CHAPTER TEN:Experimental Research One Way DesignsDemonstration of Causality:Association- There is an association between the variables and these associations are probabilistic. (Does not provide support for causal relationship between variables)Temporal priority: A-->B so A must come before BControl of common causal variables-- use of experimental manipulation which allows researchers to rule out spurious relationshipsOne way experimental Designs:Systematic change in one of more factors (IV) to determine weather such changes determine changes in one or more other factors (DV)One IVResearch hypothesis: manipulation of IV and measurement of DVIndependent variable and experimental manipulationExperimental manipulation:Levels (conditions)Random assignment--> equivalence and controlDifference Between IV and DV associationNull: mean score on the dependent variable is the same at all levels of the independent variable except for differences due to chance, therefore manipulation has no effect on DVANOVA: compare the means on the DV across the different levels of the IV by analyzing the VARIABILITY of dependent variableOverall Variance: deviation of individual scores from overall meanGreater deviation is due to WITHIN GROUP VARIANCE or deviation of individual scores from group meansVariation in BETWEEN GROUPS VARIANCE (IV) means deviation of the GROUP means from the overall mean******What Variance should be higher? ******Repeated measures Designs: created equivalence by having the same participants in each conditionPROSIncreases statistical powerEconomy of participants is easier because fewer participants neededCONS:Carryover: each measure of DV only being influenced by the proper level. Wehn effects of one level of manipulation (IV) are still present when the DV is assessed.Practice and Fatigue: same participants multiple tasks ---> increase time between dependent measures to counterbalance this.Latin Square: many orders of conditions and each needs to be used an equal # of times. This method counterbalances this problem through its order and enabling everything to appear equally and ordered.CHAPTER 11: Experimental Research Factorial DesignsFactorial Experimental Designs- experimental designs with more than one independent variable. (a lot of information)Assess the simultaneous impact of more than one manipulated IV on the DVProvides all information that would be gained from two separate one way designs and that would not have been available if the experiments had been run separatelyPresented in ANOVA summary table (F tests and significance tests)Factor: each of the manipulated independent variablesFactorial research written in notational systems to indicate how many factors there are and how many levels there are2x3= two factors, one with two levels and one with three levels2x2x2= three factors, each with two levelsConditions (cells)- can be found through multiplication of the levels in each factorTwo Way Design Factorial Design:Addition of new independent variables to determine if the original results will hold up in new situationsAsses main effects and make predictions about interactions between the factorsCrossing factors: Conditions are arranged so that each level of the independent variable occurs with each level of the other independent variablesRandom assignment to one of the conditions to equate the conditions before the manipulations occurResearch hypothesis: makes very specific prediction about the pattern of means that is expected to be observed on the dependent measure.Schematic Diagram: where the specific predictions of the research hypothesis are notated.Main Effect: differences on the dependent measure across the levels of any one factor controlling for all other factors in the experiment is the main effect of that factor.Marginal means: when means are combined across the levels of another factor they either control for or collapse across the effects of the other factor.Interactions and Simple EffectsInteraction: patterns of means that may occur when the influence of one independent variable on the dependent variable is different at different levels of another independent variable.Simple Effects: the effect of one factor within a level of another factor is a simple effect of the FIRST factorEach main effect and each interaction have their own F testUnderstanding interactions:Line chart- visualize relationshipsLevels of one of the factors indicated on the horizontal axis while DV on vertical axisPatterns with Main effects onlyPatterns with main effects and interactions:Crossover Interaction: when the interaction is such that the simple effect in one level of the second variable is OPPOSITE rather than DIFFERENT from the sample effect in the other level of the second variableInterpretations of Main Effects when Interactions are Present:the presence of an interaction indicates that the influence of each of the two independent variables cannot be understood alone.The main effects of each of the two factors are said to be QUALIFIED by the presence of the other factor.Three way Designs:Greater number of means, main effects and interactions.Significance test on the main effects of each of the three factors.Three way Interactions:test whether all three variables simultaneously influence the dependent measure.Null hypothesis is tat the two way interactions are the same at the different levels of the third variableComplicated interpretationCostly even though it is informativeRepeated Measures:Way to create equivalence between research designsSame individuals participate in all of the conditionsMixed factorial designs- some factors are between participants (random assignment) and some are repeated measuresComparison of the condition means in experimental designsMeans Comparisons: conducted to discover which group means are significantly different from each otherPairwise Comparisons: any one condition mean is compared with any other condition meanNot appropriate to perform a statistical test on each pair  type 1 errorAs each comparison is made the experimentwise alpha increases (probability of type 1 error)WAYS TO REDUCE EXPERIMENTWISE ALPHAA priori comparison/plannedCompare only the means in which specific differences were predicted by the research hypothesis.Post hoc ComparisonsWhen specific comparisons have not been planned ahead of time, so these tests ONLY allow the researcher to conduct them if the F test is significant.Complex Comparisons:More than two means are compared