Dierendonck Christophe, de Chambrier Anne-Françoise, Fagnant Annick, Luxembourger Christophe, Tinnes-Vigne Mélanie, Poncelet Débora
Department of Education and Social Work, University of Luxembourg, Esch-Belval, Luxembourg.
University of Teacher Education, Vaud, Switzerland.
Front Psychol. 2021 Jun 22;12:680124. doi: 10.3389/fpsyg.2021.680124. eCollection 2021.
The few studies that have analyzed the factorial structure of early number skills have mainly used confirmatory factor analysis (CFA) and have yielded inconsistent results, since early numeracy is considered to be unidimensional, multidimensional or even underpinned by a general factor. Recently, the bifactor exploratory structural equation modeling (bifactor-ESEM)-which has been proposed as a way to overcome the shortcomings of both the CFA and the exploratory structural equation modeling (ESEM)-proved to be valuable to account for the multidimensionality and the hierarchical nature of several psychological constructs. The present study is the first to investigate the dimensionality of early number skills measurement through the application of the bifactor-ESEM framework. Using data from 644 prekindergarten and kindergarten children (4 to 6 years old), several competing models were contrasted: the one-factor CFA model; the independent cluster model (ICM-CFA); the exploratory structural equation modeling (ESEM); and their bifactor counterpart (bifactor-CFA and bifactor-ESEM, respectively). Results indicated acceptable fit indexes for the one-factor CFA and the ICM-CFA models and excellent fit for the others. Among these, the bifactor-ESEM with one general factor and three specific factors (Counting, Relations, Arithmetic) not only showed the best model fit, but also the best coherent factor loadings structure and full measurement invariance across gender. The bifactor-ESEM appears relevant to help disentangle and account for general and specific factors of early numerical ability. While early numerical ability appears to be mainly underpinned by a general factor whose exact nature still has to be determined, this study highlights that specific latent dimensions with substantive value also exist. Identifying these specific facets is important in order to increase quality of early numerical ability measurement, predictive validity, and for practical implications.
少数分析早期数字技能因子结构的研究主要采用验证性因子分析（CFA），但结果并不一致，因为早期算术能力被认为是单维的、多维的，甚至由一个一般因子支撑。最近，双因子探索性结构方程模型（bifactor-ESEM）——被提议作为一种克服CFA和探索性结构方程模型（ESEM）缺点的方法——被证明对于解释几种心理结构的多维性和层次性质很有价值。本研究首次通过应用双因子ESEM框架来探究早期数字技能测量的维度。使用来自644名学前班和幼儿园儿童（4至6岁）的数据，对比了几种竞争模型：单因子CFA模型；独立聚类模型（ICM-CFA）；探索性结构方程模型（ESEM）；以及它们的双因子对应模型（分别为双因子CFA和双因子ESEM）。结果表明，单因子CFA和ICM-CFA模型的拟合指数可以接受，其他模型的拟合效果很好。其中，具有一个一般因子和三个特定因子（计数、关系、算术）的双因子ESEM不仅显示出最佳的模型拟合，还显示出最佳的相干因子载荷结构以及跨性别完全测量不变性。双因子ESEM似乎有助于理清并解释早期数字能力的一般和特定因子。虽然早期数字能力似乎主要由一个确切性质仍有待确定的一般因子支撑，但本研究强调具有实质价值的特定潜在维度也存在。识别这些特定方面对于提高早期数字能力测量的质量、预测效度以及实际应用具有重要意义。
