TY - JOUR
T1 - Determining directional dependency in causal associations
AU - Pornprasertmanit, Sunthud
AU - Little, Todd D.
N1 - Funding Information:
Partial support for this project was provided by grant NSF 1053160 and by the Center for Research Methods and Data Analysis at the University of Kansas (Todd D. Little, director). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.
PY - 2012/7
Y1 - 2012/7
N2 - Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D'Agostino's K2, and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if: (a) the regression error in population is normal; (b) an unobserved explanatory variable correlates with any variables equal to or less than.2; (c) a curvilinear relation between both variables is not strong (standardized regression coefficient 2); (d) there are no bivariate outliers; and (e) both variables are continuous.
AB - Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D'Agostino's K2, and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if: (a) the regression error in population is normal; (b) an unobserved explanatory variable correlates with any variables equal to or less than.2; (c) a curvilinear relation between both variables is not strong (standardized regression coefficient 2); (d) there are no bivariate outliers; and (e) both variables are continuous.
KW - Directional dependency
KW - Excessive kurtosis
KW - Skewness
KW - Unobserved explanatory variable
UR - http://www.scopus.com/inward/record.url?scp=84863660285&partnerID=8YFLogxK
U2 - 10.1177/0165025412448944
DO - 10.1177/0165025412448944
M3 - Article
AN - SCOPUS:84863660285
SN - 0165-0254
VL - 36
SP - 313
EP - 322
JO - International Journal of Behavioral Development
JF - International Journal of Behavioral Development
IS - 4
ER -