Related Experiment Video
Updated: Jul 19, 2025
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.5K
Modeling Likert scale outcomes with trend-proportional odds with and without cluster data
Ana W Capuano1, Jeffrey D Dawson2, Marizen R Ramirez3
1Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.
Methodology : European Journal of Research Methods for the Behavioral & Social Sciences
|August 16, 2023
Summary
This study applies proportional odds and trend odds models to Likert scale data in epidemiological surveys. Findings show gender differences in perceived dishonesty and higher violence odds for children with disabilities.
Area of Science:
- Epidemiology
- Biostatistics
- Survey Methodology
Background:
- Likert scales are frequently utilized in epidemiological surveys.
- Analyzing clustered Likert scale data requires specialized statistical approaches.
- Existing methods may not fully capture the nuances of ordinal survey responses.
Purpose of the Study:
- To demonstrate the simultaneous application of proportional odds and trend odds models for Likert scale data.
- To incorporate random cluster effects into the analysis of Likert scale outcomes.
- To provide practical examples using real-world epidemiological and educational datasets.
Main Methods:
- Application of the proportional odds model and the trend odds model.
- Simultaneous modeling of Likert scale outcomes with random cluster effects.
- Utilized two distinct datasets: an aging/cognition study and a large student survey.
Main Results:
- In an aging study, Black men reported higher odds of perceived dishonesty than Black women.
- In a student survey, children with disabilities had higher odds of severe violence.
- Cumulative odds ratios increased by over 60% at higher Likert scale levels in both examples.
Conclusions:
- The proportional odds and trend odds models effectively analyze clustered Likert scale data in epidemiology.
- These models reveal significant associations between demographics/conditions and survey outcomes.
- The methodology offers a robust framework for analyzing ordinal data in public health research.
Related Concept Videos
01:09Odds Ratio
169
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
169
01:20Cluster Sampling Method
12.0K
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...
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...
12.0K
01:20Comparing the Survival Analysis of Two or More Groups
223
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...
223
01:21Friedman Two-way Analysis of Variance by Ranks
235
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...
235
01:29Contingency Table
2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.5K
01:25Statistical Methods for Analyzing Epidemiological Data
411
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
411