ABSTRACT

Measuring change in an educational or psychological construct over time is often achieved by repeatedly administering the same items to the same examinees over time and fitting a second-order latent growth curve model. However, latent growth modeling with full information maximum likelihood (FIML) estimation becomes computationally challenging when the observed response data are categorical. This study first discusses some possible options that researchers can take regarding model specification and estimation (e.g., limited-information and various FIML estimators) to circumvent the challenge. To explore the utility of a stochastic Newton-Raphson type of algorithm (i.e., Metropolis Hastings-Robbins Monro; MH-RM) implemented primarily for multidimensional item response model, a re-parameterized latent growth model is also introduced. The viability of each option is examined via Monte-Carlo simulations. Insights on the pros and cons of these options and the conditions under which they are applicable are provided for researchers.

摘要

衡量教育或心理结构随时间的变化通常是通过随着时间的推移对相同的考生重复管理相同的项目并拟合二阶潜在增长曲线模型来实现的。然而，当观察到的响应数据是分类时，具有全信息最大似然 (FIML) 估计的潜在增长建模在计算上变得具有挑战性。本研究首先讨论了研究人员可以采取的关于模型规范和估计的一些可能选项（例如，有限信息和各种 FIML 估计器）以规避挑战。为了探索主要用于多维项目响应模型的随机 Newton-Raphson 类型算法（即 Metropolis Hastings-Robbins Monro；MH-RM）的效用，还引入了重新参数化的潜在增长模型。通过蒙特卡罗模拟检查每个选项的可行性。为研究人员提供了有关这些选项的优缺点及其适用条件的见解。