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Category: Project

Description: Across many diverse areas of research, it is common to average a series of observations, and to use these averages in subsequent analyses. Research using this approach faces the challenge of knowing when these averages are “stable.” Meaning, to what extent do averages change when additional observations are included? Using averages that are not stable introduces error into any analysis, and knowing the point of stability can inform research design. The current research develops a tool, implemented in R, to assess when averages are stable. Using a sequential sampling approach, it determines how many observations are needed before additional observations would no longer meaningfully change an average. We illustrate how to use this tool with data from the impression formation literature, demonstrating that averages of some perceived traits (e.g., happy) stabilize with fewer observations than others (e.g., assertive). This tutorial provides step-by-step instructions for implementation in researchers’ own data.