Membership model for classifying individuals into the common variance model¶

Theory · All theories
Evidence · 2 claims (2 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description¶

A companion model to the Bayes factor test that moves from testing the average assumption to person-level classification. As the article states, "we introduce a membership model that allows for classifying which (and how many) individuals belong to the common variance model". It thereby identifies which individuals are adequately described by the average within-person variance in hierarchical models and which are not, complementing individually varying ICCs.

Design Implications¶

Context¶

Requirements¶

• A fitted hierarchical model with person-specific within-person variance estimates to classify against

Constraints¶

• Applied in the article to cognitive inhibition task data; classification of which and how many individuals fit the common variance model is its stated function

Target Learners¶

• Researchers assessing measurement reliability in repeated-measure designs

Target Learning Objectives¶

• Determining which individuals are adequately described by average within-person variance

Claims¶

• Few Individuals Common Variance Model Reliability Masking [+M]
• Within-person variance in cognitive inhibition tasks is heterogeneous, with person-specific reliability differing up to tenfold [+W]

Related Theories¶

• Bayes factor testing of homogeneous within-person variance in hierarchical models
• Bayes factors as interpretable weight of evidence for competing scientific theories

Examples¶

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Key Sources¶

• Donald Williams, Stephen Martin, Phillipe Rast. (2021). Putting the individual into reliability: Bayesian testing of homogeneous within-person variance in hierarchical models. Behavior Research Methods. https://doi.org/10.3758/s13428-021-01646-x