|Wolfgang Wiedermann (Ph.D., Quantitative Psychology, University of Klagenfurt, Austria) is an Assistant Professor in the Educational, School, & Counseling Psychology Department at the University of Missouri. His primary research interests include the development of methods for causal inference, methods to determine the causal direction of effects in non-experimental studies (so-called Direction Dependence Analysis; see www.ddaproject.com), and methods for intensive longitudinal data in the person-oriented research setting. He has published over 60 peer-reviewed articles and book chapters that focus on the theory and application of statistical methods in experimental and non-experimental data settings.|
In observational studies, at least three possible explanations exist for the association of two variables x and y: 1) x is the cause of y (i.e., a model of the form x → y), 2) y is the cause of x (y → x), or 3) an unmeasured confounder u is present (x ← u → y). Statistical methods that identify which of the three explanatory models fits best would be a useful adjunct to use of theory alone. The present talk introduces one such statistical method, Direction Dependence Analysis (DDA; Wiedermann & von Eye, 2015; Wiedermann & Li, 2018). DDA assesses the relative plausibility of the three explanatory models using higher moment information of the variables (i.e., skewness and kurtosis). DDA will be discussed in the context of person-oriented (non-experimental) research. Extending DDA principles to so-called (linear) vector autoregressive models (VAR) can be used to empirically evaluate causal theories of multivariate intraindividual development (e.g., which of two longitudinally observed variables is more likely to be the explanatory variable and which one is more likely to reflect the outcome). An illustrative example is provided from a study on the development of experienced mood and alcohol consumption behavior. Specifically, DDA is used to answer questions concerning the causal direction of effect of subjective mood and alcohol consumption behavior from a person-oriented perspective, i.e., whether individual changes in mood are the cause of changes in alcohol consumption (i.e., mood → alcohol reflecting the so-called “tension reduction hypothesis“; Conger, 1956; Young, Oei & Knight, 199) or whether alcohol consumption patterns cause changes in perceived mood (i.e., alcohol → mood reflecting the “hedonic motive hypothesis”; Gendolla, 2000). In the present sample, DDA supported the “tension-reduction hypothesis” suggesting that experienced mood is more likely to cause alcohol intake than vice versa. Data requirements of DDA for best-practice applications are discussed and software implementations in R and SPSS are provided.