Preprints and articles.
John Michael, Shaheed Azaad, Pernille Hemmer, Robrecht van der Wel (2026) ·
A commentary on Zapparoli et al. (2022; Cortex)
Shaheed Azaad, Nick Haslam, Yoshihisa Kashima (2026) ·
Past research has concluded that stereotype threat effects cause female chess players to underperform against male opponents. Here, we investigated whether this effect is large enough be practically meaningful, and whether it varies in line with the stereotype threat account. We analysed moves from 118,053 tournament chess games (N = 29,864 players), to test for a player × opponent gender interaction on performance, whether mixed-gender games were played more aggressively, and whether female players performed better in female-only tournaments. We also tested for moderation by the Gender Inequality Index of a player’s country, a player’s birth year, and the year in which a game was played. Equivalence testing (bounds: β = ± 0.10) found all effects to be unsubstantial. Results suggest that female tournament chess players do not experience stereotype threat effects, and that gender dispari...
Shaheed Azaad (2026) ·
The typical approach to computing standardised mean differences (d) from paired-samples (or within-subjects) designs requires knowing the often-unreported repeated-measures correlation, r_"repeated" . This adjustment enables comparison with ds from independent samples. However, when meta-analysing effects that come exclusively from paired samples, an underutilised option is to compute Cohen’s d_z, which does not require r_"repeated" . Because d_z can be computed from a range of summary and inferential statistics, it enables researchers to obtain standardised effects even when articles report their results in little detail. The present article contains equations and the corresponding R code needed to compute d_z and its variance, and adjust for small-sample bias.
Shaheed Azaad, Kassandra Friebe (2025) ·
Meta-analysis enables researchers to summarise the empirical literature on a phenomenon. Often, meta-analysts also conduct theoretically motivated moderator analyses, or meta-regressions, to determine whether the magnitude of an effect depends on relevant study characteristics (e.g., the type of stimuli or measures used). To do so requires first manually extracting information from, or coding, the meta-analysed studies. Although the veracity of moderator analyses depends on the accuracy of the coding process, many researchers opt to double-code only a fraction of their studies, leaving open the possibility of coding errors in the remaining data. Here, we simulated the impact of seemingly low rates (.02, .04, and .06) of random study miscoding on meta-regressions with categorical predictors. Results indicated that miscategorisation had a larger effect in smaller meta-analyses and when mo...