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

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.8K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
5.8K
Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
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...
13.9K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

425
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
425
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

382
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
382
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
6.1K

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

Equity in law enforcement actions following a school threat assessment.

Law and human behavior·2025
Same author

The Impact of Restorative Practices on the Use of Out-of-School Suspensions: Results from a Cluster Randomized Controlled Trial.

Prevention science : the official journal of the Society for Prevention Research·2023
Same author

Accounting for Heteroskedasticity Resulting from Between-Group Differences in Multilevel Models.

Multivariate behavioral research·2022
Same author

Using cluster-robust standard errors when analyzing group-randomized trials with few clusters.

Behavior research methods·2021
Same author

Alternatives to Logistic Regression Models when Analyzing Cluster Randomized Trials with Binary Outcomes.

Prevention science : the official journal of the Society for Prevention Research·2021
Same author

An investigation of the psychometric properties of the early identification system-student report in a middle school sample.

School psychology (Washington, D.C.)·2020
Same journal

A Simple Approach for Differential Test Functioning Based on Sum Scores.

Educational and psychological measurement·2026
Same journal

Evaluating Factor Retention in Large Factor Analysis Models: A Simulation Study Comparing 15 Methods.

Educational and psychological measurement·2026
Same journal

Agreement and Alignment in Binary Rating Tasks: Strategic Convergence as an Equilibrium Outcome.

Educational and psychological measurement·2026
Same journal

Interactions Between Termination Criteria and Ability Estimators in Computerized Adaptive Testing.

Educational and psychological measurement·2026
Same journal

Identification and Diagnosis of Misreporting in Surveys.

Educational and psychological measurement·2026
Same journal

The Aggregated Latent Profile Index: Measuring Person Profile Differentiation Within a Bootstrap-Validated Latent Profile Space.

Educational and psychological measurement·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.8K

当集群强大的推理失败时

Francis Huang1,2

  • 1University of Missouri, Columbia, USA.

Educational and psychological measurement
|December 22, 2025
PubMed
概括
此摘要是机器生成的。

集群强度标准错误 (CRSEs) 在嵌套数据中可能会失败,特别是在不平衡的集群中. 替代估计器 (CR2,CR3) 和df调整保持I型错误率,CR1和有效集群大小df也可以接受.

关键词:
集群强大的标准错误.聚类数据是聚类数据.自由度的自由度的自由度.实际样本大小是有效的.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

313

相关实验视频

Last Updated: Jan 8, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.8K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

313

科学领域:

  • 统计 统计 统计 统计
  • 教育研究教育研究
  • 数据分析 数据分析

背景情况:

  • 集群稳定性标准错误 (CRSEs) 广泛用于嵌套数据,但可能无法保持I型错误率.
  • 问题特别出现在教育数据集中常见的不平衡集群大小.
  • 在使用集群级预测器时,准确的统计推断至关重要.

研究的目的:

  • 调查CRSEs无法保持I型错误率的情况.
  • 评估替代估计器和自由度 (df) 调整.
  • 用连续和二分法预测器评估不同CRSE方法的性能.

主要方法:

  • 使用蒙特卡洛模拟来测试各种场景.
  • 评估了传统的CRSE (CR1) 估计器.
  • 评估偏差减少线性化 (CR2) 和刀 (CR3) 估计器,并进行df调整.

主要成果:

  • 带有DF调整的CR2和CR3估计器在维持I型错误率方面通常是有效的.
  • 传统的CR1估计器与基于有效集群大小的df配对也是可以接受的.
  • 性能因特定的数据特征和预测器类型而异.

结论:

  • 替代CRSE估计器和df调整可以有效地解决嵌套数据中的I型错误率问题.
  • 仔细考虑数据集特征,如集群大小平衡,对于可靠的统计推断至关重要.
  • 对嵌套数据结构的准确报告对于CRSEs的适当应用至关重要.