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The Wechsler Adult Intelligence Scale-Third Edition (WAIS-III; Wechsler, 1997b) provides factor-based index scores but allows only for pairwise comparison of these scores, producing inflated Type I error rates and reducing profile interpretability. This article provides tables for simultaneous comparison to the overall mean index score, thus reducing error rates and aiding interpretation. The Working Memory Index or Processing Speed Index can also be specifically compared when an individual is believed to have a condition, such as a learning disability or traumatic brain injury, associated with the selective depression of these indexes. Tables for the infrequency of specific differences are also provided, allowing the practitioner to note how unusual an obtained difference is in the general population.

Group cohesion is an important construct in understanding the behavior of groups. However, there has been ongoing controversy on how to define and measure this construct. Central to this debate is the structure of cohesion. The authors critically review the literature on unidimensional and multidimensional models of cohesion and describe cohesion as a multidimensional construct with primary and secondary dimensions. Primary dimensions are applicable to describing the cohesiveness of all or most types of groups, whereas secondary dimensions are applicable to describing the cohesiveness of specific types of groups. Viewing group cohesion as consisting of primary and secondary dimensions is discussed.

Evaluating the factor analysis technique as a way of examining group-cohesion, a dynamic inclination of a group to adhere to their task and be united in seeking their objectives, reveals marked improvements indicating factor analysis as a useful tool for accounting the construct of cohesion and its measures. The implications for further factor analytic work and the strengths and weaknesses of factor analysis in researching cohesion are discussed.

To determine if total lifetime physical activity (PA) is associated with better cognitive functioning with aging and if cerebrovascular function mediates this association. A sample of 226 (52.2% female) community dwelling middle-aged and older adults (66.5±6.4 years) in the Brain in Motion Study, completed the Lifetime Total Physical Activity Questionnaire and underwent neuropsychological and cerebrovascular blood flow testing. Multiple robust linear regressions were used to model the associations between lifetime PA and global cognition after adjusting for age, sex, North American Adult Reading Test results (i.e., an estimate of premorbid intellectual ability), maximal aerobic capacity, body mass index and interactions between age, sex, and lifetime PA. Mediation analysis assessed the effect of cerebrovascular measures on the association between lifetime PA and global cognition. Post hoc analyses assessed past year PA and current fitness levels relation to global cognition and cerebrovascular measures. Better global cognitive performance was associated with higher lifetime PA (p=.045), recreational PA (p=.021), and vigorous intensity PA (p=.004), PA between the ages of 0 and 20 years (p=.036), and between the ages of 21 and 35 years (p<.0001). Cerebrovascular measures did not mediate the association between PA and global cognition scores (p>.5), but partially mediated the relation between current fitness and global cognition. This study revealed significant associations between higher levels of PA (i.e., total lifetime, recreational, vigorous PA, and past year) and better cognitive function in later life. Current fitness levels relation to cognitive function may be partially mediated through current cerebrovascular function. (JINS, 2015, 21, 816–830) [ABSTRACT FROM AUTHOR]

Objectives. To develop supplementary methods for the analysis of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) in neuropsychological assessment. Design and Methods. Psychometric. Results. The following methods are made available: (a) provision of traditional confidence intervals (CIs) on index scores, (b) expression of the endpoints of CIs as percentile ranks; (c) quantification of the number of abnormally low index scores exhibited by a case and accompanying estimate of the percentage of the normative population expected to exhibit at least this number of low scores; (d) quantification of the reliability and abnormality of index score deviations from an individual's index score mean (thereby offering an alternative to the pairwise approach to index score comparisons available in the WAIS-IV manual); (e) provision of CIs on an individual's deviation scores or pairwise difference scores, (f) estimation of the percentage of the normative population expected to exhibit at least as many abnormal deviations or abnormal pairwise differences as a case; and (g) calculation of a case's Mahalanobis distance index (MDI), thereby providing a multivariate estimate of the overall abnormality of an index score profile. With the exception of the MDI, all the methods can be applied using tables provided in this paper. However, for ease and speed of application, and to reduce the possibility of clerical error, all the methods have also been implemented in a computer program. Conclusions. The methods are useful for neuropsychological interpretation of the WAIS-IV. [ABSTRACT FROM AUTHOR]

The article focuses on the beneficial impact of physical fitness on the cognitive function and the mechanism by which this occurs. It relates the meaning of the decline of the age-related cognitive which refers to the normal or expected time-dependent deterioration in certain mental abilities after the maturity of reproduction. It adds that the positional of the physical fineness for the prevention of age-related cognitive decline and dementia has implications for therapeutic strategies.

Monte Carlo research increasingly seems to favor the use of parallel analysis as a method for determining the "correct" number of factors in factor analysis or components in principal components analysis. We present a regression equation for predicting parallel analysis values used to decide the number of principal components to retain. This equation is appropriate for predicting criterion mean eigenvalues and was derived from random data sets containing between 5 and 50 variables and between 50 and 500 subjects. This relatively simple equation is more accurate for predicting mean eigenvalues from random data matrices with unities in the diagonals than a previously published equation. Moreover, given that the parallel analysis decision rule may be too dependent on chance, our equation is also used to predict the 95th percentile point in distributions of eigenvalues generated from random data matrices. Multiple correlations for all analyses were at least .95. Regression weights for predicting the first 33 mean and 95th percentile eigenvalues are given in easy-to-use tables. [ABSTRACT FROM AUTHOR]