.png){kind=icon}
.png){kind=icon}
Return
Analyzing Individual Differences in Intervention-Related Changes
.png){kind=icon}
OA
AI
DOI：10.1177/2515245920979172.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Intervention studies can be expensive and time-consuming, which is why it is important to extract as much knowledge as possible. We discuss benefits and limitations of analyzing individual differences in intervention studies in addition to traditional analyses of average group effects. First, we present a short introduction to latent change modeling and measurement invariance in the context of intervention studies. Then, we give an overview on options for analyzing individual differences in intervention-related changes with a focus on how substantive information can be distinguished from methodological artifacts (e.g., regression to the mean). The main topics are benefits and limitations of predicting changes with baseline data and of analyzing correlated change. Both approaches can offer descriptive correlational information about individuals in interventions, which can inform future variations of experimental conditions. Applications increasingly emerge in the literature-from clinical, developmental, and educational psychology to occupational psychology-and demonstrate their potential across all of psychology.
Keywords:
intervention
latent change modeling
measurement invariance
individual differences
correlated change
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
A
IF:
13.4
Papers:
311
Citations:
3.7K