通过不合并多项目量表来增强预测能力。

IF 7.6 1区心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Psychological methods Pub Date : 2023-07-20 DOI:10.1037/met0000599

David Trafimow, Michael R Hyman, Alena Kostyk

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引用次数: 0

摘要

在社会科学中，通常较小但被吹捧为“统计显著”的相关系数危及理论检验和预测。为了研究这些小系数的潜在原因，考虑了传统方程，如Spearman(1904)的经典衰减公式，Cronbach(1951)的alpha，以及Guilford和Fruchter(1973)的附加项对量表预测能力影响的方程。这些方程的含义不同于大的项目间相关性，增强或减弱预测能力。与传统做法相反，当将项目作为多项目量表组件处理时，这种相关性会降低预测能力，但当单独处理项目时，这种相关性会增加预测能力。其影响是广泛的。(PsycInfo数据库记录(c) 2023 APA，版权所有)。

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Enhancing predictive power by unamalgamating multi-item scales.

The generally small but touted as "statistically significant" correlation coefficients in the social sciences jeopardize theory testing and prediction. To investigate these small coefficients' underlying causes, traditional equations such as Spearman's (1904) classic attenuation formula, Cronbach's (1951) alpha, and Guilford and Fruchter's (1973) equation for the effect of additional items on a scale's predictive power are considered. These equations' implications differ regarding large interitem correlations enhancing or diminishing predictive power. Contrary to conventional practice, such correlations decrease predictive power when treating items as multi-item scale components but can increase predictive power when treating items separately. The implications are wide-ranging. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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来源期刊

Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-

CiteScore

13.10

自引率

7.10%

发文量

159

期刊介绍： Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.