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

Nominal Level of Measurement00:56

Nominal Level of Measurement

28.7K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
28.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

133
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,...
133
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

62
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
62
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

33.0K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
33.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56

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

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same author

Designing Learning Intervention Studies: Identifiability of Heterogeneous Hidden Markov Models.

Psychometrika·2025
Same author

Improving our understanding of predictive bias in testing.

The Journal of applied psychology·2023
Same author

A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy.

The British journal of mathematical and statistical psychology·2023
Same author

Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis.

Psychometrika·2023
Same author

Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models.

Psychometrika·2023

相关实验视频

Updated: Jul 8, 2025

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

对于名义响应数据的受限制隐性类型模型:识别和估计.

Ying Liu1, Steven Andrew Culpepper2

  • 1Department of Statistics, University of Illinois at Urbana-Champaign, Computing Applications Building, Room 152, 605 E. Springfield Ave., Champaign, IL, 61820, USA.

Psychometrika
|December 19, 2023
PubMed
概括
此摘要是机器生成的。

限制性潜伏类模型 (RLCMs) 现在对多类数据具有新的识别条件. 这一进步有助于在社会科学和心理测量研究中的诊断和分类.

关键词:
贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语认知诊断模型是一种认知诊断模型.可以识别的可识别性名称响应数据的名义响应数据.有限制的潜伏类模型.

更多相关视频

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K
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

相关实验视频

Last Updated: Jul 8, 2025

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
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K
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

科学领域:

  • 心理测量 心理测量 心理测量
  • 社会科学 社会科学 社会科学
  • 统计 统计 统计 统计

背景情况:

  • 限制性潜伏类模型 (RLCMs) 对于分析多变量二进制响应至关重要.
  • 现有的研究已经为二进制和多元数据提供了先进的识别条件.
  • 多类,名义响应数据在社会科学和心理测量学中很常见.

研究的目的:

  • 为具有多类数据的RLCM建立新的识别条件.
  • 讨论这些条件对现实世界的应用的影响.
  • 为参数推理提出贝叶斯框架.

主要方法:

  • 对多类RLCM的新型识别条件的推导.
  • 开发一个用于参数估计的贝叶斯框架.
  • 蒙特卡洛模拟研究用于参数恢复评估.

主要成果:

  • 成功建立了具有多类数据的RLCM的新识别条件.
  • 建议的贝叶斯框架证明了有效的参数恢复.
  • 该方法通过对真实数据集的应用来验证.

结论:

  • 新的可识别性条件提高了RLCM对多种类型和名义数据的适用性.
  • 贝叶斯方法为分析复杂的响应数据提供了一个强大的方法.
  • 这项研究为社会科学和心理测量研究中的诊断和分类提供了有价值的工具.