多層ベクトル・オートレグレッシブモデルにおけるレベル内潜在相互作用効果のモデリング
Jana Holtmann1, Kenneth Koslowski2
1Wilhelm-Wundt Institute for Psychology, Leipzig University, Neumarkt 9-19, 04109, Leipzig, Germany. jana.holtmann@uni-leipzig.de.
Behavior research methods
|September 5, 2025
まとめ
この研究では,時間によって変化するモダレーターを考慮して,人の内部ダイナミクスの変化を捉えるために,高度な多層の潜在的タイムシリーズモデルを導入しています. これらのモデルは複雑な心理的プロセスを より微妙に理解できます
科学分野:
- 心理科学
- 定量心理学
- 縦断データ分析
背景:
- マルチレベル (潜伏) タイムシリーズモデルは,人内のダイナミクスのためにますます使用されています.
- 現在のモデルでは,縦断的な関係に影響を与える時間変動のモダレーターがしばしば見過ごされます.
- 変化する要因によって人体内のダイナミックなプロセスがどのように影響されるかについての理解が制限されます.
研究 の 目的:
- 個人のレベルでの潜在的相互作用効果を組み込むことで,多層の潜在的タイムシリーズモデルを拡張する.
- ベイジアン推定を用いたこれらの強化モデルを適用するためのチュートリアルを提供します.
- 否定的な感情の時間的動態を調査し 思考と注意を集中させる
主な方法:
- 潜伏の相互作用効果を含むように,多層の潜伏のタイムシリーズモデルを拡張する.
- マルコフチェーンモンテカルロ (MCMC) によるベイジアン推定.
- モデルの性能と複雑さを評価するためのシミュレーション研究.
主要な成果:
- 人体内のダイナミックな分析に潜在的相互作用の効果を成功裏に組み込むことが示されています.
- ネガティブな影響,反省,そして注意を集めた経験的例を提供した.
- シミュレーションに基づくモデルの複雑性とサンプルサイズに関する推奨事項
結論:
- 強化されたモデルは,時間依存の内部のダイナミクスを研究するためのより包括的なアプローチを提供します.
- 応用研究者はこれらのモデルを使って 微妙な縦断的な関係を探求することができる.
- 複合的なランダム効果モデルでは,適切なサンプルサイズ (例えば100人につき100時間ポイント) が不可欠です.
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