Perparticipant

Perparticipant is a term used in research and data analysis to describe analyses, summaries, or reports that are computed for each participant individually, rather than aggregating data across participants or across trials. In practice, perparticipant analysis involves producing and sometimes modeling data at the level of the individual subject before any group-level aggregation, preserving information about each participant’s responses.

Perparticipant approaches are common in behavioral experiments, neuroimaging, and survey research. In behavioral studies, perparticipant summaries may include a participant’s mean accuracy, mean reaction time, or the distribution of responses. In neuroimaging, first-level analyses often generate subject-specific estimates for each participant, which are then used in higher-level, group analyses. Data processing pipelines may also perform perparticipant cleaning and quality assessment to ensure that each subject’s data meets predefined criteria.

Implementation considerations include the benefit of capturing individual differences and supporting models that account for variability between participants. Perparticipant data can be analyzed with mixed-effects or hierarchical models that include random effects for participants. However, relying exclusively on perparticipant summaries can obscure within-participant variability and may reduce statistical power for detecting group effects if not combined with appropriate aggregate analyses. Missing data at the participant level can complicate downstream analyses and may require exclusion rules or imputation.

Related concepts include mixed-effects or hierarchical modeling, within-subject designs, per-trial analyses, and per-item analyses. Perparticipant methods emphasize subject-level representation and are often a precursor to, or complement of, group-level inference.