Study Guide for Final Exam Psychology 300 Dr Stangor CHAPTER 11 EXPERIMENTAL RESEARCH FACTORIAL DESIGNS LEARNING OBJECTIVES 1 Understand what factorial designs are and what advantages they have over other experiments Experimental designs with more than one independent manipulated variable The use of more than one independent variable in a single experiment increases the amount of info that can be gained Also always cheaper in terms of number of research participants More efficient and informative 2 Determine what is meant by crossing the factors in a factorial design In factorial designs the conditions are arranged such that each level of each independent variable occurs with each level of the other independent variables Known as crossing the factors 3 Understand main effects interactions and simple effects Main effect differences on the dependent measure across the levels of any one factor controlling for all other factors in the experiment Interactions pattern of means that may occur in a factorial experimental design when the influence of one IV on the DV is different at different levels of another IV or variables Simple effects effect of one factor within a level of another factor 4 Show some of the possible patterns that interactions can take 5 Determine how data from a factorial design is presented in the research report In a factorial design the statistical tests for the main effects and the significance test of the interaction may each be significant non significant F values and significance tests are presented in an ANOVA summary table Each main effect and each interaction has its own F test 6 Define a mixed factorial design Design in which some factors are between participants and some are repeated measures 7 Understand the purpose of means comparison and what statistical techniques are used to compare means Used both in one way designs with more than 2 levels and in factorial designs Conducted to discover which group means are significantly different from each other For more specific information about the significance of the simple effects SAMPLE QUESTIONS 1 Discuss when factorial experimental designs might be used and their advantages over one way experiments More than one independent variable 2 Define main effects simple effects and interactions Chapter 14 Quasi Experimental Research Designs 103 Marginal means the means of the DV within the levels of any one factor which are combined across the levels of one or more other factors in the design Main effects differences on the dependent measure across the levels of any one factor controlling for all other factors in the experiment 3 What data from a factorial research design need to be reported in the research report and how are they so reported F values for each of the main effects and interactions Within groups sum of squares Degrees of freedom Mean squares labeled residual rather than within groups Similar to that of a one way design except that more means and F tests need to be reported 4 What are mean comparisons Differentiate pairwise and complex comparisons as well as planned and post hoc comparisons Explain how each type of comparison is used in research Pairwise comparisons most common means comparison in which any one condition mean is compared with any other condition mean o Problem is that there can be a lot of them o Not normally appropriate to conduct a statistical test on each pair of condition means b c each involves a statistical test more type 1 error Complex comparisons more than two means are compared at the same time o Usually conducted with contrast tests Post hoc comparisons means comparisons that by taking into consideration that many comparisons are being made and that these comparisons were not planned ahead of time help control for increases in the experimentwise alpha o Some cases they only allow the researchers to conduct them if the F test is significant Planned a priori comparisons compare only the means in which specific differences were predicted by the research hypothesis 5 Propose a 2 x 2 factorial experiment Name the independent variables and the dependent variables and label the levels of each Predict an interaction and state the expected form of this interaction Draw a schematic diagram of the research hypothesis Chapter 14 Quasi Experimental Research Designs 104 CHAPTER 12 EXPERIMENTAL CONTROL AND INTERNAL VALIDITY LEARNING OBJECTIVES 1 Understand the potential threats to the validity of research Threats to construct validity occurs when the measured variables used in the research are invalid because they do not adequately assess the conceptual variables they were designed to measure Threats to statistical conclusion validity occurs when the conclusions that the researcher draws about the research hypothesis are incorrect because either a Type 1 error or a Type 2 error has occurred o Type 1 researcher mistakenly rejects the null o Type 2 researcher mistakenly fails to reject the null Threats to internal validity refers to the extent to which we can trust the conclusions that have been drawn about the causal relationship between the IV and DV o The DV may actually have been caused by a confounding variable Threats to external validity refers to the extent to which the results of a research design can be generalized beyond the specific settings and participants used in the experiment to other places people and times o Claims that results are more general observed effects may actually only be found under limited conditions 2 Define experimental control Occurs to the extent that the experimenter is able to eliminate effects of the DV other than the effects of the IV The greater the experimental control the more confident we can be that the IV caused the changes in the DV 3 Determine the effects of extraneous variables on research validity Extraneous variables variables other than the IV that cause changes in the DV These aren t measured by the experimenter so their presence increases the within group variability Makes it more difficult to find differences among the experimental conditions of the dependent measure 4 Define confounding and understand how confounding reduces an experiment s internal validity Confounding means that the other variable is mixed up with the independent variable making it impossible to determine which of the variables produced the change in the DV Internal validity extent to which changes in the DV can confidently be attributed to the effect of the IV