Kids these days and kids those days: Investigating perceptions of children's social skills from 1988 to 2007.

Behavior rating scales are frequently used assessment tools designed to measure social skills. Use of norm-referenced assessments such as behavior rating scales requires examiners and test publishers to consider when norms become obsolete and norm-referenced scores can no longer be validly interpreted. A fundamental factor influencing norm obsolescence regards changes in baseline levels of targeted traits within the population. Yet, limited research exists regarding how social skills may change at a population level over time as measured by established assessment tools. Thus, the present study investigates population trends in social skills of K-12 children as rated by parents, teachers, and students by concordantly linking the Social Skills Rating System (SSRS; nparent = 833, nteacher = 1215, nstudent = 4105) and the Social Skills Improvement System-Rating Scales (SSIS-RS; nparent = 2400, nteacher = 750, nstudent = 800) using validity samples collected during the development of the SSIS-RS (nparent = 240, nteacher = 221, nstudent = 224). Analyses evaluated differences between ratings on the standardization data from 1988 and 2007 by informant, sex, grade level, and sex by grade level. After applying linear linking techniques, we conducted a series of statistical comparisons that revealed a general upward trend of ratings for the 2007 sample compared to the 1988 sample, with important differences across sex, grade level, and informant. We conclude with a discussion of the implications of these findings for consideration and assessment of children's social skills.

A Comparison of Person-Fit Indices to Detect Social Desirability Bias.

Social desirability bias (SDB) has been a major concern in educational and psychological assessments when measuring latent variables because it has the potential to introduce measurement error and bias in assessments. Person-fit indices can detect bias in the form of misfitted response vectors. The objective of this study was to compare the performance of 14 person-fit indices to identify SDB in simulated responses. The area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis was computed to evaluate the predictive power of these statistics. The findings showed that the agreement statistic (A) outperformed all other person-fit indices, while the disagreement statistic (D), dependability statistic (E), and the number of Guttman errors (G) also demonstrated high AUCs to detect SDB. Recommendations for practitioners to use these fit indices are provided.

Assessing Ability Recovery of the Sequential IRT Model With Unstructured Multiple-Attempt Data.

The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of data set as a whole in the practice of educational measurement. It is critical that methods be available for measuring ability from VLE data to improve VLE systems, monitor student progress in instructional settings, and conduct educational research. The purpose of this study is to explore the ability recovery of the multidimensional sequential 2-PL IRT model in unstructured MA data from VLEs. We conduct a simulation study to evaluate the effects of the magnitude of ability growth and the proportion of students who make two attempts, as well as the moderated effects of sample size, test length, and missingness, on the bias and root mean square error of ability estimates. Results show that the model poses promise for evaluating ability in unstructured VLE data, but that some data conditions can result in biased ability estimates.