Experiment Basics: Designs

Experiment Basics: Designs Psych 231: Research Methods in Psychology

Due this week in labs - Group project: • Methods sections • IRB worksheet • Group Project ratings sheet • Exam 2 a week and a half away • ReggieNet additions • APA Handout – your GA’s generated a short summary of APA style tips • Making tables and graphs in excel how-to’s • Fun (and informative) site: ThePsychFiles.com podcast Announcements

So far we’ve covered a lot of the general details of experiments • Now let’s consider some specific experimental designs. • Some bad (but not uncommon) designs (and potential fixes) • Some good designs • 1 Factor, two levels • 1 Factor, multi-levels • Factorial (more than 1 factor) • Between & within factors Experimental designs

Bad design example 1: Does standing close to somebody cause them to move? (theory of personal space) • “hmm… that’s an empirical question. Let’s 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”) Fix: introduce a (or some) comparison group(s) Close (.5 m) Not Close (1.0 m) Very Close (.1 m) Poorly designed experiments

Bad design example 2: • Does a relaxation program decrease the urge to smoke? • 2 groups • relaxation training group • no relaxation training group • The participants choose which group to be in Training group No training (Control) group Poorly designed experiments

Random Assignment • Bad design example 2: Non-equivalent control groups Independent Variable Dependent Variable Self Assignment Training group Measure participants No training (Control) group Measure Problem: selection bias for the two groups Fix: need to do random assignment to groups Poorly designed experiments

Bad design example 3: • Does a relaxation program decrease the urge to smoke? • Pre-test desire level • Give relaxation training program • Post-test desire to smoke Poorly designed experiments

Pre-test No Training group Post-test Measure • Bad design example 3: One group pretest-posttest design Dependent Variable Independent Variable Pre vs. Post Dependent Variable Pre-test Training group Post-test Measure participants Fix: Add another factor • Problems include: history, maturation, testing, and more Poorly designed experiments

So far we’ve covered a lot of the general details of experiments • Now let’s consider some specific experimental designs. • Some bad (but not uncommon) designs • Some good designs • 1 Factor, two levels • 1 Factor, multi-levels • Factorial (more than 1 factor) • Between & within factors Experimental designs

Good design example • How does anxiety level affect test performance? • Two groups take the same test • Grp1(low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough • Grp2 (moderate anxiety group): 5 min lecture on the importance of good grades for success • What are our IV andDV? • 1 Factor (Independent variable), two levels • Basically you want to compare two treatments (conditions) • The statistics are pretty easy, a t-test 1 factor - 2 levels

Dependent Variable Random Assignment Anxiety Low Test participants Moderate Test • Good design example • How does anxiety level affect test performance? 1 factor - 2 levels

One factor Use a t-test to see if these points are statistically different test performance low moderate low moderate anxiety Two levels • Good design example • How does anxiety level affect test performance? anxiety 60 80 Observed difference between conditions T-test = Difference expected by chance 1 factor - 2 levels

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 1 factor - 2 levels

Interpolation What happens within of the ranges that you test? test performance low moderate anxiety • Disadvantages: • “True” shape of the function is hard to see • Interpolation and Extrapolation are not a good idea 1 factor - 2 levels

Extrapolation What happens outside of the ranges that you test? test performance low moderate anxiety high • Disadvantages: • “True” shape of the function is hard to see • Interpolation and Extrapolation are not a good idea 1 factor - 2 levels

So far we’ve covered a lot of the general details of experiments • Now let’s consider some specific experimental designs. • Some bad (but not uncommon) designs • Some good designs • 1 Factor, two levels • 1 Factor, multi-levels • Factorial (more than 1 factor) • Between & within factors Experimental designs

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

Grp3 (high anxiety group): 5 min lecture on how the students must pass this test to pass the course • Good design example (similar to earlier ex.) • How does anxiety level affect test performance? • Groups take the same test • Grp1(low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough • Grp2 (moderate anxiety group): 5 min lecture on the importance of good grades for success 1 Factor - multilevel experiments

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

anxiety mod high low test performance 60 80 low mod high anxiety 60 1 Factor - multilevel experiments

2 levels 3 levels test performance test performance low mod high low moderate anxiety anxiety • Advantages • Gives a better picture of the relationship (functions other than just straight lines) • Generally, the more levels you have, the less you have to worry about your range of the independent variable 1 Factor - multilevel experiments

Disadvantages • Needs more resources (participants and/or stimuli) • Requires more complex statistical analysis (ANOVA [Analysis of Variance] & follow-up pair-wise comparisons) 1 Factor - multilevel experiments

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 Pair-wise comparisons

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

B1 B2 B3 B4 A1 A2 • Two or more factors • Some vocabulary • Factors - independent variables • Levels - the levels of your independent variables • 2 x 4 design means two independent variables, one with 2 levels and one with 4 levels • “Conditions” or “groups” is calculated by multiplying the levels, so a 2x4 design has 8 different conditions Factorial experiments

Two or more factors (cont.) • Main effects - the effects of your independent variables ignoring (collapsed across) the other independent variables • Interaction effects - how your independent variables affect each other • Example: 2x2 design, factors A and B • Interaction: • At A1, B1 is bigger than B2 • At A2, B1 and B2 don’t differ Everyday interaction = “it depends on …” Factorial experiments

Rate how much you would want to see a new movie (1 no interest, 5 high interest) • Ask men and women – looking for an effect of gender Not much of a difference Interaction effects

Maybe the gender effect depends on whether you know who is in the movie. So you add another factor: • Suppose that George Clooney might star. You rate the preference if he were to star and if he were not to star. Effect of gender depends on whether George stars in the movie or not This is an interaction Interaction effects A video lecture from ThePsychFiles.com podcast

The complexity & number of outcomes increases: • A = main effect of factor A • B = main effect of factor B • AB = interaction of A and B • With 2 factors there are 8 basic possible patterns of results: 1) No effects at all 2) A only 3) B only 4) AB only • 5) A & B • 6) A & AB • 7) B & AB • 8) A & B & AB Results of a 2x2 factorial design

Interaction of AB A1 A2 B1 mean B1 Main effect of B B2 mean B2 A1 mean A2 mean Marginal means Main effect of A Condition mean A1B1 What’s the effect of A at B1? What’s the effect of A at B2? Condition mean A2B1 Condition mean A1B2 Condition mean A2B2 2 x 2 factorial design

A Main Effect A2 A1 of B B1 30 60 B1 B Dependent Variable B2 B2 30 60 Main Effect A1 A2 of A A 45 45 30 60 Main effect of A ✓ Main effect of B X Interaction of A x B X Examples of outcomes

A Main Effect A2 A1 of B B1 60 60 B1 B Dependent Variable B2 B2 30 30 Main Effect A1 A2 of A A 60 30 45 45 Main effect of A X Main effect of B ✓ Interaction of A x B X Examples of outcomes

A Main Effect A2 A1 of B B1 60 30 B1 B Dependent Variable B2 B2 60 30 Main Effect A1 A2 of A A 45 45 45 45 Main effect of A X Main effect of B X Interaction of A x B ✓ Examples of outcomes

A Main Effect A2 A1 of B B1 30 60 B1 B Dependent Variable B2 B2 30 30 Main Effect A1 A2 of A A 45 30 30 45 ✓ Main effect of A ✓ Main effect of B Interaction of A x B ✓ Examples of outcomes

main effect anxiety of difficulty easy low mod high medium 50 hard hard test performance 35 80 35 70 Test difficulty medium 65 65 80 80 easy 80 80 80 low mod high 60 80 60 main effect anxiety of anxiety Let’s add another variable: test difficulty. Yes: effect of anxiety depends on level of test difficulty Interaction ? Anxiety and Test Performance

Advantages • Interaction effects • Always consider the interaction effects before trying to interpret the main effects • Adding factors decreases the variability • Because you’re controlling more of the variables that influence the dependent variable • This increases the statistical Power of the statistical tests • Increases generalizability of the results • Because you have a situation closer to the real world (where all sorts of variables are interacting) Factorial Designs

Disadvantages • Experiments become very large, and unwieldy • The statistical analyses get much more complex • Interpretation of the results can get hard • In particular for higher-order interactions • Higher-order interactions (when you have more than two interactions, e.g., ABC). Factorial Designs

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

So you present lists of words for recall either in color or in black-and-white. Clock Chair Cab Clock Chair Cab • What is the effect of presenting words in color on memory for those words? • Two different designs to examine this question Example

levels • Between-Groups Factor • 2-levels • Each of the participants is in only one level of the IV Clock Chair Cab Colored words participants Test Clock Chair Cab BW words

levels participants Colored words BW words Test Test • Within-Groups Factor • Sometimes called “repeated measures” design • 2-levels, All of the participants are in both levels of the IV Clock Chair Cab Clock Chair Cab

participants Colored words Colored words BW words Test Test participants Test BW words • Within-subjects designs • All participants participate in all of the conditions of the experiment. • Between-subjects designs • Each participant participates in one and only one condition of the experiment. Between vs. Within Subjects Designs

Clock Chair Cab Clock Chair Cab Colored words participants Test BW words • Advantages: • Independence of groups (levels of the IV) • Harder to guess what the experiment is about without experiencing the other levels of IV • Exposure to different levels of the independent variable(s) cannot “contaminate” the dependent variable • Sometimes this is a ‘must,’ because you can’t reverse the effects of prior exposure to other levels of the IV • No order effects to worry about • Counterbalancing is not required Between subjects designs

Clock Chair Cab Clock Chair Cab Colored words participants Test BW words • Disadvantages • Individual differences between the people in the groups • Excessive variability • Non-Equivalentgroups Between subjects designs

Colored words Test participants BW words • The groups are composed of different individuals Individual differences

Colored words Test participants BW words • Excessive variability due to individual differences • Harder to detect the effect of the IV if there is one R NR R • The groups are composed of different individuals Individual differences

Colored words Test participants BW words • Non-Equivalent groups (possible confound) • The groups may differ not only because of the IV, but also because the groups are composed of different individuals • The groups are composed of different individuals Individual differences

Strive for Equivalent groups • Created equally - use the same process to create both groups • Treated equally - keep the experience as similar as possible for the two groups • Composed of equivalent individuals • Random assignment to groups - eliminate bias • Matching groups - match each individuals in one group to an individual in the other group on relevant characteristics Dealing with Individual Differences

matched matched matched matched Red Short 21yrs Blue tall 23yrs Green average 22yrs Brown tall 22yrs • Matched groups • Trying to create equivalent groups • Also trying to reduce some of the overall variability • Eliminating variability from the variables that you matched people on Group A Group B Red Short 21yrs Blue tall 23yrs Green average 22yrs Color Height Age Brown tall 22yrs Matching groups

Experiment Basics: Designs