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

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

Opioid Message Fatigue in the United States: Prevalence and Associations with Stigma and Policy Support.

Substance use & misuse·2026
Same author

A staged Bayesian framework for longitudinal prediction of adolescent depression using add health data.

BMC medical research methodology·2026
Same author

Implementation mechanisms used in national efforts to improve community services to keep individuals with mental illness out of local jails.

Implementation science communications·2025
Same author

Efficacy and moderators of cognitive behavioural therapy versus interpersonal psychotherapy for adult depression: study protocol of a systematic review and individual participant data meta-analysis.

BMJ open·2025
Same author

Are Operations Backed by Best Practices in American Problem-Solving Courts?

Journal of substance use·2025
Same author

Exploring the Impact of Juvenile Probation Officer's Individual and Organizational Characteristics on e-Connect Performance.

Journal of correctional health care : the official journal of the National Commission on Correctional Health Care·2025

Related Experiment Video

Updated: Jul 2, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Reducing confounding in natural experiments: evaluating a multilevel matching approach.

Jill Viglione1, Niloofar Ramezani2, Teneshia Thurman3

  • 1Department of Criminal Justice, University of Central Florida, Orlando, FL, USA.

Journal of Experimental Criminology
|July 1, 2026
PubMed
Summary

Rigorous multilevel matching effectively reduced confounding variables in a national experiment evaluating the Stepping Up (SU) Initiative. This method enhances the isolation of SU initiative effects on behavioral health services for justice-involved individuals.

Keywords:
Behavioral healthCriminal justice systemMultilevel matchingNatural experiment

More Related Videos

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

Related Experiment Videos

Last Updated: Jul 2, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

Area of Science:

  • Criminal Justice
  • Public Health
  • Behavioral Health

Background:

  • The Stepping Up (SU) Initiative aims to improve evidence-based behavioral health services for justice-involved populations.
  • Evaluating such initiatives requires robust methods to control for confounding factors in natural experiments.

Purpose of the Study:

  • To assess the effectiveness of multilevel matching procedures in a large national experiment.
  • To determine if multilevel matching can mitigate bias from confounding variables in outcome assessments.

Main Methods:

  • Utilized data from surveys of administrators in 133 SU and 133 matched comparison counties.
  • Employed hierarchical linear modeling to test the efficacy of the multilevel case-controlled matching procedure.
  • Reduced confounding factors from 9 to 4 across three tested models.

Main Results:

  • The matching procedure successfully accounted for baseline differences in county characteristics.
  • Matched models showed fewer significant predictors, indicating successful control of confounding variables.
  • Identified recent funding changes as a variable for future matching considerations.

Conclusions:

  • Multilevel case-controlled matching effectively reduces bias from confounding variables in outcome data.
  • This matching methodology is recommended for natural experimental designs in criminal justice and health research.