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Experiment Basics: Control Psych 231: Research Methods in Psychology Slide 2 Announcements Due this week in labs - Group project: Methods sections IRB worksheet (including a consent form) Recommended/required: Questionnaires/examples of stimuli, etc. things that you want to have ready for pilot week (week 10) Group Project ratings sheet Exam 2 two weeks from today Slide 3 Experimental Control Mythbusters examine: YawningYawning What sort of sampling method? Why the control group? Should they have confirmed? Probably not, if you do the stats, with this sample size the 4% difference isnt big enough to reject the null hypothesisProbably not Slide 4 Experimental Control Our goal: To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV. Control is used to: Minimize excessive variability To reduce the potential of confounds (systematic variability not part of the research design) Slide 5 Experimental Control Our goal: To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV. T = NR exp + NR other + R NR exp : Manipulated independent variables (IV) NR other : extraneous variables (EV) which covary with IV Random (R) Variability Nonrandom (NR) Variability Imprecision in measurement (DV) Randomly varying extraneous variables (EV) Condfounds Our hypothesis: the IV will result in changes in the DV the variability in our IV Slide 6 Experimental Control: Weight analogy Variability in a simple experiment: R NR other Treatment group Control group T = NR exp + NR other + R R NR exp NR other Absence of the treatment ( NR exp = 0 ) perfect experiment - no confounds ( NR other = 0 ) Slide 7 Experimental Control: Weight analogy Variability in a simple experiment: R NR exp R Treatment group Control group T = NR exp + NR other + R Difference Detector Our experiment is a difference detector Slide 8 Experimental Control: Weight analogy If there is an effect of the treatment then NR exp will 0 R NR exp R Treatment group Control group Difference Detector Our experiment can detect the effect of the treatment Our experiment can detect the effect of the treatment Slide 9 Things making detection difficult Potential Problems Confounding Excessive random variability Difference Detector Slide 10 Potential Problems Confound If an EV co-varies with IV, then NR other component of data will be present, and may lead to misattribution of effect to IV IV DV EV Co-vary together Slide 11 Confounding R NR exp NR other R Difference Detector Experiment can detect an effect, but cant tell where it is from Experiment can detect an effect, but cant tell where it is from Confound Hard to detect the effect of NR exp because the effect looks like it could be from NR exp but could be due to the NR other Slide 12 Confounding R NR other R Difference Detector Confound Hard to detect the effect of NR exp because the effect looks like it could be from NR exp but could be due to the NR other R NR exp NR other R Difference Detector These two situations look the same These two situations look the same There is not an effect of the IV There is an effect of the IV Slide 13 Potential Problems Excessive random variability If experimental control procedures are not applied Then R component of data will be excessively large, and may make NR exp undetectable Slide 14 Excessive random variability R NR exp R Difference Detector Experiment cant detect the effect of the treatment Experiment cant detect the effect of the treatment If R is large relative to NR exp then detecting a difference may be difficult Slide 15 Reduced random variability But if we reduce the size of NR other and R relative to NR exp then detecting gets easier RR NR exp Difference Detector Our experiment can detect the effect of the treatment Our experiment can detect the effect of the treatment So try to minimize this by using good measures of DV, good manipulations of IV, etc. Slide 16 Controlling Variability How do we introduce control? Methods of Experimental Control Constancy/Randomization Comparison Production Slide 17 Methods of Controlling Variability Constancy/Randomization If there is a variable that may be related to the DV that you cant (or dont want to) manipulate Control variable: hold it constant Random variable: let it vary randomly across all of the experimental conditions Slide 18 Methods of Controlling Variability Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment Training group No training (Control) group Without control groups if is harder to see what is really happening in the experiment It is easier to be swayed by plausibility or inappropriate comparisons Useful for eliminating potential confounds Slide 19 Methods of Controlling Variability Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment 1 week of Training group 2 weeks of Training group Sometimes there are a range of values of the IV 3 weeks of Training group Slide 20 Methods of Controlling Variability Production The experimenter selects the specific values of the Independent Variables 1 week of Training group 2 weeks of Training group 3 weeks of Training group Need to do this carefully Suppose that you dont find a difference in the DV across your different groups Is this because the IV and DV arent related? Or is it because your levels of IV werent different enough Slide 21 Experimental designs So far weve covered a lot of the about details experiments generally Now lets consider some specific experimental designs. Some bad (but common) designs Some good designs 1 Factor, two levels 1 Factor, multi-levels Between & within factors Factorial (more than 1 factor) Slide 22 Poorly designed experiments Bad design example 1: Does standing close to somebody cause them to move? hmm thats an empirical question. Lets see what happens if So you stand closely to people and see how long before they move Problem: no control group to establish the comparison group (this design is sometimes called one-shot case study design) Slide 23 Poorly designed experiments Bad design example 2: Testing the effectiveness of a stop smoking relaxation program The participants choose which group (relaxation or no program) to be in Slide 24 Poorly designed experiments Non-equivalent control groups participants Training group No training (Control) group Measure Self Assignment Independent Variable Dependent Variable Random Assignment Problem: selection bias for the two groups, need to do random assignment to groups Problem: selection bias for the two groups, need to do random assignment to groups Bad design example 2: Slide 25 Poorly designed experiments Bad design example 3: Does a relaxation program decrease the urge to smoke? Pretest desire level give relaxation program posttest desire to smoke Slide 26 Poorly designed experiments One group pretest-posttest design participantsPre-test Training group Post-test Measure Independent Variable Dependent Variable Problems include: history, maturation, testing, and more Pre-test No Training group Post-test Measure Add another factor Bad design example 3: Slide 27 1 factor - 2 levels Good design example How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades dont matter, just trying is good enough 1 Factor (Independent variable), two levels Basically you want to compare two treatments (conditions) The statistics are pretty easy, a t-test Slide 28 1 factor - 2 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable Good design example How does anxiety level affect test performance? Slide 29 anxiety low moderate 8060 lowmoderate test performance anxiety One factor Two levels Use a t-test to see if these points are statistically different T-test = Observed difference between conditions Difference expected by chance Good design example How does anxiety level affect test performance? 1 factor - 2 levels Slide 30 Advantages: Simple, relatively easy to interpret the results Is the independent variable worth studying? If no effect, then usually dont bother with a more complex design Sometimes two levels is all you need One theory predicts one pattern and another predicts a different pattern 1 factor - 2 levels Slide 31 low moderate test performance anxiety What happens within of the ranges that you test? Interpolation Disadvantages: True shape of the function is hard to see Interpolation and Extrapolation are not a good idea 1 factor - 2 levels Slide 32 Extrapolation lowmoderate test performance anxiety What happens outside of the ranges that you test? Disadvantages: True shape of the function is hard to see Interpolation and Extrapolation are not a good idea 1 factor - 2 levels high Slide 33 1 Factor - multilevel experiments For more complex theories you will typically need more complex designs (more than two levels of one IV) 1 factor - more than two levels Basically you want to compare more than two conditions The statistics are a little more difficult, an ANOVA (Analysis of Variance) Slide 34 Good design example (similar to earlier ex.) How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades dont matter, just trying is good enough 1 Factor - multilevel experiments Grp3 (high anxiety group): 5 min lecture on how the students must pass this test to pass the course Slide 35 1 factor - 3 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable High Test Slide 36 1 Factor - multilevel experiments anxiety low mod high 8060 lowmod test performance anxiety high Slide 37 1 Factor - multilevel experiments Advantages Gives a better picture of the relationship (function) Generally, the more levels you have, the less you have to worry about your range of the independent variable Slide 38 Relationship between Anxiety and Performance lowmoderate test performance anxiety 2 levels highlowmod test performance anxiety 3 levels Slide 39 1 Factor - multilevel experiments Disadvantages Needs more resources (participants and/or stimuli) Requires more complex statistical analysis (analysis of variance and pair-wise comparisons) Slide 40 Pair-wise comparisons The ANOVA just tells you that not all of the groups are equal. If this is your conclusion (you get a significant ANOVA) then you should do further tests to see where the differences are High vs. Low High vs. Moderate Low vs. Moderate