Graña Diego F, Kreitchmann Rodrigo S, Sorrel Miguel A, Garrido Luis Eduardo, Abad Francisco J
Universidad Autónoma de Madrid, Madrid, Spain.
Universidad Nacional de Educación a Distancia, Madrid, Spain.
Educ Psychol Meas. 2025 Sep 8:00131644251358226. doi: 10.1177/00131644251358226.
Forced-choice (FC) questionnaires have gained increasing attention as a strategy to reduce social desirability in self-reports, supported by advancements in confirmatory models that address the ipsativity of FC test scores. However, these models assume a known dimensionality and structure, which can be overly restrictive or fail to fit the data adequately. Consequently, exploratory models can be required, with accurate dimensionality assessment as a critical first step. FC questionnaires also pose unique challenges for dimensionality assessment, due to their inherently complex multidimensional structures. Despite this, no prior studies have systematically evaluated dimensionality assessment methods for FC data. To fill this gap, the present study examines five commonly used methods: the Kaiser Criterion, Empirical Kaiser Criterion, Parallel Analysis (PA), Hull Method, and Exploratory Graph Analysis. A Monte Carlo simulation study was conducted, manipulating key design features of FC questionnaires, such as the number of dimensions, items per dimension, response formats (e.g., binary vs. graded), and block composition (e.g., inclusion of heteropolar and unidimensional blocks), as well as factor loadings, inter-factor correlations, and sample size. Results showed that the Maximal Kaiser Criterion and PA methods outperformed the others, achieving higher accuracy and lower bias. Performance improved particularly when heteropolar or unidimensional blocks were included or when the questionnaire length increased. These findings emphasize the importance of thoughtful FC test design and provide practical recommendations for improving dimensionality assessment in this format.
强迫选择（FC）问卷作为一种减少自我报告中社会期望性的策略，受到了越来越多的关注，这得到了处理FC测试分数ipsativity的验证模型进展的支持。然而，这些模型假设维度和结构已知，这可能过于严格或无法充分拟合数据。因此，可能需要探索性模型，而准确的维度评估是关键的第一步。由于FC问卷固有的复杂多维结构，其维度评估也面临独特挑战。尽管如此，之前没有研究系统地评估过FC数据的维度评估方法。为填补这一空白，本研究考察了五种常用方法：凯泽准则、经验凯泽准则、平行分析（PA）、赫尔方法和探索性图形分析。进行了一项蒙特卡罗模拟研究，操纵FC问卷的关键设计特征，如维度数量、每个维度的项目数量、反应格式（如二元与分级）和块组成（如包含异极和单维块），以及因子载荷、因子间相关性和样本量。结果表明，最大凯泽准则和PA方法优于其他方法，具有更高的准确性和更低的偏差。当包含异极或单维块或问卷长度增加时，性能尤其有所提高。这些发现强调了精心设计FC测试的重要性，并为改进这种格式的维度评估提供了实用建议。
