book

Business Research Methods, 2nd Edition

Name: Business Research Methods, 2nd Edition
Author: Naval Bajpai
ISBN: 9789352861620

by Naval Bajpai

July 2017

Intermediate to advanced

851 pages

30h 21m

English

Pearson India

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Cover
Title Page
Dedication
Contents
About the Authors
Preface to the Second Edition
Preface to the First Edition
Part I Introduction to Business Research
1 Business Research Methods: An Introduction
1.1 Introduction1.2 Difference Between Basic and Applied Research1.3 Defining Business Research1.4 Roadmap to Learn Business Research Methods1.5 Business Research Methods: A Decision Making Tool in the Hands of Management1.5.1 Problem or Opportunity Identification1.5.2 Diagnosing the Problem or Opportunity1.5.3 Executing Business Research to Explore the Solution1.5.4 Implement Presented Solution1.5.5 Evaluate the Effectiveness of Decision Making1.6 Use of Software in Data Preparation and Analysis1.6.1 Introduction to MS Excel 20071.6.2 Introduction to Minitab®1.6.3 Introduction to SPSSSummaryKey TermsDiscussion QuestionsCase 1
2 Business Research Process Design
2.1 Introduction2.2 Business Research Process Design2.2.1 Step 1: Problem or Opportunity Identification2.2.2 Step 2: Decision Maker and Business Researcher Meeting to Discuss the Problem or Opportunity Dimensions2.2.3 Step 3: Defining the Management Problem and Subsequently the Research Problem2.2.4 Step 4: Formal Research Proposal and Introducing the Dimensions to the Problem2.2.5 Step 5: Approaches to Research2.2.6 Step 6: Fieldwork and Data Collection2.2.7 Step 7: Data Preparation and Data Entry2.2.8 Step 8: Data Analysis2.2.9 Step 9: Interpretation of Result and Presentation of Findings2.2.10 Step 10: Management Decision and Its ImplementationSummaryKey TermsDiscussion QuestionsCase 2

Part II Research Design Formulation
3 Measurement and Scaling
3.1 Introduction3.2 What Should be Measured?3.3 Scales of Measurement3.3.1 Nominal Scale3.3.2 Ordinal Scale3.3.3 Interval Scale3.3.4 Ratio Scale3.4 Four Levels of Data Measurement3.5 The Criteria for Good Measurement3.5.1 Validity3.5.2 Reliability3.5.3 Sensitivity3.6 Measurement Scales3.6.1 Single-Item Scales3.6.2 Multi-Item Scales3.6.3 Continuous Rating Scales3.7 Factors in Selecting an Appropriate Measurement Scale3.7.1 Decision on the Basis of Objective of Conducting a Research3.7.2 Decision Based on the Response Data Type Generated by Using a Scale3.7.3 Decision Based on Using Single- or Multi-Item Scale3.7.4 Decision Based on Forced or Non-Forced Choice3.7.5 Decision Based on Using Balanced or Unbalanced Scale3.7.6 Decision Based on the Number of Scale Points and Its Verbal DescriptionSummaryKey TermsDiscussion QuestionsCase 3Appendix
4 Questionnaire Design
4.1 Introduction4.2 What is a Questionnaire?4.3 Questionnaire Design Process4.3.1 Phase I: Pre-Construction Phase4.3.2 Phase II: Construction Phase4.3.3 Phase III: Post-Construction PhaseSummaryKey TermsDiscussion QuestionsCase 4
5 Sampling and Sampling Distributions
5.1 Introduction5.2 Sampling5.3 Why is Sampling Essential?5.4 The Sampling Design Process5.5 Random versus Non-random Sampling5.6 Random Sampling Methods5.6.1 Simple Random Sampling5.6.2 Using MS Excel for Random Number Generation5.6.3 Using Minitab for Random Number Generation5.6.4 Stratified Random Sampling5.6.5 Cluster (or Area) Sampling5.6.6 Systematic (or Quasi-Random) Sampling5.6.7 Multi-Stage Sampling5.7 Non-random Sampling5.7.1 Quota Sampling5.7.2 Convenience Sampling5.7.3 Judgement Sampling5.7.4 Snowball Sampling5.8 Sampling and Non-Sampling Errors5.8.1 Sampling Errors5.8.2 Non-Sampling Errors5.9 Sampling Distribution 5.10 Central Limit Theorem5.10.1 Case of Sampling from a Finite Population5.11 Sample Distribution of Sample Proportion pSummaryKey TermsDiscussion QuestionsNumerical ProblemsCase 5
Part III Sources and Collection of Data
6 Secondary Data Sources
6.1 Introduction6.2 Meaning of Primary and Secondary Data6.3 Benefits and Limitations of Using Secondary Data6.4 Classification of Secondary Data Sources6.4.1 Books, Periodicals, and Other Published Material6.4.2 Reports and Publication from Government Sources6.4.3 Computerized Commercial and Other Data Sources6.4.4 Media Resources6.5 Roadmap to Use Secondary Data6.5.1 Step 1: Identifying the Need of Secondary Data for Research6.5.2 Step 2: Utility of Internal Secondary Data Sources for the Research Problem6.5.3 Step 3: Utility of External Secondary Data Sources for the Research Problem6.5.4 Step 4: Use External Secondary Data for the Research ProblemSummaryKey TermsDiscussion QuestionsCase 6
7 Data Collection: Survey and Observation
7.1 Introduction7.2 Survey Method of Data Collection7.3 A Classification of Survey Methods7.3.1 Personal Interview7.3.2 Telephone Interview7.3.3 Mail Interview7.3.4 Electronic Interview7.4 Evaluation Criteria for Survey Methods7.4.1 Cost7.4.2 Time7.4.3 Response Rate7.4.4 Speed of Data Collection7.4.5 Survey Coverage Area7.4.6 Bias Due to Interviewer7.4.7 Quantity of Data7.4.8 Control Over Fieldwork7.4.9 Anonymity of the Respondent7.4.10 Question Posing7.4.11 Question Diversity7.5 Observation Techniques7.5.1 Direct versus Indirect Observation7.5.2 Structured versus Unstructured Observation7.5.3 Disguised versus Undisguised Observation7.5.4 Human versus Mechanical Observation7.6 Classification of Observation Methods7.6.1 Personal Observation7.6.2 Mechanical Observation7.6.3 Audits7.6.4 Content Analysis7.6.5 Physical Trace Analysis7.7 Advantages of Observation Techniques7.8 Limitations of Observation TechniquesSummaryKey TermsDiscussion QuestionsCase 7
8 Experimentation
8.1 Introduction8.2 Defining Experiments8.3 Some Basic Symbols and Notations in Conducting Experiments8.4 Internal and External Validity in Experimentation8.5 Threats to the Internal Validity of the Experiment8.5.1 History8.5.2 Maturation8.5.3 Testing8.5.4 Instrumentation8.5.5 Statistical Regression8.5.6 Selection Bias8.5.7 Mortality8.6 Threats to the External Validity of the Experiment8.6.1 Reactive Effect8.6.2 Interaction Bias8.6.3 Multiple Treatment Effect8.6.4 Non-Representativeness of the Samples8.7 Ways to Control Extraneous Variables8.7.1 Randomization8.7.2 Matching8.7.3 Statistical Control8.7.4 Design Control8.8 Laboratory Versus Field Experiment8.9 Experimental Designs and their Classification8.9.1 Pre-Experimental Design8.9.2 True-Experimental Design8.9.3 Quasi-Experimental Designs8.9.4 Statistical Experimental Designs8.10 Limitations of Experimentation8.10.1 Time8.10.2 Cost8.10.3 Secrecy8.10.4 Implementation Problems8.11 Test Marketing8.11.1 Standard Test Market8.11.2 Controlled Test Market8.11.3 Electronic Test Market8.11.4 Simulated Test MarketSummaryKey TermsDiscussion QuestionsCase 8
9 Fieldwork and Data Preparation
9.1 Introduction9.2 Fieldwork Process9.2.1 Job Analysis, Job Description, and Job Specification9.2.2 Selecting a Fieldworker9.2.3 Providing Training to Fieldworkers9.2.4 Briefing and Sending Fieldworkers to Field for Data Collection9.2.5 Supervising the Fieldwork9.2.6 Debriefing and Fieldwork Validation9.2.7 Evaluating and Terminating the Fieldwork9.3 Data Preparation9.4 Data Preparation Process9.4.1 Preliminary Questionnaire Screening9.4.2 Editing9.4.3 Coding9.4.4 Data Entry9.5 Data AnalysisSummaryKey TermsDiscussion QuestionsCase 9
Part IV Data Analysis and Presentation
10 Statistical Inference: Hypothesis Testing for Single Populations
10.1 Introduction10.2 Introduction to Hypothesis Testing10.3 Hypothesis Testing Procedure10.4 Two-Tailed and One-Tailed Tests of Hypothesis10.4.1 Two-Tailed Test of Hypothesis10.4.2 One-Tailed Test of Hypothesis10.5 Type I and Type II Errors10.6 Hypothesis Testing for a Single Population Mean Using the z Statistic10.6.1 p-Value Approach for Hypothesis Testing10.6.2 Critical Value Approach for Hypothesis Testing10.6.3 Using MS Excel for Hypothesis Testing with the z Statistic10.6.4 Using Minitab for Hypothesis Testing with the z Statistic10.7 Hypothesis Testing for a Single Population Mean Using the t Statistic (Case of a Small Random Sample When n < 30)10.7.1 Using Minitab for Hypothesis Testing for Single Population Mean Using the t Statistic (Case of a Small Random Sample, n < 30)10.7.2 Using SPSS for Hypothesis Testing for Single Population Mean Using the t Statistic (Case of a Small Random Sample, n < 30)10.8 Hypothesis Testing for a Population Proportion10.8.1 Using Minitab for Hypothesis Testing for a Population ProportionSummaryKey TermsDiscussion QuestionsNumerical ProblemsFormulasCase 10
11 Statistical Inference: Hypothesis Testing for Two Populations
11.1 Introduction11.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic11.2.1 Using MS Excel for Hypothesis Testing with the z Statistic for the Difference in Means of Two Populations11.3 Hypothesis Testing for the Difference Between Two Population Means Using the t Statistic (Case of a Small Random Sample, n1, n2 < 30, when Population Standard Deviation is Unknown)11.3.1 Using MS Excel for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic11.3.2 Using Minitab for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic11.3.3 Using SPSS for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic11.4 Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)11.4.1 Using MS Excel for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)11.4.2 Using Minitab for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)11.4.3 Using SPSS for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)11.5 Hypothesis Testing for the Difference in Two Population Proportions11.5.1 Using Minitab for Hypothesis Testing About the Difference in Two Population Proportions11.6 Hypothesis Testing About Two Population Variances (F Distribution)11.6.1 F Distribution11.6.2 Using MS Excel for Hypothesis Testing About Two Population Variances ( F Distribution)11.6.3 Using Minitab for Hypothesis Testing About Two Population Variances ( F Distribution)SummaryKey TermsDiscussion QuestionsNumerical ProblemsFormulasCase 11
12 Analysis of Variance and Experimental Designs
12.1 Introduction 12.2 Introduction to Experimental Designs12.3 Analysis of Variance12.4 Completely Randomized Design (One-way ANOVA)12.4.1 Steps in Calculating SST (Total Sum of Squares) and Mean Squares in One-Way Analysis of Variance12.4.2 Applying the F-Test Statistic12.4.3 The ANOVA Summary Table12.4.4 Using MS Excel for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations12.4.5 Using Minitab for Hypothesis Testing with the F Statistic for the Difference in the Means of More Than Two Populations12.4.6 Using SPSS for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations12.5 Randomized Block Design 12.5.1 Null and Alternative Hypotheses in a Randomized Block Design12.5.2 Applying the F-Test Statistic12.5.3 ANOVA Summary Table for Two-Way Classification12.5.4 Using MS Excel for Hypothesis Testing with the F Statistic in a Randomized Block Design12.5.5 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design12.6 Factorial Design (Two-way ANOVA)12.6.1 Null and Alternative Hypotheses in a Factorial Design12.6.2 Formulas for Calculating SST (Total Sum of Squares) and Mean Squares in a Factorial Design (Two-Way Analysis of Variance)12.6.3 Applying the F-Test Statistic12.6.4 ANOVA Summary Table for Two-Way ANOVA12.6.5 Using MS Excel for Hypothesis Testing with the F Statistic in a Factorial Design12.6.6 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design12.7 Post Hoc Comparisons in ANOVA12.7.1 Using SPSS for Post Hoc Comparision12.8 Three-Way ANOVA12.9 Multivariate Analysis of Variance (MANOVA): A One-way Case12.9.1 Using SPSS for MANOVASummaryKey TermsDiscussion QuestionsNumerical ProblemsFormulasCase 12
13 Hypothesis Testing for Categorical Data (Chi-Square Test)
13.1 Introduction 13.2 Defining χ2-test Statistic13.2.1 Conditions for Applying the χ2 Test13.3 χ2 Goodness-of-Fit Test13.3.1 Using MS Excel for Hypothesis Testing with χ2 Statistic for Goodness-of-Fit Test13.3.2 Hypothesis Testing for a Population Proportion Using χ2 Goodness-of-Fit Test as an Alternative Technique to the z-Test13.4 χ2 Test of Independence: Two-way Contingency Analysis13.4.1 Using Minitab for Hypothesis Testing with χ2 Statistic for Test of Independence13.5 χ2 Test for Population Variance13.6 χ2 Test of Homogeneity 389SummaryKey TermsDiscussion QuestionsNumerical ProblemsFormulasCase 13
14 Non-Parametric Statistics
14.1 Introduction14.2 Runs Test for Randomness of Data14.2.1 Small-Sample Runs Test14.2.2 Using Minitab for Small-Sample Runs Test14.2.3 Using SPSS for Small-Sample Runs Tests14.2.4 Large-Sample Runs Test14.3 Mann–Whitney U Test14.3.1 Small-Sample U Test14.3.2 Using Minitab for the Mann–Whitney U Test14.3.3 Using Minitab for Ranking14.3.4 Using SPSS for the Mann–Whitney U Test14.3.5 Using SPSS for Ranking14.3.6 U Test for Large Samples14.4 Wilcoxon Matched-Pairs Signed Rank Test14.4.1 Wilcoxon Test for Small Samples (n ≤ 15)14.4.2 Using Minitab for the Wilcoxon Test14.4.3 Using SPSS for the Wilcoxon Test14.4.4 Wilcoxon Test for Large Samples (n > 15)14.5 Kruskal–Wallis Test14.5.1 Using Minitab for the Kruskal–Wallis Test14.5.2 Using SPSS for the Kruskal–Wallis Test14.6 Friedman Test14.6.1 Using Minitab for the Friedman Test14.6.2 Using SPSS for the Friedman Test14.7 Spearman’s Rank Correlation14.7.1 Using SPSS for Spearman’s Rank CorrelationSummaryKey TermsDiscussion QuestionsFormulasNumerical ProblemsCase 14
15 Correlation and Simple Linear Regression Analysis
15.1 Measures of Association15.1.1 Correlation15.1.2 Karl Pearson’s Coefficient of Correlation15.1.3 Using MS Excel for Computing Correlation Coefficient15.1.4 Using Minitab for Computing Correlation Coefficient15.1.5 Using SPSS for Computing Correlation Coefficient15.2 Introduction to Simple Linear Regression15.3 Determining the Equation of a Regression Line15.4 Using MS Excel for Simple Linear Regression15.5 Using Minitab for Simple Linear Regression15.6 Using SPSS for Simple Linear Regression15.7 Measures of Variation15.7.1 Coefficient of Determination15.7.2 Standard Error of the Estimate15.8 Using Residual Analysis to Test the Assumptions of Regression15.8.1 Linearity of the Regression Model15.8.2 Constant Error Variance (Homoscedasticity)15.8.3 Independence of Error15.8.4 Normality of Error15.9 Measuring Autocorrelation: The Durbin–Watson Statistic15.10 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall Model15.10.1 t Test for the Slope of the Regression Line15.10.2 Testing the Overall Model15.10.3 Estimate of Confidence Interval for the Population Slope ( β1 )15.10.4 Statistical Inference about Correlation Coefficient of the Regression Model15.10.5 Using SPSS for Calculating Statistical Significant Correlation Coefficient for Example 15.215.10.6 Using Minitab for Calculating Statistical Significant Correlation Coefficient for Example 15.2SummaryKey TermsDiscussion QuestionsNumerical ProblemsFormulasCase 15
16 Multiple Regression Analysis
16.1 Introduction16.2 The Multiple Regression Model16.3 Multiple Regression Model with Two Independent Variables16.4 Determination of Coefficient of Multiple Determination (R 2), Adjusted R 2, and Standard Error of the Estimate16.4.1 Determination of Coefficient of Multiple Determination (R2)16.4.2 Adjusted R216.4.3 Standard Error of the Estimate16.5 Residual Analysis for the Multiple Regression Model16.5.1 Linearity of the Regression Model16.5.2 Constant Error Variance (Homoscedasticity)16.5.3 Independence of Error16.5.4 Normality of Error16.6 Statistical Significance Test for the Regression Model and the Coefficient of Regression16.6.1 Testing the Statistical Significance of the Overall Regression Model16.6.2 t-Test for Testing the Statistical Significance of Regression Coefficients 16.7 Testing Portions of the Multiple Regression Model16.8 Coefficient of Partial Determination16.9 Non-linear Regression Model: The Quadratic Regression Model16.9.1 Using MS Excel for the Quadratic Regression Model16.9.2 Using Minitab for the Quadratic Regression Model16.9.3 Using SPSS for the Quadratic Regression Model16.10 A Case When the Quadratic Regression Model is a Better Alternative to the Simple Regression Model16.11 Testing the Statistical Significance of the Overall Quadratic Regression Model16.11.1 Testing the Quadratic Effect of a Quadratic Regression Model16.12 Indicator (Dummy Variable Model)16.12.1 Using MS Excel for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable) 16.12.2 Using Minitab for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable) 16.12.3 Using SPSS for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable) 16.12.4 Using MS Excel for Interaction16.12.5 Using Minitab for Interaction16.12.6 Using SPSS for Interaction16.13 Model Transformation in Regression Models16.13.1 The Square Root Transformation16.13.2 Using MS Excel for Square Root Transformation16.13.3 Using Minitab for Square Root Transformation16.13.4 Using SPSS for Square Root Transformation16.13.5 Logarithm Transformation16.13.6 Using MS Excel for Log Transformation16.13.7 Using Minitab for Log Transformation16.13.8 Using SPSS for Log Transformation16.14 Collinearity16.15 Model Building16.15.1 Search Procedure16.15.2 All Possible Regressions16.15.3 Stepwise Regression16.15.4 Using Minitab for Stepwise Regression16.15.5 Using SPSS for Stepwise Regression16.15.6 Forward Selection16.15.7 Using Minitab for Forward Selection Regression16.15.8 Using SPSS for Forward Selection Regression16.15.9 Backward Elimination16.15.10 Using Minitab for Backward Elimination Regression16.15.11 Using SPSS for Backward Elimination RegressionSummaryKey TermsDiscussion QuestionsNumerical ProblemsFormulasCase 16
17 Discriminant Analysis and Logistic Regression Analysis
17.1 Discriminant Analysis17.1.1 Introduction17.1.2 Objectives of Discriminant Analysis17.1.3 Discriminant Analysis Model17.1.4 Some Statistics Associated with Discriminant Analysis17.1.5 Steps in Conducting Discriminant Analysis17.1.6 Using SPSS for Discriminant Analysis17.1.7 Using Minitab for Discriminant Analysis17.2 Multiple Discriminant Analysis17.2.1 Problem Formulation17.2.2 Computing Discriminant Function Coefficient17.2.3 Testing Statistical Significance of the Discriminant Function17.2.4 Result (Generally Obtained Through Statistical Software) Interpretation17.2.5 Concluding Comment by Performing Classification and Validation of Discriminant Analysis17.3 Logistic (or Logit) Regression Model17.3.1 Steps in Conducting Logistic Regression17.3.2 Using SPSS for Logistic Regression17.3.3 Using Minitab for Logistic RegressionSummaryKey TermsDiscussion QuestionsCase 17
18 Factor Analysis and Cluster Analysis
18.1 Factor Analysis18.1.1 Introduction18.1.2 Basic Concept of Using the Factor Analysis18.1.3 Factor Analysis Model18.1.4 Some Basic Terms Used in the Factor Analysis18.1.5 Process of Conducting the Factor Analysis18.1.6 Using Minitab for the Factor Analysis18.1.7 Using the SPSS for the Factor Analysis18.2 Cluster Analysis18.2.1 Introduction18.2.2 Basic Concept of Using the Cluster Analysis18.2.3 Some Basic Terms Used in the Cluster Analysis18.2.4 Process of Conducting the Cluster Analysis18.2.5 Non-Hierarchical Clustering18.2.6 Using the SPSS for Hierarchical Cluster Analysis18.2.7 Using the SPSS for Non-Hierarchical Cluster AnalysisSummaryKey TermsDiscussion QuestionsCase 18
19 Conjoint Analysis, Multidimensional Scaling and Correspondence Analysis
19.1 Conjoint Analysis19.1.1 Introduction19.1.2 Concept of Performing Conjoint Analysis19.1.3 Steps in Conducting Conjoint Analysis19.1.4 Assumptions and Limitations of Conjoint Analysis19.1.5 Using the SPSS for Conjoint Analysis19.2 Multidimensional Scaling19.2.1 Introduction19.2.2 Some Basic Terms Used in Multidimensional Scaling19.2.3 The Process of Conducting Multidimensional Scaling19.2.4 Using SPSS for Multidimensional Scaling19.3 Correspondence Analysis19.3.1 Introduction 19.3.2 Process of Conducting Correspondence Analysis19.3.3 Using SPSS for Correspondence AnalysisSummaryKey TermsDiscussion QuestionsCase 19
20 Confirmatory Factor Analysis, Structural Equation Modeling and Path Analysis
20.1 Introduction20.2 Establishing a Difference Between Exploratory Factor Analysis and Confirmatory Factor Analysis 20.2.1 Steps in Conducting Confirmatory Factor Analysis20.3 Development of Structural Equation Model20.4 Path Analysis20.5 Using AMOS for Structural Equation ModelingSummaryKey TermsDiscussion QuestionsCase 20
Part V Result Presentation
21 Presentation of Result: Report Writing
21.1 Introduction21.2 Organization of the Written Report21.2.1 Title Page21.2.2 Letter of Transmittal21.2.3 Letter of Authorization21.2.4 Table of Contents21.2.5 Executive Summary21.2.6 Body21.2.7 Appendix21.3 Tabular Presentation of Data21.4 Graphical Presentation of Data21.4.1 Bar Chart21.4.2 Pie Chart21.4.3 Histogram21.4.4 Frequency Polygon21.4.5 Ogive21.4.6 Scatter Plot21.5 Oral PresentationSummaryKey TermsDiscussion QuestionsCase 21
Appendices
Glossary

Content preview from Business Research Methods, 2nd Edition

Part IV

Data Analysis and Presentation

Chapter 10 Statistical Inference: Hypothesis testing for Single populations

Chapter 11 Statistical Inference: Hypothesis testing for two Populations

Chapter 12 Analysis of Variance and Experimental Design

Chapter 13 Hypothesis Testing for Categorical Data (Chi-Square Test)

Chapter 14 Non-parametric Statistics

Chapter 15 Correlation and Simple Linear Regression Analysis

Chapter 16 Multiple Regression Analysis

Chapter 17 Discriminant Analysis and Logistic Regression Analysis

Chapter 18 Factor Analysis and Cluster Analysis

Chapter 19 Conjoint Analysis, Multidimensional Scaling and Correspondence Analysis

Chapter 20 Confirmatory Factor Analysis, Structural Equation Modeling and Path Analysis

Chapter 10

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