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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

459
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...
459
Observational Studies01:11

Observational Studies

10.7K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
10.7K
Group Design02:01

Group Design

10.1K
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...
10.1K
Econometric Views (EViews)01:29

Econometric Views (EViews)

510
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
510
Influence of Parents and Peers on Identity01:23

Influence of Parents and Peers on Identity

419
Adolescence is a pivotal period of identity formation, during which individuals begin to answer questions central to their sense of self, such as "Who am I?" and "Who do I hope to become?" Both parents and peers play critical roles in guiding adolescents through this complex developmental phase.
Parental Influence on Identity Development
Parents serve as primary guides and managers in an adolescent's life, offering support instrumental in decision-making and personal growth....
419
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

216
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...
216

You might also read

Related Articles

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

Sort by
Same author

Covariates as Random Effects (CaRE): An approach to modeling high-dimensional confounding and effect heterogeneity.

International journal of epidemiology·2026
Same author

Ranking group-level outcomes with multilevel models: An information-theoretic measure of statistical separation.

Social science & medicine (1982)·2026
Same author

The Economics and Econometrics of Gene-Environment Interplay.

The Review of economic studies·2026
Same author

Extending the Median Odds Ratio (MOR), the Interval Odds Ratio (IOR), and the Proportion of Opposed Odds Ratios (POOR) for Use With 3-Level Multilevel Logistic Regression Models.

Statistics in medicine·2026
Same author

Antidepressant discontinuation in France from an intersectional perspective: a discrete-time survival analysis within the MAIHDA framework.

BMC public health·2026
Same author

A longitudinal multilevel analysis of individual- and contextual-level predictors of cross-ethnic friendships in the UK.

The British journal of social psychology·2026
Same journal

Education, Dietary Intakes and Exercise.

Oxford bulletin of economics and statistics·2023
Same journal

How did consumers react to the COVID-19 pandemic over time?

Oxford bulletin of economics and statistics·2022
Same journal

Modelling the Differing Impacts of Covid-19 in the UK Labour Market.

Oxford bulletin of economics and statistics·2022
Same journal

The Impact of Pessimistic Expectations on the Effects of COVID-19-Induced Uncertainty in the Euro Area.

Oxford bulletin of economics and statistics·2021
Same journal

Covid-19 Control and the Economy: Test, Test, Test.

Oxford bulletin of economics and statistics·2021
Same journal

Semi-parametric Regression under Model Uncertainty: Economic Applications.

Oxford bulletin of economics and statistics·2019
See all related articles

Related Experiment Video

Updated: Jan 3, 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

6.3K

The Use of Instrumental Variables in Peer Effects Models.

Stephanie von Hinke1,2,3, George Leckie4, Cheti Nicoletti5,6

  • 1Department of Economics University of Bristol 8 Woodland Road Bristol BS8 1TN UK.

Oxford Bulletin of Economics and Statistics
|November 19, 2019
PubMed
Summary
This summary is machine-generated.

Instrumental variables are crucial for identifying peer effects. Failing to include the instrument at the individual level when using peer characteristics to instrument peer outcomes introduces bias.

More Related Videos

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.6K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.3K

Related Experiment Videos

Last Updated: Jan 3, 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

6.3K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.6K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.3K

Area of Science:

  • Econometrics
  • Social Sciences
  • Causal Inference

Background:

  • Peer effects are a significant factor in various social and economic outcomes.
  • Instrumental variables (IV) methods are commonly employed to address endogeneity in estimating peer effects.
  • Existing IV strategies often rely on aggregate peer characteristics to instrument individual peer outcomes.

Purpose of the Study:

  • To demonstrate the necessity of including individual-level instruments in IV analyses of peer effects.
  • To identify and explain the source of bias when this requirement is not met.
  • To provide guidance for correct application of IV methods in peer effect research.

Main Methods:

  • The study theoretically examines the conditions under which instrumental variables can identify peer effects.
  • It analyzes a common IV strategy: instrumenting the peer average outcome with peer average characteristics.
  • The core methodological point is the requirement of including the instrument at the individual level.

Main Results:

  • Instrumenting peer average outcomes with peer average characteristics necessitates including the instrument at the individual level.
  • Failure to include the individual-level instrument leads to biased estimates of peer effects.
  • The bias arises from the direct effect of individual characteristics on the outcome, which is not accounted for without the individual-level instrument.

Conclusions:

  • Correct application of instrumental variables for peer effects requires including the instrument at the individual level.
  • Researchers must avoid the omission of the individual-level instrument to prevent biased peer effect estimates.
  • This finding has important implications for the validity of empirical studies on peer influence across disciplines.