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Full Description
Research Methodology: Best Practices for Rigorous, Credible, and Impactful Research takes a 360-degree view of understanding and doing research, helping readers become expert researchers, reviewers, and consumers of research. Renowned author, journal editor, and researcher Herman Aguinis distills the vast body of work on methodological best practices into a singular experience. Each of the 16 chapters thoroughly explains a different aspect of methodology step by step, from choosing useful and compelling research topics to reporting results accurately and credibly. Researchers at all career stages will find this text helpful to structure and conduct high-impact empirical research aimed at producing a thesis, dissertation, or journal publication. Research consumers will find instruction on how to evaluate the rigor and credibility of research conducted by others. Instructors will find the book's modular approach refreshing by assigning students the most relevant topics—from checklists of best practices to an in-depth treatment of a methodology. Filled with "how-to's" and "dos and don'ts", figures, hands-on exercises, and "Methods in Practice" boxes that summarize and apply best practices, this book is a must-have for anyone interested in producing or reading research.
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Contents
Chapter 1: How to Understand the Current Methodological Landscape
Importance of Research Methods
Research Methods: A Brief Historical Journey
Mapping Quantitative Research Methods
Mapping Qualitative Research Methods: Research Design, Measurement, and Data Analysis
Research on Research Methods: Best in Kind
Research Methods: The Present and the Future
Chapter 2: How to Conduct Ethical Research
Importance of Ethical Research
Research Ethics: Definition and Two Approaches
How to Plan Ethical Research
How to Execute Ethical Research
Special Considerations for Conducting Ethical Research in Field Settings
How to Report Research Results Ethically
How to Enforce Ethical Research
How to Resolve Ethics Complaints
Chapter 3: How to Make Contributions to Theory
Importance of Contributions to Theory
Why Theories Should Be Useful and for Whom
Important Distinctions in Discerning Good Theory
When Is New Theory Needed?
Pruning the Theoretical Landscape
How to Improve Theory Using Theory Elaboration
How to Conduct a Theory Elaboration Study
How to Bridge Levels of Analysis to Improve Theory
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
How to Balance the Internal Versus External Validity Tradeoff
How to Choose Control Variables
Illustrations of Best-Practice Recommendations Implementation
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
How to Make Sure Your Study Participants Are Who You Think They Are
Implementing Best Practices in Merging Your Data with Data from Existing Databases
How to Evaluate the Appropriateness of Your Study's Response Rate When Using Surveys
How to Deal with Publication Bias and the File Drawer Problem
Chapter 6: How to Measure Your Variables
Importance of Measurement
Definition of Measurement and Benefits of Good Measurement
Scales of Measurement
How to Create a New Measure
How to Improve Existing Measures
Chapter 7: How to Design and Conduct Experimental Research
Importance of Experiments
How to Conduct Vignette Experiments
How to Conduct Experiments Online
How to Conduct Experiments Using Virtual Reality
How to Conduct Thought Experiments
Chapter 8: How to Prepare Your Data for Analysis
Importance of Cleaning Up Your Data
How to Handle Missing Data and Data Transformations
How to Handle Outliers
How to Not Clean Up Your Hypotheses by Hypothesizing After Results are Known (HARKing)
Chapter 9: How to Conduct Quantitative Analysis, Part I: Regression-based Approaches
Importance of Regression Analysis
Moderation
Mediation
Analysis of Covariance (ANCOVA)
Using Regression to Understand Nonlinear Effects: The Too-Much-of-A-Good-Thing (TMGT) Effect
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
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
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
Meta-regression
Meta-analytic Structural Equation Modeling
Is There a Need for an Updated Meta-Analysis?
Chapter 12: How to Conduct Quantitative Analysis, Part IV: Advanced Techniques
Importance of Advanced Quantitative Techniques
Market Basket Analysis
Experience Sampling Methodology
Bayesian Analysis
Chapter 13: How to Conduct Qualitative Research
Importance of Qualitative Research
Interviews with Key Informants
Computer-Aided Text Analysis (CATA)
Methodological Literature Reviews
Mixed Methods
How to Improve Transparency and Replicability in Your Qualitative Research
Chapter 14: How to Report Your Results
Importance of How You Report Your Results
How to Report p-values
Why You Should Report Effect Size Estimates
How Your Effect Sizes Compare to Those in Social and Behavioral Research
How to Interpret the Meaning of Effect Sizes
How to Report Statistical Results: The Case of ?2 and ?2
How to Report Your Study's Limitations and Future Research Directions
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
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
How to Broaden the Meaning and Measurement of Impact
Actions that University Administrators, Researchers, and Educators Can Take to Enhance Scholarly Impact
How to Create Your Own Personal Impact Development Plan
Enhancing Your Impact: It's a Journey
