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Polychoric Correlation With Ordinal Data in Nursing Research
Nursing Research
|August 23, 2022
Summary
Polychoric correlation analysis is a superior statistical method for Likert scale data in nursing research compared to Pearson correlations. This approach offers a more accurate assessment of relationships and improves the validity of confirmatory factor analysis.
Area of Science:
- Nursing Research
- Psychometrics
- Statistical Modeling
Background:
- Nursing research frequently employs Likert scales, generating ordinal data.
- Confirmatory factor analysis (CFA) with Pearson correlations is common but violates ordinal data assumptions.
Purpose of the Study:
- To demonstrate polychoric correlations and CFA as a valid alternative for ordinal data.
- To use family perceived support from nurses as an example.
Main Methods:
- Cross-sectional data from 800 participants using the Iceland-Family Perceived Support Questionnaire.
- Comparison of polychoric versus Pearson correlations, ANOVA, and CFA.
Main Results:
- A two-factor model (cognitive and emotional support) fit the data.
- Polychoric correlations showed 13.8% higher associations than Pearson correlations.
- Pearson CFA did not fit the data, while polychoric CFA did.
Conclusions:
- Polychoric correlation offers stronger, more credible associations for ordinal Likert scales.
- Polychoric CFA can account for a larger variance proportion in nursing research.
- Researchers should consider polychoric correlation for ordinal Likert scale data analysis.
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