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

Confidence Coefficient01:24

Confidence Coefficient

9.8K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
9.8K
Critical Values01:31

Critical Values

9.3K
A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
9.3K
Confidence Intervals01:21

Confidence Intervals

9.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
9.2K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

8.4K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
8.4K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.5K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
8.5K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.8K
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...
4.8K

You might also read

Related Articles

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

Sort by
Same author

Stochastic population forecasting based on combinations of expert evaluations within the Bayesian paradigm.

Demography·2014
See all related articles

Related Experiment Video

Updated: Nov 19, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.0K

Confidence Distribution for the Ability Parameter of the Rasch Model.

Piero Veronese1, Eugenio Melilli2

  • 1Department of Decision Sciences, Bocconi University, via Roentgen 1, 20136, Milano, Italy. piero.veronese@unibocconi.it.

Psychometrika
|February 3, 2021
PubMed
Summary

This study introduces new methods for estimating ability in the Rasch model, offering improved confidence intervals. These novel approaches address computational challenges and demonstrate superior performance in simulations.

Keywords:
asymptotic expansionconfidence intervalcoverageextreme scorefiducial distributionitem response theorynatural exponential familyobjective Bayesian inference

More Related Videos

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Related Experiment Videos

Last Updated: Nov 19, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.0K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • The Rasch model is a fundamental tool in psychometrics for analyzing item response data.
  • Estimating the ability parameter accurately is crucial for test development and interpretation.
  • Existing methods for confidence intervals in the Rasch model face challenges due to the model's discrete nature.

Purpose of the Study:

  • To develop novel point estimators and confidence intervals for the ability parameter within the Rasch model.
  • To address computational complexities arising from the discrete nature of the Rasch model, especially with a large number of items.
  • To provide approximations for confidence distributions (CD) to facilitate practical application.

Main Methods:

  • Development of a confidence distribution (CD) for the Rasch model's ability parameter.
  • Derivation of first- and second-order approximations for the CD to handle large item sets.
  • Conducting simulation studies to compare the performance of new estimators and intervals against existing frequentist and Bayesian methods.
  • Utilizing interval length expansion to determine optimal sample sizes.

Main Results:

  • The proposed point estimators and confidence intervals exhibit good performance in simulation studies.
  • The novel methods are competitive with, and in some aspects superior to, standard frequentist and weakly informative Bayesian procedures.
  • Approximations of the CD effectively manage computational burdens associated with large item numbers.
  • A method for identifying adequate sample sizes for desired interval precision is established.

Conclusions:

  • The developed confidence distribution and its approximations offer a robust framework for ability estimation in the Rasch model.
  • The new frequentist approach provides a valuable alternative to existing methods, particularly in scenarios with many items.
  • The findings contribute to more precise and reliable measurement of latent traits in educational and psychological assessments.