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

Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.6K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.6K
Confidence Intervals01:21

Confidence Intervals

6.5K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.5K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

6.0K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
6.0K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

235
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...
235
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

156
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,...
156

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

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
Same author

From barriers to benefits: A personalized sleep intervention enhances sleep duration and emotional health in chronic short sleepers.

British journal of psychology (London, England : 1953)·2026
Same author

semfindr: An R Package for Identifying Influential Cases in Structural Equation Modeling.

Multivariate behavioral research·2026
Same author

How plausible is my model? Assessing model plausibility of structural equation models using Bayesian posterior probabilities (BPP).

Behavior research methods·2026
Same author

Forming bootstrap confidence intervals and examining bootstrap distributions of standardized coefficients in structural equation modelling: A simplified workflow using the R package semboottools.

Behavior research methods·2026
Same author

Common and unique latent transition analysis (CULTA) as a way to examine the trait-state dynamics of alcohol intoxication.

Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors·2025

相关实验视频

Updated: Jul 19, 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

蒙特卡罗信任区间的间接影响与缺失的数据.

Ivan Jacob Agaloos Pesigan1, Shu Fai Cheung2

  • 1Department of Psychology, Faculty of Social Sciences, University of Macau, Avenida da Universidade, Taipa, Macao SAR, China. i.j.a.pesigan@connect.um.edu.mo.

Behavior research methods
|August 7, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种快速准确的两步蒙特卡洛方法,用于在缺少数据的情况下进行中介分析的间接效应的置信区间. 这种方法在数据不完整时增强了统计的严谨性.

关键词:
全面信息最大概率最大概率间接影响的间接影响.调解 调解是一种调解.随机失踪的人是随机失踪的人.完全随机的完全失踪.蒙特卡洛方法 蒙特卡洛方法多重的归咎是多重的归咎.非参数式启动.

更多相关视频

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

相关实验视频

Last Updated: Jul 19, 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
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

科学领域:

  • 统计 统计 统计 统计
  • 量化心理学 量化心理学
  • 计量经济学 计量经济学

背景情况:

  • 在调解分析中,缺失的数据普遍存在,这使得对间接影响的准确估计变得复杂.
  • 对于置信区间的现有方法通常假定完整的数据,从而限制了它们的适用性.
  • 非参数引导和蒙特卡洛方法是为完整数据建立的,但需要适应缺失数据场景.

研究的目的:

  • 提出一种新,高效和精确的两步方法,用于在缺少数据的情况下进行中介分析中的间接效应的置信区间.
  • 调整蒙特卡洛方法,以有效地处理中介模型中缺少的数据.
  • 通过模拟研究来评估拟议方法的性能.

主要方法:

  • 提出了一种两步方法,用于产生间接影响的置信区间.
  • 第1步涉及参数估计和差异-共差矩阵计算,使用全信息最大概率 (FIML) 或多重推算 (MI) 等方法.
  • 步骤2模拟间接效应的采样分布,使用步骤1的估计值,然后进行置信区间构造.

主要成果:

  • 拟议的两步蒙特卡洛方法在生成缺少数据的间接效应的置信区间方面表现为简单,快速和准确.
  • 模拟研究证实了该方法在各种条件下的可行性.
  • 该方法在调解分析中处理不完整数据集时,为传统方法提供了可靠的替代方案.

结论:

  • 开发的两步蒙特卡洛方法为在缺少数据的情况下构建间接效应的置信区间提供了一个实际的解决方案.
  • 这种方法提高了调解分析在现实研究场景中的稳定性和适用性.
  • 这些发现对应用研究人员具有重大意义,他们试图准确地解释使用不完整数据的间接影响.