DrAshrafElsafty ResearchMehods ESLSCA July12

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

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.

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.

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.

More Examples of Research Areas in Business

Absenteeism Communication Motivation Consumer decision making Customer satisfaction Budget allocations Accounting procedures

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.

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

What’s Changing in Business that Influences Research

Critical Scrutiny of Business

Computing Power &

Battle for Analytical

Talent

Battle for Analytical

Talent

FactorsFactors

Information Overload

Shifting Global Economics

Government InterventionGovernment Intervention

Technological Connectivity

New Research Perspectives

Computing Power and Speed

Real-time Access

FactorsFactors

Lower-cost Data

Collection

Powerful Computation

Better Visualization

Integration of Data

Business Planning Drives Business Research

Organizational Mission

BusinessStrategies

BusinessTactics

Business Goals

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?

Information Value Chain

Characteristics

Data collection/ transmission

Data interpretation

Models

Decisionsupport systems

Data management

The Business Research Process

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

Two Categories of Research

Applied Basic (Pure)

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

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

Who Conducts Business Research?

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

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

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

Language of Research

Variables

ModelsModels

TheoryTheory

Terms usedin research

Constructs

Operationaldefinitions

Propositions/Hypotheses

Conceptualschemes

ConceptualschemesConceptsConcepts

Language of Research

Clear conceptualizationof concepts

Shared understandingof concepts

Success of

Research

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

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.

Sound Reasoning

Exposition Argument

InductionDeduction

Types of Discourse

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

The interviewing in this survey will be especially difficult and expensive

Deductive Reasoning

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

Why Didn’t Sales Increase?

Tracy’s Performance

A Variable Is the Property Being Studied

VariableVariable

EventEvent ActAct

CharacteristicCharacteristic TraitTrait

AttributeAttribute

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

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?

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.

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

Provide framework for organizing resulting conclusions

Characteristics of Strong Hypotheses

A Strong

Hypothesis Is

A Strong

Hypothesis Is

AdequateAdequate

TestableTestable

Better than rivals

Theory within Research

The Role of Reasoning

A Model within Research

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

Researchers

Encounter problems State problems Propose hypotheses Deduce outcomes Formulate rival

hypotheses Devise and conduct

empirical tests Draw conclusions

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)

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.

Preliminary Information Gathering

Nature of information to be gathered: – Background information of the organization.

– Prevailing knowledge on the topic.

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.

Data sources

Textbooks Academic and professional journals Theses Conference proceedings Unpublished manuscripts Reports of government departments and corporations Newspapers The Internet

Searching for Literature

Most libraries have the following electronic resources at their disposal:– Electronic journals

– Full-text databases

– Bibliographic databases

– Abstract databases

The Problem Statement

Examples of Well-Defined Problem Statements– To what extent 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?

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

Stage 1: Clarifying the Research Question

Management-research question hierarchy process begins by identifying the management dilemma

Management-Research Question Hierarchy

SalePro’s Hierarchy

Formulating the Research Question

Types of Management Questions

The Research Question

Determine necessary evidence

Set scope of

Examine variables

Break questions

Evaluate hypotheses

Fine-TuningFine-Tuning

Performance ConsiderationsPerformance Considerations

Investigative Questions

Attitudinal IssuesAttitudinal Issues

Behavioral IssuesBehavioral Issues

Chapter 4

The Research Process - Theoretical Framework & Hypothesis Development

Theoretical Framework

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

Theoretical Framework

Basic steps:– Identify and label the variables correctly

– State the relationships among the variables: formulate hypotheses

– Explain how or why you expect these relationships

Variable

Any concept or construct that varies or changes in value

Main types of variables:– Dependent variable– Independent variable– Moderating variable – Mediating variable

(In)dependent Variables

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

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

Independent variable (IV)– Influences the DV in either positive or negative way. The

variance in the DV is accounted for by the IV.

Example

Moderators

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

Mediating Variable Mediating variable

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

Hypothesis

A proposition that is empirically testable. It is an empirical statement concerned with the relationship among variables.

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

Can be:– Directional– Non-directional

Exercise

Give the hypotheses for the following framework:

Service qualityCustomer switching

Switching cost

Exercise

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

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

Relationships Among Variable Types

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).

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).

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).

Chapter 5

The Research Process – Elements of Research Design

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

Research Design

Purpose of the Study

Exploration Description Hypothesis Testing

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

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

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

are switching to other service providers?

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

characteristics of the variables of interest in a situation.

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

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

Hypothesis testing:– Studies that engage in hypotheses testing usually

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

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

company will increase if he doubles the advertising dollars.

Type of Investigation

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

relationship.

Correlational study– identification of the important factors “associated with”

the problem.

Study Setting

Contrived: artificial setting

Non-contrived: the natural environment where work proceeds normally

Population to be Studied

Unit of analysis:– Individuals

– Dyads

– Groups

– Organizations

– Cultures

Time Horizon

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

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

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

Stage 1: Clarifying the Research Question

Management-research question hierarchy process begins by identifying the management dilemma

Stage 2: Proposing Research

Budget Types Rule-of-thumb Departmental Task

The Research Proposal

Delivery

Legally-bindingcontract

ObligationsObligations

Written proposals establish

Methods

TimingTiming

BudgetsBudgets

ExtentExtentPurposePurpose

Stage 3: Research Design

Stage 3: Designing the Research

The Research

Project

The Research

Project

Research Design

SamplingDesign

Pilot TestingPilot Testing

Data Characteristics

Abstractness Verifiability Elusiveness Closeness

Reducing data to manageable sizeReducing data to manageable size

Steps in Data Analysis and Interpretation

Developing summariesDeveloping summaries

Looking for patternsLooking for patterns

Applying statistical techniquesApplying statistical techniques

Parts of the Research Report

Research Report

ExecutiveSummaryExecutiveSummary

ResearchOverviewResearchOverview

TechnicalAppendixTechnicalAppendix

ImplementationStrategies

The Research Report Overview

Problem’s backgroundProblem’s background

Summary of exploratory findingsSummary of exploratory findings

Research design and proceduresResearch design and procedures

ConclusionsConclusions

Research Process Problems to Avoid

•Ill-defined management problem

•Unresearchable questions

•Politically-motivated research

Research Process Problems to Avoid

•Company Database Strip-Mining•The Favored-Technique Syndrome

Exploratory Phase Search Strategy

Search Strategy

Discovery/ AnalysisSecondary Sources

Individual Depth Interviews

Expert Interview

GroupDiscussions

Integration of Secondary Data into the Research Process

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

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

Levels of Information

Primary Sources:MemosLetters

InterviewsSpeeches

LawsInternal records

Secondary Sources:

EncyclopediasTextbooksHandbooksMagazines

NewspapersNewscasts

Tertiary Sources:Indexes

BibliographiesInternet

search engines

Integrating Secondary Data

Information Sources

Encyclopedias

DirectoriesDirectories

HandbooksHandbooks

TypesTypes

Indexes/Bibliographies

DictionariesDictionaries

Evaluating Information Sources

Authority

FormatFormat

AudienceAudience

EvaluationFactors

Purpose

ScopeScope

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

Data-Mining Process

Searching Databases vs. the Web

What Is Research Design?

BlueprintBlueprint

PlanPlan

GuideGuide

FrameworkFramework

What Tools Are Used in Designing Research?

MindWriter Project Plan in Gantt chart format

What Tools Are Used in Designing Research?

Design in the Research Process

Descriptors of Research Design

Experimental Effects

Perceptual AwarenessPerceptual Awareness

Research EnvironmentResearch

Environment

DescriptorsDescriptors

Question Crystallization Data Collection

MethodData Collection

Method

Time Dimension

Topical Scope

Purpose of Study

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

Desired Outcomes of Exploratory Studies

Established range and scope of possible management decisions

Established major dimensions of research task

Defined a set of subsidiary questions that can guide research design

Desired Outcomes of Exploratory Studies (cont.)

Developed hypotheses about possible causes of management dilemma

Learned which hypotheses can be safely ignored

Concluded additional research is not needed or not feasible

Commonly Used Exploratory Techniques

Secondary Data AnalysisSecondary

Data Analysis

Focus GroupsFocus

Groups

Experience Surveys

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?

Focus Groups

Group discussion 6-10 participants Moderator-led 90 minutes-2 hours

Environment

Method

Time Dimension

Topical Scope

Purpose of Study

Data Collection Method

Monitoring Communication

Environment

Method

Time Dimension

Topical Scope

Purpose of Study

The Time Dimension

Cross-sectional

Longitudinal

Environment

Method

Time Dimension

Topical Scope

Purpose of Study

The Topical Scope

Statistical Study Breadth Population inferences Quantitative Generalizable findings

Case Study Depth Detail Qualitative Multiple sources of

information

Environment

Method

Time Dimension

Topical Scope

Purpose of Study

The Research Environment

Field conditions

Lab conditions

Simulations

Environment

Method

Time Dimension

Topical Scope

Purpose of Study

Reporting Descriptive

Casual -Explanatory

Causal -Predictive

Descriptive Studies

When?When?

How much?How much? What?What?

Where?

Descriptive Studies

Descriptions of population characteristics

Estimates of frequency of characteristics

Discovery of associations among variables

Environment

Method

Time Dimension

Topical Scope

Purpose of Study

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

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

Causation and Experimental Design

Random Assignment

Control/ Matching

Mills Method of Agreement

Mills Method of Difference

Causal Studies

AsymmetricalAsymmetrical

ReciprocalReciprocal

SymmetricalSymmetrical

Understanding Casual Relationships

Property

Response

Stimulus

Behavior

Disposition

Asymmetrical Casual Relationships

Stimulus-Response

Disposition-Behavior

Property-Behavior

Property-Disposition

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.

Covariation between A and B

Evidence of Causality

Time order of eventsTime order of events

No other possible causes of B

Environment

Method

Time Dimension

Topical Scope

Purpose of Study

Participants’ Perceptional Awareness

No deviation perceived

Deviations perceived as unrelated

Deviations perceived as researcher-induced

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

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

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

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

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Chapter 6

Measurement of Variables: Operational Definition

Measurement

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

(Characteristics of) 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.

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.

Operationalizing Concepts

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

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

Example

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Chapter 7

Measurement of Variables: Scaling, Reliability, Validity

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

Nominal Scale

A nominal scale is one that allows the researcher to assign subjects to certain categories or groups.

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

What is your gender?O MaleO Female

Nominal Scale

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

Ordinal Scale

Interval Scale

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

Interval scale

Circle the number that represents your feelings at this particular moment best. There are no right or wrong answers. Please answer every question.

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

I disagree completely 1 2 3 4 5 I agree completely

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

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

Interval scale

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?

Ratio Scale

Properties of the Four Scales

Goodness of Measures

Validity

Reliability

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

Stability

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

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

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

Internal Consistency

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

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

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

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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.

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Chapter 8

Data Collection Methods

Sources of Data

Primary data: information obtained firsthand by the researcher on the variables of interest for the specific purpose of the study.

Examples: individuals, focus groups, panels

Secondary data: information gathered from sources already existing.

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

Personal Interview Advantages

– Can clarify doubts about questionnaire

– Can pick up non-verbal cues

– Relatively high response/cooperation

– Special visual aids and scoring devises can be used

Disadvantages– High costs and time intensive

– Geographical limitations

– Response bias / Confidentiality difficult to be assured

– Some respondents are unwilling to talk to strangers

– Trained interviewers

Telephone Interview

Advantages– Discomfort of face to face is avoided

– Faster / Number of calls per day could be high

– Lower cost

Disadvantages– Interview length must be limited

– Low response rate

– No facial expressions

Self-administered

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

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

Principles of Questionnaire Design.

Questionnaire Design Definition

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

Steps1. Determine the content of the questionnaire

2. Determine the form of response

3. Determine the wording of the questions

4. Determine the question sequence

5. Write cover letter

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

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

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

Question Wording

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

Dresdner has poor customer operational support

– Avoid double negatives

Limit the length of the questionsRules of thumb:

– < 20 words

– < one full line in print

4. Question Sequence

192Personal and sensitive data at the end

5. Cover Letter

The cover letter is the introductory page of the questionnaire

It includes:– Identification of the researcher

– Motivation for respondents to fill it in

– Confidentiality

– Thanking of the respondent

Structured Observations

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

Quantitative in nature

Different types– Personal observation

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

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

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

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.

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

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

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

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

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

Experimental Design

Exercise

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

Advantages– High degree of control– High internal validity / replication– Less costly and less expensive

Disadvantages– Artificiality => reactive error– Demand artifacts– Lower external validity

Compared to field experiments, lab experiments have the following

213213

Chapter 10

Sampling

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

Relevant Terms - 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.

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.

Relevant Terms - 3

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

Statistics versus Parameters

Advantages of Sampling

Less costs Less errors due to less fatigue Less time Destruction of elements avoided

The Sampling Process

Major steps in sampling:– Define the population.

– Determine the sample frame

– Determine the sampling design

– Determine the appropriate sample size

– Execute the sampling process

Sampling Techniques

Probability versus nonprobability sampling

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

Sampling Techniques

Probability Sampling– Simple Random Sampling

– Systematic Sampling

– Stratified Random Sampling

– Cluster Sampling

Nonprobability Sampling– Convenience Sampling

– Judgment Sampling

– Quota Sampling

Simple Random Sampling

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

Characteristics– Highly generalizable

– Easily understood

– Reliable population frame necessary

Systematic Sampling

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

between 1 and n

Characteristics– Idem simple random sampling

– Easier than simple random sampling

– Systematic biases when elements are not randomly listed

Cluster Sampling Procedure

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

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

Stratified Sampling

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

• Proportionate• Disproportionate

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

(Dis)proportionate Stratified Sampling

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

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

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

of strata

Example

Overview

Choice Points in Sampling Design

Tradeoff between precision and confidence

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

Sample size: guidelines

In general: 30 < n < 500

Categories: 30 per subcategory

Multivariate: 10 x number of var’s

Experiments: 15 to 20 per condition

Sample Size for a Given Population Size

Sample Size for a Given

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)

Editing Data

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

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

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

Transforming Data

Getting a Feel for the Data

Frequencies

Descriptive Statistics: Central Tendencies and Dispersions

Reliability Analysis

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.

Choosing the Appropriate Statistical Technique

Testing Hypotheses on a Single Mean

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

Testing Hypotheses about Two Related Means

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

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

Testing Hypotheses about Two Related Means - 2

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

Testing Hypotheses about Two Unrelated Means

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

Testing Hypotheses about Several Means

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

Regression Analysis

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

Scatter plot

30 40 50 60 70 80 90

PHYS_ATTR

Simple Linear Regression

0̂0̂0̂0̂0̂0̂ `0

iii XY 10

Ordinary Least Squares Estimation

2i Minimize e

Analyze Regression Linear

Model Summary

.841 .707 .704 5.919Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

8195.319 1 8195.319 233.901 .000

3398.640 97 35.038

11593.960 98

Regression

Residual

Model1

Sum ofSquares df Mean Square F Sig.

SPSS cont’d

Coefficients

34.738 2.065 16.822 .000

.520 .034 .841 15.294 .000

(Constant)

PHYS_ATTR

Model1

B Std. Error

UnstandardizedCoefficients

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

Model Summary

.841 .707 .704 5.919Model1

Measure of Overall Fit: R2

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

R2 = total variation – unexplained variation

total variation

R2 takes on any value between zero and one:– R2 = 1: Perfect match between the line and the data points.

– R2 = 0: There is no linear relationship between x and y.

Model Summary

.841 .707 .704 5.919Model1

= r(Likelihood to Date, Physical Attractiveness)

Model Significance

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

H1: Not H0

Model Significance

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

H1: Not H0

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

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

(SSe/(n – 1 – k)

SSReg = explained variation by regression

SSe = unexplained variation by regression

8195.319 1 8195.319 233.901 .000

3398.640 97 35.038

11593.960 98

Regression

Residual

Model1

Parameter significance

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

H0: j = 0

H1: j 0

Test-statistic: t = bj/SEj ~ tn-k-1

with bj = the estimated coefficient for j

SEj = the standard error of bj

SPSS cont’d

Coefficients

34.738 2.065 16.822 .000

.520 .034 .841 15.294 .000

(Constant)

PHYS_ATTR

Model1

B Std. Error

t Sig.

Conceptual Model

Physical Attractiveness

Likelihood to Date

Multiple Regression Analysis

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

Conceptual Model

Perceived Intelligence

+Likelihood

to Date

Model Summary

.844 .712 .706 5.895Model1

8257.731 2 4128.866 118.808 .000

3336.228 96 34.752

11593.960 98

Regression

Residual

Model1

Coefficients

31.575 3.130 10.088 .000

.050 .037 .074 1.340 .183

.523 .034 .846 15.413 .000

(Constant)

PERC_INTGCE

PHYS_ATTR

Model1

B Std. Error

t Sig.

Conceptual Model

Likelihood to Date

Gender

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

level of reward) that affects the direction and/or strength of the relation between dependent and independent variable

Analytical representation

Y = ß0 + ß1X1 + ß2X2 + ß3X1X2

with Y = DVX1 = IVX2 = Moderator

Model Summary

.910 .828 .821 4.601Model1

9603.938 4 2400.984 113.412 .000

1990.022 94 21.170

11593.960 98

Regression

Residual

Model1

Moderators

Coefficients

32.603 3.163 10.306 .000

.000 .043 .000 .004 .997

.496 .027 .802 18.540 .000

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

.127 .058 .369 2.177 .032

(Constant)

PERC_INTGCE

PHYS_ATTR

GENDER

PI_GENDER

Model1

B Std. Error

t Sig.

interaction significant effect on dep. var.

Moderators

Conceptual Model

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

With Y = DVX = IVM = mediator

Step 1

Mode l Summary

.963 .927 .923 3.020Model1

10745.603 5 2149.121 235.595 .000

848.357 93 9.122

11593.960 98

Regression

Residual

Model1

Step 1 cont’d

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

t Sig.

significant effect on dep. var.

Step 2

Mode l Summary

.977 .955 .955 2.927Model1

17720.881 1 17720.881 2068.307 .000

831.079 97 8.568

18551.960 98

Regression

Residual

Model1

Step 2 cont’d

Coefficients

8.474 1.132 7.484 .000

.820 .018 .977 45.479 .000

(Constant)

COMM_INTER

Model1

B Std. Error

t Sig.

significant effect on mediator

Step 3

Mode l Summary

.966 .934 .930 2.885Model1

10828.336 6 1804.723 216.862 .000

765.624 92 8.322

11593.960 98

Regression

Residual

Model1

Step 3 cont’d

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

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.

Analysis of Qualitative Data

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

Steps:– data reduction

– data display

– drawing and verifying conclusions

Data Reduction

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

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

Data Display

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

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

Drawing Conclusions

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

Reliability in Qualitative Research

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

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

Validity in Qualitative Research

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

validity)

– can be generalized or transferred to other contexts or settings (external validity).

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.

The Written Report

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

Examples– Simple descriptive report

– Comprehensive report, offering alternative solutions

Characteristics of a Well-Written Report

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

Contents of Research Report

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

– Purpose of the study– Background – Problem statement

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

Oral Presentation

Deciding on the Content

Visual Aids – For instance graphs, charts, tables

The presenter

The presentation

Handling questions

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