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• List of Figures
• List of Tables
• Preface
• Acknowledgments
• About the Author
• Chapter 1 How to Understand the Current Methodological Landscape
• Importance of Research Methods
• Research Methods: A Brief Historical Journey
• 1917–1925: Who Are You?
• The Roaring ‘20s, the Depression, and World War II
• The Baby Boom and Beyond: 1946–1969
• 1970–1989: Measurement and Its Discontents
• 1990–Present: From One to Many
• The State of Social and Behavioral Science Methods
• Mapping Quantitative Research Methods
• Quantitative Topics: Research Design
• Quantitative Topics: Measurement
• Quantitative Topics: Data Analysis
• Mapping Qualitative Research Methods: Research Design, Measurement, and Data Analysis
• Research on Research Methods: Best in Kind
• Research Methods: The Present and the Future
• Which Are the Topical Areas That Have Been Consistently Popular over Time?
• Which Are the Methodological Areas That Are Becoming Increasingly Popular?
• What Will the Future Look Like?
• Constructive While Replication
• Embracing Methods That Allow Us to Study the Exceptionally Good and Bad
• Not Allowing Misguided or Incomplete Analyses to Survive the Review Process
• Shifting Emphasis Toward Research Design and Measurement
• Increased Theory Specificity
• Discussion Questions
• Key Terms
• Notes
• Chapter 2 How to Conduct Ethical Research
• Importance of Ethical Research
• Research Ethics: Definition and Two Approaches
• How to Plan Ethical Research
• Recruiting and Selecting Research Participants
• University Participant Pools
• Volunteers
• How to Execute Ethical Research
• Right to Informed Consent and Informed Consent Form
• Description of the Research
• Ability to Decline or Withdraw Participation Without Negative Consequences
• Information on Conditions That Might Influence Willingness to Participate
• Right to Privacy
• Right to Confidentiality and Anonymity
• Right to Protection from Deception
• Right to Debriefing
• Dehoaxing
• Desensitizing
• Special Considerations for Conducting Ethical Research in Field Settings
• How to Report Research Results Ethically
• Misrepresentation of Research Results
• Censoring
• Plagiarism and Authorship (Mis)Credit
• Data Sharing
• How to Enforce Ethical Research
• Definition and Prevalence of Research Misconduct
• Preventing Misconduct
• Familiarize Yourself with Codes of Ethics
• Obtain IRB Approval
• Participate in Replication Projects
• Secure Feedback from an Expert in Your Area of Research
• How to Resolve Ethics Complaints
• Discussion Questions
• Key Terms
• Notes
• Chapter 3 How to Make Contributions to Theory
• Importance of Contributions to Theory
• Why Theories Should Be Useful and for Whom
• Criticality of Theory for Leaders and Managers
• Criticality of Theory for Researchers
• Criticality of Theory for Students
• Criticality of Theory for Policymakers
• Important Distinctions in Discerning Good Theory
• Accurate Explanation
• Insightful Prediction
• Good Theory Versus Potentially Good Theory
• Good Unit Theory and Good Programmatic Theory
• When Is New Theory Needed?
• The Unit Theory Perspective
• Practical Applications
• Improved Predictions
• Relevant Questions
• The Programmatic Theory Perspective
• Parsimony
• Coherence
• Pruning the Theoretical Landscape
• Theory Falsification
• How Does Falsification Work with Theory?
• Falsification Versus Failed Confirmation
• How to Falsify
• Theoretical Precision
• Why Is Theoretical Precision Important?
• How to Be More Precise
• How to Improve Theory Using Theory Elaboration
• Approaches to Theorizing: Similarities and Differences
• Theory Generation
• Theory Testing
• Theory Elaboration
• Decision Process for Choosing Whether to Adopt a Theory Elaboration Perspective
• Question 1: Is There an Existing Theory and Access to Data That May Be Used to Explain or Offer Insight into the Focal Phenomenon?
• Question 2: Is the Explanation Provided by the Existing Theory Controversial, Ambiguous, or Inadequate?
• Question 3: Is There Potential to Collect Additional Data to Integrate with Existing Theory to Develop, Enhance, or Extend Existing Theory?
• How to Conduct a Theory Elaboration Study
• Five Ways to Advance Theory
• Implementation Approaches and Tactics
• Contrasting
• Construct Specification
• Structuring
• Relation Between Theory Elaboration and Other Ways of Theorizing
• Theory Elaboration and Grounded Theory
• Theory Elaboration and Abductive Reasoning
• How to Bridge Levels of Analysis to Improve Theory
• Examples of Micro-Macro Gaps in Need of Bridging: Useful, Specific, and Actionable Suggestions for Future Research
• Corporate Governance
• Entrepreneurship
• Applied Psychology and Human Resources Management
• Management
• Key Questions Related to Methodological Issues to Help Guide Bridging Efforts
• Discussion Questions
• Key Terms
• Notes
• Chapter 4 How to Plan Your Research
• Importance of Research Planning
• How to Address an Important Issue and Conduct Research with a Practical End in Mind
• How to Strengthen Causal Inferences
• Counterfactual Thinking
• Matching
• Control Variables
• Instrumental Variables
• Quasi Experiments
• How to Balance the Internal Versus External Validity Tradeoff
• Experience Sampling Methodology
• eLancing
• Virtual Reality
• How to Choose Control Variables
• Explain Why and How Controls Relate to Focal Variables
• Provide Theoretical Justifications for Each Control Variable
• Required Steps in the Process of Statistical Control
• Illustrations of Best-Practice Recommendations Implementation
• Discussion Questions
• Key Terms
• Notes
• Chapter 5 How to Collect Your Sample Using Surveys and Other Means
• Importance of Sample Size
• Why Large Sample Size Is Not Always the Best Option
• Unwritten Rule #1: Determine Required Sample Size by Conducting a Power Analysis Using Cohen’s Definitions of Small, Medium, and Large Effect Size
• What Should You Keep in Mind About Unwritten Rule #1?
• Unwritten Rule #2: Increase the a Priori Type I Error Rate to .10 Because of Your Small Sample Size
• What Should You Keep in Mind About Unwritten Rule #2?
• How to Make Sure Your Study Participants Are Who You Think They Are
• Best-practice Recommendations for Improving the Validity of Web-based Data Collection
• How to Compute IP Threat Scores and Use Internet Service Provider (ISP) Designation to Identify Targeted and Non-targeted Participants
• Implementing Best Practices in Merging Your Data with Data from Existing Databases
• Benefits Regarding Research Design and Measurement
• Benefits Regarding Control Variables
• Benefits Regarding Missing Data Management
• Benefits Regarding Outlier Management
• Selecting Archival Databases and Variables
• How to Evaluate the Appropriateness of Your Study’s Response Rate When Using Surveys
• Importance of Response Rates and Their Trend Over Time
• Functional Response Rate and Dysfunctional Response Rate
• Response Rate Validity Framework
• Researcher-Participant Relationship
• Participant Qualifications
• Participant Motivation
• Survey Length and Complexity
• Number of Times the Survey Is Administered
• Cultural and National Context
• How to Deal with Publication Bias and the File Drawer Problem
• File Drawer Problem
• Recommendations to Address Publication Bias and the File Drawer Problem
• Discussion Questions
• Key Terms
• Notes
• Chapter 6 How to Measure Your Variables
• Importance of Measurement
• Definition of Measurement and Benefits of Good Measurement
• Scales of Measurement
• Nominal Scales
• Ordinal Scales
• Interval Scales
• Ratio Scales
• How to Create a New Measure
• Determining a Measure’s Purpose
• Defining the Attribute
• Developing a Measurement Plan
• Writing Items
• Conducting a Pilot Study and Item Analysis
• Distractor Analysis
• Item Difficulty
• Item Discrimination
• Implementing Item Response Theory (IRT)
• Selecting Items
• Establishing Norms
• Estimating Reliability
• Methods for Estimating Reliability
• Interpreting Reliability and the Standard Error of Measurement
• Improving Reliability
• Gathering Validity Evidence
• Content-related Evidence
• Criterion-related Evidence
• Construct-related Evidence
• Improving Validity
• How to Improve Existing Measures
• How to Improve Construct Validity
• How to Improve Measurement Properties
• Discussion Questions
• Key Terms
• Notes
• Chapter 7 How to Design and Conduct Experimental Research
• Importance of Experiments
• How to Conduct Vignette Experiments
• Paper People Studies
• Policy Capturing and Conjoint Analysis Studies
• Planning an EVM Study
• Decision Point #1: Deciding Whether EVM Is a Suitable Approach
• Decision Point #2: Choosing the Type of EVM
• Decision Point #3: Choosing the Type of Research Design
• Decision Point #4: Choosing the Level of Immersion
• Decision Point #5: Specifying the Number and Levels of the Manipulated Factors
• Decision Point #6: Choosing the Number of Vignettes
• Implementing an EVM Study
• Decision Point #7: Specifying the Sample and Number of Participants
• Decision Point #8: Choosing the Setting and Timing for Administration
• Decision Point #9: Choosing the Best Method for Analyzing the Data
• Reporting Results of an EVM Study
• Decision Point #10: Choosing How Transparent to Be in the Final Presentation of Methodology and Results
• How to Conduct Experiments Online
• Benefits of Online Experiments
• Overcoming the Lack of Generalizability Challenge
• Overcoming the Omitted Measurement of an Important Variable Challenge
• Overcoming the Less-than-ideal Operationalization of Construct Challenge
• Overcoming the Lack of Confidence Regarding Causality Challenge in Non-experimental Research
• Overcoming Additional Challenges: Participant Bias and Selective Survival
• Benefits of Using Amazon’s Mechanical Turk (MTurk)
• Large and Diverse Participant Pool
• Ease of Access and Speed of Data Collection
• Reasonable Cost
• Flexibility Regarding Research Design Choice
• Threats to Consider when Conducting Research Using Amazon’s Mechanical Turk (MTurk)
• MTurker Inattention
• Self-selection Bias
• High Attrition Rates
• Inconsistent English Language Fluency
• Vulnerability to Web Robots (or “bots”)
• MTurker Social Desirability Bias
• Best-practice Recommendations for Using Amazon’s Mechanical Turk (MTurk)
• Planning Stage
• Implementation Stage
• Reporting Stage
• How to Conduct Experiments Using Virtual Reality
• Benefits of Using Immersive VR Technology
• Potential Pitfalls of Using Immersive VR Technology
• How to Conduct Thought Experiments
• Unique Defining Characteristics of Thought Experiments
• Taxonomy of Thought Experiments
• Type I: Early Theory Stage and Theory Confirmation Purpose
• Type II: Early Theory Stage and Theory Disconfirmation Purpose
• Type III: Late Theory Stage and Theory Confirmation Purpose
• Type IV: Late Theory Stage and Theory Disconfirmation Purpose
• Thought Experiments: Best-practice Recommendations
• Deciding Whether to Conduct a Thought Experiment and Which Type
• Planning a Thought Experiment: Theory Considerations
• Executing a Thought Experiment: Research Design Considerations
• Reporting Results of a Thought Experiment
• Discussing Implications of a Thought Experiment
• Discussion Questions
• Key Terms
• Notes
• Chapter 8 How to Prepare Your Data for Analysis
• Importance of Cleaning Up Your Data
• How to Handle Missing Data and Data Transformations
• Missing Data
• Data Transformations
• How to Handle Outliers
• How to Identify Outliers
• Making Decisions on How to Define, Identify, and Handle Outliers
• Error Outliers
• Defining Error Outliers
• Identifying Error Outliers
• Handling Error Outliers
• Interesting Outliers
• Defining Interesting Outliers
• Identifying Interesting Outliers
• Handling Interesting Outliers
• Influential Outliers
• Regression
• Structural Equation Modeling
• Multilevel Modeling
• How to Not Clean Up Your Hypotheses by Hypothesizing After Results are Known (HARKing)
• Epistemological Background of HARKing
• HARKing Mechanisms
• Prevalence of and Motivation for HARKing
• Overfitting, Complexity, and Predictive Precision
• HARKing, Results Visibility, and Effect Size Estimates
• Different Forms of HARKing
• Inadvertent Cherry-picking and Question Trolling: How Multivariate Procedures Produce Comparable Biases
• Detecting and Deterring HARKing
• Strategies for Reducing HARKing
• Discussion Questions
• Key Terms
• Notes
• Chapter 9 How to Conduct Quantitative Analysis, Part I: Regression-based Approaches
• Importance of Regression Analysis
• Moderation
• Moderation: Problems and Solutions
• Problem #1: Lack of Attention to Measurement Error
• Problem #2: Variable Distributions Are Assumed to Include the Full Range of Possible Values
• Problem #3: Unequal Sample Size Across Moderator-based Categories
• Problem #4: Insufficient Statistical Power
• Problem #5: Artificial Dichotomization of Continuous Moderators
• Problem #6: Presumed Effects of Correlations Between Product Term and Its Components
• Problem #7: Interpreting First-order Effects Based on Models Excluding Product Terms
• Problem #8: Graphs That Exaggerate the Size of Interaction Effects
• Mediation
• Mediation: Problems and Solutions
• Problem #1: Requiring a Significant Relation Between the Antecedent and the Outcome
• Problem #2: Disregarding the Magnitude of the Indirect Effect
• Problem #3: Testing the Direct Effect as a Condition for Mediation
• Problem #4: Including a Direct Effect Without Conceptual Justification
• Problem #5: Testing Mediation With Cross-Sectional Data
• Problem #6: Lack of Attention to Measurement Error
• Analysis of Covariance (ANCOVA)
• R Program for Errors-In-Variables (EIV) Regression
• Using Regression to Understand Nonlinear Effects: The Too-Much-of-A-Good-Thing (TMGT) Effect
• A Formalized Meta-theory of the TMGT Effect
• Evidence and Explanation of the TMGT Effect
• What Are the Implications of the TMGT Effect for Your Future Research?
• What Are the Implications for Theory Development?
• What Are the Implications for Theory Testing?
• Statistical Power and Effect Size
• Restriction of Range
• Advanced Meta-analytic Methods
• Advanced Growth Modeling Methods
• Discussion Questions
• Key Terms
• Notes
• Chapter 10 How to Conduct Quantitative Analysis, Part II: Multilevel Modeling
• Importance of Multilevel Modeling
• Fundamentals of Multilevel Modeling
• Visual Representation of Types of Variances Modeled in Multilevel Analysis
• Analytic Representation of Sources of Variance Modeled in Multilevel Analysis
• The Importance of Statistical Power in Multilevel Modeling
• Multilevel Modeling: Step-by-Step Illustration
• Step 1: Null Model
• Step 2: Random Intercept and Fixed Slope Model
• Step 3: Random Intercept and Random Slope Model
• Step 4: Cross-level Interaction Model
• Why You Need to Examine More than Just the Traditional Intraclass Correlation
• Best-practice Recommendations for Conducting and Reporting Multilevel Analysis and Cross-level Interaction Effects
• Pre-Data Collection Recommendations
• Issue #1: What is the Operational Definition of a Cross-level Interaction Effect?
• Issue #2: What is the Statistical Power to Detect an Existing Cross-level Interaction Effect?
• Issue #3: Can Cross-level Interaction Effects Involving a Categorical L2 or Standardized Predictor Be Tested?
• Post Data Collection Recommendations
• Issue #4: What Sources of Potential Variance Should I Examine?
• Issue #5: How Should I Re-scale (i.e., Center) Predictors and Why?
• Issue #6: How Can I Graph a Cross-level Interaction Effect?
• Issue #7: Are Cross-level Interaction Effects Symmetrical?
• Issue #8: How Can I Estimate More than One Cross-level Interaction Effect?
• Issue #9: How Can I Estimate Cross-level Interaction Effects Involving Variables at Three Different Levels of Analysis?
• Issue #10: What is the Practical Significance of the Cross-level Interaction Effect?
• Issue #11: What Information Should Be Reported Based on Multilevel Modeling Analyses?
• Discussion Questions
• Key Terms
• Notes
• Chapter 11 How to Conduct Quantitative Analysis, Part III: Meta-analysis
• Importance of Meta-analysis
• Meta-analysis: Choices, Choices, Choices
• How to Conduct a State-of-the-science Meta-analysis
• Stage 1: Data Collection
• Literature Search and Screening
• Coding of the Primary Studies
• Stage 2: Data Preparation
• Treatment of Multiple Effects Sizes
• Outlier Identification and Management
• Publication Bias
• Stage 3: Data Analysis
• Average Effect Sizes
• Heterogeneity of Effect Sizes
• Moderator (i.e., Interaction-effect) Search
• Stage 4: Reporting
• Transparency and Reproducibility
• Future Research Directions
• Meta-regression
• Meta-regression: Best-practice Recommendations
• Conducting a Meta-regression Study
• Reporting Results of a Meta-regression Study
• Meta-analytic Structural Equation Modeling
• MASEM: Implementation
• Step 1: Specify Variables and Conceptual Models
• Step 2: Meta-analytic Procedures
• Step 3: Structural Equation Modeling
• Step 4: Reporting Procedures
• Is There a Need for an Updated Meta-Analysis?
• Discussion Questions
• Key Terms
• Notes
• Chapter 12 How to Conduct Quantitative Analysis, Part IV: Advanced Techniques
• Importance of Advanced Quantitative Techniques
• Market Basket Analysis
• Advantages of Using Market Basket Analysis
• Advantage 1: MBA Allows for Inductive Theorizing
• Advantage 2: MBA Can Address Contingency Relationships
• Advantage 3: MBA Does Not Rely on Often Untenable Assumptions
• Advantage 4: MBA Allows the Use of “Unusable” and “Messy” Data
• Advantage 5: MBA Can Help Build Dynamic Theories
• Advantage 6: MBA Can Be Used to Assess Multilevel Relationships
• Advantage 7: MBA Is Practitioner-friendly
• Steps Involved in Using Market Basket Analysis
• Step 1: Determine the Suitability of MBA
• Step 2: Define the “Transactions”
• Step 3: Collect Data
• Step 4: Check MBA Requirements
• Step 5: Derive Association Rules and Their Strength
• Step 6: Interpret Association Rules
• Experience Sampling Methodology
• Four Major Strengths of ESM
• Strength 1: ESM Captures Dynamic Person-by-Situation Interactions Over Time
• Strength 2: ESM Enhances Ecological Validity
• Strength 3: ESM Allows for an Examination of Between- and Within-Person Variability
• Strength 4: ESM Mitigates Memory (i.e., Retrospective) Biases
• Designing and Implementing ESM
• Sample Size and the ESM Survey
• Scheduling and Signaling Devices
• ESM and Cell Phones
• Participant Recruitment and Orientation
• Data Structure and Analysis
• Bayesian Analysis
• Bayesian Multiple Linear Regression
• Establishing the Prior Distribution
• Computing the Posterior Distribution
• Accepting the Null Value
• Summary of Rich Information Provided by Bayesian Analysis
• Synthesis of Advantages of Bayesian Data Analysis
• Advantage 1: Use of Prior Knowledge
• Advantage 2: Joint Distribution of Parameters
• Advantage 3: Assessment of Null Hypothesis
• Advantage 4: Ability to Test Complex Models
• Advantage 5: Unbalanced or Small Sample Sizes
• Advantage 6: Multiple Comparisons
• Advantage 7: Power Analysis and Replication Probability
• Discussion Questions
• Key Terms
• Notes
• Chapter 13 How to Conduct Qualitative Research
• Importance of Qualitative Research
• Interviews with Key Informants
• Questions and Challenges to Address When Conducting Key Informant (KI) Interviews
• Research Design
• Data Collection
• Reporting of Results
• Computer-Aided Text Analysis (CATA)
• Best-practice Recommendations for Improving the Accuracy of CATA
• Transient Error
• Specific Factor Error
• Algorithm Error
• Methodological Literature Reviews
• Best-practice Recommendations and Checklist
• Mixed Methods
• How to Improve Transparency and Replicability in Your Qualitative Research
• Transparency Criteria in Qualitative Research
• Transparency Criteria and Three Types of Replicability
• Measures and Data Collection
• Recommendations for Enhancing Qualitative Research Transparency
• Kind of Qualitative Method
• Research Setting
• Your Position Along the Insider-Outsider Continuum
• Sampling Procedures
• Relative Importance of the Participants/Cases
• Documenting Interactions with Participants
• Saturation Point
• Unexpected Opportunities, Challenges, and Other Events
• Management of Power Imbalance
• Data Coding and First-Order Codes
• Data Analysis and Second- or Higher-Order Codes
• Data Disclosure
• Discussion Questions
• Key Terms
• Notes
• Chapter 14 How to Report Your Results
• Importance of How You Report Your Results
• How to Report p-values
• Conventional α Values Are Arbitrary and Misleading
• Solution: Use α Values Based on the Relative Seriousness of Type I Versus Type II Error
• The Problem With Reporting Crippled p-values
• Solution: Report Precise p-values
• Why You Should Report Effect Size Estimates
• Confusion Between Statistical Significance and Magnitude of the Effect or Relation
• Solution: Report the Magnitude of the Effect
• How Your Effect Sizes Compare to Those in Social and Behavioral Research
• Context-Specific Effect Size Benchmarks
• How to Interpret the Meaning of Effect Sizes
• Confusion Between Magnitude of the Effect and Practical Significance
• Solution: Report Practical Significance
• How to Report Statistical Results: The Case of χ2 and η2
• Chi-Squared (χ2)
• Eta-Squared (η2)
• How to Report Your Study’s Limitations and Future Research Directions
• Limitations
• Disclosing Limitations
• Describing Limitations
• Future Directions
• Describing Directions for Future Research
• Discussion Questions
• Key Terms
• Notes
• Chapter 15 How to Improve the Transparency, Reproducibility, and Replicability of Your Research
• Importance of Transparency and Replicability of Your Research
• How to Improve Transparency about Your Theory
• How to Improve Transparency about Your Research Design
• How to Improve Transparency about Your Measures
• How to Improve Transparency about Your Data Analysis
• How to Improve Transparency about Your Results
• How to Motivate Authors to Improve Transparency
• General Recommendations
• Open-Science Recommendations
• Changing the Incentive Structure
• Improving Access to Training Resources
• Promoting Shared Values
• Discussion Questions
• Key Terms
• Notes
• Chapter 16 How to Enhance the Impact of Your Research
• Importance of the Impact of Your Research
• Scholarly Impact: Internal and External
• On the Use and Misuse of Journal Lists as an Indicator of Scholarly Impact
• “An A is an A”: The New Bottom Line for Valuing Academic Research
• The Use of Journal Lists in the Social and Behavioral Sciences
• Reasons for the Use of Journal Lists
• Positive and Negative Consequences of the New Bottom Line for Valuing Academic Research
• How to Broaden the Meaning and Measurement of Impact
• Scholarly Impact: A Multidimensional and Multistakeholder Model
• A Pluralist Conceptualization of Scholarly Impact: Multiple Stakeholders and Measures
• Actions that University Administrators, Researchers, and Educators Can Take to Enhance Scholarly Impact
• University Administrators
• Recommendation #1: Align Scholarly Impact Goals with Actions and Resource-Allocation Decisions
• Recommendation #2: Ensure that Performance Management and Reward Systems Are Consistent with Impact Goals
• Recommendation #3: Be Strategic in Selecting a Journal List
• Recommendation #4: Develop a Strong Doctoral Program
• Recommendation #5: Promote Practical Knowledge and Applications
• Current and Future Researchers and Educators
• Recommendation #1: Develop Your Personal Scholarly Impact Plan
• Recommendation #2: Become an Academic Decathlete
• Recommendation #3: Find Ways to Affect Multiple Impact Dimensions Simultaneously
• Recommendation #4: Leverage Social Media to Broaden Impact on External Stakeholders
• How to Create Your Own Personal Impact Development Plan
• Personal Impact Development Plans: Why You Need Them
• Reflective Impact Competencies
• Communicative Impact Competencies
• Behavioral Impact Competencies
• Personal Impact Development Plans: Overall Content
• Personal Impact Development Plans: Developmental Activities
• Personal Impact Development Plans: Role of Context and Profession and University Leaders
• Enhancing Your Impact: It’s a Journey
• Discussion Questions
• Key Terms
• Notes
• Glossary
• Endnotes
• Index