The GPE model's parameters are recoverable via Bayesian estimation in simulation¶

Claim · All claims
Evidence · 1 study · theoretical r? · q2
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In a parameter recovery simulation with 300 fictitious participants, estimated parameter values closely matched true values, with differences centered around zero. → The Dynamic Interplay Between Goal Setting 2022

Evidence¶

The Dynamic Interplay Between Goal Setting 2022¶

The Dynamic Interplay Between Goal Setting, Performance, and Emotions in Self-Regulated Learning: A Computational Modeling Approach — Supplementary Materials. (2022). https://gpemodel.shinyapps.io/gpe-model/

q2 · i? · theoretical · r?

Simulation study generating data for 300 fictitious participants from the hypothesized model using population-level values chosen with a Shiny app, then re-estimating parameters hierarchically. The article reports that differences between true and estimated values "centered around zero" (Supplementary Figure S2).

"Results show these differences centered around zero, indicating that the model and Bayesian estimation could recover parameters well (Supplementary Figure S2)."

Discussion¶

Related Claims¶

• Alternate models isolate the roles of goal success/failure, GPD effects, and emotion effects in the GPE framework — related
• The general polytomous testlet model recovers true item parameters well in simulation, with average correlations of 0.9680, 0.8608, and 0.9982 for discrimination and difficulty parameters — possibly the same claim (merge candidate)
• Study 1's best-fitting model yields significant GPD and emotion effects on goals and performance — related