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 Intervals01:21

Confidence Intervals

10.8K
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...
10.8K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.1K
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...
10.1K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.7K
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...
11.7K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.0K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
9.0K
Confidence Coefficient01:24

Confidence Coefficient

10.7K
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...
10.7K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K

You might also read

Related Articles

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

Sort by
Same author

Social Media Usage and Its Association With the Social Media Addiction Questionnaire Scale Among Early Adolescents.

JAACAP open·2026
Same author

umx version 4.5: Extending Twin and Path-Based SEM in R with CLPM, MR-DoC, Definition Variables, Ωnyx Integration, and Censored Distributions.

Twin research and human genetics : the official journal of the International Society for Twin Studies·2026
Same author

Advancing models of PTSD-AUD comorbidity: protocol for a multimethod framework using genetics and ecological momentary assessment.

European journal of psychotraumatology·2026
Same author

Contribution of Emotion Dynamics to Adolescent Psychosocial Well-Being: Protocol for a Longitudinal Study.

JMIR research protocols·2026
Same author

Sex-specific associations between loneliness and hair cortisol among older African American adults.

Psychoneuroendocrinology·2026
Same author

Social Determinants of Health and Pediatric Long COVID in the US.

JAMA pediatrics·2026
Same journal

Maximum Likelihood and Bayesian Estimation in Cross-Domain Latent Growth Curve Modeling: The Impact of Reliability, Sample Size, and Missing Data.

Structural equation modeling : a multidisciplinary journal·2026
Same journal

Dynamic Modeling with Intensive Longitudinal Data: One-Step and Two-Step DSEM Approaches.

Structural equation modeling : a multidisciplinary journal·2026
Same journal

Accommodating Continuous Time Metrics within the Discrete-time Latent Change Score Model Using Definition Variables.

Structural equation modeling : a multidisciplinary journal·2025
Same journal

Does Cluster-Robust Estimation Provide Within-Study Effects? A Comparison of Individual Participant Data Methods in MASEM.

Structural equation modeling : a multidisciplinary journal·2025
Same journal

Two-Step Multilevel Latent Class Analysis in the Presence of Measurement Non-Equivalence.

Structural equation modeling : a multidisciplinary journal·2025
Same journal

Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations.

Structural equation modeling : a multidisciplinary journal·2025
See all related articles

Related Experiment Video

Updated: Feb 12, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
14:05

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

29.8K

Likelihood-based confidence intervals for a parameter with an upper or lower bound.

Joshua N Pritikin1, Lance M Rappaport1, Michael C Neale1

  • 1Department of Psychiatry and Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University.

Structural Equation Modeling : a Multidisciplinary Journal
|March 27, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel, user-friendly method for implementing corrections to likelihood-based confidence intervals (CIs) in statistical models. This improves the accuracy and interpretability of parameter estimates, especially in complex models.

Keywords:
Wald testWilks testconfidence intervalslikelihood ratio test

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.5K
Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation
05:52

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Published on: March 8, 2018

19.8K

Related Experiment Videos

Last Updated: Feb 12, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
14:05

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

29.8K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.5K
Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation
05:52

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Published on: March 8, 2018

19.8K

Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Confidence intervals (CIs) express estimate precision.
  • Standard error (SE)-based CIs are common, but likelihood-based CIs offer robustness to model reparameterization.
  • Bounded parameters in latent variable models require corrections for likelihood-based CIs.

Purpose of the Study:

  • To introduce a novel, automatic implementation for correcting bounded likelihood-based confidence intervals.
  • To provide an accessible tool for applied researchers.
  • To demonstrate the accuracy and utility of the proposed correction method.

Main Methods:

  • Developed an automatic implementation for likelihood-based CI corrections.
  • Conducted a simulation study using a latent growth curve model.
  • Illustrated the method with a multilevel confirmatory factor analysis.

Main Results:

  • The novel implementation simplifies the application of a known but complex correction for bounded parameters.
  • Simulation results demonstrate the accuracy of the correction.
  • The method is practical for applied researchers using latent variable models.

Conclusions:

  • The automatic correction method enhances the reliability of likelihood-based confidence intervals in latent variable models.
  • This facilitates more accurate and interpretable parameter estimation in complex statistical analyses.
  • The approach is valuable for researchers in statistics, psychometrics, and quantitative psychology.