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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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Rating norms should be calculated from cumulative link mixed effects models.
Jack E Taylor1, Guillaume A Rousselet2, Christoph Scheepers2
1School of Psychology and Neuroscience, University of Glasgow, 62 Hillhead Street, Glasgow, G12 8QB, UK. j.taylor.3@research.gla.ac.uk.
Behavior Research Methods
|September 14, 2022
Summary
This study introduces cumulative link mixed effects models (CLMMs) for analyzing Likert rating norms. CLMMs offer more accurate item norming and disentangle response biases compared to traditional methods.
Area of Science:
- Psychometrics
- Statistical Modeling
Background:
- Traditional Likert rating norm studies report means and standard deviations (SDs).
- These traditional statistics distort relative item distances due to the ordinal nature of Likert data.
- Existing methods face statistical issues in accurately representing rating norms.
Purpose of the Study:
- To introduce cumulative link mixed effects models (CLMMs) as a superior method for analyzing Likert rating norms.
- To demonstrate that CLMMs provide more accurate item norming.
- To show CLMMs can yield summary statistics free from response biases.
Main Methods:
- Utilized cumulative link mixed effects models (CLMMs) for ordinal scale data.
- Conducted simulation studies to evaluate CLMM performance.
- Reanalyzed an existing rating norms dataset using CLMMs.
Main Results:
- CLMMs accurately model inter-item relations in ordinal scales.
- CLMMs provide improved estimates for item norming compared to traditional methods.
- CLMM-derived statistics are disentangled from participant response biases.
Conclusions:
- Cumulative link mixed effects models (CLMMs) offer a more statistically sound approach for Likert rating norm analysis.
- CLMMs address limitations of traditional mean and SD reporting.
- This method enhances the accuracy and interpretability of rating norm data.
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