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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.1K
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...
7.1K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.1K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.1K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.7K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.7K
Decision Making: P-value Method01:09

Decision Making: P-value Method

7.2K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
7.2K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.4K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.4K

You might also read

Related Articles

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

Sort by
Same author

Increasing the statistical power of animal experiments with historical control data.

Nature neuroscience·2021
Same author

Explained Variance and Intraclass Correlation in a Two-Level AR(1) Model.

Multivariate behavioral research·2017
Same author

Testing the hypothesis of tissue selectivity: the intersection-union test and a Bayesian approach.

Bioinformatics (Oxford, England)·2009
Same author

Examining the psychometric characteristics of the Dutch childhood health assessment questionnaire: room for improvement?

Rheumatology international·2006
Same author

[Construction of a scale to signal personality disorders in the elderly].

Tijdschrift voor gerontologie en geriatrie·2004
Same author

Cultural differences in functional status measurement: analyses of person fit according to the Rasch model.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2001

Related Experiment Video

Updated: Mar 26, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Confirmatory Latent Class Analysis: Model Selection Using Bayes Factors and (Pseudo) Likelihood Ratio Statistics.

H Hoijtink

    Multivariate Behavioral Research
    |January 30, 2016
    PubMed
    Summary

    This study explores model selection for latent class models using Bayesian methods. Bayes factors and pseudo-likelihood ratio statistics demonstrate superior performance compared to traditional maximum likelihood criteria.

    More Related Videos

    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

    3.8K
    A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
    15:00

    A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

    Published on: February 7, 2025

    1.2K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K
    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

    3.8K
    A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
    15:00

    A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

    Published on: February 7, 2025

    1.2K

    Area of Science:

    • Statistics
    • Machine Learning
    • Psychometrics

    Background:

    • Latent class models (LCMs) are used for analyzing categorical data by identifying unobserved subgroups.
    • Assigning meaningful interpretations to latent classes often requires incorporating inequality constraints on class-specific probabilities.
    • Different constraint sets lead to distinct LCMs, necessitating robust model selection techniques.

    Purpose of the Study:

    • To investigate and compare model selection criteria for latent class models with inequality constraints.
    • To evaluate the performance of Bayesian selection criteria against traditional maximum likelihood methods.
    • To demonstrate the advantages of Bayesian approaches in avoiding common flaws associated with maximum likelihood selection.

    Main Methods:

    • Utilizing Bayes factors for Bayesian model selection.
    • Employing (pseudo) likelihood ratio statistics evaluated with posterior predictive p-values.
    • Conducting a simulation study to assess the properties of different selection criteria under controlled conditions.

    Main Results:

    • Bayesian selection criteria, specifically Bayes factors and pseudo-likelihood ratio statistics, do not exhibit the same limitations as maximum likelihood based criteria.
    • Simulation results indicate that Bayes factors and the pseudo-likelihood ratio statistic possess favorable properties for model selection in this context.
    • The proposed Bayesian methods offer a more reliable approach to selecting appropriate latent class models.

    Conclusions:

    • Bayesian model selection criteria provide a robust alternative to maximum likelihood methods for latent class models with inequality constraints.
    • The study highlights the practical utility and superior performance of Bayes factors and pseudo-likelihood ratio statistics.
    • The findings are further illustrated with a practical example, demonstrating the application of these methods.