Conditional Direction of Dependence Modeling: Application and Implementation in SPSS

Abstract

Conditional Direction Dependence Analysis (CDDA) has recently been proposed as a statistical framework to test reverse causation (x → y vs. y → x) and potential of confounding (x ← c → y) of variable relations in linear models when moderation is present. Similar to standard DDA, CDDA assumes that the “true” predictor is a continuous, non-normal, exogenous variable. Under non-normality, a conditional causal effect of one variable does not only change means, variances, and covariances, but also the distributional shape (i.e., skewness, kurtosis, co-skewness, and co-kurtosis) of another variable given the moderator. Such distributional changes can be used to study underlying mechanisms of heterogenous causal effects. The present study introduces conditional direction of dependence modeling and presents SPSS macros to make CDDA easily accessible to applied researchers. A real-world data example from the field of gambling addiction research is used to introduce the functionality of CDDA SPSS macros. Limitations of CDDA due to violated assumptions and poor data quality are discussed. The CDDA installation package is available at no charge from www.ddaproject.com.