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Five-Factor Theory of Personality01:29

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The five-factor model, often called the Big Five personality traits, is widely accepted in psychology as a comprehensive framework for understanding personality. These five traits — Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism — are often remembered using the acronym OCEAN.
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Implicit personality theory explains how individuals make assumptions about the relationships between personality traits, behaviors, and character types. When people learn that someone possesses a particular trait, they tend to infer the presence of other related characteristics, forming a cohesive impression. This cognitive shortcut plays a crucial role in social interactions and interpersonal judgments.Central Traits and Their InfluenceSolomon Asch's seminal 1946 study highlighted the power...
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Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.
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Hans and Sybil Eysenck developed a widely recognized theory of personality, which emphasizes the role of temperament and genetically based differences in shaping individual traits. Their theory posits that biological factors primarily determine personality and can be understood through two main dimensions: extroversion/introversion and neuroticism/stability.
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基于分数的测试与固定效应的人参数在项目响应理论:检测模型错误规范包括差异性项目功能.

Rudolf Debelak1,2, Charles C Driver1

  • 1University of Zurich, Zurich, Switzerland.

Applied psychological measurement
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PubMed
概括
此摘要是机器生成的。

一个新的基于分数的测试有效地检测物品响应理论 (IRT) 模型中的模型错误规范,即使使用大型数据集. 这种方法简化了差异性项目功能 (DIF) 选,为复杂的评估提供了更快,更强大的替代方案.

关键词:
项目因素分析 项目因素分析进行大规模评估.的测量不变性.模型错误的规格错误

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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
  • 统计建模 统计建模

背景情况:

  • 项目响应理论 (IRT) 模型被广泛用于教育评估.
  • 检测模型错误规范,例如差异项目功能 (DIF),对于有效的测试解释至关重要.
  • 传统的DIF检测方法可能是计算密集型,需要复杂的模型设置,特别是对于大规模数据集.

研究的目的:

  • 引入一个基于分数的快速统计测试,用于检测IRT模型中的模型错误规范.
  • 为在人参数被视为固定的效应时提供DIF查的有效方法,适用于非常大的数据集.
  • 开发一个计算效率高,更简单的替代现有的DIF选方法.

主要方法:

  • 开发了一个基于分数的近似测试,用于IRT模型的错误规范.
  • 该测试与受约束的联合最大概率 (CJML) 和联合最大后期 (JMAP) 估计器兼容.
  • 整合了一个基于spline的残留步骤来控制I型错误率,当共变量与能力相关时.

主要成果:

  • 模拟显示了名义错误率和用于检测DIF的高功率,具有足够的响应数据 (每个受试者15-20个响应).
  • 该方法被证明是可扩展和实用的,在一个大型数据集 (1602个项目,57,684名学生) 的应用中,确定了13%的项目与性别相关的DIF.
  • 拟议的测试显示了与传统的DIF检测方法的高相关性.

结论:

  • 开发的基于分数的测试为大规模评估提供了强大的,计算轻量的诊断.
  • 当经典的随机效应方法是不可行的,或者当能力组结构和DIF效应形状是未知的或复杂的时,它特别有价值.
  • 这种方法提高了心理测量分析中DIF查的效率和简单性.