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This article addresses a fundamental question in the study of socio-emotional skills, personality traits, and related constructs: "To score or not to score?" When researchers use test scores or scale scores (i.e., fallible point estimates of a skill or trait) as predictors in multiple regression, measurement error in these scores tends to attenuate regression coefficients for the skill and inflate those of the covariates. Unlike for cognitive assessments, it is not fully established how severe this bias can be in socio-emotional skill assessments, that is, how well test scores recover the true regression coefficients - compared with methods designed to account for measurement error: structural equation modeling (SEM) and plausible values (PV). The different types of scores considered in this study are standardized mean scores (SMS), regression factor scores (RFS), empirical Bayes modal (EBM) score, weighted maximum likelihood estimates (WLE), and expected a posteriori (EAP) estimates. We present a simulation study in which we compared these approaches under conditions typical of socio-emotional skill and personality assessments. We examined the performance of five types of test scores, PV, and SEM with regard to two outcomes: (1) percent bias in regression coefficient of the skill in predicting an outcome; and (2) percent bias in the regression coefficient of a covariate. We varied the number of items, factor loadings/item discriminations, sample size, and relative strength of the relationship of the skill with the outcome. Results revealed that whereas different types of test scores were highly correlated with each other, the ensuing bias in regression coefficients varied considerably. The magnitude of bias was highest for WLE with short scales of low reliability. Bias when using SMS or WLE test scores was sometimes large enough to lead to erroneous research conclusions with potentially adverse implications for policy and practice (up to 55% for the regression coefficient of the skill and 20% for that of the covariate). EAP, EBM, and RFS performed better, producing only small bias in some conditions. Additional analyses showed that the performance of test scores also depended on whether standardized or unstandardized scores were used. Only PV and SEM performed well in all scenarios and emerged as the clearly superior options. We recommend that researchers use SEM, and preferably PV, in studies on the (incremental) predictive power of socio-emotional skills.

Measures of cognitive or socio-emotional skills from large-scale assessments surveys (LSAS) are often based on advanced statistical models and scoring techniques unfamiliar to applied researchers. Consequently, applied researchers working with data from LSAS may be uncertain about the assumptions and computational details of these statistical models and scoring techniques and about how to best incorporate the resulting skill measures in secondary analyses. The present paper is intended as a primer for applied researchers. After a brief introduction to the key properties of skill assessments, we give an overview over the three principal methods with which secondary analysts can incorporate skill measures from LSAS in their analyses: (1) as test scores (i.e., point estimates of individual ability), (2) through structural equation modeling (SEM), and (3) in the form of plausible values (PVs). We discuss the advantages and disadvantages of each method based on three criteria: fallibility (i.e., control for measurement error and unbiasedness), usability (i.e., ease of use in secondary analyses), and immutability (i.e., consistency of test scores, PVs, or measurement model parameters across different analyses and analysts). We show that although none of the methods are optimal under all criteria, methods that result in a single point estimate of each respondent's ability (i.e., all types of "test scores") are rarely optimal for research purposes. Instead, approaches that avoid or correct for measurement error - especially PV methodology - stand out as the method of choice. We conclude with practical recommendations for secondary analysts and data-producing organizations.