Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores.
Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores.
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DOI：
10.1080/00223891.2010.496477
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发表时间：
2010-11
影响因子：
3.4
通讯作者：
Haviland MG
中科院分区：
文献类型：
--
作者：
Reise SP;Moore TM;Haviland MG
心理测量的应用通常会导致项目响应数据，可以说是与一维（一个单一的共同因素）和多维潜在结构（通常是由项目的包裹，挖掘类似的内容域）一致。因此，结构模糊性导致似乎无休止的“验证性”因素分析研究，其中的研究问题是量表分数是否可以解释为反映单一性状的变化。一个替代更常见的一维，相关性状，或二阶表示的措施的潜在结构是一个双因素模型。然而，双因素结构在人格评估界还没有得到很好的理解，因此很少被应用。为了解决这个问题，在此，我们：a）描述在概念化和建模多维性中出现的问题，B）描述探索性（包括Schmid-Leiman和目标双因子旋转）和验证性双因子建模，c）区分双因子和二阶模型，d）提出双因子分析特别有价值的背景（例如，用于评估分量表的可解释性，确定分数反映单个变量的程度，即使数据是多维的，并评估应用一维项目反应理论测量模型的可行性）。我们强调，确定的维度是一个相关的，但不同的问题，无论是确定在何种程度上分数反映一个单一的个体差异变量或确定多维度对IRT项目参数估计的影响。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规模分数，并适当地适合一维IRT模型。
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 and target bifactor rotations) and confirmatory bifactor modeling, c) differentiate between bifactor and second-order models, 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 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.
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