Latent profile analysis (LPA) identifies heterogeneous subgroups based on continuous indicators that represent different dimensions. It is a common practice to measure each dimension using items, create composite or factor scores for each dimension, and use these scores as indicators of profiles in LPA. In this case, measurement models for dimensions are not included and potential noninvariance across latent profiles is not modeled in LPA. This simulation study examined the robustness of LPA in terms of class enumeration and parameter recovery when the noninvariance was unmodeled by using composite or factor scores as profile indicators. Results showed that correct class enumeration rates of LPA were relatively high with small degree of noninvariance, large class separation, large sample size, and equal proportions. Severe bias in profile indicator mean difference was observed with intercept and loading noninvariance, respectively. Implications for applied researchers are discussed.

潜在概况分析 (LPA) 根据代表不同维度的连续指标识别异质子组。一种常见的做法是使用项目来衡量每个维度，为每个维度创建复合或因子分数，并将这些分数用作 LPA 中的配置文件的指标。在这种情况下，不包括维度的测量模型，并且 LPA 中未对潜在配置文件中的潜在非不变性进行建模。该模拟研究在使用复合或因子分数作为概况指标对非不变性未建模时，检查了 LPA 在类枚举和参数恢复方面的稳健性。结果表明，LPA的类枚举正确率较高，具有不变性程度小、类分离大、样本量大、比例相等的特点。截距和加载非不变性分别观察到轮廓指标平均差的严重偏差。讨论了对应用研究人员的影响。