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