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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Raymond Cattell's trait theory offers a structured framework for understanding personality by distinguishing between two critical traits: surface and source traits. Surface traits are observable patterns of behavior, such as indecisiveness, anxiety, and irrational fears. These traits are less stable, varying across situations and over time. This means that they are less helpful in understanding the deeper aspects of an individual's personality.
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在探索性的多维物件响应理论中的因子保留.

Changsheng Chen1,2, Robbe D'hondt2,3, Celine Vens2,3

  • 1Faculty of Psychology and Educational Sciences, KU Leuven, Campus KULAK, Kortrijk, Belgium.

Educational and psychological measurement
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概括
此摘要是机器生成的。

在探索性的多维物件响应理论 (MIRT) 中,确定因素的数量至关重要. 基于直方形的渐变增强决策树 (HistGBDT) 和最小平均部分 (MAP) 等机器学习方法显著优于因子保留的传统统计方法.

关键词:
这就是MIRT MIRT.探索性的多维物品响应理论.维护因子保留因子机器学习是机器学习.多维性的多维性.

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科学领域:

  • 心理测量 心理测量 心理测量
  • 教育测量教育的测量
  • 数据科学数据科学数据科学

背景情况:

  • 多维物件响应理论 (MIRT) 在教育和心理评估中被广泛使用.
  • 准确的因子保留对于有效的探索性MIRT分析至关重要.
  • 统计和机器学习 (ML) 方法在MIRT中的因子保留方面的比较性能尚不清楚.

研究的目的:

  • 为了比较各种统计和ML方法在探索性MIRT中的因子保留的有效性.
  • 确定最准确的方法来确定MIRT分析中的因素数量.

主要方法:

  • 在不同条件下使用MIRT模拟了72万个二分法响应数据集.
  • 比较统计方法 (例如,凯泽标准,并行分析,MAP,探索图分析) 和ML方法 (例如,随机森林,HistGBDT,XGBoost,ANN).
  • 基于对不同数据特征的正确计量比例进行评估的方法性能.

主要成果:

  • 最小平均部分 (MAP),随机森林 (RF),基于直方图的梯度增强决策树 (HistGBDT),XGBoost和人工神经网络 (ANN) 显示出卓越的性能.
  • 历史GBDT通常优于其他方法,特别是在将统计方法的结果作为特征时.
  • 随着数据缺失的增加和样本大小的减少,因素保留的准确性下降;一些传统方法显示出一致的过量或不足的因素.

结论:

  • 机器学习方法,特别是HistGBDT,在探索性MIRT中为因子保留提供了显著的优势.
  • 建议从业人员使用MAP和HistGBDT,以便在MIRT中进行可靠的因子确定.
  • 了解数据缺失和样本大小的影响对于可靠的MIRT分析至关重要.