Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Confidence Coefficient

Confidence Coefficient

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 both the...

Interpretation of Confidence Intervals

Interpretation of Confidence Intervals

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...

Bonferroni Test

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

Critical Values

Critical Values

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...

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...

Decision Making: Traditional Method

Decision Making: Traditional Method

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...

You might also read

Related Articles

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

Sort by

Same author

Estimating the Minimum Sample Size for Neural Network Model Fitting-A Monte Carlo Simulation Study.

Behavioral sciences (Basel, Switzerland)·2025

Same author

Attention control mediates the relationship between mental imagery vividness and emotion regulation.

Consciousness and cognition·2024

Same author

Combined effects of pharmacological interventions and intermittent theta-burst stimulation on motor sequence learning.

bioRxiv : the preprint server for biology·2024

Same author

Monte Carlo simulation with confusion matrix paradigm - A sample of internal consistency indices.

Frontiers in psychology·2024

Same author

Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR).

Heliyon·2023

Same author

Effects of Trained Peer vs. Teacher Feedback on EFL Students' Writing Performance, Self-Efficacy, and Internalization of Motivation.

Frontiers in psychology·2021

Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026

Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026

Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026

Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026

Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026

Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 25, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Evaluating the performance of different procedures for constructing confidence intervals for coefficient alpha: a

Ying Cui¹, Johnson Li

¹Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, Canada. yc@ualberta.ca

The British Journal of Mathematical and Statistical Psychology

|February 3, 2012

Summary

This summary is machine-generated.

This study compared methods for creating confidence intervals for reliability coefficients in educational and psychological testing. Findings inform accurate reliability estimation and test comparisons.

More Related Videos

The α-test: Rapid Cell-free CD4 Enumeration Using Whole Saliva

The α-test: Rapid Cell-free CD4 Enumeration Using Whole Saliva

Published on: May 16, 2012

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Related Experiment Videos

Last Updated: May 25, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The α-test: Rapid Cell-free CD4 Enumeration Using Whole Saliva

The α-test: Rapid Cell-free CD4 Enumeration Using Whole Saliva

Published on: May 16, 2012

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Area of Science:

Educational Measurement
Psychological Measurement
Psychometrics

Background:

Reliability is crucial for accurate assessment in education and psychology.
Confidence intervals for reliability coefficients are essential for evaluating estimate accuracy and comparing measurement tools.
Existing methods for confidence intervals of coefficient alpha require evaluation under diverse conditions.

Purpose of the Study:

To evaluate and compare parametric and non-parametric methods for constructing confidence intervals of coefficient alpha.
To assess the impact of various factors on the performance of these confidence intervals.
To provide guidance on selecting appropriate methods for reliability analysis.

Main Methods:

A simulation study was conducted to compare different confidence interval construction methods for coefficient alpha.
Six factors were manipulated: number of items, number of subjects, population coefficient alpha, deviation from parallelism, and item response distribution and type.
Coverage and width of confidence intervals were compared across simulation conditions.

Main Results:

The study identified significant differences in the coverage and width of various confidence intervals across manipulated factors.
Performance varied depending on the number of items, subjects, and the degree of deviation from essential parallelism.
Certain parametric and non-parametric methods demonstrated superior performance under specific conditions.

Conclusions:

The choice of method for constructing confidence intervals for coefficient alpha significantly impacts reliability estimation.
Understanding the influence of study characteristics is vital for accurate interpretation of reliability coefficients.
Recommendations are provided for selecting robust confidence interval methods in educational and psychological measurement research.