.png){kind=icon}
返回
Analyzing Individual Differences in Intervention-Related Changes
.png){kind=icon}
OA
AI
DOI：10.1177/2515245920979172.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
intervention
latent change modeling
measurement invariance
individual differences
correlated change
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
A
IF:
13.4
论文数:
320
被引数:
3.7K
机构
引用论文
.png){kind=icon}
Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results许多分析师，一个数据集: 透明分析选择的变化如何影响结果OAAI
OAAI分享收藏What to do When Scalar Invariance Fails: The Extended Alignment Method for Multi-Group Factor Analysis Comparison of Latent Means Across Many Groups当标量不变性失败时该怎么办: 多组因子分析的扩展对齐方法跨多个组的潜在均值比较
OAAI
OAAI分享收藏
查看更多内容