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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

119
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...
119
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

134
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...
134
Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.6K
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
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

1.6K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
1.6K
One-Way ANOVA: Equal Sample Sizes01:15

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...
3.2K

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

Cognitive-affective and behavioral pain mechanisms in individuals with chronic low back pain: a network analysis.

Pain·2026
Same author

A systematic review and Meta-Analytic Gaussian Network Aggregation of anxious symptoms.

Clinical psychology review·2026
Same author

Mapping the network structure of dementia and its associated factors among older adults in Singapore: evidence from two national cross-sectional studies.

BMC geriatrics·2026
Same author

Mapping the dynamics of idiographic network models to the network theory of psychopathology.

Behavior research methods·2026
Same author

The invariance partial pruning approach to the network comparison in time-series and panel data.

Psychological methods·2026
Same author

Publisher Correction: Movement tracking of psychological processes: A tutorial using mousetrap.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
Same journal

Psychometric functions from multiple responses : Dedicated to the memory of Colin L. Mallows.

Behavior research methods·2026
Same journal

Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems.

Behavior research methods·2026
Same journal

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.

Behavior research methods·2026
Same journal

Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.

Behavior research methods·2026
查看所有相关文章

相关实验视频

Updated: May 27, 2025

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

在多级向量自回归模型中测试组差异.

Jonas M B Haslbeck1,2, Sacha Epskamp3, Lourens J Waldorp4

  • 1Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands. jonashaslbeck@protonmail.com.

Behavior research methods
|February 20, 2025
PubMed
概括
此摘要是机器生成的。

研究人员开发了新的统计测试,用于在不同组之间比较多级向量自回归 (VAR) 模型. 这些在R中实施的方法使得在复杂的时间序列数据中对组差异进行可靠的推断.

关键词:
组对比 组对比 组对比网络模型 网络模型时间序列建模时间序列建模在VAR模型中.矢量自回归模型 矢量自回归模型

更多相关视频

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K

相关实验视频

Last Updated: May 27, 2025

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
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K

科学领域:

  • 计算统计学 计算统计学
  • 时间序列分析时间序列分析
  • 多层次建模多层次建模

背景情况:

  • 多级向量自回归 (VAR) 模型被广泛用于分析多个受试者的纵向数据.
  • 在这些模型中调查群体差异 (例如,患者与对照) 是至关重要的,但缺乏标准化的推理方法.
  • 现有的比较跨组多层VAR模型的方法并不容易获得或被广泛采用.

研究的目的:

  • 引入和评估新的统计测试,以推断多层VAR模型中的群体差异.
  • 在R统计环境中提供这些测试的实际实施.
  • 通过模拟研究来评估拟议方法在检测群体差异方面的性能.

主要方法:

  • 在多级VAR模型中进行群组比较的参数测试的开发和解释.
  • 开发和解释一个非参数顺序测试,用于可靠的组比较.
  • 使用mlVAR R-package和使用mnet R-package的教程来执行这两项测试.

主要成果:

  • 该研究成功实施并评估了在多层VAR模型中对群体差异的两个不同的统计测试.
  • 模拟研究表明,这些测试的性能能够准确地恢复已知的群体差异.
  • R-package mnet提供了一个可重现的框架,用于将这些方法应用于经验情感数据.

结论:

  • 本文所介绍的参数和非参数测试为在多级VAR分析中进行组对比提供了可访问和可靠的方法.
  • 这些工具增强了在动态网络结构中调查个人间差异的能力.
  • 提供的R实现和教程有助于在心理学和神经科学研究中应用这些先进的统计技术.