1. G.O. Wesolowsky
Statistical Detection of Cheating on Multiple Choice Exams: Software, Implementation, and Controversy
George O. Wesolowsky
Professor Emeritus of Management Science
De Groote School of Business
McMaster University, Hamilton. Ontario,
Essentials:
Bubbling in or selecting choices
Unauthorized communication – one way or two way. Commonly known as copying, which is cheating

2. Outline of this Presentation
G.O. Wesolowsky
Outline of this Presentation
Introduction: Cheating on multiple choice tests
How I got into this.
Outline of statistical detection methodology
Practical capabilities of SCheck
Common attitudes to detection and prevention
Recommendations

3. Ideal* Writing Conditions
G.O. Wesolowsky
Ideal* Writing Conditions
* I have seen more than 30% cheating under such conditions

4. Less than Ideal Writing Conditions
G.O. Wesolowsky

5. Prevalence of MC Tests and Exams
G.O. Wesolowsky
Prevalence of MC Tests and Exams
30% ? of marks in UG classes given through MC
At McGill (20000 undergrads) in the Fall Semester of 2002:
Finals: 83 courses,15072 students
Midterms: 70+ courses, students

6. How They Do It: Copying Sampler
G.O. Wesolowsky
How They Do It: Copying Sampler
Peeking
Passing (papers or whole exams
Signaling. One invigilator told me a student was twitching so much all over his face and hand he thought at first it was a seizure
Clandestine electronic communication
Cartoon of a toilet communications base from a web sit of a Scandinavian company selling electronic counter measures.

7. How They Do It: Types of Cheating not Resulting in Similar Responses
G.O. Wesolowsky
How They Do It: Types of Cheating not Resulting in Similar Responses
I am an impostor
Usually not vulnerable to statistical detection

8. G.O. Wesolowsky
A guide to cheating during tests and examinations From Wikibooks, the open-content textbooks collection
Contents
[hide]
1 Preamble
1.1 A few definitions to consider
1.2 The rewards/dangers of cheating
1.2.1 Rationales of cheating
1.2.2 Rationales of prosecuting cheaters
1.2.3 Possible Penalties
2 General notes
3 Techniques
3.1 Copying from a person
3.1.1 Application of codes
3.2 Copying from a pre-written source
3.2.1 Directly from textbook/notes
3.2.2 Cheat Sheet
3.3 Precautions
3.4 Copying from a planted source
3.5 Locating Cheating Material on the Web
3.6 Test previewing

9. Some Statistics plagiarized text (CAI)
G.O. Wesolowsky
Some Statistics plagiarized text (CAI)
CAI Research Conducted By Don McCabe (Released In June, 2005) is typical of many studies: As part of CAI’s Assessment Project, almost 50,000 undergraduates on more than 60 campuses have participated in a nationwide survey of academic integrity since the fall of The results were disturbing, provocative, and challenging.
On most campuses, 70% of students admitted to some cheating. Close to one-quarter of the participating students admitted to serious test cheating in the past year and half admitted to one or more instances of serious cheating on written assignments.
Faculty are reluctant to take action against suspected cheaters.
In Assessment Project surveys involving almost 10,000 faculty, 44% of those who were aware of student cheating in their course in the last three years, have never reported a student for cheating to the appropriate campus authority. Students suggest that cheating is higher in courses where it is well known that faculty members are likely to ignore cheating.

10. One Method of Cheating Detection
G.O. Wesolowsky
One Method of Cheating Detection
My favorite method.

11. Questionable Statistical Detection
G.O. Wesolowsky
It is not infrequent that instructors, when confronted by a suspected cheating situation, invent their own methodology on the spot. This is usually what I call ‘outlier methodology’. The basis is some way of using the number of wrong answers that two students have in common.
It could be simply a count of such 'wrong matches', a proportion, a run length, a ratio with other counts, or a multivariate plot of such variables. The idea is to look for outliers and attribute them to cheating.

12. G.O. Wesolowsky
Example
Bonnie and Clyde engaged in suspicious behavior. A comparison of responses revealed:
“Bonnie and Clyde are surprisingly similar; 23 matches out of 23 wrong.”
.....C......B C BD D..DB.A..ABD.B..A..CB...B ABAC..A..C
Both chose C, which is wrong
. = correct

13. But then: The instructor wrote a program:
G.O. Wesolowsky
But then:
The instructor wrote a program:
“My script just returns any match that has a high percentage of matching errors (and sufficient errors to convince you that some thing's up!) “
“Holmes and Watson are surprisingly similar; 8 matches out of 11 wrong.”
D......D B.....D.A BBA BD.B.....
CD..BCC....BB...DC..CA..D.EC.....D......AD...B.DDAB...B..A..A.AA....CDB.AA.C......BDDBE.B..
Intuitive override: “ I had found by chance (Bonnie and Clyde), but what about the rest? It's very unlikely that Holmes and Watson were cheating, but I think it's likely that the others were”.
This instructor then concluded that statistical detection is not really reliable. Bad statistical detection often discredits the good.

14. Aside: A Better “quickie” Index
G.O. Wesolowsky
Aside: A Better “quickie” Index
A better but not good simple index is the Harpp-Hogan index, which is the number of wrong matches divided by the number of differences. One is supposed to be suspicious when it is > 1. For Holmes and Watson this works out to 8/32. 

15. Problems with “Simple” Indices or combinations thereof
G.O. Wesolowsky
Problems with “Simple” Indices or combinations thereof
The value of the indices can depend in an unknown way on class size, number of questions, number of choices, etc.
They use very little information. Capability of students and difficulty of questions are often not incorporated
The risk of “false accusations” is not predictable
Many combinations of indices and plots are possible, and they may point in different directions.

16. How I Got Into This Request from an administrator
G.O. Wesolowsky
How I Got Into This
Request from an administrator
Two students were suspected in another course, how many exactly similar answers did they have in my course?
Probability tree diagrams
Checked the literature
Wesolowsky G.O. (2000) "Detecting Excessive Similarity in Answers on Multiple Choice Exams", Journal of Applied Statistics, Vol. 27,

17. pki pji 1 - pki 1 - pji 1 - pki 1 - pki 1 - pki match w1i match w1i
G.O. Wesolowsky
match
Probability correct
pji
w1i
Cond. probability wrong
match
1 - pki
w1i
Question i
1 - pji
w2i
w2i
Probability of a match by students j and k on question I = sum of match probabilities
1 - pki
match
w3i
1 - pki
w3i
match
A matching answer occurs on a question if both students get the same right answer or the same wrong answer
What is needed are the probabilities of a correct answer for each student, and the conditional probability of a wrong answer
Sum the product of the probabilities along each of the “match” branches
w4i
1 - pki
w4i
match

18. G.O. Wesolowsky
Assumptions
The probability that a student gets an answer right depends on the ability of the student and the difficulty of the question
The probability of a match on wrong questions depends on the ‘popularity’ of wrong answers
Independencies as implicit in the diagram

19. But how to we estimate wli and pji ?
G.O. Wesolowsky
But how to we estimate wli and pji ?

20. depends on two things 1 1 Above average student Below average student
G.O. Wesolowsky
depends on two things
Above average student 1
Below average student
Proportion of class that answered correctly on question i
The ability of the student and the difficulty of the question 1

21. Finding cj = proportion of questions answered correctly by student j
G.O. Wesolowsky
Finding
cj = proportion of questions answered correctly by student j
1) aj is the student ability index
2) The function is borrowed from location theory.
3) For each student, the aj estimate is obtained by making sure that the modeled proportion correct is equal to the proportion correct actually obtained.
Find by solving

22. G.O. Wesolowsky
P value for each pair of students = probability of the observed number of matches or more
Question q
Question 1
Question j M M M
1) If the probability of a match on each question were the same, this p-value could be found by the binomial distribution. A Bernoulli process always has the same probability of success
2) Here, the probability of a success (match on a question) is different for every question
3) The probability distribution is called the compound binomial distribution
Compound Binomial Distribution because the probability of a match is different on each question

23. Example of SCheck Output
G.O. Wesolowsky
** pair = ** Harpp-Hogan stat = #wr.mat/#diff = 19.00
##################################################################
Zb = 'equivalent' z from the BVP model
Significance of Zb on a pre-selected pair = 1.5E-15
Significance bound (Bonferroni)
on program selected pairs = 1.3E-11
#matches = 33 | 34 (mu,s)=( , )
prop. right for 2 = prop. right for 78 = 0.412
Quest. range = [ ] #students = 132
.d.abccd.e .e.abedb.. ...da..b.. ea.e
.d.abccdee .e.abedb.. ...da..b.. ea.e
estimated match probabilities:
** pair = ** Harpp-Hogan stat = #wr.mat/#diff = 19.00
##################################################################
Zb = 'equivalent' z from the BVP model
Significance of Zb on a pre-selected pair = 1.5E-15
Significance bound (Bonferroni)
on program selected pairs = 1.3E-11
#matches = 33 | 34 (mu,s)=( , )
prop. right for 2 = prop. right for 78 = 0.412
Quest. range = [ ] #students = 132
.d.abccd.e .e.abedb.. ...da..b.. ea.e
.d.abccdee .e.abedb.. ...da..b.. ea.e
estimated match probabilities:

24. Data Dredging The number of student pairs examined is n(n-1)/2.
G.O. Wesolowsky
Data Dredging
The number of student pairs examined is n(n-1)/2.
For 693 students this is 239,778 pairs
suspicious
An oversight by many statistical detection methods. Consider a standardized normally distributed index of similarity .
It might seem that a Z of 4 would be very unusual. But a dotplot shows otherwise. With so many pairs, rare single draw occurrences become common.
The level of suspiciousness has to be raised.

25. “Unusual” Z’s Depend on Class Size
G.O. Wesolowsky
“Unusual” Z’s Depend on Class Size
Class size
No. of pairs
P(Zmax >3)
P(Zmax >4)
P(Zmax >5)
P(Zmax >6) 2 1
3.167E-5
2.8665E-7
9.8659E-10
100
4950
4.8836E-6
400
79800
E-5
1000
499500
5000
Analogy. Suppose the Z’s are independent.
The probability of seeing a Z > 5 in a class is 3 in 10 million.
But in a class of 5000, the probability is .97

26. Multiply the Pvalue by n(n-1)/2
G.O. Wesolowsky
Multiply the Pvalue by n(n-1)/2
** pair = ** Harpp-Hogan stat = #wr.mat/#diff = 19.00
##################################################################
Zb = 'equivalent' z from the BVP model
Significance of Zb on a pre-selected pair = 1.5E-15
Significance bound (Bonferroni)
on program selected pairs = 1.3E-11
#matches = 33 | 34 (mu,s)=( , )
prop. right for 2 = prop. right for 78 = 0.412
Quest. range = [ ] #students = 132
.d.abccd.e .e.abedb.. ...da..b.. ea.e
.d.abccdee .e.abedb.. ...da..b.. ea.e
estimated match probabilities:
A similarity this unusual will occur at most 1.3 times, on the average, per 100 billion classes.

27. G.O. Wesolowsky
Important!
The significance (probability that a similarity that high will occur for an innocent pair) is different for a pair that is pre-selected by, say, suspicious behavior, from that of a pair that was selected purely by the program. In other words, the former case does not need as high a level of similarity evidence. Scheck, therefore, allows pre-selected pairs to be forced into the analysis

28. Features of SCheck New: Two Type I methods for setting cutoffs
G.O. Wesolowsky
Developed from experience with large scale testing, research into cheating psychology, tribunal cases, different data formats, etc.
New: Two Type I methods for setting cutoffs
Adjustment for “speed tests”
Interactive or stored option choice
Batch processing of multiple files
Optional Excel grades output
Files with all components necessary for verification of calculations
Diagnostic graph
Optional fine tuning (T parameter)
Compact and intuitive question diagnostics
Utility programs (format translators)
Up to students
Up to 200 questions
Up to 27 choices, numbers or letters
True or false or multiple choice in any combination
Select a contiguous block of questions
Option for pre-selected student pairs
Option for similarity scores for all students
Options for removing student identification from input and output files
Since 1998, quite a few features were added.
Important ones are:

29. G.O. Wesolowsky

30. G.O. Wesolowsky

31. G.O. Wesolowsky
Can choose a file, and then just hit <Enter> key until output appears.

32. G.O. Wesolowsky
1) Many choices, which could also be placed in a single file to avoid the interactive mode.

33. G.O. Wesolowsky
This box and the previous one allow selection of a block of questions. Useful if,say, some questions only gather information.

34. G.O. Wesolowsky

35. G.O. Wesolowsky

36. This forces suspect pairs into the output for analysis.
G.O. Wesolowsky
This forces suspect pairs into the output for analysis.

37. 1) The students pre-chosen don’t have to be adjacent
G.O. Wesolowsky
1) The students pre-chosen don’t have to be adjacent

38. G.O. Wesolowsky
This means that a false positive should occur in fewer that 1 in a 100 classes or runs.

39. A marginal improvement in the model
G.O. Wesolowsky
A marginal improvement in the model

40. Straightness = normality Slope indicates stdev of Z’s
G.O. Wesolowsky
Vertical red line indicates similarity cutoff. Position depends on class size
Straightness = normality
Slope indicates stdev of Z’s
Innocent class is symmetrical within the lines
Typical class with no identified cheaters
One possibly suspicious outlier, but outliers can happen by chance.

41. G.O. Wesolowsky

42. Forced pairs in NAM file
G.O. Wesolowsky
This pair was forced in as an illustration. The program would not have selected it otherwise. It has a negative Z and I hence not suspicious,
Students are identified

43. Forced pairs in OUT file
G.O. Wesolowsky
Students are not identified, but there is more information

44. Diagnostics on questions
G.O. Wesolowsky
Diagnostics on questions
1) The program gives all the information necessary to manually check the model
2) Also has tables to check if answer keys are correct, questions are not flawed, etc.
3) This is not item analysis but I think more useful.

45. It’s Cheating time
G.O. Wesolowsky
Finally, what if there were dark deeds done during the test?

46. Detected Pairs Summary of significances of identified pairs
G.O. Wesolowsky
Detected Pairs
Summary of significances of identified pairs
pair Z A Priori Bonferroni
Signif Signif.
2, E E-11
2, E E-10
36, E E-6
36, E E-3
36, E E-4
60, E E-3
69, E E-7
69, E E-4
70, E E-7
78, E E-9
All pairs were found in adjacent seating
But some individuals have similarity links to more than one person!

47. G.O. Wesolowsky 36 69 70 72 2 78 97 60
119
These students were all in adjacent seating and proximity copying was possible
132 students
teamwork

48. Have you ever seen anything like it?
G.O. Wesolowsky
Have you ever seen anything like it?
(Contact at a testing agency)
When I saw something like this previously, the question was a comment: “There is something wrong with your program”

49. Pair Z Pair Z Pair Z Pair Z 16, 39 6.453 16, 41 5.453 16, 42 6.090
G.O. Wesolowsky
Pair Z
Pair Z
Pair Z
Pair Z
16,
16,
16,
16,
16,
16,
16,
39,
39,
39,
39,
39,
39,
39,
39,
39,
39,
41,
41,
41,
41,
41,
41,
41,
41,
42,
42,
42,
42,
42,
42,
42,
42,
43,
43,
43,
44,
44,
44,
44,
46,
46,
46,
46,
46,
49,
50,
50,
50,
57,
64,
65,
82,
82,
82,
86,
87,
87,
91,
91,
91,
93,
93,
100,
109,
113,
113,
113,
113,
117,
118,
120,
120,
120,
120,
122,
122,
124,
Students seem to have a lot of partners, to whom they are linked with high similarities

50. Really enthusiastic teamwork!
G.O. Wesolowsky 82
113 16 87 49
117
109 39
109
119 50
110
110 41
120 57 86 92
122
118 42 64
124
138 43
137 65 91 93
100
107
1) How did “the firm” manage this?
2) I asked and was told this was sensitive and confidential, so I will never know.
3) The student numbers have adjacency tendencies, so it could be proximity copying, as opposed to electronics, but how? 44
141 69 46
35 suspects, 79 pairs, 200 students

51. A NEW OPTION SCheck Version 8a7. #### 03-05-2009 11:21:34
G.O. Wesolowsky
A NEW OPTION
SCheck Version 8a7. #### :21:34
Bonferroni program selected significance bound is 0.01
_____________________________________________________________
** pair = ** Harpp-Hogan stat = #wr.mat/#diff = 2.12
##################################################################
Zb = 'equivalent' z from the BVP model
Significance of Zb on a pre-selected pair = 9.4E-8
Approximate significance of program selected pair = 3.7E-5
Signif. bound (Bonferroni) on program selected pairs = 5.6E-3
#matches = 42 | 50 (mu,s)=( , )
prop. right for 48 = prop. right for 188 = 0.580
Quest. range = [ ] NRT = #students = 348
.b.b...aa. a.ba..baa. ..cb..b.c. .c.e.aad.e .....b.a.b
.b.b...aa. a.ba..baa. ..cbb.b.c. .c...d..ce .....b...a
estimated match probabilities:

52. History of Applications
G.O. Wesolowsky
History of Applications
Early studies for economics department
For individual instructors
Large assessment organizations
National education departments
“This is the only instance where separate examination venues have shown up and I have used your analysis on probably about 30,000 candidates.”

53. G.O. Wesolowsky
Dallas Morning News Study on Cheating on the TAKS test (June 2007), an Application of SCheck
DMN:
“ The test scores of more than 50,000 students show evidence of cheating. Some of those students were the innocent victims of others copying their answers. But experts say most were likely either deliberately copying answers or had their answer sheets doctored by school staff. “
TEA response
“Officials at the Texas Education Agency have consistently argued that statistical analysis can't prove cheating and that they must rely on other forms of evidence – like getting teachers to confess to misbehavior – in their investigations. TEA decided not to use data drawn from student answer sheets – even with evidence of widespread copying in a classroom. “

54. G.O. Wesolowsky
Common Objections: We studied together, we are from a similar background, we are twins, etc.
Some direct studies have been done.
Note that a huge number of pairs is being looked at.
We would expect that there would be a large number of highly similar pairs that couldn’t have cheated
Would expect that if prevention is implemented the high similarity rate would continue

55. Both expectations have been proven false in thousands of data sets.
G.O. Wesolowsky
Both expectations have been proven false in thousands of data sets.
Prior to electronic cheating methods, no very high similarities lacking the opportunity to cheat (adjacent seating) were found.
Multiple versions of exams cause a drastic decrease in the cheating rate.

56. Pitfalls in interpretation
G.O. Wesolowsky
Pitfalls in interpretation
High marks make detection difficult*
Non-responses can invalidate the model
Cheating must be substantial for detection
Too much cheating can invalidate the model
Hierarchical questions violate assumptions
Data sets may be too small
*In the extreme, if one student copies from another with a perfect
test, there is no way statistical detection can distinguish this case from the
case of two brilliant students with perfect papers. Scheck knows this.

57. Effective Prevention Measures
G.O. Wesolowsky
Effective Prevention Measures
Multiple versions
Randomized or assigned seating
Seat spacing
Invigilation
Electronic counter-measures
“Education on integrity” & ethics indoctrination

58. G.O. Wesolowsky
December 2002 exams McGill University. Proper prevention measures are in place.
finals
midterms 2 17

59. G.O. Wesolowsky
End Part 1

60. Some Controversial Personal Opinions
G.O. Wesolowsky
Some Controversial Personal Opinions
Multiple choice tests provide a substantial component of grades in undergraduate courses. Statistical detection of copying or collusion on such tests has proven to be quite successful, and has in turn demonstrated that simple and non-intrusive prevention measures are very effective. Why then is this subject conspicuously absent in most discussions on cheating prevention strategies?

61. G.O. Wesolowsky
The most common general attitudes of student leaders, instructors, and administrators towards cheating are remarkably similar
See no Evil, Hear no Evil, Speak no Evil
Genuine concensus

62. G.O. Wesolowsky
Comments
A “look the other way” strategy is actually the best one if the goal is to protect the integrity reputation of a university
Why? Media assume that the cheating problem is proportional to the number of reported cases or the amount of discussion about cheating. Keeping quiet, therefore, creates the impression that there is no problem.
A university that tries to do something about cheating often gets a reputation for being infested with cheating. (Otherwise, why would they talk about it?)
No good deed goes unpunished.

63. Instructor: Not on my tests you won’t!
G.O. Wesolowsky
Instructor Attitudes
Instructor: Not on my tests you won’t!

64. #1 Believe in justice? Feel personal betrayal?
G.O. Wesolowsky
Believe in justice?
Feel personal betrayal?
Not very many of these, and so their effect on the system is relatively minor.

65. G.O. Wesolowsky #2

66. G.O. Wesolowsky
Instructor: I’m not a Policeman or Prison Guard, I’m an Educator and Researcher
Translation: I am not going to charge any students with cheating or take any special prevention measures
This sounds idealistic and has the added benefit of saving a lot of work and avoiding unpleasant confrontations
1) Find may more of these.
2) Variations, such as I’m too busy with research.

67. G.O. Wesolowsky
Comments
Implicit assumption: The university’s main function is to provide an education and grades are merely an unimportant side-issue.
I disagree: The main product of a university is grades and degrees and diplomas. Without these, even if it continued to give a good education, the university would be out of business overnight.
On the other hand, degrees without education (diploma mills) are a growing phenomenon.
The assumption of a degree is that a required level of academic achievement has been met. Grades and degrees without this level of achievement are defective.
By giving degrees with based on defective grades it is giving the public a defective product.

68. Postscript: Education versus Grades
G.O. Wesolowsky
Postscript: Education versus Grades
By JANE ARMSTRONG
Friday, January 27, 2006 Posted at 5:20 AM EST
From Friday's Globe and Mail
“Professor David Weale called it a "January clearance" -- and clear out they did. Dismayed by his crowded classroom, the history teacher at the University of Prince Edward Island offered his students a deal some couldn't resist: Drop this Christianity class and you'll get a B minus. “
Rule of 20?
“The offer, also dubbed the "Weale deal" worked. The next week, about 20 of the 95 students were gone. So too is Prof. Weale after shocked administrators caught wind of the unorthodox academic transaction.”

69. G.O. Wesolowsky
Addendum
“Vice-president Gary Bradshaw said the school had no choice but to suspend Prof. Weale while a disciplinary probe begins. Offering students a credit without doing the work "strikes at the very heart of the academic principles," Mr. Bradshaw said.”
The next week, he sweetened the offer, saying he'd give students who left a mark of 68. Students mulled it over during the break and negotiated the deal up to 70, which is a B minus. Departing students were required to send Prof. Weale an and pay the $450 for the course.

70. Student Leaders and Instructors
G.O. Wesolowsky
Student Leaders and Instructors

71. Student Leaders and Instructors: A Matter of Trust*
G.O. Wesolowsky
Student Leaders and Instructors: A Matter of Trust*
Student Leader: “If you take prevention measures you show you don’t trust us and that cheating is expected. This will only increase cheating. Anyway, cheating is very rare.”
Translation: My constituency would feel threatened.
Instructor: “Cheating prevention and detection poisons the atmosphere and destroys the vital rapport and atmosphere of trust I have with my students”
Translation: My teaching evaluations would go down.
*paraphrased

72. G.O. Wesolowsky
Administration

73. Administration: The Best Solution is ‘Ethics Reengineering’
G.O. Wesolowsky
“E-tegrity board members, …,have developed a range of new initiatives to integrate integrity into the college’s culture.”
“… suggest a more broadly focused approach that creates an educational community valuing academic integrity and focusing on the moral and ethical development of students”
Sounds good and plays well in the media. Often arises out of bad publicity on cheating incidents.
1) Will it work if it is seriously implemented?
McCabe versus the economists
2) Will it ever be implemented beyond the talk level?
Web pages, articles in school newsletters, “integrity officers”, inviting McCabe.
Usual outcomes: a) effort fades away b) majority of students and instructors are not involved.
3) Contrast this with prevention measures on MC tests, which have been shown to virtually eliminate cheating.
The re-engineering initiatives usually crop up after some bad publicity. Local or national
Economists: Cheating depends on opportunity payoffs and risks, - As Reagan would say” there they go again.”
Psychologists: linking cheating to psychopathic tendencies. One study uses my software.
2) In the case of business schools it could be because of misbehavior in corporations.
3) The great majority of students are unaffected.

74. Comments on Honor Codes
G.O. Wesolowsky
Comments on Honor Codes

75. Honor Codes* Traditional Honor Codes Pledge and ceremonies
G.O. Wesolowsky
Honor Codes*
Traditional Honor Codes
Pledge and ceremonies
Proctoring of tests not allowed
Students in charge of tribunals
Students obliged to report infractions (rarely enforced)
Modified Honor Codes
Proctoring allowed
*McCabe

76. G.O. Wesolowsky
Quote on Honor codes
In this same study that found over 75 percent of students admitting to cheating, McCabe saw that only 57 percent of students cheated at schools with honor codes. Chronic cheating also seems to reduce with honor codes. At schools without codes, 1 in 5 students admits to cheating more than twice. Only 1 in 16 students admits to the same offense at schools with honor codes.
"I think it's a question of making your students understand that academic integrity is important to the school," McCabe said. "Just the fact that it's being discussed" can heighten student's awareness and reduce cheating, he concluded.
Administrator friendly comment.

77. Do Honor Codes Work? Comments
G.O. Wesolowsky
Do Honor Codes Work? Comments
?Only? 57%
Response bias:
‘Honor’ rhetoric leads to fewer admissions of cheating?
Fear of Draconian penalties?
Control for other variables: Types of exams, subject matter etc.
Low response rates → non-response bias
“it is clear the response rate is below desired levels, averaging between 10% and 15% and varying from as little as 5% to 10% on some large campuses to over 50% on a limited number of small, residential campuses”

78. Advantages of Honor Codes
G.O. Wesolowsky
Advantages of Honor Codes
Media friendly
Reduce the number of reported cases of dishonesty
Disadvantages of Honor Codes?

79. G.O. Wesolowsky

80. G.O. Wesolowsky

81. G.O. Wesolowsky
Recommendations*
Make statistical testing (even without identifying the students involved) a non-optional part of the scanning report.
Institute mandatory or strongly “encouraged” prevention measures: multiple versions, assigned seating, electronic counter-measures, etc..
As a last and least important step, use statistical testing to support charges of academic dishonesty. In other words, use it this way only after the cheating is cleaned up.
Observation: Statistical evidence is very unlikely to be sufficient by itself. Other evidence, such as a proctor’s observations, will be required to make charges stick.
* Plan is plagiarized from McGill

82. G.O. Wesolowsky
End