1. R.G. Bias | rbias@ischool.utexas.edu | Name that tune. Song title? Performer(s)? 1

2. R.G. Bias | rbias@ischool.utexas.edu | Scientific Method “Finding New Information” 3/22/2010 2

3. R.G. Bias | rbias@ischool.utexas.edu | First...  Reaction to a mid-course class evaluation. –Just 8 respondents. –Mostly positive. –One answer that troubled me: Course objectives and assignments are clearly stated. 25% (2) 50% (4) 12.5% (1) 12.5% (1) 0% (0)  Level-setting. Where are we and where are we headed? 3

4. R.G. Bias | rbias@ischool.utexas.edu | Objectives After this class (or these 3 weeks of classes) you will (it is my hope!):  know something about how scientists (information scientists) gather new information.  AND you’ll be good at evaluating information others offer you.  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.

5. R.G. Bias | rbias@ischool.utexas.edu | 5  There are three kinds of lies: lies, damned lies, and statistics. –Benjamin Disraeli (1804 – 1881), British politician

6. R.G. Bias | rbias@ischool.utexas.edu | 6  Statistics are like a bikini. What they reveal is suggestive, but what they conceal is vital. –Aaron Levenstein, U.S. politician

7. R.G. Bias | rbias@ischool.utexas.edu | 7  The statistics on sanity are that one out of every four Americans is suffering from some form of mental illness. Think of your three best friends. If they're okay, then it's you. –Rita Mae Brown, U.S. author

8. R.G. Bias | rbias@ischool.utexas.edu | 8 First...  There are two components of this and any class: Instruction and Evaluation.  Let’s get the evaluation out of the way, early.  Need one volunteer.

9. R.G. Bias | rbias@ischool.utexas.edu | 9 “Research shows...”  Finger length is a good (and quick!) indicator of intelligence.  One volunteer – measure your finger length in cm. However.

10. R.G. Bias | rbias@ischool.utexas.edu | 10 Hmmmm...  Everyone in the class will get a grade of “C”  But still, we can continue with the “instruction” part of the course.

11. R.G. Bias | rbias@ischool.utexas.edu | 11 Oh, so maybe...  Just THIS person isn’t too smart.  Or maybe finger length is NOT a good indicator of intelligence.

12. R.G. Bias | rbias@ischool.utexas.edu | 12 Now, an experiment  I will hand you each a slip of paper. Please read it an do NOT let anyone else read it. –Women receive a white slip of paper. –Men receive a green slip of paper.  After everyone has read his/her slip of paper and refolded it, I’ll show some letters of the alphabet, one at a time, for one second each.  After the last one, I’ll say “Go,” and ask you to write down the letters, in order.  Any questions?

13. R.G. Bias | rbias@ischool.utexas.edu | 13 OK, pencils down.

14. R.G. Bias | rbias@ischool.utexas.edu | 14 J F M A M J J A S O N D

15. R.G. Bias | rbias@ischool.utexas.edu | 15 Write down the letters.  In order!

16. R.G. Bias | rbias@ischool.utexas.edu | 16 Answers JFMAMJJASONDJFMAMJJASOND

17. R.G. Bias | rbias@ischool.utexas.edu | 17 Exp. 1 -- Data All correctNot all correct Total Men7 (47%)815 Women3 (13%)2023 Total

18. R.G. Bias | rbias@ischool.utexas.edu | 18 Who among you... ... is a statistical wizard? ... has experience conducting research?

19. R.G. Bias | rbias@ischool.utexas.edu | 19 Many ways to learn new things  Method of Authority –trusted authority tells you something  Method of Reason –follow basic logical laws from philosophy  Modeling  Trial-and-error  Intuition  Scientific Method –belief on the basis of experience

20. R.G. Bias | rbias@ischool.utexas.edu | 20 Three Paths to “Belief” 1 – Naïve acceptance. 2 – Cynicism. 3 – Critical skepticism.

21. R.G. Bias | rbias@ischool.utexas.edu | 21 Critical Skepticism!  Rabbit pie story.

22. R.G. Bias | rbias@ischool.utexas.edu | 22 What you’ll learn the next 3 weeks  Reliability. (“Oh, just measure it however.”)  Validity. (Finger length a good indicator of intelligence?)  Sampling – picking a representative sample and then generalizing to a larger population  Why larger samples are better

23. R.G. Bias | rbias@ischool.utexas.edu | 23 What you’ll learn (cont’d.):  How to represent a group of numbers, meaningfully. –Frequency distributions –Measures of central tendency –Measures of dispersion (spread) –Graphs/Tables  Operationalizing variables (“intelligence”)  Probability  Correlation

24. R.G. Bias | rbias@ischool.utexas.edu | 24 What you’ll learn (cont’d.):  Different measurement scales  What makes a good research question?  Experimental design –Independent and dependent variables –Controls, counterbalancing, and confounds –Hypothesis testing –Inferential statistics (is THAT number really bigger than THIS number?)

25. R.G. Bias | rbias@ischool.utexas.edu | 25 More than anything else... ... scientists are skeptical.  “Scientific skepticism is a gullible public’s defense against charlatans and others who would sell them ineffective medicines and cures, impossible schemes to get rich, and supernatural explanations for natural phenomena.”

26. R.G. Bias | rbias@ischool.utexas.edu | 26 Research Methods Researchers are... -like detectives – gather evidence, develop a theory. -like judges – decide if evidence meets scientific standards. -like juries – decide if evidence is “beyond a reasonable doubt.”

27. R.G. Bias | rbias@ischool.utexas.edu | 27 Science... ... Is a cumulative affair. Current research builds on previous research.  The Scientific Method: –is empirical (acquires new knowledge via direct observation and experimentation) –entails systematic, controlled observations. –is unbiased, objective. –entails operational definitions. –is valid, reliable, testable, critical, skeptical.

28. R.G. Bias | rbias@ischool.utexas.edu | 28 CONTROL ... is the essential ingredient of science, distinguishing it from nonscientific procedures.  The scientist, the experimenter, manipulates the Independent Variable (IV – “treatment – at least two levels – “experimental and control conditions”) and controls other variables.

29. R.G. Bias | rbias@ischool.utexas.edu | 29 More control  After manipulating the IV (because the experimenter is independent – he/she decides what to do)...  He/she measures the effect on the Dependent Variable (what is measured – it depends on the IV).

30. R.G. Bias | rbias@ischool.utexas.edu | 30 Key Distinction  IV vs. Individual Differences variable  The scientist MANIPULATES an IV, but SELECTS an Individual Differences variable (or “subject” variable).  Can’t manipulate a subject variable. –“Select a sample. Have half of ‘em get a divorce.”  Consider an Individual Difference, or Subject Variable, as a TYPE of IV.

31. R.G. Bias | rbias@ischool.utexas.edu | 31 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.”

32. R.G. Bias | rbias@ischool.utexas.edu | 32 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.

33. R.G. Bias | rbias@ischool.utexas.edu | 33 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.

34. R.G. Bias | rbias@ischool.utexas.edu | 34 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

35. R.G. Bias | rbias@ischool.utexas.edu | 35 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.

36. R.G. Bias | rbias@ischool.utexas.edu | 36 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).

37. R.G. Bias | rbias@ischool.utexas.edu | References  Hinton 37

38. R.G. Bias | rbias@ischool.utexas.edu | Going forward...  Annotated bibliography due 3/24 (this Wednesday – same drill, hard-copy in class, digital copy to your TA).  Read those two books that are on reserve in the library – To know a fly and How to lie with statistics.  See you Wednesday. 38