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The construct validity of English and Spanish phonological awareness (PA) tasks was examined with a sample of 812 kindergarten children from 71 transitional bilingual education program classrooms located in 3 different types of geographic regions in California and Texas. Tasks of PA, including blending nonwords, segmenting words, and phoneme elision, were measured in Spanish and in English and analyzed via multilevel confirmatory factor analysis at the task level. Results showed that the PA tasks defined a unitary construct at both the student and classroom levels in each language. English and Spanish PA factors were related to each other (.93 and .83 at the student and classroom levels, respectively) as well as to word reading, both within languages (correlations estimated between .74 and .93) and across languages (correlations estimated between .47 and .79). Although the PA constructs were statistically separable in each language, the high correlation between Spanish and English PA indicates considerable overlap in these abilities.

We present a simple technique for evaluating multiple-choice questions and their answers beyond the usual measures of difficulty and the effectiveness of distractors. The technique involves the construction and qualitative consideration of item response curves and is based on item response theory from the field of education measurement. To demonstrate the technique, we apply item response curve analysis to three questions from the Force Concept Inventory. Item response curve analysis allows us to characterize qualitatively whether these questions are efficient, where efficient is defined in terms of the construction, performance, and discrimination of a question and its answer choices. This technique can be used to develop future multiple-choice examination questions and a better understanding of results from existing diagnostic instruments.

Several years ago, we introduced the idea of item response curves (IRC), a simplistic form of item response theory (IRT), to the physics education research community as a way to examine item performance on diagnostic instruments such as the Force Concept Inventory (FCI). We noted that a full-blown analysis using IRT would be a next logical step, which several authors have since taken. In this paper, we show that our simple approach not only yields similar conclusions in the analysis of the performance of items on the FCI to the more sophisticated and complex IRT analyses but also permits additional insights by characterizing both the correct and incorrect answer choices. Our IRC approach can be applied to a variety of multiple-choice assessments but, as applied to a carefully designed instrument such as the FCI, allows us to probe student understanding as a function of ability level through an examination of each answer choice. We imagine that physics teachers could use IRC analysis to identify prominent misconceptions and tailor their instruction to combat those misconceptions, fulfilling the FCI authors’ original intentions for its use. Furthermore, the IRC analysis can assist test designers to improve their assessments by identifying nonfunctioning distractors that can be replaced with distractors attractive to students at various ability levels.

Much of learning disabilities research relies on categorical classification frameworks that use psychometric tests and cut points to identify children with reading or math difficulties. However, there is increasing evidence that the attributes of reading and math learning disabilities are dimensional, representing correlated continua of severity. We discuss issues related to categorical and dimensional approaches to reading and math disabilities, and their comorbid associations, highlighting problems with the use of cut points and correlated assessments. Two simulations are provided in which the correlational structure of a set of cognitive and achievement data are simulated from a single population with no categorical structures. The simulations produce profiles remarkably similar to reported profile differences, suggesting that the patterns are a product of the cut point and the correlational structure of the data. If dimensional approaches better fit the attributes of learning disability, new conceptualizations and better methods to identification and intervention may emerge, especially for comorbid associations of reading and math difficulties.

The current study describes the development and psychometric properties of a new measure targeting sensitivity to change of core autism spectrum disorder (ASD) symptoms, the Autism Impact Measure (AIM). The AIM uses a 2-week recall period with items rated on two corresponding 5-point scales (frequency and impact). Psychometric properties were examined using a large sample (n = 440) of children with ASD enrolled in the Autism Treatment Network. The exploratory factor analysis indicated four factors and resulted in a 25-item questionnaire with excellent overall model fit. Test-retest reliability, cross-informant reliability, and convergent validity with other measures of ASD symptoms and overall functioning were strong. The AIM is a reliable and valid measure of frequency and impact of core ASD symptoms.

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