Anunciação Luis, Squires Jane, Landeira-Fernandez J, Singh Ajay
Department of Psychology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
College of Education, University of Oregon, Eugene, Oregon.
J Neurosci Rural Pract. 2022 Feb 11;13(2):186-195. doi: 10.1055/s-0041-1741503. eCollection 2022 Apr.
A wide range of exploratory methods is available in psychometrics as means of gathering insight on existing data and on the process of establishing the number and nature of an internal structure factor of a test. Exploratory factor analysis (EFA) and principal component analysis (PCA) remain well-established techniques despite their different theoretical perspectives. Network analysis (NA) has recently gained popularity together with such algorithms as the Next Eigenvalue Sufficiency Test. These analyses link statistics and psychology, but their results tend to vary, leading to an open methodological debate on statistical assumptions of psychometric analyses and the extent to which results that are generated with these analyses align with the theoretical basis that underlies an instrument. The current study uses a previously published data set from the Ages & Stages Questionnaires: Social-Emotional to explore, show, and discuss several exploratory analyses of its internal structure. To a lesser degree, this study furthers the ongoing debate on the interface between theoretical and methodological perspectives in psychometrics. From a sample of 22,331 sixty-month-old children, 500 participants were randomly selected. Pearson and polychoric correlation matrices were compared and used as inputs in the psychometric analyses. The number of factors was determined via well-known rules of thumb, including the parallel analysis and the Hull method. Multidimensional solutions were rotated via oblique methods. R and Factor software were used, the codes for which are publicly available at https://luisfca.shinyapps.io/psychometrics_asq_se/ . Solutions from one to eight dimensions were suggested. Polychoric correlation overcame Pearson correlation, but nonconvergence issues were detected. The Hull method achieved a unidimensional structure. PCA and EFA achieved similar results. Conversely, six clusters were suggested via NA. The statistical outcomes for determining the factor structure of an assessment diverged, varying from one to eight domains, which allowed for different interpretations of the results. Methodological implications are further discussed.
在心理测量学中，有多种探索性方法可用于深入了解现有数据以及确定测试内部结构因素的数量和性质的过程。探索性因素分析（EFA）和主成分分析（PCA）尽管理论视角不同，但仍然是成熟的技术。网络分析（NA）最近与下一个特征值充分性检验等算法一起受到欢迎。这些分析将统计学与心理学联系起来，但它们的结果往往各不相同，引发了关于心理测量分析的统计假设以及这些分析产生的结果与工具所基于的理论基础的契合程度的公开方法学辩论。本研究使用先前发表的《年龄与阶段问卷：社会情感》数据集来探索、展示和讨论其内部结构的几种探索性分析。在较小程度上，本研究进一步推动了心理测量学中理论与方法视角之间的持续辩论。 从22331名60个月大的儿童样本中随机选取了500名参与者。比较了皮尔逊相关矩阵和多列相关矩阵，并将其用作心理测量分析的输入。通过众所周知的经验法则确定因素数量，包括平行分析和赫尔方法。多维解决方案通过斜交方法进行旋转。使用了R和Factor软件，其代码可在https://luisfca.shinyapps.io/psychometrics_asq_se/上公开获取。 提出了从一到八个维度的解决方案。多列相关克服了皮尔逊相关，但检测到了不收敛问题。赫尔方法实现了单维结构。PCA和EFA取得了相似的结果。相反，通过NA提出了六个聚类。 确定评估因素结构的统计结果各不相同，从一到八个领域不等，这使得对结果有不同的解释。进一步讨论了方法学意义。
