{
  "abstract": "When should researchers use linear mixed-effects models (LMMs) versus repeated-measures analysis of variance (RM-ANOVA) for analysing repeated-measures data? I first outline the conceptual and statistical differences between the two approaches: RM-ANOVA operates on aggregated difference scores, whereas LMMs model trial-level data using fixed and random effects to account for dependencies among observations. I then describe the key limitations of RM-ANOVA: its inability to handle missing data, partially within-participants factors, continuous within-participants covariates, and complex dependency structures such as crossed random effects designs. I also discuss the conceptual and computational challenges associated with LMMs, such as model specification and convergence issues. Using two simulation studies, I then demonstrate that when both methods are appropriate, RM-ANOVA -- especially with Greenhouse-Geisser correction -- generally provides better control of Type I error rates and statistical power equal to or greater than that of LMMs. Consequently, I recommend using RM-ANOVA in designs where RM-ANOVA is appropriate. LMMs should be reserved for cases that require the additional flexibility, such as complex designs, missing data, or advanced modelling goals.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Henrik Singmann"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/296xf7cg8jnm3.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/e7bjz_v1",
  "fulltext_url": null,
  "id": "296xf7cg8jnm3",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/e7bjz_v1/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Quantitative Psychology"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Statistical Methods"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7216215559",
  "pdf_url": "https://osf.io/e7bjz/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/296xf7cg8jnm3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:17Z",
      "native_id": "e7bjz",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/e7bjz_v1/"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "bepress:Statistical Methods",
      "subfield": "statistics"
    }
  ],
  "title": "When to Use Mixed Models and When RM-ANOVA - An Argument for Statistical Parsimony",
  "title_text": "When to Use Mixed Models and When RM-ANOVA - An Argument for Statistical Parsimony",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:17Z",
  "url": "https://research.draconitic.com/paper/296xf7cg8jnm3",
  "venue": "PsyArXiv",
  "versions": []
}