Abstract

The effect of an independent variable on random slopes in growth modeling with latent variables is conventionally used to examine predictors of change over the course of a study. This article demonstrates that the same effect of a covariate on growth can be obtained by using final status centering for parameterization and regressing the random intercepts (or the intercept factor scores) on both the independent variable and a baseline covariate–the framework used to study change with classical regression analysis. Examples are provided that illustrate the application of an intercept-focused approach to obtain effect sizes–the unstandardized regression coefficient, the standardized regression coefficient, squared semi-partial correlation, and Cohen’s f²–that estimate the same parameters as respective effect sizes from a classical regression analysis. Moreover, statistical power to detect the effect of the predictor on growth was greater when using random intercepts than the conventionally used random slopes.

 抽象的

在具有潜在变量的增长模型中，自变量对随机斜率的影响通常用于检查研究过程中变化的预测因素。本文证明，通过使用最终状态中心进行参数化并对自变量和基线协变量（用于研究变化的框架）进行随机截距（或截距因子得分）回归，可以获得协变量对增长的相同影响与经典回归分析。提供的示例说明了应用截距为中心的方法来获取效应大小（非标准化回归系数、标准化回归系数、平方半偏相关系数和 Cohen's f ² ），这些示例根据 a 估计与各自效应大小相同的参数。经典回归分析。此外，使用随机截距时检测预测变量对生长影响的统计功效比传统使用的随机斜率更大。