Things to Remember - Methods.docx

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Department
Psychology
Course
PSYC 2360
Professor
Naseem Al- Aidroos
Semester
Winter

Description
Things to Remember - Methods Final Exam Two Factor Designs  when we have 2 independent variables each with 2+ levels  we calculate an F statistic like in one-way ANOVAs where o , if the null hypothesis is rejected that means that F > 1 and there is a significant difference between the groups larger than what we would expect if it was due to chance alone. If the null hypothesis is not rejected, F = 1 therefore there was not a big enough difference between the groups to be considerably different than what we would expect if due to chance  marginal means are when we calculate the mean of the entire row or column  main effects are when we look to see if the marginal means are the same or different for a given factor o if there is a significant main effect (the marginal means for the factor’s levels are different) we can say that that factor has a significant effect on the dependent variable, ignoring the effects of the other factor  an interaction is when the effects of one factor on the dependent variable depends on the level of the other factor (when the lines intersect) o to see if we have an interaction we do decomposition and see if we can get back the cell means by only knowing the marginal means and the grand mean a) get the grand mean (mean of all cells) b) subtract grand mean from each cell c) get the new marginal mean of the cells d) add the marginal means and the grand mean and see if you get the cell means back - if you do, there is no interaction - if you do not, the leftovers are the interaction o when we have a significant interaction it just tells us there is a difference between the means but it does not tell us the pattern of the means, therefore we use simple main effects  simple main effects are when we look at the effect of a factor on the dependent variable across a level of the other factor o when we have a significant interaction, the main effects should be interpreted with caution because the effect of one factor DEPENDS on the level of the other  if we were to do a three way interaction (3 independent variables) it gets a lot more complex… o 1 three way interaction o 3 two way interactions o 3 main effects  many repeated measures designs are factorial and have the need for counterbalancing o e.g. Latin square design (each condition appears in each order and equally follows each other condition)  when you have more than 2 levels in the factor, a simple main effect will not tell you where the differences are within the means therefore we need to conduct mean comparisons o pairwise comparisons - we compare any mean with any other mean, causes type 1 error probability to increase o planned or priori comparisons - we compare two means that we had expected would be different o posthoc comparisons - we only do comparisons if we have a significant effect o complex comparisons - we compare more than 2 means at a time Internal Validity  there are a few threats to validity o construct validity - is the test measuring what it is supposed to measure o statistical conclusion validity - is our conclusion valid o internal validity - the extent to which we can say that our independent variable is causing the dependent variable o external validity - the extent to which our findings generalize  experimental control is when we try to make sure and control as many variables as we can - the greater experimental control, the more internally valid o we can have extraneous variables that noise the data and cause random error however we can still have an experiment that is internally valid with extraneous variables o we can also have confounding variables in which we cannot say that our test is internally valid because confounding variables always produce an alternative pathway and we have no way of knowing which pathway is causing the change in the dependent variable  since people are different, there are different designs we can do in order to control for as many people differences as we can o limited populations design - similar to convenience sampling where we sample from population like university in which subjects will be similar to each other (homogeneous) in a variety of factors, this increases our statistical power by decreasing the effects of extraneous variables o before and after designs - are similar to repeated measures designs except the group is only in one condition and the dependent variable is measured twice - once before the manipulation is presented and once after the manipulation and participants serve as their own controls, this still has the problems of retesting effects such as fatigue, practice, guessing the research hypothesis o matched groups designs are when the sample are measured on a variable and then based on these scores, are put into groups, this works as long as the variable they are being scored on is related to the dependent variable, often this is not necessary and random assignment is enough, this reduces the differences between the groups and increases the power of the test  participants are enough different and the experimenter does not want to introduce any additional differences there
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