MODEL SELECTION: FIXED-EFFECT VS. RANDOM-EFFECTS MODELS

Please remember that There are two distinct aggregation models: fixed effects and random effects models. The fixed effects model presupposes that all studies in the meta-analysis come from the same population and that the true effect size remains consistent across all studies. Thus, the variation in effect size is presumed to stem from differences within each study, such as sampling error. In contrast, the random effects model is more intricate if the effects on the population vary from study to study. This assumption is based on the idea that the observed studies are a selection of samples drawn from a broader universe of studies. Random effects models encompass two sources of variation in a given effect size: within and between studies. (Kaufmann & Reips, 2024).

When conducting meta-analyses, effect sizes are combined using either fixed-effect or random-effects models. The choice to utilize one of these models relies on the assumption about the distribution of the effect sizes:

1. Fixed-Effect Model: This model assumes that all studies estimate the same true effect size and that observed variations are due to sampling error alone. It gives more weight to more extensive studies and is appropriate when studies are very similar regarding participants, interventions, and outcomes.
2. Random-Effects Model: This model assumes that effect sizes vary across studies due to both within-study sampling error and between-study heterogeneity. It incorporates an additional variance component, allowing for a more generalized inference about the effect size. The random effects model will likely produce a more cautious estimate with a wider confidence interval, prompting a mindful approach to the results. However, the conclusions from the two models typically align when there is no heterogeneity. It is more appropriate when there is significant heterogeneity among the included studies.