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LIS 397.1 Introduction to Research in Library and Information Science Summer, 2003 Day 4

LIS 397.1 Introduction to Research in Library and Information Science Summer, 2003 Day 4. Old Business. Typo on syllabus Here’s the REAL reading schedule: Before week 1 – Nothing Before week 2 – Huff Best Hinton, Ch. 1, 2, 3 S, Z, & Z, Ch. 1, 2, 3 Before Week 3 – Dethier

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LIS 397.1 Introduction to Research in Library and Information Science Summer, 2003 Day 4

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  1. LIS 397.1Introduction to Research in Library and Information ScienceSummer, 2003Day 4

  2. Old Business • Typo on syllabus • Here’s the REAL reading schedule: • Before week 1 – Nothing • Before week 2 – • Huff • Best • Hinton, Ch. 1, 2, 3 • S, Z, & Z, Ch. 1, 2, 3 • Before Week 3 – • Dethier • Hinton, Ch. 4, 5, 6, 7, 8 • S, Z, & Z, Ch. 7, 8, 14 • Harris

  3. Updated Reading Sched. (Cont’d.) • Before Week 4 – • S, Z, &, Z, Ch. 4, 5, 6, 10, 11 • Cronin • Groman articles • Before Week 5 – • Hinton, Ch. 9 – 16, 19 • S, Z, & Z, Ch. 12, 13 • Before Week 6 – • MacCoun • (No S, Z, & Z, Ch. 16) • Also, I listed the “17th day of class” wrongly – it’s 7/10/2003.

  4. More Old Business • Web site • Room • Normal curve example

  5. From Jaisingh (2000)

  6. Calculating percentiles • From Runyon et al. (2000)

  7. 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.

  8. 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.

  9. http://www.who.int/csr/sars/country/2003_06_06/en/ • http://www.econmodel.com/phillips/index2.php?graph=4969.png • http://www.time.com/time/covers/1101030505/sars/index.html • http://factfinder.census.gov/bf/_lang=en_vt_name=DEC_2000_SF3_U_DP3_geo_id=01000US.html • http://www.understandingusa.com/chaptercc=14&cs=302.html • http://www.pbs.org/wgbh/pages/frontline/shows/race/economics/analysis.html • http://www.ischool.utexas.edu/~annashin/StatisticsRepresentation.doc • http://www.austin360.com/search/content/shared/money/salarysurvey/browse.html • http://money.cnn.com/2003/05/07/pf/saving/q_jobless_grads/index.htm • http://www.dps.state.la.us/TIGER/PollFrequencies.htm • http://story.news.yahoo.com/news?tmpl=story2&u=/030529/246/486h6.html • http://www.blooberry.com/indexdot/history/browsers.htm

  10. So far . . . • . . . we’ve talked of summarizing ONE distribution of scores. • By ordering the scores. • By organizing them in graphs/tables/charts. • By calculating a measure of central tendency and a measure of dispersion. • What happens when we want to compare TWO distributions of scores?

  11. “Now, why would I want to do that”? • Is your child taller or heavier? • Is this month’s SAT test any easier or harder than last month’s? • Is my 91 in my Research Methods class better than my 95 in my Digital Libraries class? • Is the new library lay-out better than the old one? • Can more employees sign up, more quickly, for benefits with our new intranet site than with our old one? • Did my class perform better on the TAKS test than they did on the TAAS test?

  12. Well? • COULD it be the case that your 91 in your Research Methods class is better than your 95 in your Digital Libraries class? • How?

  13. What if . . . • The mean in Research Methods was 50, and the mean in Digital Libraries was 99? • (What, besides the fact that everyone else is trying to drop the Research class!) • So:

  14. The Point • As I said yesterday, you need to know BOTH a measure of central tendency AND a measure of spread to understand a distribution. • BUT STILL, this can be convoluted . . . • “Well, daughter, how are you doing in grad school this semester”?

  15. “Well, Mom . . . • “. . . I have a 91 in Research Methods but the mean is 50 and the standard deviation is 12, but I only have a 95 in Digital Libraries, whereas the mean in that class is 99 with a standard deviation of 1.” • Of course, your mom’s reaction will be, “Just call home more often, dear.”

  16. Wouldn’t it be nice . . . • . . . if there could be one score we could use for BOTH classes, for BOTH the TAKS test and the TAAS test, for BOTH your child’s height and weight? • There is – and it’s called the “standard score,” or “z score.” (Get ready for another headache.)

  17. Standard Score • z = (X - µ)/σ • “Hunh”? • Each score can be expressed as the number of standard deviations it is from the mean of its own distribution. • “Hunh”? • (X - µ) – This is how far the score is from the mean. (Note: Could be negative! No squaring, this time.) • Then divide by the SD to figure out how many SDs you are from the mean.

  18. Z scores (cont’d.) • z = (X - µ)/σ • Notice, if your score (X) equals the mean, then z is, what? • If your score equals the mean PLUS one standard deviation, then z is, what? • If your score equals the mean MINUS one standard deviation, then z is, what?

  19. An example

  20. Let’s calculate σ – Test 1

  21. Let’s calculate σ – Test 2

  22. So . . . z = (X - µ)/σ • Kris had a 76 on both tests. • Test 1 - µ = 61, σ = 9 • So her z score was (76-61)/9 or 15/9 or 1.67. So we say that Kris’s score was 1.67 standard deviations above the mean. • Test 2 - µ = 83, σ = 5.4 • So her z score was (76-83)/5.4 or -7/5.4 or –1.3. So we say that Kris’s score was 1.3 standard deviations BELOW the mean. • Given what I said yesterday about two-thirds of the scores being within one standard deviation of the mean . . . .

  23. z = (X - µ)/σ • If I tell you that the average IQ score is 100, and that the SD of IQ scores is 16, and that Bob’s IQ score is 2 SD above the mean, what’s Bob’s IQ? • If I tell you that your 75 was 1.5 standard deviations below the mean of a test that had a mean score of 90, what was the SD of that test?

  24. Notice . . . • The mean of all z scores (for a particular distribution) will be zero, as will be their sum. • With z scores, we transform raw scores into standard scores. • These standard scores are RELATIVE distances from their (respective) means. • All are expressed in units of σ.

  25. Probability • Remember all those decisions we talked about, last week. • VERY little of life is certain. • One person wore her new Snoopy shirt because she THOUGHT it would make her feel happy. (Or maybe she thought to herself: “The probability that wearing this shirt will make me happy is ≥ .50.”) (But I doubt it.)

  26. Prob. (cont’d.) • Life’s a gamble! • Just about every decision is based on a probable outcomes. • None of you raised your hands last Wednesday when I asked for “statistical wizards.” Yet every one of you does a pretty good job of navigating an uncertain world. • None of you touched a hot stove (on purpose.) • All of you made it to class.

  27. Probabilities • Always between one and zero. • Something with a probability of “one” will happen. (e.g., Death, Taxes). • Something with a probability of “zero” will not happen. (e.g., My becoming a Major League Baseball player). • Something that’s unlikely has a small, but still positive, probability. (e.g., probability of someone else having the same birthday as you is 1/365 = .0027, or .27%.)

  28. Just because . . . • . . . There are two possible outcomes, doesn’t mean there’s a “50/50 chance” of each happening. • When driving to school today, I could have arrived alive, or been killed in a fiery car crash. (Two possible outcomes, as I’ve defined them.) Not equally likely. • But the odds of a flipped coin being “heads,” . . . .

  29. Prob (cont’d.) • Probability of something happening is • # of “successes” / # of all events • P(one flip of a coin landing heads) = ½ = .5 • P(one die landing as a “2” = 1/6 = 1.67 • P(some score in a distribution of scores is greater than the median) = ½ = .5 • P(some score in a normal distribution of scores is greater than the mean but has a z score of 1 or less is . . . ? • P(drawing a diamond from a complete deck of cards) = ?

  30. Probability Rules (and Rocks!) • Addition Rule (And rule): If there are two or more mutually exclusive outcomes. • Chances of rolling a two or a three, on one die. 1/6 + 1/6 = 2/6 • Multiplication Rule (Or rule): Prob. of getting BOTH of two or more independent outcomes. • Chances of rolling a two and THEN a three, on one die. 1/6 x 1/6 = 1/36

  31. Think this through. • What are the odds (“what are the chances”) (“what is the probability”) of getting two “heads” in a row? • Three heads in a row? • Three flips the same (heads or tails) in a row?

  32. So then . . . • WHY were the odds in favor of having two people in our class with the same birthday? • Think about the problem! • What if there were 367 people in the class. • P(2 people with same b’day) = 1.00

  33. Happy B’day to Us • But we had 46. • Probability that the first person has a birthday: 1.00. • Prob of the second person having the same b’day: 1/365 • Prob of the third person having the same b’day as Person 1 and Person 2 is 1/365 + 1/365 – the chances of all three of them having the same birthday.

  34. Sooooo . . . • http://www.people.virginia.edu/~rjh9u/birthday.html

  35. Homework Keep reading. See you tomorrow.

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