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Gary L. Canivez - One of the best experts on this subject based on the ideXlab platform.
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construct validity of the wechsler intelligence scale for children fourth uk edition with a referred irish sample wechsler and cattell horn carroll model comparisons with 15 subtests
British Journal of Educational Psychology, 2017Co-Authors: Gary L. Canivez, Marley W. Watkins, Rebecca Good, Kate James, Trevor JamesAbstract:Background Irish educational psychologists frequently use the Wechsler Intelligence Scale for Children – Fourth UK Edition (WISC–IVUK; Wechsler, 2004, Wechsler Intelligence Scale for Children-Fourth UK Edition, London, UK, Harcourt Assessment) in clinical Assessments of children with learning difficulties. Unfortunately, reliability and validity studies of the WISC–IVUK standardization sample have not yet been reported. Watkins et al. (2013, International Journal of School and Educational Psychology, 1, 102) found support for a bifactor structure with a large sample (N = 794) of Irish children who were administered the 10 WISC–IVUK core subtests in clinical Assessments of learning difficulties and dominance of general intelligence. Because only 10 subtests were available, Cattell–Horn–Carroll (CHC; McGrew, 1997, 2005, Contemporary Intellectual Assessment: Theories, tests, and issues, New York, NY: Guilford; Schneider & McGrew, 2012, Contemporary Intellectual Assessment: Theories, tests, and issues, New York, NY, Guilford Press) models could not be tested and compared. Aim, Sample and Method The present study utilized confirmatory factor analyses to test the latent factor structure of the WISC–IVUK with a sample of 245 Irish children administered all 15 WISC–IVUK subtests in evaluations assessing learning difficulties in order to examine CHC- and Wechsler-based models. One through five, oblique first-order factor models and higher order versus bifactor models were examined and compared using CFA. Results Meaningful differences in fit statistics were not observed between the Wechsler and CHC representations of higher-order or bifactor models. In all four structures, general intelligence accounted for the largest portions of explained common variance, whereas group factors accounted for small to miniscule portions of explained common variance. Omega-hierarchical subscale coefficients indicated that unit-weighted composites that would be generated by WISC–IVUK group factors (Wechsler or CHC) would contain little unique variance and thus be of little value. Conclusion These results were similar to those from other investigations, further demonstrating the replication of the WISC–IV factor structure across cultures and the importance of focusing primary interpretation on the FSIQ.
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examination of the structural convergent and incremental validity of the reynolds Intellectual Assessment scales rias with a clinical sample
Psychological Assessment, 2012Co-Authors: Jason M Nelson, Gary L. CanivezAbstract:Empirical examination of the Reynolds Intellectual Assessment Scales (RIAS; C. R. Reynolds & R. W. Kamphaus, 2003a) has produced mixed results regarding its internal structure and convergent validity. Various aspects of validity of RIAS scores with a sample (N = 521) of adolescents and adults seeking psychological evaluations at a university-based clinic were examined. Results from exploratory factor analysis indicated only 1 factor, and confirmatory factor analysis (CFA) indicated that the 1-factor model was a good fit and a better fit than the 2-factor model. Hierarchical factor analysis indicated the higher order, general intelligence factor accounted for the largest amount of variance. Correlations with other measures of verbal/crystallized and nonverbal/fluid intelligence were supportive of the convergent validity of the Verbal Intelligence Index but not the Nonverbal Intelligence Index. Joint CFA with these additional measures resulted in a superior fit of the 2-factor model compared with the 1-factor model, although the Odd-Item-Out subtest was found to be a poor measure of nonverbal/fluid intelligence. Incremental validity analyses indicated that the Composite Intelligence Index explained a medium to large portion of academic achievement variance; the NIX and VIX explained a small amount of remaining variance. Implications regarding interpretation of the RIAS when assessing similar individuals are discussed.
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higher order exploratory factor analysis of the reynolds Intellectual Assessment scales with a referred sample
Journal of School Psychology, 2007Co-Authors: Jason M Nelson, Gary L. Canivez, Will Lindstrom, Clifford V HattAbstract:The factor structure of the Reynolds Intellectual Assessment Scales (RIAS; [Reynolds, C.R., & Kamphaus, R.W. (2003). Reynolds Intellectual Assessment Scales. Lutz, FL: Psychological Assessment Resources, Inc.]) was investigated with a large (N=1163) independent sample of referred students (ages 6–18). More rigorous factor extraction criteria (viz., Horn's parallel analysis (HPA); [Horn, J.L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.], and Minimum Average Partial (MAP) analysis; [Velicer, W.F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327.]), in addition to those used in RIAS development, were investigated. Exploratory factor analyses using both orthogonal and oblique rotations and higher-order exploratory factor analyses using the Schmid and Leiman [Schmid, J., and Leiman, J.M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 53–61.] procedure were conducted. All factor extraction criteria indicated extraction of only one factor. Oblique rotations resulted in different results than orthogonal rotations, and higher-order factor analysis indicated the largest amount of
Jason M Nelson - One of the best experts on this subject based on the ideXlab platform.
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examination of the structural convergent and incremental validity of the reynolds Intellectual Assessment scales rias with a clinical sample
Psychological Assessment, 2012Co-Authors: Jason M Nelson, Gary L. CanivezAbstract:Empirical examination of the Reynolds Intellectual Assessment Scales (RIAS; C. R. Reynolds & R. W. Kamphaus, 2003a) has produced mixed results regarding its internal structure and convergent validity. Various aspects of validity of RIAS scores with a sample (N = 521) of adolescents and adults seeking psychological evaluations at a university-based clinic were examined. Results from exploratory factor analysis indicated only 1 factor, and confirmatory factor analysis (CFA) indicated that the 1-factor model was a good fit and a better fit than the 2-factor model. Hierarchical factor analysis indicated the higher order, general intelligence factor accounted for the largest amount of variance. Correlations with other measures of verbal/crystallized and nonverbal/fluid intelligence were supportive of the convergent validity of the Verbal Intelligence Index but not the Nonverbal Intelligence Index. Joint CFA with these additional measures resulted in a superior fit of the 2-factor model compared with the 1-factor model, although the Odd-Item-Out subtest was found to be a poor measure of nonverbal/fluid intelligence. Incremental validity analyses indicated that the Composite Intelligence Index explained a medium to large portion of academic achievement variance; the NIX and VIX explained a small amount of remaining variance. Implications regarding interpretation of the RIAS when assessing similar individuals are discussed.
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higher order exploratory factor analysis of the reynolds Intellectual Assessment scales with a referred sample
Journal of School Psychology, 2007Co-Authors: Jason M Nelson, Gary L. Canivez, Will Lindstrom, Clifford V HattAbstract:The factor structure of the Reynolds Intellectual Assessment Scales (RIAS; [Reynolds, C.R., & Kamphaus, R.W. (2003). Reynolds Intellectual Assessment Scales. Lutz, FL: Psychological Assessment Resources, Inc.]) was investigated with a large (N=1163) independent sample of referred students (ages 6–18). More rigorous factor extraction criteria (viz., Horn's parallel analysis (HPA); [Horn, J.L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.], and Minimum Average Partial (MAP) analysis; [Velicer, W.F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327.]), in addition to those used in RIAS development, were investigated. Exploratory factor analyses using both orthogonal and oblique rotations and higher-order exploratory factor analyses using the Schmid and Leiman [Schmid, J., and Leiman, J.M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 53–61.] procedure were conducted. All factor extraction criteria indicated extraction of only one factor. Oblique rotations resulted in different results than orthogonal rotations, and higher-order factor analysis indicated the largest amount of
John H Kranzler - One of the best experts on this subject based on the ideXlab platform.
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Intellectual Assessment of children and youth in the united states of america past present and future
International journal of school and educational psychology, 2016Co-Authors: John H Kranzler, Nicholas Benson, Randy G FloydAbstract:This article briefly reviews the history of Intellectual Assessment of children and youth in the United States of America, as well as current practices and future directions. Although administration of intelligence tests in the schools has been a longstanding practice in the United States, their use has also elicited sharp controversy over time. At present, intelligence tests are primarily used in school settings for determining eligibility for special education and related services. In clinical settings, intelligence tests are used for diagnostic, predictive, and treatment planning purposes. Over the past decade, the use of contemporary theory, particularly the Cattell-Horn-Carroll theory (CHC), has increasingly been used for test development and interpretation.
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independent examination of the wechsler adult intelligence scale fourth edition wais iv what does the wais iv measure
Psychological Assessment, 2010Co-Authors: Nicholas F Benson, David M Hulac, John H KranzlerAbstract:Published empirical evidence for the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) does not address some essential questions pertaining to the applied practice of Intellectual Assessment. In this study, the structure and cross-age invariance of the latest WAIS-IV revision were examined to (a) elucidate the nature of the constructs measured and (b) determine whether the same constructs are measured across ages. Results suggest that a Cattell-Horn-Carroll (CHC)-inspired structure provides a better description of test performance than the published scoring structure does. Broad CHC abilities measured by the WAIS-IV include crystallized ability (Gc), fluid reasoning (Gf), visual processing (Gv), short-term memory (Gsm), and processing speed (Gs), although some of these abilities are measured more comprehensively than are others. Additionally, the WAIS-IV provides a measure of quantitative reasoning (QR). Results also suggest a lack of cross-age invariance resulting from age-related differences in factor loadings. Formulas for calculating CHC indexes and suggestions for interpretation are provided.
Clifford V Hatt - One of the best experts on this subject based on the ideXlab platform.
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higher order exploratory factor analysis of the reynolds Intellectual Assessment scales with a referred sample
Journal of School Psychology, 2007Co-Authors: Jason M Nelson, Gary L. Canivez, Will Lindstrom, Clifford V HattAbstract:The factor structure of the Reynolds Intellectual Assessment Scales (RIAS; [Reynolds, C.R., & Kamphaus, R.W. (2003). Reynolds Intellectual Assessment Scales. Lutz, FL: Psychological Assessment Resources, Inc.]) was investigated with a large (N=1163) independent sample of referred students (ages 6–18). More rigorous factor extraction criteria (viz., Horn's parallel analysis (HPA); [Horn, J.L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.], and Minimum Average Partial (MAP) analysis; [Velicer, W.F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327.]), in addition to those used in RIAS development, were investigated. Exploratory factor analyses using both orthogonal and oblique rotations and higher-order exploratory factor analyses using the Schmid and Leiman [Schmid, J., and Leiman, J.M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 53–61.] procedure were conducted. All factor extraction criteria indicated extraction of only one factor. Oblique rotations resulted in different results than orthogonal rotations, and higher-order factor analysis indicated the largest amount of
Lea E Witta - One of the best experts on this subject based on the ideXlab platform.
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a confirmatory analysis of the factor structure and cross age invariance of the wechsler adult intelligence scale third edition
Psychological Assessment, 2004Co-Authors: Gordon E Taub, Kevin S Mcgrew, Lea E WittaAbstract:In the Wechsler Adult Intelligence Scale—Third Edition (WAIS–III; D. Wechsler, 1997), the manual reports several confirmatory factor analyses in support of the instrument’s latent factor structure. In practice, examiners frequently compare an examinee’s score from a current administration of the WAIS–III with the results from a previous test administration. Implicit in test–retest score comparisons is evidence that scores retain similar interpretive meaning across time. Establishing an instrument’s factorial invariance provides the foundation for this practice. This study investigated the factorial invariance of the WAIS–III across the instrument’s 13 age groups. The overall results from this study generally support both configural and factorial invariance of the WAIS–III when the 11 primary tests are administered. The Wechsler Adult Intelligence Scale—Third Edition (WAIS– III; Wechsler, 1997) represents the latest edition of this intelligence battery. The Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1955), in its various incarnations, is perhaps the most widely used test of adult intelligence (Boake, 2002; Sattler, 2001). Even though the basic format of the WAIS–III remains the same (68% of the items were retained from the WAIS–R; Wechsler, 1981), the WAIS–III represents a significant departure from the WAIS–R. Similar to the Wechsler Intelligence Scale for Children—Third Edition (WISC–III; Wechsler, 1991), the WAIS–III incorporates four index scores (i.e., Verbal Comprehension, Perceptual Organization, Working Memory, and Processing Speed) rather than the three scores used in the WAIS–R. The new instrument’s factor structure is more in line with the design of contemporary intelligence tests and psychometrically based hierarchical models of intelligence (see Carroll, 1993, 1997; McGrew & Woodcock, 2001). The new factor structure of the WAIS–III measures specific cognitive abilities associated with each of the 11 tests, which in turn are subsumed by one of the four first-order broad factors. At the apex of the model is a general factor of intelligence or g. To support the construct validity of the four-factor model of the WAIS–III, the authors offer extensive confirmatory factor analyses across all of the instrument’s 13 age groups, which range from 16 to 89 years of age (The Psychological Corporation, 1997). Even with these significant revisions, however, the WAIS–III leaves a simple, yet important question unanswered. Especially pertinent to the practice of applied Intellectual Assessment is the lack of empirical evidence to support the interpretation of the primary WAIS–III tests as measures of the same latent first-order cognitive constructs throughout the instrument’s 13 age groups. In other words, empirical evidence of the factorial invariance of the WAIS– III is not contained in the instrument’s technical manual. The purpose of this study was threefold. The first purpose was to investigate the configural invariance of the four-factor theoretical model across the WAIS–III’s 13 age-differentiated groups. The second purpose was to test the invariance or stability of the four-factor theoretical model across the instrument’s 13 age groups (e.g., is the structure of the theoretical four-factor model the same for 16 –17-year-olds as it is for 85– 89-year-olds?). The third purpose was to test the invariance of the measurementmodel of the WAIS–III’s four-factor theoretical model across the instrument’s 13 age-differentiated groups (i.e., from age 16 to 89). This was an investigation of the extent to which the 11 primary tests included in the calculation of the first-order factors of the WAIS–III are equally valid measures of the four theoretical factors across all 13 age groups.