors
Information Overload
Shifting Global Economics
Shifting Global Economics
Government InterventionGovernment Intervention
Technological Connectivity
Technological Connectivity
New Research Perspectives
New Research Perspectives
1-10
Computing Power and Speed
Real-time Access
Real-time Access
FactorsFactors
Lower-cost Data
Collection
Powerful Computation
Powerful Computation
Better Visualization
Tools
Better Visualization
Tools
Integration of Data
Integration of Data
1-11
Business Planning Drives Business Research
Organizational Mission
BusinessStrategies
BusinessTactics
Business Goals
1-12
Research May Not Be Necessary
Can It Pass These Tests? Can information be applied to a critical decision? Will the information improve managerial decision
making? Are sufficient resources available?
1-13
Information Value Chain
Characteristics
Data collection/ transmission
Data interpretation
Models
Decisionsupport systems
Data management
1-14
The Business Research Process
1-15
Characteristics of Good Research
Clearly defined purposeClearly defined purpose
Detailed research processDetailed research process
Thoroughly planned designThoroughly planned design
High ethical standardsHigh ethical standards
Limitations addressedLimitations addressed
Adequate analysisAdequate analysis
Unambiguous presentationUnambiguous presentation
Conclusions justifiedConclusions justified
CredentialsCredentials
1-16
Two Categories of Research
Applied Basic (Pure)
1-17
Four Types of Studies
Reporting
Explanatory Predictive
Descriptive
Internal Researchers
Advantages:– Better acceptance from staff
– Knowledge about organization
– Would be an integral part of implementation and evaluation of the research recommendations.
Disadvantages– Less fresh ideas
– Power politics could prevail
– Possibly not valued as “expert” by staff
18
External Researchers
Advantages– Divergent and convergent thinking– Experience from several situations in different
organizations– Better technical training, usually
Disadvantages– Takes time to know and understand the organization– Rapport and cooperation from staff not easy– Not available for evaluation and implementation– Costs
19
1-20
Who Conducts Business Research?
1-21
Key Terms Applied research Business intelligence
system (BIS) Business research Control Decision support system Descriptive studies Explanatory Studies
Management dilemma Predictive studies Pure research Reporting studies Return on Investment (ROI) Scientific method Strategy Tactics
2222
Chapter 2
Scientific Investigation,
Thinking like a Researcher
Hallmarks of Scientific Research:
Hallmarks or main distinguishing characteristics of scientific research: – Purposiveness – Rigor – Testability – Replicability – Precision and Confidence – Objectivity – Generalizability – Parsimony
23
3-24
Research and Intuition
“If we ignore supernatural inspiration,intuition is based on two things: experienceand intelligence. The more experience I havewith you, the more likely I am to encounterrepetition of activities and situations that helpme learn about you. The smarter I am, the moreI can abstract from those experiences to findconnections and patterns among them.”
Jeffrey Bradshow, creator of thesoftware that searches databases
3-25
Language of Research
Variables
ModelsModels
TheoryTheory
Terms usedin research
Terms usedin research
Constructs
Operationaldefinitions
Operationaldefinitions
Propositions/Hypotheses
Propositions/Hypotheses
Conceptualschemes
ConceptualschemesConceptsConcepts
3-26
Language of Research
Clear conceptualizationof concepts
Shared understandingof concepts
Success of
Research
3-27
Job Redesign Constructs and Concepts
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
28
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.
29
3-30
Sound Reasoning
Exposition Argument
InductionDeduction
Types of Discourse
3-31
Inner-city household interviewing is especially difficult and expensive
Inner-city household interviewing is especially difficult and expensive
This survey involves substantial inner-city
household interviewing
This survey involves substantial inner-city
household interviewing
© 2002 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
The interviewing in this survey will be especially difficult and expensive
The interviewing in this survey will be especially difficult and expensive
Deductive Reasoning
3-32
Inductive Reasoning
Why didn’t sales increase during our promotional event?– Regional retailers did not have sufficient stock to fill
customer requests during the promotional period– A strike by employees prevented stock from arriving in
time for promotion to be effective– A hurricane closed retail outlets in the region for 10 days
during the promotion
3-33
Why Didn’t Sales Increase?
3-34
Tracy’s Performance
3-35
A Variable Is the Property Being Studied
VariableVariable
EventEvent ActAct
CharacteristicCharacteristic TraitTrait
AttributeAttribute
3-36
Propositions and Hypotheses
Brand Manager Jones (case) has a higher-than-average achievement motivation (variable).
Brand managers in Company Z (cases) have a higher-than-average achievement motivation (variable).
Generalization
3-37
Hypothesis Formats
Descriptive Hypothesis In Detroit, our potato
chip market share stands at 13.7%.
American cities are experiencing budget difficulties.
Research Question What is the market
share for our potato chips in Detroit?
Are American cities experiencing budget difficulties?
3-38
Relational Hypotheses
Correlational Young women (under 35)
purchase fewer units of our product than women who are older than 35.
The number of suits sold varies directly with the level of the business cycle.
Causal An increase in family
income leads to an increase in the percentage of income saved.
Loyalty to a grocery store increases the probability of purchasing that store’s private brand products.
3-39
The Role of Hypotheses
Guide the direction of the studyGuide the direction of the study
Identify relevant factsIdentify relevant facts
Suggest most appropriate research design
Suggest most appropriate research design
Provide framework for organizing resulting conclusions
Provide framework for organizing resulting conclusions
3-40
Characteristics of Strong Hypotheses
A Strong
Hypothesis Is
A Strong
Hypothesis Is
AdequateAdequate
TestableTestable
Better than rivals
Better than rivals
3-41
Theory within Research
3-42
The Role of Reasoning
3-43
A Model within Research
3-44
The Scientific Method
Direct observationDirect observation
Clearly defined variablesClearly defined variables
Clearly defined methodsClearly defined methods
Empirically testableEmpirically testable
Elimination of alternativesElimination of alternatives
Statistical justificationStatistical justification
Self-correcting processSelf-correcting process
3-45
Researchers
Encounter problems State problems Propose hypotheses Deduce outcomes Formulate rival
hypotheses Devise and conduct
empirical tests Draw conclusions
3-46
Key Terms Argument Case Concept Conceptual scheme Construct Deduction Empiricism Exposition Hypothesis
–Correlational–Descriptive–Explanatory–Relational
Hypothetical construct
Induction Model Operational definition Proposition Sound reasoning Theory Variable
– Control– Confounding (CFV)– Dependent (DV)– Extraneous (EV)– Independent (IV)– Intervening (IVV)– Moderating (MV)
4747
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 sales volume of a product is not picking up. – Minority group members are not advancing in their
careers. – 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.
48
Preliminary Information Gathering
Nature of information to be gathered: – Background information of the organization.
– Prevailing knowledge on the topic.
49
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. – Enhances testability and replicability of the findings. – Reduces the risk of “reinventing the wheel”. – Confirms that the problem is perceived as relevant and
significant.
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Data sources
Textbooks Academic and professional journals Theses Conference proceedings Unpublished manuscripts Reports of government departments and corporations Newspapers The Internet
51
Searching for Literature
Most libraries have the following electronic resources at their disposal:– Electronic journals
– Full-text databases
– Bibliographic databases
– Abstract databases
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The Problem Statement
Examples of Well-Defined Problem Statements– To what extent do the structure of the organization and type of
information systems installed account for the variance in the perceived effectiveness of managerial decision making?
– 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?
53
The Research Proposal
Key elements:– Purpose of the study– Specific problem to be investigated. – Scope of the study– Relevance of the study– Research design:
• Sampling design • Data collection methods • Data analysis
– Time frame – Budget– Selected Bibliography
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5-55
The Business Research Process
5-56
Stage 1: Clarifying the Research Question
Management-research question hierarchy process begins by identifying the management dilemma
5-57
Management-Research Question Hierarchy
5-58
SalePro’s Hierarchy
5-59
Formulating the Research Question
5-60
Types of Management Questions
5-61
The Research Question
Determine necessary evidence
Determine necessary evidence
Set scope of
study
Set scope of
study
Examine variables
Examine variables
Break questions
down
Break questions
down
Evaluate hypotheses
Evaluate hypotheses
Fine-TuningFine-Tuning
5-62
Performance ConsiderationsPerformance Considerations
© 2002 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
Investigative Questions
Attitudinal IssuesAttitudinal Issues
Behavioral IssuesBehavioral Issues
6363
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).
64
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
65
Variable
Any concept or construct that varies or changes in value
Main types of variables:– Dependent variable– Independent variable– Moderating variable – Mediating variable
66© 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.
67© 2009 John Wiley & Sons Ltd.www.wileyeurope.com/college/sekaran
Example
68
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.
69
Mediating 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.
70
Hypothesis
A proposition that is empirically testable. 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
71
Exercise
72
Give the hypotheses for the following framework:
Service qualityCustomer switching
Switching cost
Exercise
73
Give the hypotheses for the following framework:
Customer satisfaction
Service qualityCustomer switching
Argumentation
The expected relationships / hypotheses are an integration of:
– Exploratory research
– Common sense and logical reasoning
74
3-75
Independent and Dependent Variable Synonyms
Independent Variable (IV) Predictor Presumed cause Stimulus Predicted from… Antecedent Manipulated
Dependent Variable (DV) Criterion Presumed effect Response Predicted to…. Consequence Measured outcome
3-76
Relationships Among Variable Types
3-77
Relationships Among Variable Types
3-78
Relationships Among Variable Types
3-79
Moderating Variables (MV)
The introduction of a four-day week (IV) will lead to higher productivity (DV), especially among younger workers (MV)
The switch to commission from a salary compensation system (IV) will lead to increased sales (DV) per worker, especially more experienced workers (MV).
The loss of mining jobs (IV) leads to acceptance of higher-risk behaviors to earn a family-supporting income (DV) – particularly among those with a limited education (MV).
3-80
Extraneous Variables (EV)
With new customers (EV-control), a switch to commission from a salary compensation system (IV) will lead to increased sales productivity (DV) per worker, especially among younger workers (MV).
Among residents with less than a high school education (EV-control), the loss of jobs (IV) leads to high-risk behaviors (DV), especially due to the proximity of the firing range (MV).
3-81
Intervening Variables (IVV)
The switch to a commission compensation system (IV) will lead to higher sales (DV) by increasing overall compensation (IVV).
A promotion campaign (IV) will increase savings activity (DV), especially when free prizes are offered (MV), but chiefly among smaller savers (EV-control). The results come from enhancing the motivation to save (IVV).
8282
Chapter 5
The Research Process – Elements of Research Design
4-83
Evaluating the Value of Research
Option AnalysisOption Analysis
Decision TheoryDecision Theory
Prior or Interim EvaluationPrior or Interim Evaluation
Ex Post Facto EvaluationEx Post Facto Evaluation
84
Research Design
4-85
The Business Research Process
Purpose of the Study
Exploration Description Hypothesis Testing
86
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?
87
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.
88
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.
89
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.
90
Study Setting
Contrived: artificial setting
Non-contrived: the natural environment where work proceeds normally
91
Population to be Studied
Unit of analysis:– Individuals
– Dyads
– Groups
– Organizations
– Cultures
92
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
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4-94
Stage 1: Clarifying the Research Question
Management-research question hierarchy process begins by identifying the management dilemma
4-95
Stage 2: Proposing Research
Budget Types Rule-of-thumb Departmental Task
4-96
The Research Proposal
Delivery
Legally-bindingcontract
Legally-bindingcontract
ObligationsObligations
Written proposals establish
Written proposals establish
Methods
TimingTiming
BudgetsBudgets
ExtentExtentPurposePurpose
4-97
Stage 3: Research Design
4-98
Stage 3: Designing the Research
The Research
Project
The Research
Project
Research Design
Research Design
SamplingDesign
SamplingDesign
Pilot TestingPilot Testing
4-99
Data Characteristics
Abstractness Verifiability Elusiveness Closeness
4-100
Reducing data to manageable sizeReducing data to manageable size
© 2002 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
Steps in Data Analysis and Interpretation
Developing summariesDeveloping summaries
Looking for patternsLooking for patterns
Applying statistical techniquesApplying statistical techniques
4-101
Parts of the Research Report
Research Report
Research Report
ExecutiveSummaryExecutiveSummary
ResearchOverviewResearchOverview
TechnicalAppendixTechnicalAppendix
ImplementationStrategies
ImplementationStrategies
4-102
The Research Report Overview
Problem’s backgroundProblem’s background
Summary of exploratory findingsSummary of exploratory findings
Research design and proceduresResearch design and procedures
ConclusionsConclusions
4-103
Research Process Problems to Avoid
•Ill-defined management problem
•Unresearchable questions
•Politically-motivated research
4-104
Research Process Problems to Avoid
•Company Database Strip-Mining•The Favored-Technique Syndrome
5-105
Exploratory Phase Search Strategy
Search Strategy
Discovery/ AnalysisSecondary Sources
Individual Depth Interviews
Expert Interview
GroupDiscussions
5-106
Integration of Secondary Data into the Research Process
5-107
Objectives of Secondary Searches
Expand understanding of management dilemma Gather background information Identify information that should be gathered Identify sources for and actual questions that
might be used Identify sources for and actual sample frames that
might be used
5-108
Conducting a Literature Search
Define management dilemmaDefine management dilemma
Consult books for relevant termsConsult books for relevant terms
Use terms to searchUse terms to search
Locate/review secondary sourcesLocate/review secondary sources
Evaluate value of each source and content
Evaluate value of each source and content
5-109
Levels of Information
Primary Sources:MemosLetters
InterviewsSpeeches
LawsInternal records
Secondary Sources:
EncyclopediasTextbooksHandbooksMagazines
NewspapersNewscasts
Tertiary Sources:Indexes
BibliographiesInternet
search engines
5-110
Integrating Secondary Data
5-111
Information Sources
Encyclopedias
DirectoriesDirectories
HandbooksHandbooks
TypesTypes
Indexes/Bibliographies
DictionariesDictionaries
5-112
Evaluating Information Sources
Authority
FormatFormat
AudienceAudience
EvaluationFactors
EvaluationFactors
Purpose
ScopeScope
5-113
The Evolution of Data Mining
Evolutionary Step Investigative Question Enabling Technologies Characteristics
Data collection (1960s) “What was my average total revenue over the last five years?”
Computers, tapes, disks Retrospective, static data delivery
Data access (1980s) “What were unit sales in California last December?”
Relational databases (RDBMS), structured query language (SQL), ODBC
Retrospective, dynamic data delivery at record level
Data navigation (1990s) “What were unit sales in California last December? Drill down to Sacramento.”
Online analytic processing (OLAP), multidimensional databases, data warehouses
Retrospective, dynamic data delivery at multiple levels
Data mining (2000) “What’s likely to happen to Sacramento unit sales next month? Why?”
Advanced algorithms, multiprocessor computers, massive databases
Prospective, proactive information delivery
5-114
Data-Mining Process
5-115
Searching Databases vs. the Web
6-116
What Is Research Design?
BlueprintBlueprint
PlanPlan
GuideGuide
FrameworkFramework
6-117
What Tools Are Used in Designing Research?
6-118
MindWriter Project Plan in Gantt chart format
What Tools Are Used in Designing Research?
6-119
Design in the Research Process
6-120
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-121
Degree of Question Crystallization
Exploratory Study Loose structure Expand understanding Provide insight Develop hypotheses
Formal Study Precise procedures Begins with hypotheses Answers research
questions
6-122Approaches for Exploratory Investigations
Participant observation Film, photographs Projective techniques Psychological testing Case studies Ethnography Expert interviews Document analysis Proxemics and Kinesics
6-123
Desired Outcomes of Exploratory Studies
Established range and scope of possible management decisions
Established range and scope of possible management decisions
Established major dimensions of research task
Established major dimensions of research task
Defined a set of subsidiary questions that can guide research design
Defined a set of subsidiary questions that can guide research design
6-124
Desired Outcomes of Exploratory Studies (cont.)
Developed hypotheses about possible causes of management dilemma
Developed hypotheses about possible causes of management dilemma
Learned which hypotheses can be safely ignored
Learned which hypotheses can be safely ignored
Concluded additional research is not needed or not feasible
Concluded additional research is not needed or not feasible
6-125
Commonly Used Exploratory Techniques
Secondary Data AnalysisSecondary
Data Analysis
Focus GroupsFocus
Groups
Experience Surveys
Experience Surveys
6-126
Experience Surveys What is being done? What has been tried in the past with or without
success? How have things changed? Who is involved in the decisions? What problem areas can be seen? Whom can we count on to assist or participate in the
research?
6-127
Focus Groups
Group discussion 6-10 participants Moderator-led 90 minutes-2 hours
6-128
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-129
Data Collection Method
Monitoring Communication
6-130
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-131
The Time Dimension
Cross-sectional
Longitudinal
6-132
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-133
The Topical Scope
Statistical Study Breadth Population inferences Quantitative Generalizable findings
Case Study Depth Detail Qualitative Multiple sources of
information
6-134
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-135
The Research Environment
Field conditions
Lab conditions
Simulations
6-136
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-137
Purpose of the Study
Reporting Descriptive
Casual -Explanatory
Causal -Predictive
6-138
Descriptive Studies
When?When?
How much?How much? What?What?
Who?
Where?
6-139
Descriptive Studies
Descriptions of population characteristics
Descriptions of population characteristics
Estimates of frequency of characteristics
Estimates of frequency of characteristics
Discovery of associations among variables
Discovery of associations among variables
6-140
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-141
Experimental Effects
Experiment Study involving the
manipulation or control of one or more variables to determine the effect on another variable
Ex Post Facto Study After-the-fact report on
what happened to the measured variable
6-142
Ex Post Facto Design
Fishing Club Member Non-Fishing-Club Member
Age High Absentee Low Absentee High Absentee Low Absentee
Under 30 years 36 6 30 48
30 to 45 4 4 35 117
45 and over 0 0 5 115
6-143
Causation and Experimental Design
Random Assignment
Control/ Matching
6-144
Mills Method of Agreement
6-145
Mills Method of Difference
6-146
Causal Studies
AsymmetricalAsymmetrical
ReciprocalReciprocal
SymmetricalSymmetrical
6-147
Understanding Casual Relationships
Property
Response
Stimulus
Behavior
Disposition
6-148
Asymmetrical Casual Relationships
Stimulus-Response
Disposition-Behavior
Property-Behavior
Property-Disposition
6-149
Exhibit 6-6 Types of Asymmetrical Causal Relationships
Relationship Type Nature of Relationship Examples
Stimulus-response An event or change results in a response from some object.
• A change in work rules leads to a higher level of worker output.• A change in government economic policy restricts corporate financial decisions.• A price increase results in fewer unit sales.
Property-disposition An existing property causes a disposition.
• Age and attitudes about saving.• Gender attitudes toward social issues.• Social class and opinions about taxation.
Disposition-behavior A disposition causes a specific behavior.
• Opinions about a brand and its purchase.• Job satisfaction and work output.• Moral values and tax cheating.
Property-behavior An existing property causes a specific behavior.
• Stage of the family life cycle and purchases of furniture.• Social class and family savings patterns.• Age and sports participation.
6-150
Covariation between A and B
Covariation between A and B
Evidence of Causality
Time order of eventsTime order of events
No other possible causes of B
No other possible causes of B
6-151
Descriptors of Research Design
Experimental Effects
Perceptual AwarenessPerceptual Awareness
Research EnvironmentResearch
Environment
DescriptorsDescriptors
Question Crystallization Data Collection
MethodData Collection
Method
Time Dimension
Time Dimension
Topical Scope
Purpose of Study
6-152
Participants’ Perceptional Awareness
No deviation perceived
Deviations perceived as unrelated
Deviations perceived as researcher-induced
6-153
Descriptors of Research DesignCategory OptionsThe degree to which the research question has been crystallized
• Exploratory study• Formal study
The method of data collection • Monitoring• Communication Study
The power of the researcher to produce effects in the variables under study
• Experimental• Ex post facto
The purpose of the study • Reporting• Descriptive• Causal-Explanatory• Causal-Predictive
The time dimension • Cross-sectional• Longitudinal
The topical scope—breadth and depth—of the study
• Case• Statistical study
The research environment • Field setting• Laboratory research• Simulation
The participants’ perceptional awareness of the research activity
• Actual routine• Modified routine
4-154
Key Terms Census Data
–Primary data–Secondary data
Data analysis Decision rule exploration Investigative questions Management dilemma
Management question Management-research
question hierarchy Pilot test Research design Research process Research questions Sample Target population
6-155
Key Terms Asymmetrical relationship Case study Causal study Causation Children’s panels Communication study Control Control group Correlation Cross-sectional study
Descriptive study Ethnographic research Ex post facto design Experience Experiment Exploratory study Field conditions Focus group Formal study Individual depth interview Intranet
6-156
Key Terms (cont.) Laboratory conditions Longitudinal study Matching Monitoring Primary data Qualitative techniques Random assignment
Reciprocal relationship Research design Secondary data Simulation Statistical study Symmetrical relationship
157157
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.
158
(Characteristics of) Objects
Objects include persons, strategic business units, companies, countries, kitchen appliances, restaurants, shampoo, yogurt and so on.
Examples of characteristics of objects are arousal seeking tendency, achievement motivation, organizational effectiveness, shopping enjoyment, length, weight, ethnic diversity, service quality, conditioning effects and taste.
159
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 subjective nature.
160
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.
161
Example
162
163163
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.
164
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
165
Nominal Scale
166
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
167
Ordinal Scale
168
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.
169
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
170
Interval scale
171
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?
172
Ratio Scale
173
Properties of the Four Scales
174
Goodness of Measures
175
Validity
176
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).
177
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.
178
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.
179
180180
1.1.Illustrate Illustrate thethe needed operationalization for : needed operationalization for :- Need for Cognition, OR- Need for Cognition, OR- Shopping Enjoyment.- Shopping Enjoyment.
2. Select among the studied scales, the needed 2. Select among the studied scales, the needed types to measure 4 of your elements, make sure types to measure 4 of your elements, make sure to use all scales types.to use all scales types.
181181
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.
182
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
183
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
184
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
185
Principles of Questionnaire Design.
186
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 questionnaire
2. Determine the form of response
3. Determine the wording of the questions
4. Determine the question sequence
5. Write cover letter
187
1. Questionnaire content
FrameworkNeed information for all constructs in framework
Measurement: Operationalizing– Objective construct:
• 1 element/items=> 1 question
– Subjective construct: • multiple elements/items
=> multiple questions
188
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
Cfr. Measurement: Response scales
189
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
190
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
191
4. Question Sequence
192Personal 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
193
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)
194
195195
Chapter 9
Experimental Designs
Causal Research
Research conducted to identify cause-and-effect relationships among variables when the research has already been narrowly defined
196
Evidence for Causality
Covariation– Evidence of the extent to which X and Y occur
together or vary together in the way predicted by the hypothesis
Time order of occurrence of variable– Evidence that shows X occurs before Y
Elimination of other possible causal factors– Evidence that allows the elimination of factors other
than X as the cause of Y A logical explanation
– About why the independent variable affects the dependent variable.
197
Experiment
Data collection method in which one or more IVs are manipulated in order to measure their effect on a DV, while controlling for exogenous variables in order to test a hypothesis
Cause and effect relationship is established by– Manipulation of independent variable
– Controlling for exogenous factors
198
Manipulation of IV
ManipulationCreation of different levels of the IV to assess the impact on the DV
Treatment levelsThe arbitrary or natural groups a researcher makes within the IV
Evidence for causality– Covariation (difference between groups)– Time order control
199
Exogenous Variables
Controlling for exogenous/confounding variables– Eliminating other possible causal factors– Eliminating alternative explanations
Experimental designs available
Two types of exogenous variables– Related to participants– Related other, environmental factors
200
Related to Participants
Selection bias: improper assignment of participants to the experimental groups
– Matched groups: Match the different groups as closely as possible in terms of age, interest, expertise etc.– Random assignment: Randomly assign members to different treatment groups. The differences will be randomly distributed. Systematic bias will reduce.–Statistical control: Measuring the external variables and adjusting for their effect through statistical methods
Mortality: Loss of participants during the experiment
201
Related to other actors
History effects: External events occurring at the same time that
may affect the DV
Maturation effects: Changes in the participants as a passage of
time that may affect the DV
Testing effects: The experiment itself affect the responses
– Main testing effect: prior responses affect later responses
– Interactive testing effect: prior responses affect perception of IV
Instrumentation effects: Changes in measuring instrument
202
Experimental Design
203
Experimental Design
204
Experimental Design
205
Experimental Design
206
Experimental Design
207
Experimental Design
208
Experimental Design
209
Exercise
210
An organization would like to introduce one of two types of new manufacturing processes to increase the productivity of workers. Both involve heavy investment in expensive technology. The company wants to test the efficacy of each process in one of its small plants.
Propose an experiment, using:- Pretest posttest control group design- Posttest control group design
And calculate for each design the specific effect of each new process on the productivity.
Validity
Internal validity– Determination of whether the effect is actually caused by the
manipulation of treatments and not by other, exogenous variables
External validity– Determination of whether the cause-and-effect relationships found
in the experiment can be generalized
211
Advantages– High degree of control– High internal validity / replication– Less costly and less expensive
Disadvantages– Artificiality => reactive error– Demand artifacts– Lower external validity
212
Compared to field experiments, lab experiments have the following
213213
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.
214
Relevant Terms - 1
Population refers to the entire group of people, events, or things of interest that the researcher wishes to investigate.
An element is a single member of the population.
A sample is a subset of the population. It comprises some members selected from it.
215
Relevant Terms - 2
Sampling unit: the element or set of elements that is available for selection in some stage of the sampling process.
A subject is a single member of the sample, just as an element is a single member of the population.
216
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.
217
Statistics versus Parameters
218
Advantages of Sampling
Less costs Less errors due to less fatigue Less time Destruction of elements avoided
219
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
220
Sampling Techniques
Probability versus nonprobability sampling
Probability sampling: elements in the population have a known and non-zero chance of being chosen
221
Sampling Techniques
Probability Sampling– Simple Random Sampling
– Systematic Sampling
– Stratified Random Sampling
– Cluster Sampling
Nonprobability Sampling– Convenience Sampling
– Judgment Sampling
– Quota Sampling
222
Simple Random Sampling
Procedure– Each element has a known and equal chance of being selected
Characteristics– Highly generalizable
– Easily understood
– Reliable population frame necessary
223
Systematic Sampling
Procedure– Each nth element, starting with random choice of an element
between 1 and n
Characteristics– Idem simple random sampling
– Easier than simple random sampling
– Systematic biases when elements are not randomly listed
224
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
225
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
226
(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
227
Example
228
229
Overview
230
Overview
231
Overview
232
Choice Points in Sampling Design
Tradeoff between precision and confidence
233
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
234
Sample Size for a Given Population Size
235
Sample Size for a Given
236
237237
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)
238
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.
239
Transforming Data
240
Getting a Feel for the Data
241
Frequencies
242
Descriptive Statistics: Central Tendencies and Dispersions
243
Reliability Analysis
244
245245
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.
Type II error (): the probability of failing to reject the null hypothesis given that the alternative hypothesis is actually true.
Statistical power (1 - ): the probability of correctly rejecting the null hypothesis.
246
Choosing the Appropriate Statistical Technique
247
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.
248
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.
249
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.
250
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.
251
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.
252
Regression Analysis
Simple regression analysis is used in a situation where one metric independent variable is hypothesized to affect one metric dependent variable.
253
Scatter plot
254
30 40 50 60 70 80 90
PHYS_ATTR
20
40
60
80
100
LKLH
D_D
ATE
Simple Linear Regression
255
Y
X
0̂0̂0̂0̂0̂0̂ `0
0̂
iii XY 10
1̂1
Ordinary Least Squares Estimation
256
Yi
Xi
Yiei
n
1i
2i Minimize e
ˆ
SPSS
Analyze Regression Linear
257
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
258
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 sense
2. 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
259
SPSS
260
Model Summary
.841 .707 .704 5.919Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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.
261
SPSS
262
Model Summary
.841 .707 .704 5.919Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
= r(Likelihood to Date, Physical Attractiveness)
Model Significance
H0: 0 = 1 = ... = m = 0 (all parameters are zero)
H1: Not H0
263
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
264
SPSS
265
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.
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
266
SPSS cont’d
267
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.
Conceptual Model
268
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.
269
Conceptual Model
270
Perceived Intelligence
Physical Attractiveness
+
+Likelihood
to Date
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.
271
Conceptual Model
272
Perceived Intelligence
Physical Attractiveness
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
273
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.
274
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.
275
Moderators
Conceptual Model
276
Perceived Intelligence
Physical Attractiveness
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
277
With Y = DVX = IVM = mediator
Step 1
278
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.
Step 1 cont’d
279
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.
Step 2
280
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.
Step 2 cont’d
281
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
Step 3
282
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.
Step 3 cont’d
283
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.
284284
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.
285
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
286
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.
287
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.
288
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.
289
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).
290
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).
291
292292
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.
293
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
294
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
295
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
296
Oral Presentation
Deciding on the Content
Visual Aids – For instance graphs, charts, tables
The presenter
The presentation
Handling questions
297
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