Criterion Referenced Factor Analysis (CRFA): Method and Illustration
CRFA is a valid method for evaluating relationships between criteria and “factors'' initially identified from both the predictor and the criterion variables. Among others, it may be applied to classical problems involving: 1) Changes in complex task ability requirements as a function of practice, 2) Impacts of environmental stressors on personality or performance factors overtime, or 3) Residential Lifestyle Factor Impacts on Energy-Use (as herein). CRFA differs from traditional battery developments in its: (a) Initial inclusion of both criterion and predictor variables for factor identifications, but (b) Exclusion of criterion in the ultimate calculation of factor-scores. This avoids the vexing confounding of criterion variance in factor score estimates, and ultimately provides for unconfounded analyses of criterion and factor relationships. A “Big-Data'' illustration of CRFA is presented that highlights the stability of model results for independent samples across years. The primary model of interest built upon a USA-representative survey (N = 2,165) sample of 17 variables adapted from RECS-2005 (USEIA, 2019). These included16 lifestyle-related and an annual energy use criterion (i.e., LNKWH, Ln-transformed annual KiloWatt Hours). Unweighted least squares (ULS) factor analysis revealed a 5- Lifestyle factor solution that accounted for 45.5% of the total variation in the 17-variable set and 45.3% of the 16 less LNKWH. “Lifestyle” factor predictions – subsequently derived by CRFA less LNKWH– are found to be remarkably stable when compared to a similar sample taken 4-years earlier (RECS-2001). Specifically, (1) the proportions of LNKWH variance explained with lifestyle factor scores alone are nearly identical across the 4-year gap (2005 R2 = 0.42- and 2001 R2 = 0.38; ps <10-10), (2) these increased after external additions of household characteristics (R2 = 0.55 both fore- and back-casting; ps <10-15), and model B-weights were near identical. CRFA is strongly recommended for valid evaluations of relationships between criteria and predictor-based factor-scores, where factor characterizations are initially derived from both predictor and criterion variables.