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

Regression Toward the Mean01:52

Regression Toward the Mean

6.7K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.7K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.7K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.7K
Outliers and Influential Points01:08

Outliers and Influential Points

5.6K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.6K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

446
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...
446
Regression Analysis01:11

Regression Analysis

7.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.5K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.1K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K

You might also read

Related Articles

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

Sort by
Same author

Treating a friend to voter registration in a Divided America.

PloS one·2025
Same author

To the Editor: Let This Be the Last Call to Action to Train Residents in Addiction.

Journal of graduate medical education·2024
Same author

Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna.

Wiener klinische Wochenschrift·2022
Same author

Comparison of SARS-CoV-2 Antibody Response 4 Weeks After Homologous vs Heterologous Third Vaccine Dose in Kidney Transplant Recipients: A Randomized Clinical Trial.

JAMA internal medicine·2021
Same author

Low SARS-CoV-2 seroprevalence in the Austrian capital after an early governmental lockdown.

Scientific reports·2021
Same author

Fast Electron and Slow Hole Relaxation in InP-Based Colloidal Quantum Dots.

ACS nano·2019
Same journal

More Likely to Be Poor Whatever the Measure: Working-Age Persons with Disabilities in the United States.

Social science quarterly·2026
Same journal

Evaluating Evidentiary Standards in the Realm of Citizen Policy Evaluation.

Social science quarterly·2026
Same journal

Panethnic fate unlinked: Who finds the term "Chinese virus" acceptable?

Social science quarterly·2026
Same journal

Poverty and the Incidence of Material Hardship, Revisited.

Social science quarterly·2024
Same journal

Racial Differences in Feelings of Distress during the COVID-19 Pandemic and John Henryism Active Coping in the United States: Results from a National Survey.

Social science quarterly·2024
Same journal

Medicaid Expansions and Private Insurance 'Crowd-Out' (1999-2019).

Social science quarterly·2024
See all related articles

Related Experiment Video

Updated: Dec 11, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Defying the Rally During COVID-19 Pandemic: A Regression Discontinuity Approach.

Enrijeta Shino1, Michael Binder1

  • 1University of North Florida.

Social Science Quarterly
|August 25, 2020
PubMed
Summary
This summary is machine-generated.

During pandemics, partisan differences can reduce the "rally around the flag" effect. Governor DeSantis saw a 7-point drop in approval after his "Safer at Home" order, showing political affiliation impacts public support.

More Related Videos

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.8K
Experimental Paradigm for Measuring the Effects of Self-distancing in Young Children
07:01

Experimental Paradigm for Measuring the Effects of Self-distancing in Young Children

Published on: March 1, 2019

8.3K

Related Experiment Videos

Last Updated: Dec 11, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.8K
Experimental Paradigm for Measuring the Effects of Self-distancing in Young Children
07:01

Experimental Paradigm for Measuring the Effects of Self-distancing in Young Children

Published on: March 1, 2019

8.3K

Area of Science:

  • Political Science
  • Public Health Policy
  • Epidemiology

Background:

  • The COVID-19 pandemic placed state governors on the frontlines, managing public health responses.
  • President Trump delegated pandemic response responsibilities to individual states, increasing gubernatorial influence and scrutiny.
  • Public support for elected officials during crises, often termed the "rally around the flag" effect, is a key area of political science research.

Purpose of the Study:

  • To investigate whether partisan differences influence public support for governors during the COVID-19 pandemic.
  • To assess the impact of specific policy decisions, like stay-at-home orders, on gubernatorial approval ratings.
  • To understand the conditions under which the "rally around the flag" phenomenon may be diminished.

Main Methods:

  • Utilized a regression discontinuity design to analyze the impact of political events on public support.
  • Exploited a specific policy announcement (the "Safer at Home" order) as a discontinuity point for analysis.
  • Employed survey data from registered voters in Florida to measure changes in approval.

Main Results:

  • Governor DeSantis's approval rating decreased by 7 percentage points after the "Safer at Home" order announcement on April 1.
  • The findings indicate a measurable decline in public support linked to a specific policy decision during the pandemic.
  • Analysis suggests that pre-existing political affiliations may moderate or negate expected increases in support during a crisis.

Conclusions:

  • Partisanship can significantly blunt the "rally around the flag" effect, even during a public health crisis.
  • Gubernatorial actions and policy decisions can lead to decreases, not just increases, in public support.
  • This research provides crucial context for understanding the dynamics of public opinion and elected official support during national emergencies.