Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

1.1K
The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
1.1K
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

328
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
328
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

351
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
351
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

534
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
534
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

948
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
948
Statistical Significance01:50

Statistical Significance

21.2K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
21.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same authorSame journal

The EM Algorithm and Its Variants in Cognitive Diagnostic Models: Comparing Their Propensity for Boundaries, Extremes, Convergence, and Suboptimal Solutions.

Applied psychological measurement·2026
Same author

Bayesian fine-mapping pinpoints candidate genes and pleiotropic loci of production traits from a chicken backcrossing scheme.

BMC genomics·2026
Same author

Genetically Determined Levels of Inflammation-Related Proteins and Functional Outcome After Ischemic Stroke: A Mendelian Randomization Study.

Journal of stroke·2026
Same author

Predicting post-stroke functional outcome using explainable machine learning and integrated data.

Scientific reports·2026
Same author

Integrated Methylome-Transcriptome Analysis Reveals Epigenomic Remodeling and Rho GTPase-Linked Immune-Epithelial Crosstalk in Atopic Dermatitis.

Allergy·2026
Same author

Deep learning can automate chicken tibia-breaking strength quantification to improve animal welfare.

Poultry science·2026

Related Experiment Video

Updated: Jan 23, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

Improved Wald Statistics for Item-Level Model Comparison in Diagnostic Classification Models.

Yanlou Liu1, Björn Andersson2, Tao Xin3

  • 1Qufu Normal University, China.

Applied Psychological Measurement
|June 26, 2019
PubMed
Summary

The Wald test for selecting diagnostic classification models (DCMs) often has inflated Type I errors. New methods using sandwich-type matrices improve accuracy and power for item-level model comparisons, especially with larger sample sizes.

Keywords:
Wald testasymptotic covariance matrixdiagnostic classification modelslikelihood ratio testmodel comparison

More Related Videos

Combined Supine and Standing Imaging for Varicocele: An Improved Diagnostic Approach
04:15

Combined Supine and Standing Imaging for Varicocele: An Improved Diagnostic Approach

Published on: November 22, 2024

648
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

446

Related Experiment Videos

Last Updated: Jan 23, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K
Combined Supine and Standing Imaging for Varicocele: An Improved Diagnostic Approach
04:15

Combined Supine and Standing Imaging for Varicocele: An Improved Diagnostic Approach

Published on: November 22, 2024

648
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

446

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Diagnostic Classification Models (DCMs) are prevalent in education and psychology.
  • The Wald test is commonly used for selecting appropriate DCMs.
  • Existing Wald tests suffer from inflated Type I error rates, compromising model selection accuracy.

Purpose of the Study:

  • To propose improved Wald test statistics for item-level DCM comparisons.
  • To address the Type I error inflation issue in existing Wald tests.
  • To evaluate the performance of new Wald test variants.

Main Methods:

  • Developed Wald tests utilizing the observed information matrix.
  • Developed Wald tests utilizing a sandwich-type matrix for covariance estimation.
  • Conducted a simulation study to assess empirical behavior under various conditions.

Main Results:

  • The proposed Wald test based on the sandwich-type matrix demonstrated accurate Type I error rates.
  • This improved Wald test exhibited adequate to excellent statistical power.
  • These benefits were observed particularly with reasonably large sample sizes.

Conclusions:

  • The Wald test with a sandwich-type matrix offers a more reliable approach for item-level DCM comparisons.
  • This method mitigates Type I error inflation, enhancing diagnostic classification model selection.
  • The findings support the use of improved Wald statistics in psychometric and educational research.