Common Fallacies in Quantitative Research Methodology

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.