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Related Concept Videos

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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Wald-Wolfowitz Runs Test II01:17

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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A two-step, test-guided Mokken scale analysis, for nonclustered and clustered data.

Letty Koopman1, Bonne J H Zijlstra2, L Andries van der Ark2

  • 1Research Institute of Child Development and Education, University of Amsterdam, P. O. Box 15776, 1001 NG, Amsterdam, The Netherlands. V.E.C.Koopman@UvA.nl.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|May 13, 2021
PubMed
Summary

This study introduces a test-guided Mokken scale analysis (MSA) to improve scale construction for quality of life research. The new method accurately handles clustered data and sampling fluctuations, enhancing measurement instrument reliability.

Keywords:
Automated item selection procedureClustered data analysisMokken scale analysisTest-guided automated item selection procedure

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Area of Science:

  • Psychometrics
  • Health Sciences Research
  • Statistical Modeling

Background:

  • Mokken scale analysis (MSA) is a valuable tool for analyzing ordinal data, particularly in health-related quality of life (HRQoL) research.
  • Key features of MSA include scalability coefficients and the automated item selection procedure (AISP).
  • Existing MSA methods have limitations with clustered data and do not adequately account for sampling fluctuation in scalability coefficients.

Purpose of the Study:

  • To address the limitations of traditional MSA for clustered data and sampling fluctuations.
  • To develop an improved MSA procedure that provides accurate estimates and significance tests for scalability coefficients in clustered and nonclustered data.

Main Methods:

  • Developed point estimates and standard errors for scalability coefficients suitable for clustered data.
  • Implemented a Wald-based significance test within the AISP algorithm, creating a test-guided AISP (T-AISP).
  • Integrated T-AISP into a two-step procedure for scale construction, including within-group dependency checks for clustered data.

Main Results:

  • The T-AISP was successfully integrated into a two-step MSA framework for scale construction.
  • The procedure effectively guides scale selection for both nonclustered and clustered data.
  • Demonstrated the method's utility on clustered item scores from a quality of life questionnaire administered to 639 students in 30 classrooms.

Conclusions:

  • A robust two-step, test-guided MSA for scale construction has been developed.
  • The new method accurately accounts for sampling fluctuation of scalability coefficients.
  • The procedure is applicable to item scores from both nonclustered and clustered sampling designs, enhancing measurement in HRQoL research.