<![CDATA[When researchers test a model that represents the effect an independent variable onanotherâdependent-- variable, many researchers commonly do not further investigateabout the correctness of the causal direction of the model. Hypothesis testing of suchmodel is generally done by assuring that the model coefficients are statisticallysignificant assuming that the direction of the causality is indeed correct. Hence, thedirection of the causality of these models is simply ex ante assumed, which means that thedirection could be incorrectly stated. The effect of this mistake could be enormous,particularly if findings of the study, which adopt an incorrect causal order, are used forpolicy makingt. This study discusses two approaches in testing the causal ordering of amodel, i.e., the Granger and Simâs tests as well as SCDTs test of causality, which couldbe either used in an experimental or nonexperimental setting. Findings of two empiricalresearches written by Gudono (2006) and Chong and Chong (2002) are discussed andused as an illustration.(Keywords: causal ordering, lagged- regression, the sequential Chi-Square Differences tests (SCDTs), Type I, Type II, and Type III errors).]]>
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