1. Experimental Designs Psych 231: Research Methods in Psychology

2. Announcements Exam 2 coming up: Monday Oct 27th Review session Thursday 6:30 DeGarmo 463 Piloting experiments in lab this week

3. Experimental Designs 1 Factor - two levels Advantages: Simple, relatively easy to interpret the results, good first step, sometimes all you need Disadvantages: “ True” shape of the function is hard to see 1 Factor - more than two levels Advantages Better picture of the function Less worry about your range of the independent variable Disadvantages Needs more resources (participants and/or stimuli) Requires more complex statistical analysis (analysis of variance and pair-wise comparisons)

4. Factorial experiments Factorial Designs: Two or more factors Advantages Interaction 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) Disadvantages Experiments become very large, and can become unwieldy The statistical analyses get much more complex Interpretation of the results can get hard Need more resources

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

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

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

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

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

10. Between subjects designs 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 participants Colored words BW words Test Clock Chair Cab Clock Chair Cab Clock Chair Cab Clock Chair Cab

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

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

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

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

15. Dealing with 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

16. Matching groups Group AGroup B 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 Red Short 21yrs Blue tall 23yrs Green average 22yrs Brown tall 22yrs Color Height Age matched Red Short 21yrs matched Blue tall 23yrs matched Green average 22yrs matched Brown tall 22yrs

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

18. Within subjects designs Advantages: Don’t have to worry about individual differences Same people in all the conditions Variability between conditions is smaller (statistical advantage) Fewer participants are required

19. Within subjects designs Disadvantages Order effects: Carry-over effects Progressive error Counterbalancing is probably necessary to address these order effects

20. test Condition 2Condition 1 test Order effects Carry-over effects Transfer between conditions is possible Effects may persist from one condition into another e.g. Alcohol vs no alcohol experiment on the effects on hand-eye coordination. Hard to know how long the effects of alcohol may persist. How long do we wait for the effects to wear off?

21. Order effects Progressive error Practice effects – improvement due to repeated practice Fatigue effects – performance deteriorates as participants get bored, tired, distracted

22. Dealing with order effects Counterbalancing is probably necessary This is used to control for “order effects” Ideally, use every possible order n! ( e.g., AB = 2! = 2 orders; ABC = 3! = 6 orders, ABCD = 4! = 24 orders, etc ). All counterbalancing assumes Symmetrical Transfer The assumption that AB and BA have reverse effects and thus cancel out in a counterbalanced design

23. Counterbalancing Simple case Two conditions A & B Two counterbalanced orders: AB BA participants Colored words BW words Test Colored words BW words Test Not so simple analysis: need to analyze as a factorial design, with 2 factors (word color & order)

24. Counterbalancing More than two conditions: Often it is not practical to use every possible ordering n! ( e.g., AB = 2! = 2 orders; ABC = 3! = 6 orders, ABCD = 4! = 24 orders, etc ) Common Solution: Partial counterbalancing Latin square designs – a form of partial counterbalancing, so that each group of trials occur in each position an equal number of times Simple case

25. Partial counterbalancing Example: consider four conditions Recall: ABCD = 4! = 24 possible orders 1) Unbalanced Latin square: each condition appears in each position (4 orders) DCBA ADCB BADC CBAD Order 1 Order 2 Order 3 Order 4

26. Partial counterbalancing 2) Balanced Latin square: each condition appears before and after all others (8 orders) ABDC BCAD CDBA DACB ABCD BCDA CDAB DABC Example: consider four conditions Recall: ABCD = 4! = 24 possible orders

27. Mixed factorial designs Mixed designs Treat some factors as within-subjects (participants get all levels of that factor) and others as between-subjects (each level of this factor gets a different group of participants). This only works with factorial (multi-factor) designs

28. Class experiment results SourceSSdfMean SquareFSig. Word Type22.4122.49.3.003 Depth of Proc120.31120.339.0<.001 Word * Depth10.5110.54.3.039 Error (within)357.11482.4 Error (btwn)456.41483.1 Main effect of both variables An interaction

29. Describing your design You need to describe: How many factors How many levels of each factor Whether the factors are within or between groups e.g., 2 (shallow/deep processing) x 2 (abstract/concrete) mixed groups factorial design abstractconcrete Shallow 4.04.2 Deep 4.95.8