The Wechsler Intelligence Scale for Children – Third Edition (WISC-III; Wechsler, 1991) and the Children’s Memory Scale (CMS; Cohen, 1997) are frequently used measures of children’s cognitive ability and memory, respectively. They are often used together to assess a child’s strengths and weaknesses to individualize recommendations for assisting them in the educational setting. However, research suggests that there may be some overlap in the abilities assessed by these instruments, making complete administration of both somewhat redundant. Furthermore, previous studies have been equivocal with regard to the assessment of children with Attention- Deficit/ Hyperactivity Disorder (ADHD) on the WISC-III. Support for the applicability of the four-factor structure of the WISC-III has been questioned, particularly in terms of its utility in the diagnosis of ADHD based on the Freedom from Distractibility Index (FFD). A combined confirmatory factor analysis was conducted on the WISC-III and CMS to determine whether a combination of their subtests could be used in lieu of complete administration of each test. The combined WISC-III/ CMS standardization sample was obtained from the Psychological Corporation for use in the confirmatory factor analyses. One-, six-, and seven-factor models were initially proposed for the analyses. Results of the combined confirmatory factor analyses indicated that all three models failed to fit the data as well as a new five-factor model that was created during modification of the six-factor model. Once the five-factor model was specified as the most appropriate model, a clinical sample from a research study was analyzed on the model to find out whether there were age and gender performance differences and also to determine how accurately the new factors differentiated between clinical and nonclinical subsamples. Results indicated that males and females performed significantly differently on the Processing Speed factor but there were no age differences. There were significant differences between the ADHD and no diagnosis groups on three factors: Verbal Comprehension, Working Memory, and Processing Speed; there were no differences on the factors between ADHD subtypes. Together, the five factors were able to correctly classify 66% of children with ADHD. Implications of these results are discussed and suggestions for future research are provided.

The primary purpose of this study was to confirm the four-factor structure of the 30-item Adlerian Personality Priority Assessment (APPA) using a split-sample cross-validation confirmatory factor analysis (CFA). The APPA is an assessment, grounded in Adlerian theory, used to conceptualize clients based on the four personality priorities most commonly used in the Adlerian literature: superiority, pleasing, control, and comfort. The secondary purpose of this study was to provide evidence for discriminant validity, examine predictive qualities of demographics, and explore the prevalence of the four priorities across demographics. For the cross validation CFA, I randomly divided the sample, 1210 undergraduates, at a large public research university (53% Caucasian, 13.1% Hispanic/Latino(a), 21.4% African American, 5.4% American Indian, and 5.8% biracial; mean age =19.8; 58.9% females), into two equal subsamples. I used Subsample 1 (n = 605) to conduct the initial CFA. I held out Subsample 2 (n = 605) to test any possible model changes resulting from Subsample 1 results and to provide further confirmation of the APPA's construct validity. Findings from the split-sample cross-validation CFA confirmed the four-factor structure of the APPA and provided support for the factorial/structure validity of the APPA's scores. Results also present initial evidence of discriminant validity and support the applicability of the instrument across demographics. Overall, these findings suggest Adlerian counselors can confidently use the APPA as a tool to conceptualize clients.

This measurement study examined the construct validity of the retell component of the Texas Middle School Fluency Assessment (Texas Education Agency, University of Houston, & The University of Texas System, 2008a) within a confirmatory factor analysis framework. The role of retell, provided after a one-minute oral reading fluency measure, was investigated by comparing the fit of a three-factor model of reading competence to the data collected on a diverse sample of seventh- and eighth-grade students (N=394). The final model demonstrated adequate to mediocre fit (χ2 = 97.316 {32}; CFI = 0.958; TLI = 0.941; RMSEA = .081). Results suggest that retell was a significant contributor to comprehension (Δχ2=16.652{1}, p < .001), fluency (Δχ2=10.882{1}, p = .001), and word identification (Δχ2=7.84{1}, p = .005). However, the χ2 difference was greater for comprehension, as was the factor loading for comprehension (.250, p < .001) compared to fluency (.194, p < .001) and word identification .167, p < .001). Retell did, however, have a large residual variance (.938), suggesting it did not function well as a measure of comprehension in its current state with low inter-rater reliability (K = .37). Narrative retell scores (.352, p< .001) were better predictors of comprehension than expository retell scores (from .2221 to .264, p < .001) or the combination of all three scores (Δχ2=134.261{19}; p < .001), but average retell scores produced a more parsimonious model than narrative retell scores alone (ΔAIC = 58.275; ΔBIC = 58.275). Average retell was only weakly correlated to other measures of comprehension (from r = .155 to r = .257, p < .01). However, the relationship was stronger than the relationship between retell and other measures of fluency (from r = .158 to r = .183, p < .01) or word identification (r = .132, p < .05). In addition, retell did not demonstrate differential item functioning when student characteristics (e.g., primary language, socioeconomic status, ability level) were entered as covariates, even though there were overall latent differences. / text

Background: Support for the four factor construct validity of the third edition of the Wechsler Adult Intelligence Scale (WAIS-III) has been found in clinical and non clinical populations but some studies question whether more complex models consistent with the concepts of fluid and crystallised intelligence provide a better explanation of the data. The WAIS-III is frequently used in the diagnosis of learning disability, however, previous exploratory factor analysis of data from a population with low IQ did not support the explicit four factor structure of the WAIS-III. Method: A confirmatory factor analysis of the WAIS-III was carried out on data from people with severe and significant learning disability and people with borderline IQ (IQ = 70-79). Results: The data from the borderline IQ sample and the sample with significant learning disability showed at best a weak fit to the explicit four factor models and more complex five or six factor models. However fit of the data from the sample with severe learning disability was poor for all models. Discussion: The findings show little support for the explicit four factor construct validity of the WAIS-III for people with borderline IQ or significant or severe intellectual impairment. Some support is found for the direction taken by the new Wechsler children’s and adult scales (WISC-IV & WAIS-IV) in aligning interpretation of the scales more closely to concepts such as fluid and crystallised theory. The research also suggests the cut-off point of IQ 70 is not reflective of an actual difference in cognitive profile as measured by the WAIS-III. Limitations of this study and implications for further research are also discussed.

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The purpose of this research was to explore the factorial validity of the National Survey of Student Engagement (NSSE), a survey widely used by institutions of higher education. Specifically, using data collected from first-year students and seniors at The University of Tennessee, Knoxville (UT), this research addressed three research questions. First, to what extent does the five-factor model of NSSE (i.e., the benchmark model) exhibit factorial validity? Second, to what extent is Pike’s (2006b) scalelet model of the NSSE factorially valid? Finally, is there a model that depicts the NSSE data better than the models consisting of benchmarks or scalelets? The participants of this study were first-year (n = 981) and senior (n = 944) students at UT who completed the online version of the NSSE in the spring of 2009. Using confirmatory factor analysis, results suggested poor model fit for both the benchmark model and Pike’s (2006b) scalelet model. Exploratory factor analysis with oblique rotation (Promax) resulted in a six-factor solution consisting of 27 items that accounted for approximately 39 percent of variance. The six-factor model failed, however, to exhibit sufficient model fit when confirmatory factor analysis was applied to a different data set (i.e., NSSE data collected in the spring of 2010). Overall, results suggest that much more validation research is needed for the National Survey of Student Engagement to ensure that its use among institutions of higher education is appropriate.

No description available.

This study examined equity in the context of the allocation of park and recreation resources within a community. The contributions made by this study include: extending the original taxonomy of equity models proposed by Crompton and Wicks (1988); development of a theoretical framework for their original model; providing a current synthesis of equity based literature; advancing the Equity Implementation Model (Wicks & Crompton, 1989) by developing an instrument capable of measuring residents’ perceptions and preferences of park and recreation resource allocation in their community; empirically confirming the legitimacy of alternate dimensions of equity through Structural Equation Modeling; applying information gained from using the instrument to determine the usefulness of selected variables in predicting equity preferences; and comparing data on equity preferences with those of prevailing perceptions to illustrate the utility of the instrument in guiding resource allocation decisions. Five of the original operationalizations of equity were validated (Compensatory, Taxes Paid, Direct Price, Efficiency and Advocacy). An additional operationalization, Professional Judgment, was included and also validated, while one of the original dimensions suggested by Crompton and Wicks, Equal Outcomes, could not be distinctively conceptually differentiated and so was discarded. The operationalizations of Equal Inputs and Equal Opportunity could not be differentiated to reflect distinctively different equity concepts. However, further efforts should be invested in operationalizing these two equity concepts, since they do appear to be conceptually different. Using confirmatory factor analysis, a model consisting of all seven operationalizations (Compensatory, Taxes Paid, Direct Price, Efficiency, Advocacy, Professional Judgment and Equality) was an acceptable fit and all paths were significant at the .05 level, suggesting that the proposed 23-item, seven-dimension scale, P&R-EQUITY, effectively measures seven facets of residents’ perceptions of equity in the allocation of park and recreation resources. Two additional operationalizations (Demonstrated Use and Coproduction Opportunities) emerged during the research which suggested that Demonstrated Interest was inadequately operationalized, so future efforts could be focused on operationalizing those three. The scale developed in this study is intended to help officials make appropriate decisions when allocating park and recreation resources.

The primary purpose of this study was to find out which variables lie behind the fact that patients who respond well to treatment of chronic pain differs from those who do not. We used logistic regression to predict group belonging based on the self-reported health surveys, i.e if different answers in the surveys can predict whether a patient is “responsive” or “unresponsive”. By bootstrapping 176 samples, and aggregating the results from 176 logistic regressions based on the sub-samples, we calculate an averaged model. The variables anxiety and physical health were significant in 76% and 70% of the models respectively, while depression was significant in 30% of the models. Gender was significant in 15% of the models and health status in 0,006%. The averaged model correctly classified the most unresponsive patients at cut-off value 0.5. As the cut –off value was increased, the number of correctly classified unresponsive patients decreased while the number of correctly classified responsive patients increased, as well as unresponsive patients classified as responsive. We concluded that the model did not discriminate enough between the two groups. We were also interested in finding out how the variables anxiety, depression, heath status, willingness to participate in activities as well as engagement in activities, mental and physical health relate with one another. The results from confirmatory factor analysis showed that a patient’s health status is highly related to their physical health and activity engagement while pain willingness and engagement in activity were least related. Furthermore, the analysis showed that mental health is highly related with anxiety and health status, indicating that mental health is indeed important to reflect upon when considering the health status of a patient.

The abilities of life insurers can be divided and measured from various aspects. Through the use of structural equation modeling, we investigate the relations among solvency, operation ability and profitability in year 1994, 1995 and 1996. After within-year analysis and longitudinal data analysis, we found that operation ability has a positive influence on the size and income of life insurers and has a slight negative effect on the return on capital during these years. While the effect of solvency, asset risk and product risk on return on capital is not significant. / text