Sample Size Justification

An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable information given the inferential goals of the researcher. In this overview article six approaches are discussed to justify the sample size in a quantitative empirical study: 1) collecting data from (almost) the entire population, 2) choosing a sample size based on resource constraints, 3) performing an a-priori power analysis, 4) planning for a desired accuracy, 5) using heuristics, or 6) explicitly acknowledging the absence of a justification. An important question to consider when justifying sample sizes is which effect sizes are deemed interesting, and the extent to which the data that is collected informs inferences about these effect sizes. Depending on the sample size justification chosen, researchers could consider 1) what the smallest effect size of interest is, 2) which minimal effect size will be statistically significant, 3) which effect sizes they expect (and what they base these expectations on), 4) which effect sizes would be rejected based on a confidence interval around the effect size, 5) which ranges of effects a study has sufficient power to detect based on a sensitivity power analysis, and 6) which effect sizes are expected in a specific research area. Researchers can use the guidelines presented in this article, for example by using the interactive form in the accompanying online Shiny app, to improve their sample size justification, and hopefully, align the informational value of a study with their inferential goals.

Call for Registered Reports - Ideology Dataset

Registered Reports using a large, existing dataset of individual differences and implicit and explicit measures related to ideology Coordinating Team: Kathleen Schmidt, Charlie Ebersole, and Brian Nosek Deadline to request exploratory data: December 15, 2022 Deadline to submit Stage 1 Registere...

Power Analysis for the Random Intercept Cross-Lagged Panel Model Using the powRICLPM R-Package

The random intercept cross-lagged panel model (RI-CLPM) is a popular model among psychologists for studying reciprocal effects in longitudinal panel data. Although various texts and software packag...

Mastodon Twitter Crossposter

Battling the Curse of Dimensionality

Materials for Applied Data Science profile course INFOMDA2 Battling the curse of dimensionality.