The comprehensive approach to analyzing multivariate constructs

Many psychological constructs of interest to personality psychologists, such as personality, behavior, and emotions, are made up of many variables. Moreover, similarity metrics, such as self-other agreement, profile similarity, or behavioral consistency, result from calculations conducted across many variables. When analyzed using a comprehensive approach, such multivariate constructs present unique analytic challenges. Such challenges are not well addressed in standard graduate statistics textbooks or presently available in standard commercial software. This article introduces the ‘multicon’ package, freely available in the R statistical package, designed to aid researchers interested in taking a comprehensive approach to analyzing multivariate constructs. Realistic examples from personality psychology are provided to demonstrate the utility of this package.     

Qualitative aspects of pantomime recognition defect in aphasia

This study investigated the performance pattern of aphasic patients on a pantomime recognition test in which one of the four response alternatives was an object semantically related to the correct choice. Aphasics with defective pantomime recognition made 71% of their total errors on the semantic foils, and for the majority of such patients, semantic errors represented 80 to 100% of total errors. Only aphasies with objective evidence of confusion failed to make highly frequent semantic errors. The findings suggest that most aphasies who fail pantomime recognition are impaired because of semantically vague understanding of the pantomimes' intended meaning rather than complete lack of understanding of the pantomime.     

Modelling monotonic effects of ordinal predictors in Bayesian regression models

Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under- or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which it is reasonable to assume their effects to be monotonic. The parameterization of such monotonic effects is realized in terms of a scale parameter b representing the direction and size of the effect and a simplex parameter modelling the normalized differences between categories. This ensures that predictions increase or decrease monotonically, while changes between adjacent categories may vary across categories. This formulation generalizes to interaction terms as well as multilevel structures. Monotonic effects may be applied not only to ordinal predictors, but also to other discrete variables for which a monotonic relationship is plausible. In simulation studies we show that the model is well calibrated and, if there is monotonicity present, exhibits predictive performance similar to or even better than other approaches designed to handle ordinal predictors. Using Stan, we developed a Bayesian estimation method for monotonic effects which allows us to incorporate prior information and to check the assumption of monotonicity. We have implemented this method in the R package brms, so that fitting monotonic effects in a fully Bayesian framework is now straightforward.     

Identifying the relevant aspects of a problem text

Forty-nine subjects judged the relevancy of sentence parts of a word problem (the Allsports problem). Patterns of subjects' judgments suggest three problem-solving heuristics: a SETS heuristic, a TIME heuristic, and a QUESTION heuristic. Presentation of the question before the problem tends to suppress SETS and TIME heuristics. A computer program (ATTEND) is presented to simulate subjects' behavior on the Allsports problem. The program is context-sensitive in that it can change a relevance judgment upon the acquisition of further information. Averaged subject judgments and ATTEND judgments agree for 87% of the items.     

A new goodness-of-fit measure for probit models: Surrogate R2

Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R² used for linear models. Such a measure has long been sought to achieve ‘comparability’ of different empirical models across multiple samples addressing similar social questions. To this end, we propose a novel R² measure for probit models using the notion of surrogacy – simulating a continuous variable $S$ as a surrogate of the original discrete response (Liu & Zhang, Journal of the American Statistical Association, 113, 845 and 2018). The proposed R² is the proportion of the variance of the surrogate response explained by explanatory variables through a linear model, and we call it a surrogate R². This paper shows both theoretically and numerically that the surrogate R² approximates the OLS R² based on the latent continuous variable, preserves the interpretation of explained variation, and maintains monotonicity between nested models. As no other pseudo R², McKelvey and Zavoina's and McFadden's included, can meet all the three criteria simultaneously, our measure fills this crucial void in probit model inference.     

SPECTERS OF THE NINETEENTH CENTURY: Charles Taylor and the Problem of Historicism

This paper identifies and analyzes the problem of historicism in Charles Taylor's work overall, but with particular emphasis on his most recent publication, A Secular Age. I circumscribe the problem of historicism through reference to the nineteenth‐century German philosophical tradition in which it developed, in particular in the thought of Wilhelm Dilthey. I then trace the structural similarities between the notions of history to be found in the thought of Taylor and Dilthey and how these structural similarities raise worries associated with the problem of historicism. I argue that the structural aporia of historicism evident in Taylor's work brings to light a live philosophical problem that is basic to theoretical debates in the study religion.     

Kierkegaard's Critique of Eudaimonism: A Reassessment

Interpreters are less univocal than one might think in assessing Søren Kierkegaard's attitude toward eudaimonism. Through an analysis of several key texts from across Kierkegaard's authorship, I argue that existing interpretations do not convincingly address the relationship between Kierkegaard's critique of eudaimonism and his mid‐nineteenth‐century context, which was dominated by post‐Kantian idealists. While I am sympathetic to aspects of deontological and aretaic interpretations, a contextual reading shows that his critique centers on what he diagnoses as the enclosure of the modern self. This puts his critique of eudaimonism in the purview of his moral psychology and in continuity with his critique of romanticism.     

One version of direct response priming requires automatization of the relevant associations but not awareness of the prime

Priming is the influence of one event on performance during a second event. One type of priming is known as semantic priming because it biases interpretation of the subsequent stimulus. Another type, direct response priming, biases responding directly without semantic mediation. Research reviewed in this article indicates that two versions of the second type, direct response priming, can be distinguished. One version, explicit priming, requires awareness of the prime. The other version, associative response priming, occurs even if the prime is masked and not phenomenally visible. This version, which is attributed to associations relating specific sensory events to movements of particular muscles, is enabled only if the association has previously been automatized by brief practice in which the to-be-primed response is made to the stimulus that subsequently appears as the prime. Associative response priming can be explained by a simple stimulus–response interpretation; other varieties of priming are more theoretically challenging.