Department of Psychology, Faculty of Social Sciences, University of Macau, Avenida da Universidade, Taipa, Macao SAR, China.
Department of Psychology, The University of Hong Kong, Hong Kong SAR, China.
Behav Res Methods. 2024 Aug;56(5):4862-4882. doi: 10.3758/s13428-023-02224-z. Epub 2023 Oct 5.
Mediation, moderation, and moderated mediation are common in behavioral research models. Several tools are available for estimating indirect effects, conditional effects, and conditional indirect effects and forming their confidence intervals. However, there are no simple-to-use tools that can appropriately form the bootstrapping confidence interval for standardized conditional indirect effects. Moreover, some tools are restricted to a limited type of models. We developed an R package, manymome, which can be used to estimate and form confidence intervals for indirect effects, conditional effects, and conditional indirect effects, standardized or not, using a two-step approach: model parameters are estimated either by structural equation modeling using lavaan or by a set of linear regression models using lm, and then the coefficients are used to compute the requested effects and form confidence intervals. It can be used when there are missing data if the model is fitted by structural equation modeling. There are only a few limitations on some aspects of a model, and no inherent limitations on the number of predictors, the number of independent variables, or the number of moderators and mediators. The goal is to have a tool that allows researchers to focus on model fitting first and worry about estimating the effects later. The use of the model is illustrated using a few numerical examples, and the limitations of the package are discussed.
中介、调节和中介调节在行为研究模型中很常见。有几种工具可用于估计间接效应、条件效应和条件间接效应，并形成它们的置信区间。然而，没有简单易用的工具可以适当地为标准化条件间接效应形成自举置信区间。此外，一些工具仅限于有限类型的模型。我们开发了一个 R 包 manymome，可以使用两步法估计和形成间接效应、条件效应和条件间接效应的置信区间，无论是否标准化：使用 lavaan 通过结构方程建模或使用一组使用 lm 的线性回归模型来估计模型参数，然后使用这些系数来计算所需的效应并形成置信区间。如果使用结构方程建模拟合模型，则可以在存在缺失数据的情况下使用。该模型的某些方面只有很少的限制，并且对预测器的数量、自变量的数量或调节变量和中介变量的数量没有内在限制。其目标是拥有一个工具，使研究人员首先关注模型拟合，然后再担心估计效应。使用几个数值示例来说明模型的使用，并讨论了该包的局限性。
