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

Data Validation

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

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...

Prevalence and Incidence

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...

Formulating and Validating Nursing Diagnosis II

Formulating and Validating Nursing Diagnosis II

Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

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.

You might also read

Related Articles

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

Sort by

Same author

Comparative Test Evaluation: Methods and Challenges.

Journal of gambling studies·2018

Same author

Test Performance Variation Between Settings and Populations.

Journal of gambling studies·2017

Same author

The validation of screening tests: meet the new screen same as the old screen?

Journal of gambling studies·2011

Same author

A test of the false positive bias hypothesis.

Journal of gambling studies·2007

Same author

Reflections on accuracy.

Journal of gambling studies·2006

Same author

A comment on the utility of prevalence estimates of pathological gambling.

Journal of gambling studies·2006

Search research articles

Related Experiment Video

Updated: May 13, 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

Setting criterion thresholds for estimating prevalence: what is being validated?

Blase Gambino¹

¹American Academy of Health Care Providers in the Addictive Disorders, 10 Ellet Street, Boston, MA, 02122, USA, blasegambino@comcast.net.

Journal of Gambling Studies

|March 26, 2013

Summary

Estimating problem gambling prevalence requires understanding test score interpretation and clinical relevance, not just test validation. This study recommends pragmatic empirical methods for accurate prevalence estimation.

Related Experiment Videos

Last Updated: May 13, 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

Area of Science:

Psychology
Public Health
Epidemiology

Background:

Estimating problem gambling prevalence faces challenges due to misconceptions and flawed assumptions.
Current methods often fail to validate test score interpretations for specific purposes and define clinical relevance of case definitions.
Misunderstandings regarding test value interpretation at criterion thresholds and replication of threshold validation are prevalent.

Purpose of the Study:

To address misconceptions and flawed assumptions in problem gambling prevalence estimation.
To clarify the validation process, focusing on test score interpretation rather than the test itself.
To recommend alternative, pragmatic empirical approaches for accurate prevalence estimation.

Main Methods:

Critically analyze common assumptions and misinterpretations in problem gambling research.
Examine the role of clinical and epidemiologic relevance in case definitions.
Discuss the interpretation of test values and criterion thresholds.
Propose alternative methods for test evaluation and prevalence estimation.

Main Results:

The validation of test score interpretation, not just the test, is crucial.
Defining the clinical and epidemiologic relevance of case definitions is often overlooked.
The dichotomy versus continuum distinction in problem gambling is a false choice.
Emphasis on overestimation of prevalence is misdirected.

Conclusions:

Accurate problem gambling prevalence estimation requires a nuanced understanding of measurement and definition.
Pragmatic empirical approaches offer a more reliable method for interpreting prevalence estimates.
Future research should focus on robust validation of score interpretation and clinically relevant case definitions.