A Challenge to Orthodoxy in Psychology: Thomas Sowell and Social Justice.

Psychologists address social-justice problems in their research and applied work, and their scholarly efforts have been influenced by assumptions, constructs, and hypotheses from the political left. Recently, some psychologists have called for increased intellectual and political diversity in psychology, particularly as such diversity may lead to improved problem-solving. As an attempt to increase intellectual diversity in psychology, we review here the scholarship of Thomas Sowell. His work represents a rich source of hypotheses for psychologists' future research. We focus on his views on the importance of freedom; the extent to which reforms can reduce freedom; the importance of free markets to human flourishing; the role of free markets in producing costs for discrimination; the way spontaneously ordered systems can contain knowledge that can be overlooked in reforms; and the importance of culture and cultural capital. We will also discuss Sowell's more thoroughgoing economic analyses of problems and solutions and his analyses of contingencies operating on politicians and reformers, as well as his views on conflicts in fundamental visions about human nature and the pivotal role of improvements in minority education.

Differences in specific learning disability identification with the Woodcock-Johnson IV.

The Ability Achievement Discrepancy model remains the primary identification method used by school personnel. This study examined identification of a specific learning disability using the Ability Achievement Discrepancy model with the Woodcock-Johnson IV (WJ-IV). Two different test scores can be used to represent the ability construct: one that maintains the overlap between intelligence quotient (IQ) and basic psychological processes (i.e., general intellectual ability) and one that mostly removes the overlap between IQ and basic psychological processes (i.e., fluid-crystallized intelligence). The study included 3,736 individuals from the WJ-IV standardization sample to ascertain whether different proportions of individuals were identified by the 2 methods as well as identify which tests contributed to the differences. χ2 tests of independence and absolute ratios were used to examine the proportion of individuals identified; a multivariate analysis of variance and follow-up Tukey honestly significant differences were conducted to determine whether the groups of individuals identified in each model differed on their academic achievement scores, and Mann-Whitney U tests were used to identify the tests that contributed to differences in identification rates. The results indicated that different proportions of individuals were identified as a function of the IQ score used, even though achievement scores were generally similar across identification methods. Black students were overrepresented and White students were underrepresented compared with their proportion in the total sample. Discrepancy profiles largely varied as a function of the internal psychometrics of the WJ-IV rather than characteristics of the individual. Implications for practice and methodological limitations are reviewed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Gaining from discretization of continuous data: The correspondence analysis biplot approach.

According to Stevens's classification of measurement, continuous data can be either ratio or interval scale data. The relationship between two continuous variables is assumed to be linear and is estimated with the Pearson correlation coefficient, which assumes normality between the variables. If researchers use conventional statistics (t test or analysis of variance) or factor analysis of correlation matrices to study gender or race differences, the data are assumed to be continuous and normally distributed. If continuous data are discretized, they become ordinal; thus, discretization is widely considered to be a downgrading of measurement. However, discretization is advantageous for data analysis, because it provides interactive relationships between the discretized variables and naturally measured categorical variables such as gender and race. Such interactive relationship information between categories is not available with the ratio or interval scale of measurement, but it is useful to researchers in some applications. In the present study, Wechsler intelligence and memory scores were discretized, and the interactive relationships were examined among the discretized Wechsler scores (by gender and race). Unlike in previous studies, we estimated category associations and used correlations to enhance their interpretation, and our results showed distinct gender and racial/ethnic group differences in the correlational patterns.

Understanding immigrants, schooling, and school psychology: Contemporary science and practice.

Immigration into the United States is a particularly salient topic of current contemporary educational, social, and political discussions. The school-related needs of immigrant children and youth can be well served by rigorous research and effective school psychology preservice training and preparation. This overview highlights key definitions, demographic statistics, and current resources related to immigration in U.S. society. This special topic section on understanding immigrants, schooling, and school psychology features articles relevant to this important topic. We conclude with a call for this effort to serve as a springboard for future discussions, scholarship, and school psychology training in preparing practitioners for serving children who are immigrants. (PsycINFO Database Record

Using Profile Analysis via Multidimensional Scaling (PAMS) to identify core profiles from the WMS-III.

Profile Analysis via Multidimensional Scaling (PAMS) is a procedure for extracting latent core profiles in a multitest data set. The PAMS procedure offers several advantages compared with other profile analysis procedures. Most notably, PAMS estimates individual profile weights that reflect the degree to which an individual's observed profile approximates the shape and scatter of latent core profiles. The PAMS procedure was applied to index scores of nonreplicated participants from the standardization sample (N = 1,033) for the Wechsler Memory Scale--Third Edition (D. Tulsky, J. Zhu, & M. F. Ledbetter, 2002). PAMS extracted discrepant visual memory and auditory memory versus working memory core profiles for the complete 16- to 89-year-old sample and discrepant working memory and auditory memory versus working memory core profiles for the 75- to 89-year-old cohort. Implications for use of PAMS in future research are discussed.

Confirmatory Factor Analysis and Profile Analysis via Multidimensional Scaling.

This paper describes the Confirmatory Factor Analysis (CFA) parameterization of the Profile Analysis via Multidimensional Scaling (PAMS) model to demonstrate validation of profile pattern hypotheses derived from multidimensional scaling (MDS). Profile Analysis via Multidimensional Scaling (PAMS) is an exploratory method for identifying major profiles in a multi-subtest test battery. Major profile patterns are represented as dimensions extracted from a MDS analysis. PAMS represents an individual observed score as a linear combination of dimensions where the dimensions are the most typical profile patterns present in a population. While the PAMS approach was initially developed for exploratory purposes, its results can later be confirmed in a different sample by CFA. Since CFA is often used to verify results from an exploratory factor analysis, the present paper makes the connection between a factor model and the PAMS model, and then illustrates CFA with a simulated example (that was generated by the PAMS model) and at the same time with a real example. The real example demonstrates confirmation of PAMS exploratory results by using a different sample. Fit indexes can be used to indicate whether the CFA reparameterization as a confirmatory approach works for the PAMS exploratory results.

Estimating Cognitive Profiles Using Profile Analysis via Multidimensional Scaling (PAMS).

Two of the most popular methods of profile analysis, cluster analysis and modal profile analysis, have limitations. First, neither technique is adequate when the sample size is large. Second, neither method will necessarily provide profile information in terms of both level and pattern. A new method of profile analysis, called Profile Analysis via Multidimensional Scaling (PAMS; Davison, 1996), is introduced to meet the challenge. PAMS extends the use of simple multidimensional scaling methods to identify latent profiles in a multi-test battery. Application of PAMS to profile analysis is described. The PAMS model is then used to identify latent profiles from a subgroup (N = 357) within the sample of the Woodcock-Johnson Psychoeducational Battery-Revised (WJ-R; McGrew, Werder, & Woodcock, 1991; Woodcock & Johnson, 1989), followed by a discussion of procedures for interpreting participants' observed score profiles from the latent PAMS profiles. Finally, advantages and limitations of the PAMS technique are discussed.