This study is a partial replication of L. Hu and P. M. Bentler's (1999) fit criteria work. The purpose of this study was twofold: (a) to determine whether cut-off values vary according to which model is the true population model for a dataset and (b) to identify which of 13 fit indexes behave optimally by retaining all of the correct models while simultaneously rejecting all of the misspecified models in a manner invariant across sample size and data distribution. The authors found that for most indexes the results do not vary depending on which model serves as the correct model. Furthermore, the search for an optimal cut-off value led to a new discovery about the nature of McDonald's measure of centrality and the root mean square error of approximation. Unlike all other indexes considered in this study, the cut-off value of both indexes actually decreases for incorrect models as sample size increases. This may suggest that power calculations are more likely to be optimal when based on those indices.

The authors examined the psychometric properties of the Counseling Competencies Scale (CCS; University of Central Florida Counselor Education Faculty, 2009), an instrument designed to assess trainee competencies as measured in their counseling skills, dispositions, and behaviors. There was strong internal consistency for the 4‐factor model for midterm data (.927) and the 5‐factor model for final data (.933). Interrater reliability for the total CCS score was .570, and criterion‐related validity (correlation between the total score on the final CCS and semester grade) yielded a moderate correlation (r= .407, p < .01). Thus, the results provide initial support for using the CCS to assess counseling students’ professional competencies.

The latest version of the Wechsler Intelligence Scale for Children, the WISC-III, is generating the research and professional interest that would be expected of this venerable tool. The WISC-III manual anticipated this interest with considerable information about the reliability and validity of the new scale. Nevertheless, questions remain about the constructs measured by the WISC-III. This research had multiple purposes. It sought to determine (a) whether the WISC-III measures the same constructs (whatever those constructs are) across its 11 year age span, and (b) what constructs are measured by the WISC-III. This research also sought to illustrate the methodology appropriate for such analyses: Multi-sample, hierarchical confirmatory factor analyses were performed on the WISC-III standardization data. The covariance matrices for the 11 age levels were statistically indistinguishable; the test does measure the same constructs across ages. Four factors, as anticipated in the manual, were supported in the hierarchical analysis, and again were remarkably consistent across ages. Loadings of first-order factors on the second-order g (general intelligence) factor argue against naming the third factor Freedom from Distractibility, however. Rather, this factor, with its very high loading on g, may be a measure of Quantitative Reasoning. The development of the latest version of the Wechsler Intelligence Scale forChildren, the WISC-III, must have felt like a tightrope walk to its publisher, the Psychological Corporation (Psyc Corp). Its predecessor, the WISC-R, was the most widely used individual intelligence test for children, with a wealth of information supporting its reliability and validity (cf. Kaufman, 1979). Psyc Corp could not change the test too much without risking losing customers. At the same time, the WISC-R was increasingly being criticized as being out-ofdate; its norms, its psychometric and theoretical base, and even its styling, wereWe are grateful to

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.

Mangine, GT, Hoffman, JR, Gonzalez, AM, Townsend, JR, Wells, AJ, Jajtner, AR, Beyer, KS, Boone, CH, Wang, R, Miramonti, AA, LaMonica, MB, Fukuda, DH, Witta, EL, Ratamess, NA, and Stout, JR. Exercise-induced hormone elevations are related to muscle growth. J Strength Cond Res 31(1): 45-53, 2017-Partial least squares regression structural equation modeling (PLS-SEM) was used to examine relationships between the endocrine response to resistance exercise and muscle hypertrophy in resistance-trained men. Pretesting (PRE) measures of muscle size (thickness and cross-sectional area) of the vastus lateralis and rectus femoris were collected in 26 resistance-trained men. Participants were randomly selected to complete a high-volume (VOL, n = 13, 10-12RM, 1-minute rest) or high-intensity (INT, n = 13, 3-5RM, 3-minute rest) resistance training program. Blood samples were collected at baseline, immediately postexercise, 30-minute, and 60-minute postexercise during weeks 1 (week 1) and 8 (week 8) of training. The hormonal responses (testosterone, growth hormone [22 kD], insulin-like growth factor-1, cortisol, and insulin) to each training session were evaluated using area-under-the-curve (AUC) analyses. Relationships between muscle size (PRE), AUC values (week 1 + week 8) for each hormone, and muscle size (POST) were assessed using a consistent PLS-SEM algorithm and tested for statistical significance (p ≤ 0.05) using a 1,000 samples consistent bootstrapping analysis. Group-wise comparisons for each relationship were assessed through independent t-tests. The model explained 73.4% (p < 0.001) of variance in muscle size at POST. Significant pathways between testosterone and muscle size at PRE (p = 0.043) and muscle size at POST (p = 0.032) were observed. The ability to explain muscle size at POST improved when the model was analyzed by group (INT: R = 0.882; VOL: R = 0.987; p < 0.001). No group differences in modal quality were found. Exercise-induced testosterone elevations, independent of the training programs used in this study, seem to be related to muscle growth.