Jove
Visualize
Contact Us

Related Concept Videos

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

395
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
395
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

118
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,...
118
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

246
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
246
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

115
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
115
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

You might also read

Related Articles

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

Sort by
Same author

Treating Noneffortful Responses as Missing.

Educational and psychological measurement·2024
Same author

A Note on the Relation Between the Angle of the Reference Composite and Liu, Li, and Liu's Method 4 for Domain Scores.

Applied psychological measurement·2021
Same author

A Note on the Odds Ratio DIF Index.

Applied psychological measurement·2020
Same author

Revised Parallel Analysis With Nonnormal Ability and a Guessing Parameter.

Educational and psychological measurement·2019
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 Experiment Video

Updated: Jun 19, 2025

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.0K

Item Parameter Recovery: Sensitivity to Prior Distribution.

Christine E DeMars1, Paulius Satkus2

  • 1James Madison University, Harrisonburg, VA, USA.

Educational and Psychological Measurement
|July 26, 2024
PubMed
Summary

For item response theory models, applying Bayesian priors to marginal maximum likelihood estimation improves parameter estimates, especially with small sample sizes. Priors on c-parameters are beneficial for samples >= 500, while both a- and c-parameters need priors for samples of 100.

Keywords:
BMEIRTMMAPMMLprior distribution

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

Related Experiment Videos

Last Updated: Jun 19, 2025

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.0K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

Area of Science:

  • Psychometrics
  • Statistical modeling
  • Educational measurement

Background:

  • Marginal maximum likelihood (MML) is a common estimation method for item response theory (IRT) models.
  • Bayesian priors are often incorporated into MML estimation for three-parameter logistic (3PL) models, particularly with small sample sizes, to address estimation challenges.
  • Guidance on selecting appropriate priors for MML estimation is limited.

Purpose of the Study:

  • To investigate the impact of prior distributions on parameter estimation in 3PL IRT models using MML.
  • To determine the effectiveness of priors on different parameters (a, b, c) across various sample sizes.
  • To evaluate the influence of prior mode and strength on parameter estimate bias and root mean squared error (RMSE).

Main Methods:

  • Simulation study with varying sample sizes (≤1000).
  • Estimation of 3PL IRT models using marginal maximum likelihood.
  • Application of Bayesian priors to item parameters (a, b, c).
  • Analysis of parameter bias and RMSE under different prior conditions.

Main Results:

  • Without priors, small sample sizes (≤1000) often resulted in extreme and implausible parameter estimates.
  • Priors on c-parameters improved estimation for samples of 500 or more.
  • Priors on both a- and c-parameters were necessary for samples of size 100.
  • Parameter bias was influenced by the prior mode, but not significantly by prior strength (unless extremely informative).
  • RMSE for a- and b-parameters showed minimal dependence on prior mode or strength.
  • RMSE for c-parameters was affected by the prior mode for c.

Conclusions:

  • Bayesian priors are crucial for stable MML estimation of 3PL IRT models, especially with limited data.
  • Strategic application of priors, particularly on c-parameters, can mitigate estimation issues.
  • The choice of prior mode is important for reducing bias in c-parameter estimates.