共通因子と複合物の間の内蔵モデル比較
Danielle Siegel1, Victoria Savalei2, Mijke Rhemtulla1
1Department of Psychology, University of California, Davis.
Psychological methods
|February 17, 2026
まとめ
心理学の研究はしばしば反射的なモデルを使用しますが,形成的なモデルはデータに適しているかもしれません. この研究では,反射型と形成型モデルが内蔵されていることが示され,キ二乗差のテストのような統計的テストを使用して比較が可能です.
科学分野:
- 心理的な測定法
- 構造方程式モデリング
- サイコメトリクス サイコメトリクス
背景:
- 反射的確認因子分析 (CFA) は,心理学研究における支配的な測定モデルである.
- 形成的な測定モデルは,ある種の心理学的データに対して理論的に適切であるとますます認識されています.
- 既存の構造方程式モデリング (SEM) フレームワークでは,形成性複合材料の直接的な仕様が欠けている.
研究 の 目的:
- 反射的なCFAの理論的および経験的なネスティング構造,形成的なヘンセラー-オガサワラ確認複合分析,およびSEM内の形成的な擬似指標モデルを実証する.
- 組み込みモデル比較技術,特にキ平方差分テストおよびRMSEA (RMSEA_D) の有用性を評価し,反射型および形成型測定モデルを区別する.
- 測定モデルの選択と比較に関する研究者のための実践的なガイドラインと経験的例を提供すること.
主な方法:
- 構造方程式モデリング (SEM) フレームワーク内の3つの内蔵測定モデルの仕様:反射的なCFA,形成的なHenseler-Ogasawara確認複合分析,および形成的な擬似指標モデル.
- 構造コンポーネントが飽和したときに,これらのモデル間の階層的な関係の理論的および経験的実証.
- 異なる条件下 (サンプルサイズ,指標重量,外部相関) で反射型と形成型モデルの差別化におけるチ2差分試験とRMSEA_Dのパフォーマンスを評価するためのシミュレーション研究.
主要な成果:
- 反射的なCFA,形成的なヘンセラー・オガサワラ確認複合分析,形成的な擬似指標モデルが,飽和した構造条件下で相互に組み込まれていることが示されています.
- Chi-square差のテストとRMSEA_Dは,反射的および形成的測定モデルを確実に区別する可能性を示しているが,パフォーマンスはサンプルサイズ,指標重量,およびコンストラクート相関によって異なります.
- 経験的な例は,反射的および形成的測定アプローチの間の実用的な差別化のために,内蔵モデル比較ツールの適用を例示しています.
結論:
- 反射的および形成的測定モデルは,組み込みモデル比較技術を使用して,統一されたSEMフレームワーク内で公式に比較することができます.
- 研究者は,反射的な測定モデルと形成的な測定モデルの間の選択を実証的に検証するために,チ平方差分テストのような統計的テストを活用することができます.
- 結論は,理論的に正当な場合,心理学研究における形成的な測定モデルのより広範な採用と厳格な評価を支持しています.
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