Methods for the Comparison of Differential Item Functioning across Assessments.

An important aspect of the educational and psychological evaluation of individuals is the selection of scales with appropriate evidence of reliability and validity for inferences and uses of the scores for the population of interest. One key aspect of validity is the degree to which a scale fairly assesses the construct(s) of interest for members of different subgroups within the population. Typically, this issue is addressed statistically through assessment of differential item functioning (DIF) of individual items, or differential test functioning (DTF) of sets of items within the same measure. When selecting an assessment to use for a given application (e.g., measuring intelligence), or which form of an assessment to use for a test administration, researchers need to consider the extent to which the scales work with all members of the population. Little research has examined methods for comparing the amount or magnitude of DIF/DTF present in two or more assessments when deciding which assessment to use. The current study made use of 7 different statistics for this purpose, in the context of intelligence testing. Results demonstrate that by using a variety of effect sizes, the researcher can gain insights into not only which scales may contain the least amount of DTF, but also how they differ with regard to the way in which the DTF manifests itself.

Increasing Nursing Students' Knowledge of Autism Spectrum Disorder by Using a Standardized Patient.

Nursing students participated in a simulation using a standardized patient role-playing an adolescent with autism spectrum disorder (ASD). The researchers used student feedback to develop and improve a simulation aimed at increasing learner skills and knowledge for treating ASD patients. Students indicated that the standardized patient provided realism not obtained when using static manikins or high-fidelity simulators. Students strongly agreed or agreed that classroom instruction prior to the simulation was important to increasing their knowledge of ASD. Overall, the simulation provided students an opportunity to practice and develop their clinical skills in caring for patients with ASD.

The prediction of intelligence in preschool children using alternative models to regression.

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Diagnosing attention disorders with measures of neurocognitive functioning.

The validity of the Differential Ability Scales (DAS; Elliott, 1990) in predicting attention disorders in school-aged children was examined. The participants were 40 children diagnosed with attention deficit/hyperactivity disorder (ADHD) and 40 normal children between 7 and 12 years of age. Comorbidity was controlled by excluding children with ADHD who had a concurrent DSM-IV diagnosis such as reading disability, learning disability, mood disorder, or behavioral disorder. In addition, groups did not differ significantly by sex, age, racial/ethnic identification, or parental educational level. A stepwise discriminant analysis indicated the DAS correctly classified 72.5% of the total sample when either the Sequential and Quantitative Reasoning subtest or the Recall of Digits subtest was used to predict group membership. In general, the DAS core subtests were found to be good measures of cognitive ability in children classified with ADHD and did not appear to be influenced by attention problems.