1. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 1 INF 397C Introduction to Research in Library and Information Science Fall, 2003 Day 2

2. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 2 Standard Deviation σ = SQRT(Σ(X - µ) 2 /N) (Does that give you a headache?)

3. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 3 USA Today has come out with a new survey - apparently, three out of every four people make up 75% of the population. –David Letterman (1947 - )

4. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 4 Statistics: The only science that enables different experts using the same figures to draw different conclusions. –Evan Esar (1899 - 1995), US humorist

5. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 5 Frequency Distributions

6. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 6 Percentiles/Deciles

7. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 7 Scales The data we collect can be represented on one of FOUR types of scales: –Nominal –Ordinal –Interval –Ratio “Scale” in the sense that an individual score is placed at some point along a continuum.

8. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 8 Nominal Scale Describe something by giving it a name. (Name – Nominal. Get it?) Mutually exclusive categories. For example: –Gender: 1 = Female, 2 = Male –Marital status: 1 = single, 2 = married, 3 = divorced, 4 = widowed –Make of car: 1 = Ford, 2 = Chevy... The numbers are just names.

9. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 9 Ordinal Scale An ordered set of objects. But no implication about the relative SIZE of the steps. Example: –The 50 states in order of population: 1 = California 2 = Texas 3 = New York... 50 = Wyoming

10. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 10 Interval Scale Ordered, like an ordinal scale. Plus there are equal intervals between each pair of scores. With Interval data, we can calculate means (averages). However, the zero point is arbitrary. Examples: –Temperature in Fahrenheit or Centigrade. –IQ scores

11. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 11 Ratio Scale Interval scale, plus an absolute zero. Sample: –Distance, weight, height, time (but not years – e.g., the year 2002 isn’t “twice” 1001).

12. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 12 Scales (cont’d.) It’s possible to measure the same attribute on different scales. Say, for instance, your midterm test. I could: Give you a “1” if you don’t finish, and a “2” if you finish. “1” for highest grade in class, “2” for second highest grade,.... “1” for first quarter of the class, “2” for second quarter of the class,”... Raw test score (100, 99,....). –(NOTE: A score of 100 doesn’t mean the person “knows” twice as much as a person who scores 50, he/she just gets twice the score.)

13. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 13 Scales (cont’d.) NominalOrdinalIntervalRatio Name=== Mutually- exclusive === Ordered== Equal interval = Absolute Zero Gender, Yes/No Class rank, ratings Days of wk., temp. Inches, dollars

14. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 14 Critical Skepticism Remember the Rabbit Pie example from yesterday. The “critical consumer” of statistics asked “what do you mean by ’50/50’”? Let’s look at some other situations and claims.

15. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 15 Company is hurting. We’d like to ask you to take a 50% cut in pay. But if you do, we’ll give you a 60% raise next month. OK? Problem: Base rate.

16. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 16 Sale! “Save 100%” I doubt it.

17. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 17 Probabilities “It’s safer to drive in the fog than in the sunshine.” (Kinda like “Most accidents occur within 25 miles of home.” Doesn’t mean it gets safer once you get to San Marcos.) Navy literature around WWI: –“The death rate in the Navy during the Spanish- American war was 9/1000. For civilians in NYC during the same period it was 16/1000. So... Join the Navy. It’s safer.”

18. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 18 Are all results reported? “In an independent study [ooh, magic words], people who used Doakes toothpaste had 23% fewer cavities.” How many studies showed MORE cavities for Doakes users?

19. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 19 Sampling problems “Average salary of 1999 UT grads – “$41,000.” How did they find this? I’ll bet it was average salary of THOSE WHO RESPONDED to a survey. Who’s inclined to respond?

20. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 20 Correlation ≠ Causation Around the turn of the century, there were relatively MANY deaths of tuberculosis in Arizona. What’s up with that?

21. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 21 Remember... I do NOT want you to become cynical. Not all “media bias” is intentional. Just be sensible, critical, skeptical. As you “consume” statistics, ask some questions...

22. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 22 ??? Who says so? (A Zest commercial is unlikely to tell you that Irish Spring is best.) How does he/she know? (That Zest is “the best soap for you.”) What’s missing? (One year, 33% of female grad students at Johns Hopkins married faculty.) Did somebody change the subject? (“Camrys are bigger than Accords.” “Accords are bigger than Camrys.”) Does it make sense? (“Study in NYC: Working woman with family needed $40.13/week for adequate support.”)

23. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 23 Quote on front of Huff book: “It ain’t so much the things we don’t know that get us in trouble. It’s the things we know that ain’t so.” Artemus Ward, US author Being a critical consumer of statistics will keep you from knowing things that ain’t so.

24. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 24 Claims “Better chance of being struck by lightening than being bitten by a shark.” Tom Brokaw – Tranquilizers. What are some claims you all heard/read?

25. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 25 Break

26. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 26 Before the break... We learned about frequency distributions. I asserted that a frequency distribution, and/or a histogram (a graphical representation of a frequency distribution), was a good way to summarize a collection of data. There’s another, even shorter-hand way.

27. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 27 Measures of Central Tendency Mode –Most frequent score (or scores – a distribution can have multiple modes) Median –“Middle score” –50 th percentile Mean - µ (“mu”) –“Arithmetic average” –ΣX/N

28. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 28 Let’s calculate some “averages” From old data.

29. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 29 A quiz about averages 1 – If one score in a distribution changes, will the mode change? __Yes __No __Maybe 2 – How about the median? __Yes __No __Maybe 3 – How about the mean? __Yes __No __Maybe 4 – True or false: In a normal distribution (bell curve), the mode, median, and mean are all the same? __True __False

30. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 30 More quiz 5 – (This one is tricky.) If the mode=mean=median, then the distribution is necessarily a bell curve? __True __False 6 – I have a distribution of 10 scores. There was an error, and really the one highest score is 5 points HIGHER than previously thought. a) What does this do to the mode? __ Increases it __Decreases it __Nothing __Can’t tell b) What does this do to the median? __ Increases it __Decreases it __Nothing __Can’t tell c) What does this do to the mean? __ Increases it __Decreases it __Nothing __Can’t tell 7 – Which of the following must be an actual score from the distribution? a) Mean b) Median c) Mode d) None of the above

31. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 31 OK, so which do we use? Means allow further arithmetic/statistical manipulation. But... It depends on: –The type of scale of your data Can’t use means with nominal or ordinal scale data With nominal data, must use mode –The distribution of your data Tend to use medians with distributions bounded at one end but not the other (e.g., salary). (Look at our “Number of MLB games” distribution.) –The question you want to answer “Most popular score” vs. “middle score” vs. “middle of the see-saw” “Statistics can tell us which measures are technically correct. It cannot tell us which are ‘meaningful’” (Tal, 2001, p. 52).

32. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 32 Have sidled up to SHAPES of distributions Symmetrical Skewed – positive and negative Flat

33. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 33 Why...... isn’t a “measure of central tendency” all we need to characterize a distribution of scores/numbers/data/stuff? “The price for using measures of central tendency is loss of information” (Tal, 2001, p. 49).

34. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 34 Note... We started with a bunch of specific scores. We put them in order. We drew their distribution. Now we can report their central tendency. So, we’ve moved AWAY from specifics, to a summary. But with Central Tendency, alone, we’ve ignored the specifics altogether. –Note MANY distributions could have a particular central tendency! If we went back to ALL the specifics, we’d be back at square one.

35. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 35 Measures of Dispersion Range Semi-interquartile range Standard deviation –σ (sigma)

36. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 36 Range Like the mode... –Easy to calculate –Potentially misleading –Doesn’t take EVERY score into account. What we need to do is calculate one number that will capture HOW spread out our numbers are from that Central Tendency. –“Standard Deviation”

37. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 37 New distribution Number of semesters all 2003 UT ISchool graduates spent in graduate school. (I made this up.) Measures of central tendency. Go with mean. So, how much do the actual scores deviate from the mean?

38. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 38 So... Add up all the deviations and we should have a feel for how disperse, how spread, how deviant, our distribution is. Σ(X - µ)

39. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 39 Damn! OK, so mathematicians at this point do one of two things. Take the absolute value or square ‘em. We square ‘em. Σ(X - µ) 2 Then take the average of the squared deviations. Σ(X - µ) 2 /N But this number is so BIG!

40. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 40 OK...... take the square root (to make up for squaring the deviations earlier). σ = SQRT(Σ(X - µ) 2 /N) Now this doesn’t give you a headache, right? I said “right”?

41. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 41 Hmmm... ModeRange Median????? MeanStandard Deviation

42. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 42 We need... A measure of spread that is NOT sensitive to every little score, just as median is not. SIQR: Semi-interquartile range. (Q3 – Q1)/2

43. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 43 To summarize ModeRange-Easy to calculate. -Maybe be misleading. MedianSIQR-Capture the center. -Not influenced by extreme scores. Mean (µ) SD (σ) -Take every score into account. -Allow later manipulations.

44. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 44 Graphs Graphs/tables/charts do a good job (done well) of depicting all the data. But they cannot be manipulated mathematically. Plus it can be ROUGH when you have LOTS of data. Let’s look at your examples.

45. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 45 Some rules...... For building graphs/tables/charts: –Label axes. –Divide up the axes evenly. –Indicate when there’s a break in the rhythm! –Keep the “aspect ratio” reasonable. –Histogram, bar chart, line graph, pie chart, stacked bar chart, which when? –Keep the user in mind.

46. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 46 Who wants to guess...... What I think is the most important sentence in S, Z, & Z (2003), Chapter 2?

47. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 47 p. 19 Penultimate paragraph, first sentence: “If differences in the dependent variable are to be interpreted unambiguously as a result of the different independent variable conditions, proper control techniques must be used.”

48. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 48 http://highered.mcgraw- hill.com/sites/0072494468/student_view0 /statistics_primer.htmlhttp://highered.mcgraw- hill.com/sites/0072494468/student_view0 /statistics_primer.html Click on Statistics Primer.

49. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 49 Homework LOTS or reading. See syllabus. Send a table/graph/chart that you’ve read this past week. Send email by noon, Monday, 9/8/2003. See you next week.