Theoretical, Statistical, and Substantive Issues in the Assessment of Construct Dimensionality
指出常用方法（如因子分析）用于理想点反应过程数据（如自评态度、情绪）时，会错误推断构念维度，例如产生虚假维度导致否定双极性，并以情绪数据为例说明对组织研究的实质影响，最后提出分析步骤以规避此问题。
Can one accurately infer the dimensionality of constructs such as emotions (i.e., happy–sad), work–family spillover (i.e., positive–negative), or job performance (i.e., organizational citizenship behaviors and counterproductive work behaviors) with commonly used methods? In this article, the authors show how the misapplication of commonly used methods (e.g., factor analysis [FA]) to data originating from an ideal point response process (i.e., self-reported typical behaviors: attitudes, personality, emotions, or interests) can lead to incorrect theoretical and statistical inferences. The authors demonstrate that principal components analysis (PCA) produces an additional spurious dimension despite Likert scaling procedures (i.e., reverse scoring and excluding items with low item-total correlations to improve scale reliability). This incorrectly leads to a conclusion against bipolarity. The authors illustrate the substantive implications for organizational research with emotions data showing that the misapplication of FA could underlie the longstanding debate on the bipolarity of affect. To circumvent this potential problem, the authors propose analytic steps to determine if the recovered constructs are spurious. Additionally, the authors lay out specific issues that need to be considered when evaluating the bipolarity of self-reported typical behavior constructs such as work–family spillover and job performance.