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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

37
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...
37
Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.9K
Random Error01:04

Random Error

880
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
880
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

2.8K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.8K
Probability Distributions01:32

Probability Distributions

6.9K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.9K
Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K

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

[Developmental status and prospect of musical electroacupuncture].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2015
Same author

Utility of Tc-PEG4-E[PEG4-c(RGDfK)]2 in Posttherapy Surveillance of Patients with Reelevated Carcinoembryonic Antigen Levels.

Medical principles and practice : international journal of the Kuwait University, Health Science Centre·2015
Same author

Characterization of the impurities and isomers in cefetamet pivoxil hydrochloride by liquid chromatography/time-of-flight mass spectrometry and ion trap mass spectrometry.

Journal of pharmaceutical and biomedical analysis·2015
Same author

(68)Ga-labeled 3PRGD2 for dual PET and Cerenkov luminescence imaging of orthotopic human glioblastoma.

Bioconjugate chemistry·2015
Same author

An exploratory study on 99mTc-RGD-BBN peptide scintimammography in the assessment of breast malignant lesions compared to 99mTc-3P4-RGD2.

PloS one·2015
Same author

Chemoradiation therapy reduces aldehyde dehydrogenase 1 expression in cervical cancer but does not improve patient survival.

Medical oncology (Northwood, London, England)·2015

相关实验视频

Updated: Jun 25, 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

个人随机效应模型用于在受访者中特征分布的差异.

Rui Wu1,2, Xuliang Gao1, Shiquan Pan1

  • 1School of Psychology, Guizhou Normal University, Guiyang, China.

Scientific reports
|May 25, 2024
PubMed
概括

一个新的个人随机效应模型解决了经典物品响应理论 (IRT) 模型中的局限性. 这种增强的模型考虑了响应概率的个体差异,提供了更准确的参数估计和更好的模型适合教育评估.

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

5.9K
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

6.3K

相关实验视频

Last Updated: Jun 25, 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

5.9K
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

6.3K

科学领域:

  • 教育测量和心理测量学
  • 社会科学中的统计学
  • 项目响应理论 (IRT) 建模模型

背景情况:

  • 经典测量通常假定均性,但物品响应理论 (IRT) 模型可能无法完全捕捉个体响应变化.
  • 标准IRT模型假设相同潜伏特征的受访者具有相同的选项概率,这可能是一个限制性假设.
  • 现有的模型可能无法充分考虑人体内反应模式的变化.

研究的目的:

  • 为项目响应理论提出和评估一种新的个人随机效应模型.
  • 纳入人内差异,以解释具有相似潜伏特征的个体之间的不同选择概率.
  • 为了提高参数估计的准确性和模型适合在教育和心理评估.

主要方法:

  • 开发一种新的个人随机效应模型,其中包含了人内差异.
  • 使用马尔科夫链蒙特卡洛 (MCMC) 方法进行参数估计.
  • 通过模拟研究和对现实数据的分析进行验证 (PISA的PRESUPP规模).

主要成果:

  • 建议的个人随机效应模型与经典IRT模型相比,可以产生更准确的参数估计.
  • 新模型成功地估计了反映受访者能力分布的尺度参数,考虑到个人内部的差异.
  • 已证明较低的根平均平方误差 (RMSE) 和高级模型适用于模拟和真实数据分析.

结论:

  • 个人随机效应模型通过考虑个人反应异质性,比传统的IRT模型提供了显著的进步.
  • 该模型在大规模评估中提供了对受访者能力和反应行为的更细致的理解.
  • 这些发现表明,使用这种增强的建模方法时,教育测量的精度和可靠性得到了提高.