Chapter 3 Further Reading
Foundational Methodological Papers
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.
This landmark paper demonstrated through simulation that standard research practices — collecting data until significance is reached, reporting only significant outcomes from multiple tests — dramatically inflate false-positive rates in psychology. Required reading for anyone who wants to understand why the replication crisis happened. Readable without specialized statistical training.
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716.
The paper behind the "39% replication rate" finding. Describes the methodology for the large-scale replication effort and presents the results honestly, including the many ways "replication success" is not a simple binary judgment. An important document for understanding what the replication crisis actually showed.
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83.
The paper that introduced the WEIRD acronym and documented the scope of sampling bias in psychological research. The main article is followed by extensive peer commentaries (many of which push back helpfully on specific claims) and a response from the authors. Reading the commentary section gives a realistic sense of how scientific argument works.
On Effect Sizes and Statistical Interpretation
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.
Cohen's own short, accessible overview of statistical power, effect sizes, and the d benchmarks he proposed. At just 5 pages, this is one of the most influential brief papers in psychology. Useful for understanding what the author actually meant — which is often different from how the benchmarks have been applied.
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29.
A practical argument for moving from null hypothesis significance testing toward effect size estimation and confidence intervals. Accessible to undergraduates with basic statistics training and includes worked examples.
On Cross-Cultural Research Methods
Van de Vijver, F. J. R., & Leung, K. (1997). Methods and data analysis for cross-cultural research. SAGE.
The standard methodological reference for cross-cultural psychology. Covers measurement equivalence, translation procedures, sampling design, and statistical analysis in cross-cultural contexts. Technical in places but invaluable for anyone designing or evaluating multi-country research.
Accessible Entry Points
Spiegelhalter, D. (2019). The art of statistics: How to learn from data. Basic Books.
An engaging, non-technical introduction to statistical reasoning by a leading statistician and communicator. Covers probability, significance, effect sizes, and the problems with how statistics are typically presented in science journalism. Ideal for students who want to strengthen their quantitative literacy without a statistics course.
Gelman, A., & Loken, E. (2014). The statistical crisis in science. American Scientist, 102(6), 460–465.
A brief, readable account of how "the garden of forking paths" in data analysis leads to inflated false-positive rates, even among well-meaning researchers who are not engaging in intentional misconduct. Freely available online.
On Attraction Research Methodology Specifically
Finkel, E. J., & Eastwick, P. W. (2008). Speed-dating. Current Directions in Psychological Science, 17(3), 193–197.
Finkel and Eastwick's own overview of their speed-dating research program, written for a general psychological science audience. Good companion to Case Study 3.1. Explains their methodology and its implications clearly and without jargon.
Joel, S., Eastwick, P. W., & Finkel, E. J. (2017). Is romantic desire predictable? Machine learning applied to initial romantic attraction. Psychological Science, 28(10), 1478–1489.
A methodologically innovative study using machine learning to predict romantic desire from individual characteristics — and finding that individual-level predictors explain very little variance. A sobering complement to the conventional attraction research literature.