Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Stratified Sampling Method

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

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 from...

Comparing the Survival Analysis of Two or More Groups

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...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

You might also read

Related Articles

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

Sort by

Same author

Resistance to doxorubicin-induced proteinuria and proteolytic activation of ENaC in 129S2/SvPas mice.

Physiological reports·2025

Same author

Fluoroscopy-guided celiac plexus block - Trans-Aortic approach.

Interventional pain medicine·2025

Same author

Improving YOLO-based breast mass detection with transfer learning pretraining on the OPTIMAM Mammography Image Database.

Computers in biology and medicine·2025

Same author

A new multi-object tracking pipeline based on computer vision techniques for mussel farms.

Journal of the Royal Society of New Zealand·2024

Same author

Pooled Analysis of Physical Activity, Sedentary Behavior, and Sleep Among Children From 33 Countries.

JAMA pediatrics·2024

Same author

Generalized Mantel-Haenszel Estimators for Simultaneous Differential Item Functioning Tests.

Educational and psychological measurement·2023

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

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

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

Published on: September 27, 2019

The analysis of stratified multiple responses.

Ivy Liu¹, Thomas Suesse

¹School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600 Wellington 6140, New Zealand. iliu@mcs.vuw.ac.nz

Biometrical Journal. Biometrische Zeitschrift

|January 26, 2008

Summary

This study introduces two methods, generalized estimating equations (GEE) and generalized Mantel-Haenszel (GMH), for analyzing survey data with multiple responses. These statistical approaches effectively handle complex qualitative variables in stratified analyses.

Related Experiment Videos

Last Updated: Jul 8, 2026

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

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

Published on: September 27, 2019

Area of Science:

Statistics
Epidemiology
Survey Methodology

Background:

Surveys frequently collect qualitative data where respondents can select multiple options.
This scenario, termed 'multiple responses,' requires specialized analytical techniques.
Stratification variables are common in survey data, necessitating methods that account for them.

Purpose of the Study:

To present and compare two statistical approaches for analyzing survey data with multiple responses.
To provide methods for handling stratified qualitative variables where multiple outcomes are possible.
To extend existing statistical frameworks to accommodate complex survey response patterns.

Main Methods:

The generalized estimating equations (GEE) approach, utilizing logit models.
The generalized Mantel-Haenszel (GMH) approach, extending traditional estimators.
Application to data with stratification variables and dependent observations across strata.

Main Results:

Both GEE and GMH methods offer viable strategies for analyzing multiple response data.
These methods allow for robust inferences in the presence of stratification.
The study demonstrates the applicability of these techniques to dependent observational data.

Conclusions:

The GEE and GMH approaches provide effective tools for analyzing complex survey data with multiple responses.
These methods are suitable for situations involving stratification and dependent observations.
The findings enhance the analytical capabilities for qualitative survey variables.