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

Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.5K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

226
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...
226
Introduction to Test of Independence01:21

Introduction to Test of Independence

2.3K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.3K
Fisher's Exact Test01:08

Fisher's Exact Test

591
Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
591
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K

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

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same author

Standard Errors for Reliability Coefficients.

Psychometrika·2025
Same author

How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data.

Multivariate behavioral research·2025
Same author

Interrater Reliability for Interdependent Social Network Data: A Generalizability Theory Approach.

Multivariate behavioral research·2025
Same author

Investigating the Ordering Structure of Clustered Items Using Nonparametric Item Response Theory.

Educational and psychological measurement·2024
Same author

Beyond Pearson's Correlation: Modern Nonparametric Independence Tests for Psychological Research.

Multivariate behavioral research·2024

相关实验视频

Updated: Jul 15, 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

3.4K

对于大型稀缺应急表的分类边际模型的最大增强实证概率估计.

L Andries van der Ark1, Wicher P Bergsma2, Letty Koopman3

  • 1Research Institute of Child Development and Education, University of Amsterdam, P.O. Box 15776, 1001, NG, Amsterdam, The Netherlands. L.A.vanderArk@uva.nl.

Psychometrika
|September 26, 2023
PubMed
概括

最大增强的经验概率 (MAEL) 估计为分析大,稀疏的分类数据提供了解决方案. 这种新方法克服了复杂模型最大实证概率 (MEL) 的局限性.

关键词:
克伦巴赫的阿尔法分类边际模型的分类边际模型.大量的分类数据集.边际同质性的边际同质性最大的经验概率估计估计.最大的概率估计估计.可扩展性系数的可扩展性系数

更多相关视频

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
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

6.9K

相关实验视频

Last Updated: Jul 15, 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

3.4K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
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

6.9K

科学领域:

  • 统计 统计 统计 统计
  • 计算统计学 计算统计学
  • 数据分析 数据分析

背景情况:

  • 分类边际模型 (CMM) 对于依赖性分类数据是有效的,当依赖性不是主要关注点时.
  • 对于CMM的最大概率 (ML) 估计,由于指数级增长的应急表,随着变量数量的增加,它变得在计算上不可行.
  • 最大实证概率 (MEL) 估计提供了一个具有最佳异面效率的替代方案,但与大,稀疏的表格作斗争.

研究的目的:

  • 解决最大实证概率 (MEL) 估计在大型,稀疏的应急表中的分解.
  • 为分类边际模型 (CMM) 引入一种新的估计方法,该方法对于大型数据集在计算上是可行的.
  • 为分析复杂的分类数据结构提供强大的统计工具.

主要方法:

  • 最大增强经验概率 (MAEL) 估计的发展.
  • MAEL涉及用精心挑选的细胞增强经验概率支持.
  • 为了评估MAEL的性能,进行了模拟研究.

主要成果:

  • 经验最大概率 (MEL) 估计被证明对大型,稀疏的应急表是不可靠的.
  • 拟议的最大增强实证概率 (MAEL) 方法证明了有限样本的良好性能.
  • 即使对非常大的应急表来说,MAEL也有效,克服了以前方法的局限性.

结论:

  • 最大增强经验概率 (MAEL) 估计为CMM提供了可行的和强大的最大概率 (ML) 和最大经验概率 (MEL) 的替代方案.
  • 在传统方法失败的情况下,MAEL特别适合分析大型和稀疏的分类数据集.
  • 拟议的方法提高了CMM在复杂的统计建模场景中的适用性.