The gold standard for modeling multiple indicator measurement data is confirmatory factor analysis (CFA), which has many statistical advantages over traditional exploratory factor analysis (EFA). In most CFA applications, items are assumed to be pure indicators of the construct they intend to measure. However, despite our best efforts, this is often not the case. Cross-loadings incorrectly set to zero can only be expressed through the correlations between the factors, leading to biased factor correlations and to biased structural (regression) parameter estimates. This article introduces a third approach, which has emerged in the psychometric literature, viz., unrestricted factor analysis (UFA). UFA borrows strengths from both traditional EFA and CFA. In simulation studies, we show that ignoring cross-loadings even as low as .2 can substantially bias factor correlations when CFA is used and that even the commonly used guideline RMSEA ≤ .05 may be too lenient to guard against non-negligible bias in factor correlations in CFA. Next, we present two empirical applications using Schwartz’s value theory, and electronic service quality. In the first case, UFA leads to much better model fit and more plausible regression estimates. In the second case, the difference is less dramatic but nevertheless, UFA provides richer results. We provide recommendations on when to use UFA vs. CFA.

对多指标测量数据进行建模的金标准是验证性因子分析 (CFA)，与传统的探索性因子分析 (EFA) 相比，它具有许多统计优势。在大多数 CFA 应用程序中，项目被假定为他们打算测量的结构的纯粹指标。然而，尽管我们尽了最大努力，但通常情况并非如此。错误地设置为零的交叉载荷只能通过因子之间的相关性来表示，从而导致有偏差的因子相关性和有偏差的结构（回归）参数估计。本文介绍了心理测量学文献中出现的第三种方法，即无限制因素分析 (UFA)。UFA 借鉴了传统 EFA 和 CFA 的优势。在模拟研究中，我们表明忽略交叉载荷甚至低至 . 当使用 CFA 时，2 可以显着偏向因子相关性，即使常用的指南 RMSEA ≤ .05 也可能过于宽松，无法防止 CFA 中因子相关性出现不可忽略的偏差。接下来，我们展示了使用 Schwartz 的价值理论和电子服务质量的两个实证应用。在第一种情况下，UFA 导致更好的模型拟合和更合理的回归估计。在第二种情况下，差异不那么显着，但 UFA 提供了更丰富的结果。我们提供有关何时使用 UFA 与 CFA 的建议。在第一种情况下，UFA 导致更好的模型拟合和更合理的回归估计。在第二种情况下，差异不那么显着，但 UFA 提供了更丰富的结果。我们提供有关何时使用 UFA 与 CFA 的建议。在第一种情况下，UFA 导致更好的模型拟合和更合理的回归估计。在第二种情况下，差异不那么显着，但 UFA 提供了更丰富的结果。我们提供有关何时使用 UFA 与 CFA 的建议。