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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

332
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...
332
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

2.6K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
2.6K
Crossover Experiments01:16

Crossover Experiments

3.9K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
3.9K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

2.5K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
2.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

325
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
325
Test for Homogeneity01:23

Test for Homogeneity

2.1K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Nothing to See Here? A Non-Inferiority Approach to Parallel Trends.

Statistics in medicine·2026
Same author

Association of School-Based Health Center Availability With Child Mental Health Outcomes.

Health services research·2025
Same author

The effect of providing Medicare Advantage enrollees diagnosed with cancer additional time to reassess enrollment.

Health affairs scholar·2025
Same author

Network Analysis to Define Pediatric Acute Care Regions in Wisconsin.

Health services research·2025
Same author

Regionalization of Hip Fracture Care in Five High-Income Countries.

Health services research·2025
Same author

Insulin Out-of-Pocket Spending Caps and Employer-Sponsored Insurance: Changes in Out-of-Pocket and Total Costs for Insulin and Healthcare.

Health services research·2025

Related Experiment Video

Updated: Oct 4, 2025

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

6.0K

Difference-in-differences for categorical outcomes.

John A Graves1, Carrie Fry1, J Michael McWilliams2

  • 1Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

Health Services Research
|February 8, 2022
PubMed
Summary
This summary is machine-generated.

Difference-in-difference (DID) methods were developed for categorical outcomes to assess the Affordable Care Act's Medicaid expansion. The study found Medicaid expansion increased transitions to public insurance and decreased transitions to private insurance among the uninsured.

Keywords:
control groupsdata scienceeconometrichealth care reformhealth policyinsurancemethodsmodelsregression analysisstatistical

More Related Videos

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:24

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

962
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

941

Related Experiment Videos

Last Updated: Oct 4, 2025

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

6.0K
Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:24

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

962
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

941

Area of Science:

  • Health Economics
  • Biostatistics
  • Public Health Policy

Background:

  • The Affordable Care Act's Medicaid expansion aimed to increase health insurance coverage.
  • Estimating the causal impact of such policies on insurance coverage requires robust statistical methods.
  • Difference-in-difference (DID) estimators are commonly used but require careful application for categorical outcomes.

Purpose of the Study:

  • To develop and discuss advanced difference-in-difference (DID) estimators for categorical outcomes.
  • To apply these novel DID methods to estimate the effect of the Affordable Care Act's Medicaid expansion on health insurance coverage.
  • To explore the scale-dependence of DID assumptions for marginal versus transition effect estimates.

Main Methods:

  • Developed novel difference-in-difference (DID) methods for panel data with categorical outcomes.
  • Specified a new target estimand focusing on outcome category transitions under treatment versus control.
  • Analyzed secondary data from the Survey on Income and Program Participation (SIPP) panel (2014) covering 16,027 individuals aged 18-62.

Main Results:

  • The study estimated a differential increase in transitions from uninsured to public insurance coverage following Medicaid expansion.
  • A differential decrease in transitions from uninsured to private, non-group coverage was observed.
  • The analysis also indicated a differential decrease in the proportion of individuals remaining uninsured.

Conclusions:

  • The application of DID methods highlights the scale-dependence of assumptions, particularly when comparing marginal and transition effects.
  • Studying transitions across outcome values provides nuanced insights into policy impacts that changes in marginals alone may obscure.
  • The findings demonstrate the utility of transition-based DID approaches for understanding complex changes in health insurance coverage.