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Related Concept Videos

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...
Blinding01:11

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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...
Controls in Experiments01:13

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
Blind Procedures02:07

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was...

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Related Experiment Video

Updated: Jun 13, 2026

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
07:40

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

Evaluating Group-Based Interventions When Control Participants Are Ungrouped.

Daniel J Bauer1, Sonya K Sterba, Denise Dion Hallfors

  • 1University of North Carolina at Chapel Hill.

Multivariate Behavioral Research
|April 17, 2010
PubMed
Summary

This study introduces a new statistical model for intervention research where only treated individuals are grouped. This approach corrects for correlated outcomes in treated groups, improving inference accuracy for treatment effects.

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Area of Science:

  • Social Sciences
  • Psychology
  • Educational Research

Background:

  • Intervention studies often randomize individuals but administer treatments in groups.
  • This group setting can lead to correlated outcomes among treated individuals due to shared experiences or provider effects.
  • Existing statistical models may not account for this clustered data structure, potentially causing biased results.

Purpose of the Study:

  • To present an alternative statistical model that accommodates the specific data structure of group-administered treatments.
  • To provide a method that explicitly models the clustering of outcomes within treatment groups.
  • To enable formal assessment of treatment effect variation across groups and within-group similarity.

Main Methods:

  • Development of a novel statistical model designed for individually randomized group-administered treatments.
  • The model accounts for correlated outcomes in treated groups while assuming independence in control groups.
  • Application and demonstration using data from the Reconnecting Youth program.

Main Results:

  • The proposed model offers a valid test for overall treatment effects.
  • It allows for the quantification of treatment effect heterogeneity across different treatment groups.
  • The model can detect evidence of individuals within treatment groups becoming more similar.

Conclusions:

  • Standard statistical models may yield biased inferences in intervention studies with group-administered treatments.
  • The presented model provides a more accurate and nuanced approach to analyzing such data.
  • This method enhances the understanding of treatment effects and group dynamics in intervention research.