Rasch Analysis and Interval-Level Scaling of the Positive and Negative Affect Schedule (PANAS) Across Cultures

Area of Science:

• Psychology
• Psychometrics

Background:

• Accurate measurement of affective states is crucial for statistical analysis.
• The Positive and Negative Affect Schedule (PANAS) has satisfactory psychometric properties but uses ordinal data, limiting parametric statistical applications.

Purpose of the Study:

• To assess the psychometric properties of the PANAS using the Rasch model.
• To enhance the precision and validity of PANAS scores for parametric statistical analysis.
• To evaluate the cross-cultural equivalence of the PANAS.

Main Methods:

• Rasch model analysis was applied to PANAS responses from 1000 individuals across Germany, Ghana, India, and New Zealand.
• Testlet creation approach was used to improve model fit.
• Convergent and discriminant validity were assessed.

Main Results:

• Both positive and negative affect subscales of the PANAS showed satisfactory model fit.
• The subscales demonstrated strong reliability, structural validity, and measured a single underlying construct.
• The PANAS functioned equivalently across diverse demographic groups and countries.
• An algorithm was developed to convert ordinal PANAS scores to interval data.

Conclusions:

• The Rasch model analysis supports the use of the PANAS as a precise and valid measure of affective states.
• The developed algorithm enhances the utility of the PANAS for parametric statistical analyses.
• The PANAS demonstrates cross-cultural validity and reliability for measuring positive and negative affect.