The distinct contributions of cause–effect order and reasoning type in judgments of causality

ABSTRACT

People generally perceive a stronger link between smoking and cancer than between cancer and smoking. Generally, prior research on asymmetrical causal reasoning has not distinguished predictive (searching for effects) and diagnostic reasoning (searching for causes) from the order in which causes and effects are presented. Across 6 studies (overall N = 627), we show that order and reasoning have an additive influence on the causality perception: causes, spatially or temporally presented before the effect, strengthen the causality attribution associated to predictive (vs. diagnostic) frames. Moreover, we show that order and reasoning frame are bi-directionally related, as the cause-first order triggers predictive reasoning and vice versa, and people mentally maintain the cause-first order when envisaging a causal relation. Besides its methodological contribution to the causal reasoning literature, this research demonstrates the powerful role of word order in causal reasoning. Implications for the role of word order in communication and risk prevention are discussed.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.