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

What are Estimates?01:06

What are Estimates?

9.3K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
9.3K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

4.0K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
4.0K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.5K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

6.9K
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...
6.9K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.8K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.8K
Behrens–Fisher Test00:57

Behrens–Fisher Test

340
The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
340

You might also read

Related Articles

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

Sort by
Same authorSame journal

The Association Between Sepsis Coding and Payment to U.S. Hospitals.

Health services research·2026
Same author

Rhode Island's Affordability Standards Led To Substantial Reductions In Hospital Staffing And Labor Costs By 2022.

Health affairs (Project Hope)·2026
Same author

Pre-Claim Review And Traditional Medicare Home Health Spending: Evidence From 4 States.

Health affairs (Project Hope)·2026
Same author

Third-Party Convener Firms And The Rise Of Geographically Dispersed, High-Earning Medicare ACOs.

Health affairs (Project Hope)·2026
Same author

Trends in Broker Enrollment and Spending in Medicare Advantage.

JAMA internal medicine·2026
Same author

Exposure to the new Medicare Advantage risk adjustment model varies across insurers.

Health affairs scholar·2026

Related Experiment Video

Updated: Apr 19, 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

6.5K

Why We Should Not Be Indifferent to Specification Choices for Difference-in-Differences.

Andrew M Ryan1, James F Burgess2, Justin B Dimick3

  • 1University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI.

Health Services Research
|December 16, 2014
PubMed
Summary

Difference-in-differences (DID) models require careful specification. Matching methods improve accuracy when groups differ, while clustered standard errors or permutation tests enhance inference in DID analysis.

Keywords:
Hospitalseconometricshealth economicshealth policyquality of care

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Related Experiment Videos

Last Updated: Apr 19, 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

6.5K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Area of Science:

  • Health Services Research
  • Econometrics
  • Statistical Modeling

Background:

  • Difference-in-differences (DID) models are widely used to estimate treatment effects.
  • Model specification choices can significantly impact the accuracy of DID estimates.
  • Understanding these impacts is crucial for reliable policy evaluation.

Purpose of the Study:

  • To evaluate how different specification choices affect the accuracy of estimates in DID models.
  • To compare the performance of various DID model specifications under different scenarios.
  • To provide guidance on best practices for DID analysis.

Main Methods:

  • A Monte Carlo simulation experiment was conducted using process-of-care quality data.
  • Three scenarios were simulated based on the correlation between treatment probability and pre-intervention performance.
  • Alternative DID models were estimated, varying data intervals, comparison groups, and inference methods.

Main Results:

  • Model specification performance varied dramatically when treatment probability correlated with pre-intervention levels or trends.
  • Propensity score matching yielded more accurate point estimates in these scenarios.
  • Permutation tests and clustered standard errors improved inference accuracy.

Conclusions:

  • When treatment and comparison groups differ in pre-intervention levels or trends, matching is recommended for accurate point estimates in DID models.
  • Clustered standard errors or permutation tests are advised for improved inference.
  • A checklist for DID analysis is proposed based on these findings.