Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Longitudinal Research02:20

Longitudinal Research

11.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.8K
Longitudinal Studies01:26

Longitudinal Studies

101
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
101
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

20
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
20
Cross-Sectional Research01:50

Cross-Sectional Research

11.1K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
11.1K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

122
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...
122
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same authorSame journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same author

EBF1 Deficiency Drives Prostate Cancer Progression by Interfering with the Transcriptional Regulation of <i>ITPR1</i>.

Oncology research·2026
Same author

Alteration in cerebral cortex thickness and structural covariance networks in patients with chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS).

Frontiers in neurology·2026
Same authorSame journal

Three-level vector autoregressive models.

Psychological methods·2026
Same author

Research on noise reduction of annular axial cooling fan blade with perforated structure.

Scientific reports·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
查看所有相关文章

相关实验视频

Updated: May 20, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.2K

在密集的纵向数据中的时间尺度不匹配:基于动态结构方程模型的当前问题和可能的解决方案.

Xiaohui Luo1, Yueqin Hu1, Hongyun Liu1

  • 1Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Beijing Normal University, Faculty of Psychology.

Psychological methods
|May 19, 2025
PubMed
概括
此摘要是机器生成的。

研究人员在密集的纵向数据中探索了动态关系,时间尺度不匹配. 像全路径和因子模型这样的改进模型准确地捕捉了这些复杂的相互作用,比旧方法提供了更好的方法指导.

更多相关视频

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

相关实验视频

Last Updated: May 20, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

科学领域:

  • 心理学方法 心理学方法
  • 量化心理学 量化心理学
  • 纵向数据分析 纵向数据分析

背景情况:

  • 密集的纵向数据 (ILD) 对于研究变量之间的动态关系至关重要.
  • 在ILD中变量之间的时间尺度不匹配是一个重要的分析挑战.
  • 现有的动态结构方程建模 (DSEM) 方法,如部分路径和平均得分模型都有局限性.

研究的目的:

  • 为了评估现有的DSEM模型对时间尺度不匹配的变量.
  • 为了评估改进的DSEM方法的性能:全路径,因子和调整因子模型.
  • 为分析具有时间尺度不匹配的ILD提供方法指南.

主要方法:

  • 模拟研究 (研究1, 2-1, 2-2) 将不同条件下的模型性能进行比较.
  • 评估部分路径,平均得分,全路径,因子和调整因子模型.
  • 将模型应用于具有时间尺度不匹配变量的经验数据 (研究3).

主要成果:

  • 与部分路径模型相比,全路径模型更好地捕捉了动态相互作用和特定时间效应.
  • 因子模型为时间尺度不匹配的变量提供了准确的估计,与偏差平均得分模型不同.
  • 当回归效应实质性时,调整后的因子模型比因子模型提供了边际改进.

结论:

  • 时间尺度不匹配是ILD分析中的一个关键问题.
  • 建议使用全路径和因子模型来分析与时间尺度不匹配的动态关系.
  • 这项研究为ILD研究中的数据收集和分析策略提供了宝贵的见解.