检查多层设计中聚类效应的动态:一个潜在的变量方法应用程序.
Tenko Raykov1, Ahmed Haddadi2, Christine DiStefano3
1Michigan State University, East Lansing, USA.
Educational and psychological measurement
|November 20, 2024
概括
本研究研究了多层设计中的集群效应随着时间的推移而发生的变化,使用潜在变量方法. 该方法估计了差异的增长或下降,为教育和行为研究中的时间动态提供了洞察力.
科学领域:
- 多层次建模的多层次建模
- 教育和行为研究方法的方法学.
- 潜在变量分析的分析.
背景情况:
- 聚类效应在教育和行为研究中使用的多层设计中很常见.
- 了解这些聚类效应的时间发展对于准确的数据解释至关重要.
- 现有的方法可能无法充分捕捉层次数据结构内的差异的动态性质.
研究的目的:
- 概述一种潜在变量方法,用于研究聚类效应的时间发展.
- 为了使特定水平变异随时间变化的点和间隔估计.
- 检查两个层和三个层设计中的聚类效应的稳定性.
主要方法:
- 提出一种基于潜变量方法的方法.
- 该程序估计了特定水平差异函数的增长或下降.
- 该方法适用于双层和三层多层模型.
主要成果:
- 概述的程序有效地估计了聚类效应的时间变化.
- 它允许检查集群效应随时间的稳定性.
- 经验示例证明了该方法的实用性.
结论:
- 拟议的潜变量方法为分析多层数据中的时间动态提供了一个强大的框架.
- 这种方法增强了对聚类效应如何演变的理解,为研究人员提供了有价值的见解.
- 该方法与标准的潜变量建模软件兼容.
相关概念视频
Factorial Design
13.0K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.0K
Friedman Two-way Analysis of Variance by Ranks
147
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
147
Cluster Sampling Method
11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Behavioral Genetics and Its Designs
329
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
329
Comparing the Survival Analysis of Two or More Groups
152
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
152
One-Way ANOVA: Equal Sample Sizes
3.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.2K


