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

Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

Distribution Reliability and Automation

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

You might also read

Related Articles

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

Sort by

Same author

The Impact of Model Misspecification on the Individual Causal Association in Surrogate Endpoint Evaluation.

Pharmaceutical statistics·2026

Same author

Time-Scale Target Parameters and Two-Step Estimation in Longitudinal Trials for Progressive Diseases.

Statistics in medicine·2026

Same author

Validation of Optimized Embryo Scoring Procedures: Single-Day versus 2-Day Assessment.

Gynecologic and obstetric investigation·2026

Same author

Primary carE PPi dEprescRibing (PEPPER) trial: a protocol for determining the optimal strategy for stopping chronic proton pump inhibitor therapy in primary care patients.

BMJ open·2026

Same author

Clinical Outcome Associated With Beta-Lactam Allergy Labels in Hospitalized Patients in Belgium.

Clinical and translational allergy·2026

Same author

Visual Biofeedback (VB) through Trans-Perineal Ultrasound (TPU) during the active second stage of labor to improve maternal childbirth satisfaction: protocol of a randomized controlled trial.

BMC pregnancy and childbirth·2026

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

A unified approach to multi-item reliability.

Ariel Alonso¹, Annouschka Laenen, Geert Molenberghs

¹Center for Statistics, Hasselt University, Diepenbeek, Belgium. ariel.alonso@uhasselt.be

|January 15, 2010

Summary

This study introduces two novel reliability measures for multi-item scales, offering a more general approach than existing methods. These new measures capture distinct aspects of reliability and are applied to the Positive and Negative Syndrome Scale for schizophrenia assessment.

More Related Videos

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Related Experiment Videos

Last Updated: Jun 17, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Area of Science:

Psychometrics
Psychological Measurement
Schizophrenia Assessment

Background:

Reliability of multi-item scales is crucial in psychometrics.
Existing measures like Cronbach's α often rely on restrictive, unidimensional models.
There is a need for more general reliability assessment tools.

Purpose of the Study:

To introduce two novel measures for quantifying the reliability of multi-item scales.
To develop measures based on a more general psychometric model.
To assess the reliability of the Positive and Negative Syndrome Scale.

Main Methods:

Development of two new reliability measures for multi-item scales.
Theoretical framework establishing intuitive properties and complementary value.
Application of the measures to the Positive and Negative Syndrome Scale.

Main Results:

The proposed measures capture two distinct aspects of scale reliability.
The measures satisfy a set of intuitive psychometric properties.
The reliability of the Positive and Negative Syndrome Scale was investigated using the new measures.

Conclusions:

The new reliability measures offer a more general and comprehensive approach.
These measures provide valuable insights into the reliability of complex scales.
The study advances the assessment of measurement reliability in psychometrics and clinical settings.