若手研究者育成プロジェクトワーキングペーパー

ホーム > 若手研究者育成プロジェクトワーキングペーパー > No.24 認知診断モデルにおけるモデル選択の比較 シミュレーションによる小サンプル状況下での検証

No.24 認知診断モデルにおけるモデル選択の比較 シミュレーションによる小サンプル状況下での検証

2018.04.28

 

 

 

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山口 一大

 

Some Model Comparisons in Cognitive Diagnostic Models
 Relatively Small Sample Situation with Simulation Study

Kazuhiro Yamaguchi

April, 2018

Abstract

A lot of cognitive diagnostic models (CDMs) have been developed in several decades. The objective of this study is to check how we can detect misspesifications among data generation models and analysis models in relatively small sample size situations. We employed simulation study for the purpose. We got three results. First, that Bayesian information criterion (BIC) indicated LLM (linear logistic model) as optimal model when G-DINA (generalized deterministic noisy inputs “and” gate) model was true model. Second, when the LLM and A-CDM (additive CDM) were true models, it was difficult to distinguish these model with Akaike information criterion (AIC) and BIC. Third, AIC and BIC can select R-RUM (reparametarized reduced unified model), DINA (deterministic noisy inputs “and” gate) model and DINO (deterministic noisy inputs “or” gate) model models as correct model. We discuss these results.