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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
Friedman Two-way Analysis of Variance by Ranks01:21

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...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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Updated: Jul 6, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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

An imputation strategy for incomplete longitudinal ordinal data.

Hakan Demirtas1, Donald Hedeker

  • 1Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL 60612, USA. demirtas@uic.edu

Statistics in Medicine
|March 14, 2008
PubMed
Summary
This summary is machine-generated.

A novel quasi-imputation method handles incomplete ordinal data by transforming it into a Gaussian format for multiple imputation. This approach is promising for longitudinal and clustered ordinal outcomes in psychiatric research.

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Last Updated: Jul 6, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

  • Statistics
  • Biostatistics
  • Psychiatric Research

Background:

  • Handling missing data in ordinal response variables is challenging, especially with correlated outcomes.
  • Existing imputation methods may not adequately preserve the structure of correlated ordinal data.

Purpose of the Study:

  • To propose a new quasi-imputation strategy for correlated ordinal responses.
  • To adapt existing imputation techniques for Gaussian outcomes to ordinal data.

Main Methods:

  • Collapsing ordinal levels to binary outcomes.
  • Converting correlated binary outcomes to multivariate normal outcomes.
  • Utilizing multiple imputation for Gaussian data and re-converting to ordinal scale.

Main Results:

  • The proposed method ensures the preservation of original marginal distributions and correlations after imputation.
  • The quasi-imputation strategy allows leveraging established imputation techniques for Gaussian data.
  • Demonstrated application in a psychiatric research dataset.

Conclusions:

  • The quasi-imputation strategy is a promising tool for incomplete longitudinal or clustered ordinal outcomes.
  • The method offers a flexible approach to handling complex ordinal data structures.
  • Further research can explore its performance across various scenarios.