发帖
雷达卡
=Life and Job Satisfaction in China: Exploring Longitudinal Analysis with MplusAuthor(s): Lukasz Czarnecki, Delfino Vargas Chanes
This course provides a practical introduction to longitudinal analysis in incremental steps using a real-world example of job satisfaction and life satisfaction and the Mplus statistical package. Social and behavioral researchers have used latent growth curve (LGC) and auto-regressive modeling approaches in structural equation modeling (SEM) frameworks to describe and analyze changes in individual attributes, such as behaviors, psychosocial characteristics, relationships, and health outcomes, over time. LGC models focus on intraindividual change, while auto-regressive models focus on residual change over time. When analyzing more than one co-varying or time-varying attribute, researchers have extended LGC models to parallel LGC models and auto-regressive models to cross-lagged (CL) auto-regressive models. The authors explain these conventional models well using illustrative examples of job satisfaction and life satisfaction.
The CL approach for two time-varying variables is consistent with the timesequential processes between two consecutive time points resulting in a change in the rank order of those attributes. Parallel LGC is consistent with the parallel intra-individual change process (interlocking trajectories of attributes). and also use illustrative examples to explain these extended models well in this course.
Exploring Longitudinal Analysis with Mplus .pdf
(4.55 MB, 需要: RMB 19 元)
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