Enhancing the precision of the Positive and Negative Affect Schedule (PANAS) using Rasch analysis

• DOI: 10.1007/s12144-021-01556-3
• Publication Date: 2021-03-05T20:02:21Z
• Authors: Oleg N. Medvedev; Anja Roemer; Christian U. Kräg...; Margaret H. Sandham; Richard J. Siegert
• Keywords: 0504 sociology; 05 social sciences

The Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegan, Journal of Personality and Social Psychology, 54(6), 1063–1070, 1988) is a widely used affect measure but it has the limitations of an ordinal scale such as low precision and unsuitability for use with parametric statistics. Rasch analysis is an appropriate methodology used to investigate and enhance the precision of ordinal scales through transformation from ordinal to interval scores given the data fits the model. This study applied the Partial Credit Rasch model to the adequate sample of 396 participants. Positive and Negative Affect subscales were analysed independently and local dependence was identified between several items in each subscale that affected both model fit and reliability. The best Rasch model fit was achieved for each subscale after applying recent methodological advances that involved combining locally dependent items into super-items resulting in strong reliability (PSI > 0.80) and strict unidimensionality. This permitted the development of ordinal-to-interval conversion tables. The results support robust psychometric properties of the PANAS. Using conversion tables published here researchers can improve the precision of PANAS subscales to satisfy assumptions of parametric statistics and fundamental measurement principles.