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

Affective state investigation requires precise measurement that satisfies parametric statistical assumptions for reliable and valid cross-data comparisons. The psychometric properties of the 20-item Positive and Negative Affect Schedule (PANAS) are satisfactory, but provide scores on an ordinal scale, which could be unsuitable for parametric statistics. The present study employed the Rasch model to assess the psychometric statistics of the PANAS, enhancing the scale's precision using community samples from four countries. I analysed responses from a randomly selected sample of 1000 individuals (250 from each country) out of a total sample of 1822 recruited from Germany (475), Ghana (523), India (411), and New Zealand (413). The analyses indicated that both the positive affect and negative affect subscales demonstrated satisfactory model fit after applying the testlet creation approach. Each subscale reflected a clear, single underlying construct with strong reliability and structural validity. Both scales also functioned equivalently across demographic groups, suggesting that the items measured affective states consistently, regardless of participants' sociodemographic backgrounds. The scales further exhibited strong convergent and discriminant validity. The study developed an algorithm to convert ordinal scores to interval data, enhancing precision and validity in parametric analyses.