Experimental Control cont.

1. Experimental Control cont. Psych 231: Research Methods in Psychology

2. Announcements • Re-writes of group project intros due this week in lab • Please attach the original intro draft • Group Project Methods section due next week in lab • Please bring your textbook to lab • Remember to download and read the articles for class exp soon (e.g., before next week’s labs)

3. Sources of variability (noise) • Sources of Total (T) Variability: T = NRexp + NRother +R • Our goal is to reduce R and NRother so that we can detect NRexp. • That is, so we can see the changes in the DV that are due to the changes in the independent variable(s).

4. NR NR NR R R other other exp Weight analogy • Imagine the different sources of variability as weights Treatment group control group

5. NR NR NR R R other other exp Weight analogy • If NRother and R are large relative to NRexp then detecting a difference may be difficult

6. NR NR NR R R other other exp Weight analogy • But if we reduce the size of NRother and R relative to NRexp then detecting gets easier

7. Using control to reduce problems • Potential Problems • Excessive random variability • Confounding • Dissimulation

8. Potential Problems • Excessive random variability • If control procedures are not applied, then R component of data will be excessively large, and may make NR undetectable • So try to minimize this by using good measures of DV, good manipulations of IV, etc.

9. NR NR NR R R other other exp Excessive random variability Hard to detect the effect of NRexp

10. Co-vary together Potential Problems • Confounding • If relevant EV co-varies with IV, then NR component of data will be "significantly" large, and may lead to misattribution of effect to IV IV DV EV

11. NR R R other Confounding Hard to detect the effect of NRexp becausethe effect looks like it could be from NRexp but is really (mostly) due to the NRother NR exp

12. Potential Problems • Potential problem caused by experimental control • Dissimulation • If EV which interacts with IV is held constant, then effect of IV is known only for that level of EV, and may lead to overgeneralization of IV effect • This is a potential problem that affects the external validity

13. Methods of Controlling Variability • Comparison • Production • Constancy/Randomization

14. 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

15. Methods of Controlling Variability • Production • The experimenter selects the specific values of the Independent Variables • (as opposed to allowing the levels to freely vary as in observational studies) • 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

16. 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 • you should either hold it constant (control variable) • let it vary randomly across all of the experimental conditions (random variable) • But beware confounds, variables that are related to both the IV and DV but aren’t controlled

17. 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”)

18. Poorly designed experiments • Does a relaxation program decrease the urge to smoke? • One group pretest-posttest design • Pretest desire level – give relaxation program – posttest desire to smoke

19. Poorly designed experiments • One group pretest-posttest design • Problems include: history, maturation, testing, instrument decay, statistical regression, and more Dependent Variable Independent Variable Dependent Variable participants Pre-test Training group Post-test Measure

20. Poorly designed experiments • Example: Smoking example again, but with two groups. The subjects get to choose which group (relaxation or no program) to be in • Non-equivalent control groups • Problem: selection bias for the two groups, need to do random assignment to groups

21. Poorly designed experiments • Non-equivalent control groups Self Assignment Independent Variable Dependent Variable Training group Measure participants No training (Control) group Measure

22. “Well designed” experiments • Post-test only designs Random Assignment Independent Variable Dependent Variable Experimental group Measure participants Control group Measure

23. “Well designed” experiments • Pretest-posttest design Random Assignment Independent Variable Dependent Variable Dependent Variable Experimental group Measure Measure participants Control group Measure Measure

24. Next time • Read chapters 8 & 10