探索性因素分析
验证性因素分析
心理学
统计
可靠性(半导体)
样品(材料)
项目分析
协方差
探索性分析
统计分析
心理测量学
探索性研究
临床心理学
项目反应理论
因子(编程语言)
因子分析
差异项目功能
应用心理学
比例(比率)
结构方程建模
计算机科学
发展心理学
数学
化学
程序设计语言
数据科学
色谱法
功率(物理)
物理
量子力学
标识
DOI:10.1207/s15327752jpa6803_5
摘要
The special characteristics of items—low reliability, confounds by minor, unwanted covariance, and the likelihood of a general factor—and better understanding of factor analysis means that the default procedure of many statistical packages (Little Jiffy) is no longer adequate for exploratory item factor analysis. It produces too many factors and precludes a general factor even when that means the factors extracted are nonreplicable. More appropriate procedures that reduce these problems are presented, along with how to select the sample, sample size required, and how to select items for scales. Proposed scales can be evaluated by their correlations with the factors; a new procedure for doing so eliminates the biased values produced by correlating them with either total or factor scores. The role of exploratory factor analysis relative to cluster analysis and confirmatory factor analysis is noted.
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