Measuring positive and negative affect in older adults over 56 days: comparing trait level scoring methods using the partial credit model

Positive (PA) and negative affect (NA) are important constructs in health and well-being research. Good longitudinal measurement is crucial to conducting meaningful research on relationships between affect, health, and well-being across the lifespan. One common affect measure, the PANAS, has been evaluated thoroughly with factor analysis, but not with Racsh-based latent trait models (RLTMs) such as the Partial Credit Model (PCM), and not longitudinally. Current longitudinal RLTMs can computationally handle few occasions of data. The present study compares four methods of anchoring PCMs across 56 occasions to longitudinally evaluate the psychometric properties of the PANAS plus additional items. Anchoring item parameters on mean parameter values across occasions produced more desirable results than using no anchor, using first occasion parameters as anchors, or allowing anchor values to vary across occasions. Results indicated problems with NA items, including poor category utilization, gaps in the item distribution, and a lack of easy-to-endorse items. PA items had much more desirable psychometric qualities.