Experimental Design: Single factor designs Psych 231: Research Methods in Psychology

Announcements Reminder: your group project experiment method section is due in labs this week Remember to download, print and READ the class exp articles

Methods of Controlling Variability Comparison Production Constancy/Randomization

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 typically the absence of the treatment »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 Sometimes there are just a range of values of the IV

Methods of Controlling Variability Production –The experimenter selects the specific values of the Independent Variables Need to do this carefully –Suppose that you don’t find a difference in the DV across your different groups »Is this because the IV and DV aren’t related? »Or is it because your levels of IV weren’t different enough

Methods of Controlling Variability Constancy/Randomization –If there is a variable that may be related to the DV that you can’t (or don’t want to) manipulate Control variable: hold it constant Random variable: let it vary randomly across all of the experimental conditions –But beware confounds, variables that are related to both the IV and DV but aren’t controlled

Experimental designs So far we’ve covered a lot of the about details experiments generally Now let’s consider some specific experimental designs. –1 Factor, two levels –1 Factor, multi-levels –Factorial (more than 1 factor) –Between & within factors

Poorly designed experiments Example: Does standing close to somebody cause them to move? –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”)

Single variable – One Factor designs 1 Factor (Independent variable), two levels –Basically you want to compare two treatments (conditions) –The statistics are pretty easy, a t-test T-test = Observed difference btwn conditions Difference expected by chance

1 factor - 2 levels 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 don’t matter, just trying is good enough

1 factor - 2 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable

Single variable – one Factor anxiety low moderate 8060 lowmoderate test performance anxiety One factor Two levels Use a t-test to see if these points are statistically different

Single variable – one Factor Advantages: –Simple, relatively easy to interpret the results –Is the independent variable worth studying? If no effect, then usually don’t bother with a more complex design –Sometimes two levels is all you need One theory predicts one pattern and another predicts a different pattern

Single variable – one Factor Disadvantages: –“True” shape of the function is hard to see interpolation and extrapolation are not a good idea

Interpolation low moderate test performance anxiety What happens within of the ranges that you test?

Extrapolation lowmoderate test performance anxiety What happens outside of the ranges that you test? high

Poorly designed experiments Example 1: –Testing the effectiveness of a stop smoking relaxation program –The subjects choose which group (relaxation or no program) to be in

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

Poorly designed experiments Example 2: Does a relaxation program decrease the urge to smoke? –Pretest desire level – give relaxation program – posttest desire to smoke

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

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)

1 Factor - multilevel experiments Example (same as earlier with one more group) –How does anxiety level affect test performance? Three 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 don’t matter, just trying is good enough –Grp3 (high anxiety group): 5 min lecture on how the students must pass this test to pass the course

1 factor - 3 levels participants Low Moderate Test Random Assignment Anxiety Dependent Variable High Test

1 Factor - multilevel experiments anxiety low mod high 8060 lowmod test performance anxiety high

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

Relationship between Anxiety and Performance lowmoderate test performance anxiety 2 levels highlowmod test performance anxiety 3 levels

1 Factor - multilevel experiments Disadvantages –Needs more resources (participants and/or stimuli) –Requires more complex statistical analysis (analysis of variance and pair-wise comparisons)

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

Next time Adding a wrinkle: between-groups versus within-groups factors Read chapter 11