When using multiple regression, researchers frequently wish to explore how the relationship between two variables is moderated by another variable; this is termed an interaction. Historically, two approaches have been used to probe interactions: the pick-a-point approach and the Johnson-Neyman (JN) technique. The pick-a-point approach has limitations that can be avoided using the JN technique. Currently, the software available for implementing the JN technique and creating corresponding figures lacks several desirable features–most notably, ease of use and figure quality. To fill this gap in the literature, we offer a free Microsoft Excel 2013 workbook, CAHOST (a concatenation of the first two letters of the authors' last names), that allows the user to seamlessly create publication-ready figures of the results of the JN technique.

Surveys commonly suffer from insufficient effort responding (IER). If not accounted for, IER can cause biases and lead to false conclusions. In particular, Cronbach's alpha has been empirically observed to either deflate or inflate due to IER. This paper will elucidate how IER impacts Cronbach's alpha in a variety of situations. Previous results concerning internal consistency under mixture models are extended to obtain a characterization of Cronbach's alpha in terms of item validities, average variances, and average covariances. The characterization is then applied to contaminating distributions representing various types of IER. The discussion will provide commentary on previous simulation-based investigations, confirming some previous hypotheses for the common types of IER, but also revealing possibilities from newly considered responding patterns. Specifically, it is possible that the bias can change from negative to positive (and vice versa) as the proportion of contamination increases.

This paper presents a reinforcement learning algorithm for solving infinite horizon Markov Decision Processes under the expected total discounted reward criterion when both the state and action spaces are continuous. This algorithm is based on Watkins' Q-learning, but uses Nadaraya-Watson kernel smoothing to generalize knowledge to unvisited states. As expected, continuity conditions must be imposed on the mean rewards and transition probabilities. Using results from kernel regression theory, this algorithm is proven capable of producing a Q-value function estimate that is uniformly within an arbitrary tolerance of the true Q-value function with probability one. The algorithm is then applied to an example problem to empirically show convergence as well.

People have long used language to infer associates’ personality. In quantitative research, the relationship is often analyzed by looking at correlations between a psychological construct and the Linguistic Inquiry and Word Count (LIWC)—a program that tabulates word frequencies. We compare LIWC to a machine learning (ML) language model on the task of predicting grandiose narcissism (valid N = 471).We use the ML model discussed in Cutler and Kulis and formulate it as an extension of LIWC. With a strict validation scheme, the LIWC prediction was not more accurate than chance. The ML representation did moderately better ( R2 = .043). This indicates that the ML model was able to preserve personality information where LIWC failed to do so, suggesting that precautions are warranted for social-personality research that relies solely on LIWC.

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