連続した制限された応答の応答スタイルをモデル化:モデル開発と検証
Youxiang Jiang1, Biao Zeng1, Siwei Peng2
1Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
Behavior research methods
|August 27, 2025
まとめ
この研究では,絶え間ないデータのための新しい項目応答モデルを導入し,極端とミッドポイント応答スタイルを効果的に測定します. このモデルは堅実な有効性を示し,連続測定における応答スタイル効果を軽減します.
科学分野:
- サイコメトリクス
- 統計モデリング
- 調査方法
背景:
- アイテムレスポンス理論モデルは,ライカートスケールデータのために確立されていますが,視覚アナログスケール (VAS) やスライダーバーなどの連続形式ではあまり開発されていません.
- エクストリームレスポンススタイル (ERS) やミッドポイントレスポンススタイル (MRS) などのレスポンススタイルは,連続測定形式で結果に偏りがあります.
- 既存のモデルでは,これらの応答スタイルを継続的なデータで適切に取り扱ったり,孤立させたりできません.
研究 の 目的:
- 連続した制限された応答形式を分析するための新しい項目応答モデルフレームワークを提案する.
- 内容の特徴,極端な応答スタイル (ERS),およびミッドポイント応答スタイル (MRS) を統一モデルに柔軟に組み込む.
- ERSとMRSを正確に推定し,観察された反応への影響を軽減するモデルの能力を検証する.
主な方法:
- 偽応答を利用した階層的な項目応答モデルフレームワークの開発.
- ERSとMRSの見積もりを評価するために,連続した境界応答データを用いて実証検証.
- マルコフ・チェーン・モンテ・カルロ (MCMC) 方法を用いてパラメータ回復を評価するシミュレーション研究.
主要な成果:
- 提案されたモデルは,既存のアプローチと比較して,継続的な制限された応答データに優れた適合性を示した.
- このモデルは,極端な応答スタイル (ERS) とミッドポイント応答スタイル (MRS) を効果的に推定した.
- マルコフ連鎖モンテカルロ (MCMC) 方法は,さまざまなシミュレーション条件でモデルパラメータを正確に復元しました.
結論:
- 新しいアイテムレスポンスモデルフレームワークは,継続的な制限レスポンスデータを分析するための堅牢で有効なアプローチを提供します.
- このモデルは,反応スタイル (ERS,MRS) を成功裏に分離し,測定し,観察された反応に対する有害な影響を軽減します.
- このフレームワークは,継続的な測定器のサイコメトリックモデリングを進めて,データの正確性と解釈性を向上させます.
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