Department of Psychology, University of California, Los Angeles, CA 90095, USA.
J Pers Assess. 2010 Nov;92(6):544-59. doi: 10.1080/00223891.2010.496477.
The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures (typically caused by parcels of items that tap similar content domains). As such, structural ambiguity leads to seemingly endless "confirmatory" factor analytic studies in which the research question is whether scale scores can be interpreted as reflecting variation on a single trait. An alternative to the more commonly observed unidimensional, correlated traits, or second-order representations of a measure's latent structure is a bifactor model. Bifactor structures, however, are not well understood in the personality assessment community and thus rarely are applied. To address this, herein we (a) describe issues that arise in conceptualizing and modeling multidimensionality, (b) describe exploratory (including Schmid-Leiman [Schmid & Leiman, 1957] and target bifactor rotations) and confirmatory bifactor modeling, (c) differentiate between bifactor and second-order models, and (d) suggest contexts where bifactor analysis is particularly valuable (e.g., for evaluating the plausibility of subscales, determining the extent to which scores reflect a single variable even when the data are multidimensional, and evaluating the feasibility of applying a unidimensional item response theory (IRT) measurement model). We emphasize that the determination of dimensionality is a related but distinct question from either determining the extent to which scores reflect a single individual difference variable or determining the effect of multidimensionality on IRT item parameter estimates. Indeed, we suggest that in many contexts, multidimensional data can yield interpretable scale scores and be appropriately fitted to unidimensional IRT models.
心理测量方法的应用通常会产生项目反应数据，这些数据可以同时与单维（一个单一的共同因素）和多维潜在结构（通常由反映相似内容领域的项目组合引起）相一致。因此，结构上的模糊性导致了看似无穷无尽的“验证性”因素分析研究，这些研究的问题是量表分数是否可以被解释为反映单一特征的变化。与更常见的单维、相关特征或测量潜在结构的二阶表示相比，一种替代方法是双因素模型。然而，双因素结构在人格评估领域尚未得到很好的理解，因此很少被应用。为了解决这个问题，我们在此（a）描述了在概念化和建模多维性时出现的问题，（b）描述了探索性（包括 Schmid-Leiman [Schmid & Leiman, 1957]和目标双因素旋转）和验证性双因素建模，（c）区分双因素和二阶模型，以及（d）提出了双因素分析特别有价值的情境（例如，评估子量表的合理性，确定即使数据是多维的，分数反映单一变量的程度，以及评估应用单维项目反应理论（IRT）测量模型的可行性）。我们强调，确定维度是一个与确定分数反映单一个体差异变量的程度或确定多维性对 IRT 项目参数估计的影响相关但不同的问题。事实上，我们认为，在许多情况下，多维数据可以产生可解释的量表分数，并适当地拟合于单维 IRT 模型。
