Hi James M. Clark Professor of Psychology 204-786-9757 204-774-4134 Fax [email protected] Department of Psychology University of Winnipeg Winnipeg, Manitoba R3B 2E9 CANADA
>>> "Mike Palij" <[email protected]> 09-Jan-09 9:53 AM >>> On Thu, 08 Jan 2009 20:04:23 -0800, Karl Wuensch wrote: > I'm even less conservative than Stephen. I would not apply the >Bonferroni adjustment. After all, these are PLANNED comparisons, eh? This is a curious point: Why should the state of knowledge (i.e., able to predict the size of difference, the direction of a difference, etc.) affect the probability of making an error of inference? JC: I think of it as quasi-Bayesian kind of thinking and use the analogy of perception. If you walk into a crowded room EXPECTING to see a certain person (the prediction), then you require less perceptual information (the data) in order to identify that the person is indeed present. In essence, you have a lower threshold for detection because of the expectation. Lacking the expectation, your threshold for detection is higher and you will require greater evidence. In Bayesian terms, your a priori probability is higher given the theoretical/empirical expectation, therefore you require less current evidence to achieve a certain level of a posteriori confidence than if you lacked that expectation. It is important to emphasize that your expectations in research need to be well-founded, either on the basis of theory or past research. A second point with respect to the specific case Karl is commenting on (i.e., simple effects following an interaction) is that the omnibus test of the interaction is quite insensitive except when a cross-over interaction occurs. Otherwise, the interaction is diluted into main effects and interaction. In the typical pretest-posttest by treatment/control design, for example, one expects (ideally) no change for the control group and a change for the treatment group. But this specific pattern gives rise to differences for the two main effects and the interaction, whereas the simple effects analysis correctly (?) allocates all the variability to the difference between pre and posttest for the treatment group. Illustrative numbers below. Factorial Interaction (brackets are interaction effect) Pre Post Row effect Control 20 20 20 -5 (+5) (-5) Treatment 20 40 30 +5 (-5) (+5) Col effect 20 30 25 Mg -5 +5 Simple Effects (brackets are simple effects) Pre Post Row Control 20 20 20 (0) (0) Treatment 20 40 30 (-10) (+10) In many cases, latter simple effect for treatment will be significant absent significant interaction. Take care Jim --- To make changes to your subscription contact: Bill Southerly ([email protected])
