Name that tune.

1. Name that tune.  Song title?  Performer(s)?

2. Scientific Method (continued) “Finding New Information” 3/21/2011

3. Objectives • I want to arm you with a scientist’s skepticism, and a scientist’s tools to conduct research and evaluate others’ research. • Randolph – remember to take roll.

4. Operational Definitions • Explains a concept solely in terms of the operations used to produce and measure it. • Bad: “Smart people.” • Good: “People with an IQ over 120.” • Bad: “People with long index fingers.” • Good: “People with index fingers at least 7.2 cm.” • Bad: Ugly guys. • Good: “Guys rated as ‘ugly’ by at least 50% of the respondents.”

5. Validity and Reliability • Validity: the “truthfulness” of a measure. Are you really measuring what you claim to measure? “The validity of a measure . . . the extent that people do as well on it as they do on independent measures that are presumed to measure the same concept.” • Reliability: a measure’s consistency. • A measure can be reliable without being valid, but not vice versa.

6. Theory and Hypothesis • Theory: a logically organized set of propositions (claims, statements, assertions) that serves to define events (concepts), describe relationships among these events, and explain their occurrence. • Theories organize our knowledge and guide our research • Hypothesis: A tentative explanation. • A scientific hypothesis is TESTABLE.

7. A bit more about theories • Good theories provide “precision of prediction” • The “rule of parsimony” is followed • The simplest alternative explanations are accepted • A good scientific theory passes the most rigorous tests • Testing will be more informative when you try to DISPROVE (falsify) a theory • Start with a null hypothesis

8. If results of an experiment . . . • . . . (a well-run experiment!) are consistent with theory, we say we’ve supported the theory. (NOT that it is “right.”) • Otherwise, we modify the theory. • Testing hypotheses and revising theories based on the outcomes of experiments – the long process of science.

9. Goals of Scientific Method • Description • Nomothetic approach – establish broad generalizations and general laws that apply to a diverse population • Versus idiographic approach – interested in the individual, their uniqueness (e.g., case studies) • Prediction • Correlational study – when scores on one variable can be used to predict scores on a second variable. (Doesn’t necessarily tell you “why.”) • Understanding – con’t. on next page • Creating change • Applied research

10. Understanding • Three important conditions for making a causal inference: • Covariation of events. (IV changes, and the DV changes.) • A time-order relationship. (First the scientist changes the IV – then there’s a change in the DV.) • The elimination of plausible alternative causes.

11. Experimental Design • Description and Prediction are crucial to the scientific study of behavior, but they’re not sufficient for understanding the causes. We need to know WHY. • Best way to answer this question is with the experimental method. • “The special strength of the experimental method is that it is especially effective for establishing cause-and-effect relationships.”

12. Confounding • When two potentially effective IVs are allowed to covary simultaneously. • Poor control! • Men, overall, did a better job of remembering the 12 “random” letters. But the men had received a different “clue.” • So GENDER (what type of IV? A SUBJECT variable, or indiv. differences variable) was CONFOUNDED with “type of clue” (an IV).

13. Populations and Samples • Population: the set of all cases of interest • Sample: Subset of all the population that we choose to study.

14. Logic of Experimental Research • Researchers manipulate an independent variable in an experiment to observe the effect on behavior, as assessed by the dependent variable.

15. Independent Groups Design • Each group represents a different condition as defined by the independent variable.

16. Random . . . • Random Selection vs. Random Assignment • Random Selection = every member of the population has an equal chance of being selected for the sample. • Random Assignment = every member of the sample (however chosen) has an equal chance of being placed in the experimental group or the control group. • Random assignment allows for individual differences among test participants to be averaged out.

17. Example • Theory: Vitamins help humans’ immune systems fight off disease • Hypothesis: Taking vitamin C will lead to fewer colds • IV – Vitamin C (some vs. none, every day) • DV -- # of colds in a year • H0 – Vitamin C doesn’t influence # of colds • HA – Vitamin C reduces # of colds • Select sample (random selection?) • Random assignment!

18. Another example • A researcher was interested in the affect of ambient noise on ability to concentrate. She operationally defined “noise” as a pneumatic jackhammer at 80 decibels, and ability to concentrate as time it took to complete a Sudoku puzzle. Invited 20 PSY 301 students into the lab, one at a time. First 10 completed the puzzle in quiet environs. Next 10 completed the same puzzle with the jackhammer sound just outside the window. Found that people in a quiet setting completed the puzzle on the average 2.5 minutes faster. • What’s the IV? The DV? H0? HA? Was this a good experiment?

19. Let’s step back a minute • An experiment is “personkind’s way of asking nature a question.” • I want to know if one variable (factor, event, thing) has an effect on another variable – does the IV systematically influence the DV? • I manipulate some variables (IVs), control other variables, and count on random selection to wash out the effects of all the rest of the variables.

20. Challenges to Internal Validity • Testing intact groups. (Why is the group a group? Might be some systematic differences.) • Extraneous variables. (Balance ‘em.) (E.g., experimenter). • Subject loss • Mechanical loss, OK. • Select loss, not OK. • Demand characteristics (cues and other info participants pick up on) – use a placebo, and double-blind procedure • Experimenter effects – use double-blind procedure

21. Notice • Many things influence how easy or hard it is to discover a difference. • How big the real difference is. • How much variability there is in the population distribution(s). • How much error variance there is. • Let’s talk about variance.

22. References • Hinton, P. R. Statistics explained. • Shaughnessy, Zechmeister, and Zechmeister. Experimental methods in psychology.