1. Useful Statistical Tools February 19, 2010

2. Today’s Class Aphorisms Useful Statistical Tools Probing Question Assignments Surveys

3. Aphorisms “Get close enough to know the task, but stay far enough to see the patterns.” "Humor happens, embrace it.“ "Much like improv, prom night, and getting into fights, the key to good contextual inquiry is to always say yes.“ "Learn as though you would never be able to master it; hold it as though you would be in fear of losing it.“ "Until you learn to interpret openly, you open yourself to mis-interpretation.“ "To know an answer, you must ask a question. To know a truth, you must contextually inquire the right question.” "Your participant does all the hard stuff. All you have to do is talk about it and check your work“ "You cannot learn if you already know, unless you first learn how to forget!“ "Listen to the people around you, including to those you know well -- but listen deeper.“ "Do, or do not. There is no try."

4. Any guesses From those who did not email in? Juelaila has won the first cookie There is one cookie remaining

5. Aphorisms “Get close enough to know the task, but stay far enough to see the patterns.” "Humor happens, embrace it.“ "Much like improv, prom night, and getting into fights, the key to good contextual inquiry is to always say yes.“ "Learn as though you would never be able to master it; hold it as though you would be in fear of losing it.“ "Until you learn to interpret openly, you open yourself to mis-interpretation.“ "To know an answer, you must ask a question. To know a truth, you must contextually inquire the right question.” "Your participant does all the hard stuff. All you have to do is talk about it and check your work“ "You cannot learn if you already know, unless you first learn how to forget!“ "Listen to the people around you, including to those you know well -- but listen deeper.“ "Do, or do not. There is no try."

6. Aphorisms “Get close enough to know the task, but stay far enough to see the patterns.” "Humor happens, embrace it.“ "Much like improv, prom night, and getting into fights, the key to good contextual inquiry is to always say yes.“ "Learn as though you would never be able to master it; hold it as though you would be in fear of losing it.“ "Until you learn to interpret openly, you open yourself to mis-interpretation.“ "To know an answer, you must ask a question. To know a truth, you must contextually inquire the right question.” "Your participant does all the hard stuff. All you have to do is talk about it and check your work“ "You cannot learn if you already know, unless you first learn how to forget!“ "Listen to the people around you, including to those you know well -- but listen deeper.“ "Do, or do not. There is no try."

7. Cookies! "Do, or do not. There is no try.“ – Juelaila answered first "Until you learn to interpret openly, you open yourself to mis-interpretation.“ – No answers

8. Let’s discuss A few of these aphorisms Do you think that they help us understand the idea and practice of contextual inquiry better?

9. Your thoughts? “Get close enough to know the task, but stay far enough to see the patterns.”

10. Your thoughts? "Much like improv, prom night, and getting into fights, the key to good contextual inquiry is to always say yes.“

11. Your thoughts? "Until you learn to interpret openly, you open yourself to mis-interpretation.“

12. Your thoughts? "Your participant does all the hard stuff. All you have to do is talk about it and check your work“

13. Your thoughts? "You cannot learn if you already know, unless you first learn how to forget!“

14. Comments? Questions?

15. Today’s Class Aphorisms Useful Statistical Tools Probing Question Assignments Surveys

16. Useful Statistical Tools Power Analysis Meta-Analysis Imputation

17. Power Analysis A set of methods for determining The probability that you will obtain a statistically significant result, assuming a true effect size and sample size of a certain magnitude

18. Or The reverse Given a certain true effect size, and a desired probability of obtaining a statistically significant result, what sample size is needed?

19. Why? When? Why might a researcher want to do each type of power analysis? When might a researcher want to do each type of power analysis?

20. When used Effect size + Power --> Sample Size – Usually used before running study to pick sample size Effect size + Sample Size --> Power – Usually used after running study to explain to thesis committee why more subjects are needed

21. Power analysis Can be computed from – “Effect Size”/ Cohen’s d (M1 – M2)/ (pooled SD, e.g.  ) – r – Difference in two r values – And several other metrics

22. Power analysis Can be computed for – Single-group t-test – Two-group t-test – Paired t-test – F test – Sign test – Etc., etc., etc.

23. Mathematical Details Differ for different statistical tests and metrics Possible to do this in online power calculators

24. Sign Test Example (Courtesy of John McDonald)

27. What is a good value for power? Conventionally, power = 0.80 is treated as “good” Kind of a magic number

28. Comments? Questions?

29. I need 3 volunteers

30. Play with calculator http://www.cs.uiowa.edu/~rlenth/Power/ Two-sample t-test

31. Volunteer #1 If the true effect size is 0.5  how big a sample do you need to achieve Power = 0.8?

32. Volunteer #2 If the true effect size is 0.2  how big a sample do you need to achieve Power = 0.8?

33. Volunteer #3 If your control condition gains 20 points pre-post And your experimental condition gains 40 points pre- post And the pooled standard deviation is 30 points And you have 20 students in each condition What’s your statistical power?

34. Comments? Questions?

35. How can statistical power be increased? Both in theory, and in real life

36. How can statistical power be increased? Increase sample size

37. How can statistical power be increased? Increase difference in means – Make your intervention better

38. How can statistical power be increased? Increase difference in means – Make your control condition worse Some researchers make the mistake of picking a control condition that’s impossibly good – ScienceAssistments versus ScienceAssistments, with one less potential IV This doesn’t mean you should fish for a control condition that is absurdly awful – DrScheme versus Learning programming through interpretive dance – Miley’s World versus Learning math through reading textbooks

39. How can statistical power be increased? Increase difference in means – Make your control condition worse Some researchers make the mistake of picking a control condition that’s impossibly good – ScienceAssistments versus ScienceAssistments, with one less potential IV This doesn’t mean you should fish for a control condition that is absurdly awful – DrScheme versus Learning programming through interpretive dance – Miley’s World versus Learning math through reading textbooks written in Danish

40. How can statistical power be increased? Reduce standard deviation – What methods have we discussed in class that could help us do this?

41. How can statistical power be increased? Reduce standard deviation – What methods have we discussed in class that could help us do this? Stratification

42. Comments? Questions?

43. Meta-Analysis

44. Very important point, right up front There is meta-analysis And then there are the statistical techniques used in meta-analysis – Much broader in application than just classical meta-analysis!

45. Meta-Analysis In the classic sense, integrating across a set of previous studies, to attempt to find an overall effect size or significance of finding across all those studies

46. Examples Kulik & Kulik (1991) computer-aided instruction does 0.3  better than traditional instruction Cohen, Kulik, & Kulik (1982) found that expert tutors do 2.3  better than traditional instruction; novice tutors only do 0.4  better than traditional instruction

47. Process of doing a meta-analysis Find all the studies on topic of interest Find measure of interest (effect size or statistical significance) Integrate across studies

48. Challenges What might make it difficult to Find all the studies on topic of interest ?

49. Challenges to Finding all Studies Knowing what terminology to use in literature review – many phenomena have many names – Off-task behavior, Time-on-task, Percent On-Task, Attention – Gaming the system, Systematic Guessing, Hint Abuse, Help Abuse, Executive Help-Seeking, Letaxmaning, Off-Task Gaming Behavior, Player Transformation, Goal Structure Misalignment

50. Challenges to Finding all Studies “File-Drawer Effect” – Papers with null results get rejected by conference program committees and journal reviewers – Papers with null results don’t get submitted in the first place

51. Find measure of interest Statistical significance – If you can find a p, you can turn it into a Z, and you’re good to go Using Z formula in Excel, or a Z-p table – Set direction on Z to be consistent E.g. all studies with finding X are positive All studies with finding not-X are negative

52. Find measure of interest Effect size – Transform values into correlations or Cohen’s d values

53. Why might you… Why might you want to do meta-analysis on effect size versus statistical significance?

54. Integrating Across Studies Two cases Studies are independent Studies are non-independent

55. Studies are Independent By far the statistically easier case

56. Aggregating significance tests Stouffer’s Z For N studies, each with Z value  Z sqrt(N)

57. Volunteer?

58. Example Five studies on the effects of taking gym class on mathematics performance – Two studies found positive effect of taking gym class, p= 0.02, p=0.06 – Three studies found negative effect of taking gym class, p=0.05, p=0.11, p=0.75 – One-tailed Z table on the next slide

59. Z table

60. Aggregating correlations Convert r to Fisher z’ For N studies, each with z’ value  z’  N Then convert the result back to r

61. Why Fisher z’? Equal differences between any two Fisher z’ values are equal in significance Whereas r is uneven – From r=0.8 to 0.9 is a bigger difference in significance than r=0.2 to r=0.3 – So transformation is necessary to weight all differences in correlation equally

62. Volunteer?

63. Example Five studies on the effects of learning computer programming on popularity – Two studies found positive correlation, r = 0.1, r= 0.3 – Three studies found negative correlation, r = - 0.8, r = - 0.6, r = - 0.7 – Fisher z’ table on the next slide

64. r z' 0.00 0.00000.18 0.18200.35 0.36540.53 0.59010.69 0.8480 0.86 1.2933 0.01 0.01000.19 0.19230.36 0.37690.54 0.60420.70 0.8673 0.87 1.3331 0.02 0.02000.20 0.20270.37 0.38840.55 0.61840.71 0.8872 0.88 1.3758 0.03 0.03000.21 0.21320.38 0.40010.56 0.63280.72 0.9076 0.89 1.4219 0.04 0.04000.22 0.22370.39 0.41180.57 0.64750.73 0.9287 0.90 1.4722 0.05 0.05000.23 0.23420.40 0.42360.58 0.66250.74 0.9505 0.91 1.5275 0.06 0.06010.24 0.24480.41 0.43560.59 0.67770.75 0.9730 0.92 1.5890 0.07 0.07010.25 0.25540.42 0.44770.60 0.69310.76 0.9962 0.93 1.6584 0.08 0.08020.26 0.26610.43 0.45990.61 0.70890.77 1.0203 0.94 1.7380 0.09 0.09020.27 0.27690.44 0.47220.62 0.72500.78 1.0454 0.95 1.8318 0.10 0.10030.28 0.28770.45 0.48470.63 0.74140.79 1.0714 0.96 1.9459 0.11 0.11040.29 0.29860.46 0.49730.64 0.75820.80 1.0986 0.97 2.0923 0.12 0.12060.30 0.30950.47 0.51010.65 0.77530.81 1.1270 0.98 2.2976 0.13 0.13070.31 0.32050.48 0.52300.66 0.79280.82 1.1568 0.99 2.6467 0.14 0.14090.32 0.33160.49 0.53610.67 0.81070.83 1.1881 0.15 0.15110.33 0.34280.50 0.54930.68 0.82910.84 1.2212 0.16 0.16140.34 0.35410.51 0.56270.85 1.2562 0.17 0.17170.52 0.5763

65. Comments? Questions?

66. Studies are non-independent Generally taken to mean that same sample (at least in part) is involved The case where there is non-independence due to similar (or the same) learning materials is generally not considered, as inter- correlation is low and difficult to compute

67. Math is “complex” Strube’s (1985) Adjusted Z is used instead of Stouffer’s Z in these cases – Accounts for correlation of different data points for the same subject Similar approach for effect size

68. Comments? Questions?

69. Other Uses of These Techniques

70. Non-independence in modeling Take the case where you are studying whether an EDM model is statistically significantly different than chance – N actions involving M students It is extremely invalid to do a statistical significance test involving N actions – Assumes each action is independent of each other action But it biases towards non-significance to collapse the N data points into one data point per student

71. Solution Do separate statistical significance test within each student (actions can be treated as independent of each other, once student is accounted for) Then use Stouffer’s Z to aggregate across students

72. To see examples… There is not time to discuss the math in detail today, but see examples in – Baker, Corbett, & Aleven (2008) – Baker, Corbett, Roll, & Koedinger (2008)

73. Comments? Questions?

74. Imputation

75. In data sets with large amounts of data per data point – For instance, extremely long surveys or demographic data It is common to have small amounts of missing data in each data point – E.g. variable 17 missing for students 1, 14, 90, 112, 202, 477

76. In these cases… It may be undesirable to throw out every data point that has a missing response – You might end up losing 30-40% of your data, or more, and biasing your data For instance, people who occasionally fail to respond to survey items probably differ systematically from people who dilligently and carefully answer every question

77. Imputation For each data point missing a value Find a set of “similar” data point that is not missing that value – Similar data point has low absolute difference across non-missing variables Randomly choose one of the non-missing values to fill in the missing data

78. Multiple Imputation Create 3-10 data sets in this fashion Then for all the missing data, find the mean (and SD) across all imputed data sets Use the no-longer-missing data in future analyses

79. An alternative: regression imputation Find set of linear regression functions predicting each variable from all other variables Use this function to fill in missing data

80. Advantages? Disadvantages? Multiple Imputation Regression Imputation Throwing out all data points with missing variables

81. Comments? Questions?

82. Today’s Class Aphorisms Useful Statistical Tools Probing Question Assignments Surveys

83. Probing Question Observation: Relatively few researchers use power analysis when designing their studies. Why? Are they making a mistake?

84. Today’s Class Aphorisms Useful Statistical Tools Probing Question Assignments Surveys

85. Assignment #5 Any questions?

86. Today’s Class Aphorisms Useful Statistical Tools Probing Question Assignments Surveys