Shao Kaiqi, Elahi Shirvan Majid, Alamer Abdullah
Department of Foreign Languages, Hangzhou Dianzi University, Hangzhou, China.
Department of Foreign Languages, University of Bojnord, Bojnord, Iran.
Front Psychol. 2022 May 24;13:901412. doi: 10.3389/fpsyg.2022.901412. eCollection 2022.
Assessing the association between conceptual constructs are at the heart of quantitative research in educational and psychological research. Researchers apply different methods to the data to obtain results about the correlation between a set of variables. However, the question remains, how accurate are the results of the correlation obtained from these methods? Although various considerations should be taken to ensure accurate results, we focus on the types of analysis researchers apply to the data and discuss three methods most researchers use to obtain results about correlation. Particularly, we show how correlation results in bivariate correlation, confirmatory factor analysis (CFA), and exploratory structural equation modeling (ESEM) differ substantially in size. We observe that methods that assume independence of the items often generate inflated factor correlations whereas methods that relax this assumption present uninflated, thus more accurate correlations. Because factor correlations are inflated in bivariate correlation and CFA, the discriminant validity of the constructs is often unattainable. In these methods, the size of the correlation can be very large and biased. We discuss the reasons for this variation and suggest the type of correlation that researchers should select and report.
评估概念结构之间的关联是教育和心理研究中定量研究的核心。研究人员对数据应用不同的方法，以获得关于一组变量之间相关性的结果。然而，问题仍然存在，从这些方法中获得的相关性结果有多准确？尽管应考虑各种因素以确保结果准确，但我们关注研究人员对数据应用的分析类型，并讨论大多数研究人员用于获得相关性结果的三种方法。具体而言，我们展示了双变量相关性、验证性因素分析（CFA）和探索性结构方程建模（ESEM）中的相关性结果在大小上如何存在显著差异。我们观察到，假设项目独立性的方法通常会产生膨胀的因素相关性，而放宽这一假设的方法呈现出未膨胀的，因此更准确的相关性。由于双变量相关性和CFA中的因素相关性被夸大，结构的区分效度往往无法实现。在这些方法中，相关性的大小可能非常大且有偏差。我们讨论了这种差异的原因，并建议研究人员应选择和报告的相关性类型。
