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Introduction Introduction
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Contextual Variable Fallacies Contextual Variable Fallacies
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Fallacy 1: Mistaking Mediation for Moderation and Vice Versa Fallacy 1: Mistaking Mediation for Moderation and Vice Versa
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Fallacy 2: Mediation is Tested With the Constituent Paths Rather Than the Product of the Paths Fallacy 2: Mediation is Tested With the Constituent Paths Rather Than the Product of the Paths
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Fallacy 3: Presence of Direct Effect Should be Tested as Prerequisite Evidence of Mediation Fallacy 3: Presence of Direct Effect Should be Tested as Prerequisite Evidence of Mediation
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Fallacy 4: Cross-Sectional Models Can Be Used to Test Mediation Fallacy 4: Cross-Sectional Models Can Be Used to Test Mediation
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Fallacy 5: Moderation Is Confused With Additive Effects of a Multiple Regression Equation Fallacy 5: Moderation Is Confused With Additive Effects of a Multiple Regression Equation
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Fallacy 6: Hierarchically Nested Data Structures Can Be Ignored or Should Be Avoided Fallacy 6: Hierarchically Nested Data Structures Can Be Ignored or Should Be Avoided
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Measurement Error Fallacies Measurement Error Fallacies
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The Myth about Numbers The Myth about Numbers
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Fallacy 1: Summing Across Individual Items to Derive Composite Scores Fallacy 1: Summing Across Individual Items to Derive Composite Scores
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Fallacy 2: Reliability as an Increasing Function of Test Length Fallacy 2: Reliability as an Increasing Function of Test Length
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Fallacy 3: Ignorance of Latent Mixture and Multilevel Structure Fallacy 3: Ignorance of Latent Mixture and Multilevel Structure
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Fallacy 4: Unreliability and Attenuated Effects Fallacy 4: Unreliability and Attenuated Effects
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Missing Data Fallacies Missing Data Fallacies
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Fallacy 1: Modern Missing-Data Treatments Are “Cheating” Fallacy 1: Modern Missing-Data Treatments Are “Cheating”
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Fallacy 2: Missing Data Is Not Something for Which You Can Prepare Fallacy 2: Missing Data Is Not Something for Which You Can Prepare
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Statistical Significance Fallacies Statistical Significance Fallacies
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Fallacy 1: A Significant p-Value Means the Research Hypothesis Is True Fallacy 1: A Significant p-Value Means the Research Hypothesis Is True
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Fallacy 2: Smaller p-Values Indicate a Stronger Effect Fallacy 2: Smaller p-Values Indicate a Stronger Effect
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Fallacy 3: Statistical Significance Indicates Practical Importance Fallacy 3: Statistical Significance Indicates Practical Importance
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Fallacy 4: p-Values Reflect Replicability Fallacy 4: p-Values Reflect Replicability
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Fallacy 5: Lack of Significant Findings Means a Failed Study Fallacy 5: Lack of Significant Findings Means a Failed Study
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Alternatives and Solutions Alternatives and Solutions
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Alternative Paradigms Beyond Null Hypothesis Significance Testing Alternative Paradigms Beyond Null Hypothesis Significance Testing
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Statistical Power Fallacies Statistical Power Fallacies
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Fallacy 1: Statistical Power is a Single, Un ified Concept Fallacy 1: Statistical Power is a Single, Un ified Concept
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Fallacy 2: Statistical Nonsignificance is Evidence for Null Hypothesis Being True Fallacy 2: Statistical Nonsignificance is Evidence for Null Hypothesis Being True
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Fallacy 3: Statistical Nonsignificance Combined With High Retrospective Power Is Evidence for Null Hypothesis Being True Fallacy 3: Statistical Nonsignificance Combined With High Retrospective Power Is Evidence for Null Hypothesis Being True
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Fallacy 4: Lack of Retrospective Power in Rejecting a Null Hypothesis Is Evidence for a True Effect Fallacy 4: Lack of Retrospective Power in Rejecting a Null Hypothesis Is Evidence for a True Effect
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Summary and Recommendations Summary and Recommendations
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Factor Analysis Fallacies Factor Analysis Fallacies
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Fallacy 1: Misuse of Principal Components when Common Factors Is More Appropriate for Factor Extraction in Exploratory Factor Analysis Fallacy 1: Misuse of Principal Components when Common Factors Is More Appropriate for Factor Extraction in Exploratory Factor Analysis
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Fallacy 2: Careless Selection of Number of Factors to Retain in EFA Fallacy 2: Careless Selection of Number of Factors to Retain in EFA
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Fallacy 3: Default Use of Orthogonal Rotation in EFA Fallacy 3: Default Use of Orthogonal Rotation in EFA
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Fallacy 4: Using Confirmatory Factor Analysis to Confirm Analysis Performed with Exploratory Factor Analysis Fallacy 4: Using Confirmatory Factor Analysis to Confirm Analysis Performed with Exploratory Factor Analysis
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Other Issues in Factor Analysis Other Issues in Factor Analysis
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Summary Summary
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Concluding Remarks and Summary Checklist Concluding Remarks and Summary Checklist
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Author Note Author Note
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References References
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31 Common Fallacies in Quantitative Research Methodology
Get accessLihshing Leigh Wang, Educational Studies Program, University of Cincinnati, Cincinnati, OH
Amber S. Watts, Center for Research Methods and Data Analysis and Lifespan Institute, Gerontology Center, University of Kansas, Lawrence, KS
Rawni A. Anderson, Center for Research Methods and Data Analysis, University of Kansas, Lawrence, KS
Todd D. Little, Texas Tech University, Lubbock, Texas
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Published:01 October 2013
Cite
Abstract
Since the inception of scientific revolutions, quantitative research methodology has dominated the research literatures in many disciplines. Despite its long tradition in evidence-based research and practice, many fallacies and misconceptions continue to infiltrate the ways quantitative researchers conceive, collect, analyze, and interpret data. This chapter outlines 16 common fallacies and examines in depth 6 of those that are most consequential and prevalent in published quantitative research. The six major fallacies include Contextual Variable Fallacies, Measurement Error Fallacies, Missing Data Fallacies, Significance Testing Fallacies, Statistical Power Fallacies, and Factor Analysis Fallacies. These fallacies span the entire quantitative research process—from research design, sampling, and instrumentation to statistical analysis and interpretation. By drawing implications from recent advances in quantitative methodological research, this chapter examines the theoretical frameworks of those fallacies, traces their origins and developments in applied research, and provides recommendations to address the challenge of alternative solutions. We conclude with a checklist for quantitative researchers to guard against committing those and other common fallacies. Directions for future research in advancing quantitative methodology and recommendations for strategies to correct fallacious practices are also discussed.
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