R. Steve McCallum

The Universal Nonverbal Intelligence Test

The Universal Nonverbal Intelligence Test (UNIT) can be conceptualized as a measure of intelligence, obtained nonverbally. The UNIT is designed to provide a unique measure of cognitive organization (i.e., symbolic and nonsymbolic content) and function (memory and reasoning); even so, it is first and foremost a strong measure of g,primarily because of the complex nature of its six subtests.

Toward a Research-Based Assessment of Dyslexia Using Cognitive Measures to Identify Reading Disabilities

One hundred five participants from a random sample of elementary and middle school children completed measures of reading achievement and cognitive abilities presumed, based on a synthesis of current dyslexia research, to underlie reading. Factor analyses of these cognitive variables (including auditory processing, phonological awareness, short-term auditory memory, visual memory, rapid automatized naming, and visual processing speed) produced three empirically and theoretically derived factors (auditory processing, visual processing/speed, and memory), each of which contributed to the prediction of reading and spelling skills. Factor scores from the three factors combined predicted 85% of the variance associated with letter/sight word naming, 70% of the variance associated with reading comprehension, 73% for spelling, and 61% for phonetic decoding. The auditory processing factor was the strongest predictor, accounting for 27% to 43% of the variance across the different achievement areas. The results provide practitioner and researcher with theoretical and empirical support for the inclusion of measures of the three factors, in addition to specific measures of reading achievement, in a standardized assessment of dyslexia. Guidelines for a thorough, research-based assessment are provided.

Reading Rate and Comprehension as a Function of Computerized Versus Traditional Presentation Mode: A Preliminary Study

Using a counterbalanced, randomized treatment design, 12 elementary school-aged children read under two conditions: (a) independent, silent reading; and (b) computer-assisted reading, via Kurzweil 3000. A repeated-measures multivariate analysis of variance (MANOVA) revealed no significant difference in the composite mean for comprehension and reading rate scores based on presentation strategy. Computerized text presentation (via auditory and visual means) proved no more effective than traditional reading instruction for teacher-nominated weak readers in improving reading rate and comprehension. However, a trend was noted for slower readers to show increased reading rate as a function of computer-assisted reading, with the opposite result for faster readers. Overall, results indicate that for students reading material at their instructional level, computer-assisted reading did not improve comprehension. Future research should continue to focus on the role of technology as an aid to reading instruction.

What Is the Role of Working Memory in Reading Relative to the Big Three Processing Variables (Orthography, Phonology, and Rapid Naming)?

Zero-order correlation coefficients show significant relationships between orthography, phonology, rapid naming, visual and auditory memory, and reading and spelling for 143 second through sixth graders. Although coefficients ranged from .05 to .71, most were statistically significant (65 out of 78). In addition, multiple regression analyses show the power of shortterm memory to contribute to the prediction of reading decoding and reading fluency (p < .05) beyond the power of the “big three” cognitive processing variables (phonology, rapid naming, and orthography).

Handbook of nonverbal assessment

Part I: Nonverbal Assessment. 1. Context for Nonverbal Assessment of Intelligence and Related Abilities R. Steve McCallum. 2. Best Practices in Detecting Bias in Nonverbal Tests Susan J. Maller. 3. Physiological and Psychological Influences on Multicultural and Nonverbal Assessment R. Steve McCallum. 4. Cross-Battery Assessment of Nonverbal Cognitive Ability Brian E. Wilhoit and R. Steve McCallum. Part II: Selected Nonverbal Tests and Cognitive Strategies. 5. The Universal Nonverbal Intelligence Text R. Steve McCallum. 6. Assessment with the Leiter International Performance Scale -- Revised and the S-BIT Gale Roid, Leah Nellis, and Mary McLellan. 7. Comprehensive Test of Nonverbal Intelligence Nils Pearson. 8. The General Ability Measure for Adults Achilles N. Bardos. 9. Naglieri Nonverbal Ability Tests: NNAT and MAT-EF Jack A. Naglieri. 10. Test of Nonverbal Intelligence: A Language-Free Measure of Cognitive Ability Linda Brown. 11. Raven Progressive Matrices John and Jean Raven. Part III: Nonverbal Assessment of Related Abilities. 12. Nonverbal Assessment of Academic Achievement with Special Populations Craig L. Frisby. 13. Functional Behavior Assessment of Nonverbal Behavior Christopher H. Skinner and Ruth A. Ervin. 14. Nonverbal Assessment of Personality and Psychopathology John D. Wasserman. 15. Nonverbal Neuropsychological Assessment John D. Wasserman and Robin M. Lawhorn.Author Index. Subject Index.

Predictive Validity of the Cattell-Horn Gf-Gc Constructs to Achievement:

The predictive validity of cognitive constructs taken from Cattell-Horns Gf-Gc Model was examined. Gf-Gc cognitive constructs were measured using the Woodcock-Johnson-Revised Tests of Cognitive Ability; they include processing speed, fluid reasoning, acculturation-knowledge, short-term memory, long-term retrieval, auditory processing, and visual processing. Scores from the Comprehensive Test of Basic Skills were used as the criterion measures for 104 elementary, middle, and high school students. Using multiple regression equations, various combinations of the Comprehension-Knowledge, Fluid Reasoning, and Processing Speed variables were consistently found to be the best predictors of achievement. Multiple Rs ranged from the .60s to .70s. Results provide evidence for the importance of cognitive constructs for predicting achievement and are potentially useful for understanding program planning and Aptitude x Treatment Interaction research.

Relationship of the Gesell Developmental Exam and the Bracken Basic Concept Scale to academic achievement

Abstract The Gesell Developmental Exam (GDE) and the Bracken Basic Concept Scale (BBCS) were administered to 80 graduates of kindergarten. From a stepwise multiple regression analysis the BBCS accounted for the greatest amount of variance in Arithmetic (31%), Reading (25%), and Spelling (36%) scores from the Wide Range Achievement Test—Revised (WRAT-R). The GDE accounted for an insignificant portion of the variance over the BBCS (2%). If the goal is to predict achievement from a current estimate of readiness, the BBCS may be a better choice for preschool personnel.

A Model for Screening Twice-Exceptional Students (Gifted With Learning Disabilities) Within a Response to Intervention Paradigm

The purpose of this article is to present a model for screening for twice-exceptional status (i.e., gifted students who have a learning disability). Curriculum-based measures (Monitoring Instructional Responsiveness: Reading and Monitoring Instructional Responsiveness: Math) were administered to 1,242 third-grade students within a Response to Intervention paradigm. When gifted status is tentatively defined as high performance (i.e., 84th percentile and higher) on a Monitoring Instructional Responsiveness reading probe, 5.48% of students exhibited deficits in (math) performance consistent with a significant discrepancy between reading and math (i.e., reading score – math score); 4.83% exhibited a discrepancy in reading (i.e., math score – reading). These values are based on observed scores using the following formula to define a discrepancy: 1.5(SD) × SEe. Only 2.1% exhibited a math discrepancy and 1.13% a reading discrepancy based on predicted scores, which takes regression to the mean into account. Using various cut score criteria, practitioners can select from less than 1% to about 10% for screening purposes. When using predicted (rather than observed) scores and more stringent cut score criteria, percentages decline, as expected. Recommendations for using this process for screening are provided, as are implications for best practice, particularly the impact of using more or less conservative criteria for screening twice exceptional students.

The Test of Choice for Assessment of Gifted Children: A Comparison of the K-ABC, WISC-R, and Stanford-Binet

Correlational and mean-difference analyses from 41 gifted children were conducted to determine the comparability of mental ability scores from the Wechsler Intelligence Scale for Children—Revised (WISC-R), Stanford-Binet, and the Kaufman Assessment Battery for Children (K-ABC). Correlation coefficients ranged from .03 to .74. The three mean K-ABC processing scores were (statistically) significantly lower than the IQs from the WISC-R and the Stanford-Binet for all possible comparisons. The implications of using the three tests for placement in gifted programs are discussed.

Simultaneous-Successive Processing among College Students:

The Ravens Progressive Matrices and tasks measuring memory for design, figure copying, free recall, serial recall, and digit span were ad ministered to 67 college students. Factor analytic results indicated that the first three measures loaded on a factor termed simultaneous processing while the latter three measures loaded on a successive processing factor. These results were consistent with previous research with young children indicating that these cognitive processing styles are employed by college students as well as younger students. A self-report measure of preference for simultaneous or successive cognitive processing was found to be unrelated to measured processing styles.