The Support for Economic Inequality Scale: Development and adjudication.

Past research has documented myriad pernicious psychological effects of high economic inequality, prompting interest into how people perceive, evaluate, and react to inequality. Here we propose, refine, and validate the Support for Economic Inequality Scale (SEIS)-a novel measure of attitudes towards economic inequality. In Study 1, we distill eighteen items down to five, providing evidence for unidimensionality and reliability.

The Taxonic Latent Structure and Taxometrics in Forensic Mental Health.

Recently, researchers in the field of forensic mental health have attempted to address the technical, empirical question of whether important clinical problems, such as psychopathy or malingering, constitute taxa (i.e., discrete conditions).

Selection Between Linear Factor Models and Latent Profile Models Using Conditional Covariances.

A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient inferential method suggested by the main result is examined via data simulation and is shown to have acceptable error rate control when deciding between the 2 types of models.

A proposed framework for conducting data-based test analysis.

The authors argue that the current state of applied data-based test analytic practice is unstructured and unmethodical due in large part to the fact that there is no clearly specified, widely accepted test analytic framework for judging the performances of particular tests in particular contexts. Drawing from the extant test theory literature, they propose a rationale that may be used in data-based test analysis. The components of the proposed test analytic framework are outlined in detail, as are examples of the framework as applied to commonly encountered test evaluative scenarios.

An Analysis of Meehl's MAXCOV-HITMAX Procedure for the Case of Continuous Indicators.

MAXCOV-HITMAX was invented by Paul Meehl as a tool for the detection of latent taxonic structures (i.e., structures in which the latent variable, u, is not continuously, but rather Bernoulli, distributed).

Dual scaling for the analysis of categorical data.

Dual scaling is a set of related techniques for the analysis of a wide assortment of categorical data types including contingency tables and multiple-choice, rank order, and paired comparison data. When applied to a contingency table, dual scaling also goes by the name "correspondence analysis," and when applied to multiple-choice data in which there are more than 2 items, "optimal scaling" and "multiple correspondence analysis. " Our aim of this article was to explain in nontechnical terms what dual scaling offers to an analysis of contingency table and multiple-choice data.

What does it mean that an issue is conceptual in nature?

McGrath (2005/this issue) argues that "the conceptual complexity [italics added] of the constructs psychologists choose to measure and the scales they use to measure them has played an important role in the failure to develop more accurate measurement systems" (p. 112). Although we agree with this, we argue, in this commentary, that McGrath has misdiagnosed the source of these difficulties and that this misdiagnosis originates with an unresolved articulation of the nature of a conceptual issue and of the relationship between conceptual and empirical issues in science.

An Analysis of Meehl's MAXCOV-HITMAX Procedure for the Case of Dichotomous Indicators.

MAXCOV-HITMAX was invented by Paul Meehl for the detection of latent taxonic structures (i.e., structures in which the latent variable, _, is not continuously, but rather Bernoulli, distributed).