ABSTRACT Integrative data analysis (IDA) involves obtaining multiple datasets, scaling the data to a common metric, and jointly analyzing the data. The first step in IDA is to scale the multisample item-level data to a common metric, which is often done with multiple group item response models (MGM). With invariance constraints tested and imposed, the estimated latent variable scores from the MGM serve as an observed variable in subsequent analyses. This approach was used with empirical multiple group data and different latent variable estimates were obtained for individuals with the same response pattern from different studies. A Monte Carlo simulation study was then conducted to compare the accuracy of latent variable estimates from the MGM, a single-group item response model, and an MGM where group differences were ignored. Results suggest that these alternative approaches led to consistent and equally accurate latent variable estimates. Implications for IDA are discussed.

中文翻译：

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使用多组项目响应模型估计潜在变量分数：对综合数据分析的影响

摘要 综合数据分析（IDA）涉及获取多个数据集，将数据缩放到一个通用指标，并联合分析数据。IDA 的第一步是将多样本项目级数据缩放到一个通用指标，这通常通过多组项目响应模型 (MGM) 来完成。通过测试和施加不变性约束，来自 MGM 的估计潜在变量分数在后续分析中用作观察变量。这种方法与经验多组数据一起使用，并且对于来自不同研究的具有相同反应模式的个体获得了不同的潜在变量估计值。然后进行了一项蒙特卡罗模拟研究，以比较来自 MGM、单组项目响应模型和忽略组差异的 MGM 的潜在变量估计的准确性。结果表明，这些替代方法导致了一致且同样准确的潜在变量估计。讨论了对 IDA 的影响。