Sifting through the Hyperbole: One Hundred Years of Marijuana Coverage in The New York Times

The contemporary media has often portrayed marijuana as a “slacker drug”; however, this portrayal is somewhat novel. Several scholars have argued in the early 1900s, especially in the 1930s, the media often associated marijuana with violence and mental illness. Another common argument was that marijuana was associated with Mexican immigrants. Conversely, other researchers have argued that these reports were overblown and media accounts of marijuana were not very common until recent years. The current study utilizes recently available on-line archives to conduct a more comprehensive test of how exactly marijuana was depicted in The New York Times from 1851–1950.     

A unified approach to multiple‐set canonical correlation analysis and principal components analysis

Multiple‐set canonical correlation analysis and principal components analysis are popular data reduction techniques in various fields, including psychology. Both techniques aim to extract a series of weighted composites or components of observed variables for the purpose of data reduction. However, their objectives of performing data reduction are different. Multiple‐set canonical correlation analysis focuses on describing the association among several sets of variables through data reduction, whereas principal components analysis concentrates on explaining the maximum variance of a single set of variables. In this paper, we provide a unified framework that combines these seemingly incompatible techniques. The proposed approach embraces the two techniques as special cases. More importantly, it permits a compromise between the techniques in yielding solutions. For instance, we may obtain components in such a way that they maximize the association among multiple data sets, while also accounting for the variance of each data set. We develop a single optimization function for parameter estimation, which is a weighted sum of two criteria for multiple‐set canonical correlation analysis and principal components analysis. We minimize this function analytically. We conduct simulation studies to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of functional neuroimaging data to illustrate its empirical usefulness.     

Parent,child, and contextual predictors of childhood physical punishment

Data gathered over the course of an 18‐year longitudinal study of 1025 New Zealand children were used to: (a) develop a profile of the maternal, child, and contextual factors associated with differing levels of exposure to maternal physical punishment, and (b) identify the key predictors of maternal physical punishment as reported by young people at age 18. Results revealed the presence of clear linear associations between the extent of young people's reported exposure to physical punishment and a wide range of maternal, child, and contextual factors. The key predictors of physical punishment suggested that the psychosocial profile of those mothers at greatest risk of physically punishing or mistreating their child was that of a young woman with a personal history of strict parenting who entered motherhood at an early age, and who was attempting to parent a behaviourally difficult child within a dysfunctional family environment characterized by elevated rates of inter‐parental violence and childhood sexual abuse. These findings were consistent with a cumulative risk factor model in which increasing risk factor exposure is associated with increasing levels of child physical punishment/maltreatment. Copyright © 2002 John Wiley & Sons, Ltd.     

Child‐directed teaching and social learning at 18 months of age: evidence from Yucatec Mayan and US infants

In several previous studies, 18‐month‐old infants who were directly addressed demonstrated more robust imitative behaviors than infants who simply observed another's actions, leading theorists to suggest that child‐directed interactions carried unique informational value. However, these data came exclusively from cultural communities where direct teaching is commonplace, raising the possibility that the findings reflect regularities in infants' social experiences rather than responses to innate or a priori learning mechanisms. The current studies consider infants' imitative learning from child‐directed teaching and observed interaction in two cultural communities, a Yucatec Mayan village where infants have been described as experiencing relatively limited direct instruction (Study 1) and a US city where infants are regularly directly engaged (Study 2). Eighteen‐month‐old infants from each community participated in a within‐subjects study design where they were directly taught to use novel objects on one day and observed actors using different objects on another day. Mayan infants showed relative increases in imitative behaviors on their second visit to the lab as compared to their first visit, but there was no effect of condition. US infants showed no difference in imitative behavior in the child‐directed vs. observed conditions; however, infants who were directly addressed on their first visit showed significantly higher overall imitation rates than infants who observed on their first visit. Together, these findings call into question the idea that child‐directed teaching holds automatic or universal informational value.     

Counterfactuals and Causal Explanation

This article defends the use of interventionist counterfactuals to elucidate causal and explanatory claims against criticisms advanced by James Bogen and Peter Machamer. Against Bogen, I argue that counterfactual claims concerning what would happen under interventions are meaningful and have determinate truth values, even in a deterministic world. I also argue, against both Machamer and Bogen, that we need to appeal to counterfactuals to capture the notions like causal relevance and causal mechanism. Contrary to what both authors suppose, counterfactuals are not “unscientific”—a substantial tradition within statistics and the causal modelling literature makes heavy use of them.