Determinants of moral judgments about academic dis-honesty: an experimental approach

Table 18 Pairwise Comparisons

(I) Social origins	(J) Social origins	Mean Difference (I-J)	Std. Error	Sig.d
Low	High	,245*,b	,107	,022



Table 19 Pairwise Comparisons

Gender	(I) Geographical origins	(J) Geographical origins	Mean Difference (I-J)	Std. Error	Sig.c
Female	rural	urban	,015a	,151	,920
Male	rural	urban	,252	,151	,094

Another significant effect of interaction is between “gender” and “geographical origins” of protagonist. However, Pairwise Comparisons shows that there is no difference between levels of these factors on 95% confidence level (see Table 19).

The last significant effect of interaction is between “social origins” and “geographical origins” of protagonist. However, Pairwise Comparisons shows that there is no difference on “high” social origins level and the difference between “urban” and “rural” geographical origins exists only on “low” social origins level on 95% confidence level (see Table 20). On average, students regardless of other manipulated factors would assess cheating more favorably if protagonist's social origins are low and he or she is from rural area (estimated marginal mean = 5,003) than if he or she is from urban city (estimated marginal mean = 4,646) (see Table 21 in appendices).

Table 20 Pairwise Comparisons

Social origins	(I) Geographical origins	(J) Geographical origins	Mean Difference (I-J)	Std. Error	Sig.d
Low	rural	urban	,357*	,149	,017
High	urban	rural	,148c	,153	,334

Same Pairwise Comparisons was conducted on 95% confidence level to answer the question, whether or not the assessment depends on “Type of question”. For such purposes we compared means estimated depending on two questions: about acceptability and about honesty. The found difference was significant, which means that students assess two questions differently. Students regardless of experimental manipulation with “gander”, “social origins”, “geographical origins” of protagonist and “language of instruction” would assess cheating as more acceptable behavior than honest behavior (mean difference equals 1,330) as seen Table 21. Estimated Marginal Means were 5,375 for acceptability and 4,045 for honesty (see Table 22 in appendices).

Table 21 Pairwise Comparisons

(I) type	(J) type	Mean Difference (I-J)	Std. Error	Sig.c
acceptability	honesty	1,330*,b	,049	,000



Conclusion

To be able to correctly interpret the results, we would like to emphasize the fact that not every significant outcome is meaningful. In the current investigation we had a large number of participants (N=2204) that increases statistical power (the power of a test) and decreases the probability of rejecting the null hypothesis. Therefore, even small differences would be significant in the analysis. And so it turned out in Study 1 when some main effects and effects of interactions occurred as significant but with very small effect size. Such effects were: “type of the question” * “social origins”, “type of the question” * “gender” * “language of instruction”, “type of the question” * “gender” * “social origins” * “geographical origins”, “social origins”, “gender” * “geographical origins”, “social origins” * “geographical origins” -- with partial з2 < 0.005. All of these results would be interpreted as a measurement error due to the large effect size that determines even small differences. However, we do not sacrifice any meaningful results by doing this since focusing on inessential effects can mislead the interpretation and weaken the explanatory power of the theory.

Although the hypotheses from Study 1 for main effects have not been confirmed, it would be rash to conclude that socio-demographic factors manipulated in the study do not affect students' judgments about academic dishonesty. Most likely, we did not take into account some significant covariates that could mediate students' assessments, or the operationalization of the described factors did not clearly reflect the concepts we nested. In this regard, we recommend to reflect on the conclusion that "gender", "social origins", "geographical origins" of the protagonist and "language of instruction" do not affect students' judgments about academic dishonesty cautiously.

Subjectivity

A significant result was obtained based on Study 2. The subjectivity with which the situation is "framed" affects students' assessments of acceptability and honesty of cheating. Moreover, the more the situation is "framed" as a subjective description "in the first person", the lower the assessment of acceptability and honesty received by the protagonist. On the contrary, if the situation was detached from the protagonist's point of view, if it presented an objective description, students rated the cheating as more acceptable and honest. Participants also assess cheating as more acceptable and honest in “intersubjective” settings, than in “subjective” but still lower than in “objective” settings.

Thus, framing effect does take place when describing a situation with varying degrees of subjectivity. Here we would like to distinguish the degree of subjectivity since it has been shown that the more "subjective" the situation is described, the less the protagonist is trustworthy, which is confirmed by low assessment of honesty and acceptability. The more witnesses to the teacher's poor pedagogy and the tolerance of fellow students to academic dishonesty, the more honest and acceptable the actions of the protagonist become. Interestingly, the difference in assessment was not found significant between "intersubjective" and "subjective" settings. This may indicate that the reader basically perceives the text either as a factual description or narrative by an unreliable narrator.

Using the theory of "cognitive grammar" we can say that in the described vignettes, the protagonist is always someone being observed, but in the "intersubjective" and especially "subjective" settings, he or she is represented as the one who is observing - an observer. This makes him or her when reading not a passive figure, rather an active agent, responsible and able to influence not only what is happening in the story, but also how this story is seen. The observer gives us, the reader, the meanings that it wants to convey. This makes him or her responsible for the story, which we eventually read and perceive as the only true source for lack of other evidence. These confirmations may be other storytellers in the "intersubjective" settings, but even they may be unreliable and have their own intent in the story. Therefore, the participants are justifiably skeptical towards scenarios that have a bit of subjectivity in it, to what can simply be called an opinion, and not a fact. An actual detached narrative that does not have its own interest in the story is perceived as knowledge rather than “feeling of knowing”.

In the context of Academic Dishonesty, these results highlight the importance of using a descriptive language, primarily in vignette experiments. How the situation is framed can have a much greater impact on the participants' assessments than the meaningful factors put in by the experimenters. Moreover, careless use of the description language can significantly violate the results, or the results may be intentionally distorted by making the scenario more subjective. The results also show that students perceive the difference between an opinion, opinions, and facts and that by "subjectifying" stories, students can begin to attribute blame for what is happening to the protagonist to a greater extent than if the situation were described externally. This hypothesis is suggested by the theory of “cognitive grammar” but in the current investigation it has not been tasted.

Dishonesty and acceptability

Our main hypothesis that assessments of honesty and acceptability differ was confirmed in both Studies. Regardless of other factors, assessments of the acceptability of cheating were higher than assessments of the honesty of cheating. On an eleven-point scale, they differed by more than a point, which is a significant result demonstrating the students' opinion that cheating on the exam is wrong but sometimes acceptable.

This finding is embedded into theory moral judgments. Honesty here is a "first-order" judgment. The individual determines for himself what is good and what is bad, what is right and what is wrong. These basic grounds serve to make moral judgments about good and evil, justice and injustice. In our case, the existence of the "cheating is bad" rule sets the tone for further reasoning. "Yes, cheating is bad" can be said by the participant, but still there are certain circumstances that violate the basic principle of "cheating is bad" or even change it. We see that no one has asked the question of “honest cheating". The very discipline of Academic Dishonesty already suggests an answer to the question of honesty: "cheating is bad", however, no research has been conducted to confirm this moral doctrine. The question of academic dishonesty has ceased to be a question of morality, but has become a statement of morality: "cheating is bad". By conducting the current investigation, we wanted to show that the assessment of honesty is not deontic "absolutely right" or "absolutely wrong", rather it is a bell curve where the honesty assessment can vary depending on factors.

"Second-order" judgments are moral obligations to follow the principles laid down in "first-order" judgments. Such a judgment can be a judgment about the acceptability of certain actions. The moral law within the actor does not suffer even if it is necessary to give it up for some time. It seems appropriate, therefore, to consider honesty and acceptability as the relation to "first" and "second-order" moral judgments. Stricter criteria are applied to "first-order" judgments than to "second-order" judgments, as the results of Studies demonstrate. Even those actions that were assessed by participants as "absolutely dishonest" to some extent were accepted by them. This leads us to conclude that even though "cheating is bad" this behavior is quite acceptable in some circumstances in students' minds.

This finding illustrates that the concepts of honesty and acceptability differ in the minds of students. To better understand the determinants of dishonest behavior among students, we need a better understanding of the determinants of students' judgments about academic dishonesty. It was suggested by some scholars (McCabe & Trevino, 1993) that in the institutions which have high formal ethical code, dishonest behavior is less likely to occur. However, without a deeper understanding of how the perception of dishonesty works, strengthening the normative component would not serve its purpose and all regulatory policy in vain.

Other Limitations and Suggestions for Future Study

The main limitation of the current investigation is a low level of external validity. The present study was conducted on an approximately homogeneous sample to minimize the effect of subjective variables (the difference that comes with difference between individuals) and to boost the internal validity that allows building strong causal relationships within a controlled setting.

We also focus on only one type of academic dishonesty - cheating on an exam, however in the future studies the different types of transgressive behavior should be investigated to gain more knowledge on how the perception of dishonesty varies in relation to it.

Here we investigate how dishonesty as a concept is not constant but a relative term and perceived differently depending on contextual variables among students. However, a few studies show that transgressive behavior perceived differently also among teaching staff (Aaron & Georgia, 1994), so we assume that dishonesty as a concept is not consistent even in the view of academic representatives, which could be studied in the future researches.

Also, the previous experience of cheating, which is not covered by the questionnaire, could affect participants' assessment of situations in the vignettes. Factorial experiment in general underestimates individual-differences (subjective) variables that can affect students' judgments. However, if the main effect of manipulated variables cannot explain the difference in assessments, covariates cannot do that either.

It should be noted that the study implemented between-subjects design, and participants of the experiment are deprived of the opportunity to adjust the estimates. The effects of learning and order do not function in this design of the experiment, and the model indicators become less significant.

Finally, all respondents are first-year students of the National Research University Higher School of Economics, a highly selective university with current internal regulations, including the rule of compliance with academic standards, for violation of which may result in expulsion. Thus, the HSE students' assessments may differ from students of other Russian universities.

In addition to these two Studies we are conducting another one with the support of the Centre of Sociology of Higher Education (HSE). Study 3 has a larger number of participants (N>6000) and it covers several universities across Russia. However, it is still a part of the current investigation with the use of the same dependent variable (assessment of dishonesty) but with different second dependent variable (fairness of the result). We also use different vignette scenarios with between-subjects experimental design. 8 scenarios with 2 factors are used: the result of cheating 2 (good grade vs. bad grade) X consequences of cheating 4 (severe punishment vs. mild punishment vs. lack of severe punishment vs. lack of mild punishment). Our core assumption for Study 3 is that the honesty of action can be evaluated not a priori (cheating is bad), but a posteriori - based on the result of cheating. If the student cheats but received a bad grade, then the game was not worth the candle and the honesty assessment will be higher than if the cheater managed to achieve his goal and earn a high score dishonestly. The amount of punishment, or lack of it, can also affect the assessment of honesty, since a deservedly punished cheater can evoke compassion or a sense of justice in participants' minds. On the contrary, excessive punishment can lead to a greater assessment of honesty as an excuse for the risk and severity of the situation. Another dependent variable "the result of cheating" will help to determine whether students agree or disagree with the system. A higher assessment from the participant will indicate that the academic system is perceived as unfair. A lower assessment will indicate the superiority of moral provisions over the formal actions of the student and administration. But the most interesting will be the result of coincidence evaluation for the exam from the teacher (independent variable) and the participant (the independent variable), that would mean indifference and acceptance of the system in matters of formal assessment of knowledge in students' mind (see vignettes for Study 3 in appendices).



References

1. Бугрименко, А. Г. (2006). Внутренняя и внешняя учебная мотивация у студентов педагогического вуза. Психологическая наука и образование, 11(4), 51-60.

2. Козлов, Д. В., Платонова, Д. П., & Лешуков, О. В. (2017). Где учиться и где работать: межрегиональная мобильность студентов и выпускников университетов. М.: НИУ ВШЭ.

3. Мкртчян, Н. В. (2003). Из России в Россию: откуда и куда едут внутренние мигранты. Мир России. Социология. Этнология, 12(2).

4. Мкртчян, Н. В. (2017). Миграция молодежи из малых городов России. Мониторинг общественного мнения: Экономические и социальные перемены, (1 (137)).

5. Седова, Н. Н. (2019). Жизненный успех молодежи-от Москвы до самых до окраин. Мониторинг общественного мнения: Экономические и социальные перемены, (6 (154)).

6. Aaron, R. M., & Georgia, R. T. (1994). Administrator perceptions of student academic dishonesty in collegiate institutions. NASPA Journal, 31(2), 83-91.

7. Akee, R. K., Copeland, W. E., Keeler, G., Angold, A., & Costello, E. J. (2010). Parents' incomes and children's outcomes: a quasi-experiment using transfer payments from casino profits. American Economic Journal: Applied Economics, 2(1), 86-115.

8. Anderman, E. M., & Danner, F. (2008). Achievement goals and academic cheating. Revue internationale de psychologie sociale, 21(1), 155-180.

9. Anderman, E. M., & Koenka, A. C. (2017). The relation between academic motivation and cheating. Theory Into Practice, 56(2), 95-102.

10. Anderman, E. M., Griesinger, T., & Westerfield, G. (1998). Motivation and cheating during early adolescence. Journal of Educational Psychology, 90(1), 84.

11. Ayal, S., & Gino, F. (2011). Honest rationales for dishonest behavior. The social psychology of morality: Exploring the causes of good and evil. Washington, DC: American Psychological Association.

12. Becker, G. S. (1968). Crime and punishment: An economic approach. In The economic dimensions of crime (pp. 13-68). Palgrave Macmillan, London.

13. Bukodi, E., & Goldthorpe, J. H. (2009). Class origins, education and occupational attainment: cross-cohort changes among men in Britain.

14. Bukodi, E., & Goldthorpe, J. H. (2013). Decomposing `social origins': The effects of parents' class, status, and education on the educational attainment of their children. European sociological review, 29(5), 1024-1039.

15. Burnett, S. (2002). Dishonor and distrust. Community College Week, 14(24), 6-8.

16. Burton, R. V. (1963). Generality of honesty reconsidered. Psychological Review, 70(6), 481.

17. Butler, J. (2011). Gender trouble: Feminism and the subversion of identity. routledge.

18. Byrne, A., Chandola, T., & Shlomo, N. (2018). How does parental social mobility during childhood affect socioeconomic status over the life course?. Research in Social Stratification and Mobility, 58, 69-79.

19. Cizek, G. J. (1999). Cheating on tests: How to do it, detect it, and prevent it. Routledge.

20. Crystal, D. (2012). English as a global language. Cambridge university press.

21. Dans, P. E. (1996). Self-reported cheating by students at one medical school. Academic Medicine, 71(1), S70-2.

22. Deckert, G. D. (1993). Perspectives on plagiarism from ESL students in Hong Kong. Journal of Second Language Writing, 2(2), 131-148.

23. Dunkel, P., Mishra, S., & Berliner, D. (1989). Effects of note taking, memory, and language proficiency on lecture learning for native and nonnative speakers of English. Tesol Quarterly, 23(3), 543-549.

24. Eccles, M., Arnold, J., Rubin, S., Lambarey, Z., & Belle, J. V. (2006). Technological and societal factors influencing plagiarism among information systems students. In International Science and Technology Conference (pp. 1-14).

25. Eisen, M. (1972). Characteristic self-esteem, sex, and resistance to temptation. Journal of Personality and Social Psychology, 24(1), 68.

26. Erola, J., Jalonen, S., & Lehti, H. (2016). Parental education, class and income over early life course and children's achievement. Research in Social Stratification and Mobility, 44, 33-43.

27. Friedman, A., Blau, I., & Eshet-Alkalai, Y. (2016). Cheating and Feeling Honest: Committing and Punishing Analog versus Digital Academic Dishonesty Behaviors in Higher Education. Interdisciplinary Journal of E-Learning & Learning Objects, 12.

28. Gugushvili, A., Bukodi, E., & Goldthorpe, J. H. (2017). The direct effect of social origins on social mobility chances:`Glass floors' and `glass ceilings' in Britain. European Sociological Review, 33(2), 305-316.

29. Haines, V. J., Diekhoff, G. M., LaBeff, E. E., & Clark, R. E. (1986). College cheating: Immaturity, lack of commitment, and the neutralizing attitude. Research in Higher education, 25(4), 342-354.

30. Hilbert, G. A. (1985). Involvement of nursing students in unethical classroom and clinical behaviors. Journal of Professional Nursing, 1(4), 230-234.

31. Hosny, M., & Fatima, S. (2014). Attitude of students towards cheating and plagiarism: University case study. Journal of Applied Sciences, 14(8), 748-757.

32. James, H. W. (1933). Honesty as a character trait among young people. The Journal of Educational Research, 26(8), 572-578.

33. Jensen, L. A., Arnett, J. J., Feldman, S. S., & Cauffman, E. (2002). It's wrong, but everybody does it: Academic dishonesty among high school and college students. Contemporary Educational Psychology, 27(2), 209-228.

34. Kohlberg, L., & Candee, D. (1984). The relationship of moral judgment to moral action. Morality, moral behavior, and moral development, 52, 73.

35. Korupp, S. E., Ganzeboom, H. B., & Van Der Lippe, T. (2002). Do mothers matter? A comparison of models of the influence of mothers' and fathers' educational and occupational status on children's educational attainment. Quality and Quantity, 36(1), 17-42.

36. Koul, R. (2012). Multiple motivational goals, values, and willingness to cheat. International Journal of Educational Research, 56, 1-9.

37. Krawczak, K. (2016). Objectivity, subjectivity and intersubjectivity: Integrating two Cognitive-Functional theories. Functions of Language, 23(2), 179-213.

38. Langacker, R. W. (1985). Observations and speculations on subjectivity. Iconicity in syntax, 1(985), 109.

39. Langsrud, Ш. (2003). ANOVA for unbalanced data: Use Type II instead of Type III sums of squares. Statistics and Computing, 13(2), 163-167.

40. Levin, I. P., Johnson, R. D., Russo, C. P., & Deldin, P. J. (1985). Framing effects in judgment tasks with varying amounts of information. Organizational behavior and human decision processes, 36(3), 362-377.

41. Lunney, G. H. (1970). USING ANALYSIS OF VARIANCE WITH A DICHOTOMOUS DEPENDENT VARIABLE: AN EMPIRICAL STUDY 1. Journal of educational measurement, 7(4), 263-269.

42. Macmillan, L., Tyler, C., & Vignoles, A. (2015). Who gets the top jobs? The role of family background and networks in recent graduates' access to high-status professions. Journal of Social Policy, 44(3), 487-515.

43. Mazar, N., Amir, O., & Ariely, D. (2008). The dishonesty of honest people: A theory of self-concept maintenance. Journal of marketing research, 45(6), 633-644.

44. McCabe, D. L., & Trevino, L. K. (1993). Academic dishonesty: Honor codes and other contextual influences. The journal of higher education, 64(5), 522-538.

45. McCabe, D. L., & Trevino, L. K. (1996). What we know about cheating in college longitudinal trends and recent developments. Change: The Magazine of Higher Learning, 28(1), 28-33.

46. McCabe, D. L., Treviсo, L. K., & Butterfield, K. D. (2001)a. Cheating in academic institutions: A decade of research. Ethics &Behavior, 11(3), 219-232.

47. McCabe, D. L., Trevino, L. K., & Butterfield, K. D. (2001)b. Dishonesty in academic environments: The influence of peer reporting requirements. The Journal of Higher Education, 72(1), 29-45.

48. Murdock, T. B., & Stephens, J. M. (2007). Is cheating wrong? Students' reasoning about academic dishonesty. In Psychology of academic cheating (pp. 229-251). Academic Press.

49. Murdock, T. B., Beauchamp, A. S., & Hinton, A. M. (2008). Predictors of cheating and cheating attributions: Does classroom context influence cheating and blame for cheating?. European Journal of Psychology of Education, 23(4), 477.

50. Murdock, T. B., Miller, A., & Kohlhardt, J. (2004). Effects of Classroom Context Variables on High School Students' Judgments of the Acceptability and Likelihood of Cheating. Journal of Educational Psychology, 96(4), 765.

51. Nunan, D. (2003). The impact of English as a global language on educational policies and practices in the Asia?Pacific Region. TESOL quarterly, 37(4), 589-613.

52. Park, C. (2003). In other (people's) words: Plagiarism by university students--literature and lessons. Assessment & evaluation in higher education, 28(5), 471-488.

53. Pavela, G. (1997). Applying the power of association on campus: A model code of academic integrity. JC & UL, 24, 97.

54. Prakhov, I., & Bocharova, M. (2016). Socio-economic predictors of student mobility. Higher School of Economics Research Paper No. WP BRP, 34.

55. Pulvers, K., & Diekhoff, G. M. (1999). The relationship between academic dishonesty and college classroom environment. Research in Higher Education, 40(4), 487-498.

56. Raju, P. S., Lonial, S. C., & Glynn Mangold, W. (1995). Differential effects of subjective knowledge, objective knowledge, and usage experience on decision making: An exploratory investigation. Journal of consumer psychology, 4(2), 153-180.

57. Rettinger, D. A., & Kramer, Y. (2009). Situational and personal causes of student cheating. Research in Higher Education, 50(3), 293-313.

58. Rettinger, D. A., Jordan, A. E., & Peschiera, F. (2004). Evaluating the motivation of other students to cheat: A vignette experiment. Research in Higher Education, 45(8), 873-890.

59. Sinn, J. S. (1997). The predictive and discriminant validity of masculinity ideology. Journal of Research in Personality, 31, 117-135.

60. Tibbetts, S. G., & Herz, D. C. (1996). Gender differences in factors of social control and rational choice. Deviant Behaviour, 17, 183-208.?

61. Traugott, Elizabeth Closs. 1989. On the rise of epistemic meanings in English: An example of subjectification in semantic change. Language 65. 31-55. doi: 10.2307/414841

62. Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. science, 211(4481), 453-458.

63. Ward, D. A. (1986). Self-esteem and dishonest behavior revisited. Journal of Social Psychology 126(6): 709-713.?

64. Ward, D. A., & Beck, W. L. (1990). Gender and dishonesty. Journal of Social Psychology 130(3): 333-339.?

65. Whitley, B. E., Nelson, A. B., & Jones, C. J. (1999). Gender differences in cheating attitudes and classroom cheating behavior: A meta-analysis. Sex Roles, 41(9-10), 657-680.

66. Willis, G. B. (1999). Cognitive interviewing: A “how to” guide. Research Triangle Park, NC: Research Triangle Institute.

67. Wryobeck, J. M., & Whitley Jr, B. E. (1999). Educational value orientation and peer perceptions of cheaters. Ethics & Behavior, 9(3), 231-242.

68. Xu, Z. X., & Ma, H. K. (2015). Does honesty result from moral will or moral grace? Why moral identity matters. Journal of Business Ethics, 127(2), 371-384.



Appendices

Used tables

Table 2 Levene's Test of Equality of Error Variances for Study 2

	F	df1	df2	Sig.
Acceptability Study 2	,709	2	2474	,492
Honesty Study 2	1,934	2	2474	,145

Table 3 Levene's Test of Equality of Error Variances for Study 1

	F	df1	df2	Sig.
Acceptability Study 1	1,148	14	2357	,310
Honesty Study 1	1,203	14	2357	,266

Table 4 Tests of Normality

	Kolmogorov-Smirnova	Shapiro-Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
Acceptability 2	,107	2477	,000	,959	2477	,000
Honesty 2	,111	2477	,000	,936	2477	,000

Table 5 Tests of Normality

	Kolmogorov-Smirnova	Shapiro-Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
Acceptability 1	,096	2372	,000	,948	2372	,000
Honesty 1	,135	2372	,000	,902	2372	,000

Table 6 Box's Test of Equality of Covariance Matricesa

Box's M	12,257
F	2,040
df1	6
df2	151598827,990
Sig.	,057

Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.a a. Design: Intercept + Subjectivity Within Subjects Design: type

Table 7 Multivariate Testsa

Effect	Value	F	Hypothesis df	Error df	Sig.	Partial Eta Squared
type	Pillai's Trace	,299	1057,026b	1,000	2474,000	,000	,299
	Wilks' Lambda	,701	1057,026b	1,000	2474,000	,000	,299
	Hotelling's Trace	,427	1057,026b	1,000	2474,000	,000	,299
	Roy's Largest Root	,427	1057,026b	1,000	2474,000	,000	,299
type * Subjectivity	Pillai's Trace	,002	2,016b	2,000	2474,000	,133	,002
	Wilks' Lambda	,998	2,016b	2,000	2474,000	,133	,002
	Hotelling's Trace	,002	2,016b	2,000	2474,000	,133	,002
	Roy's Largest Root	,002	2,016b	2,000	2474,000	,133	,002

Table 8 Tests of Between-Subjects Effects Transformed Variable: Average

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
Intercept	151231,443	1	151231,443	12794,067	,000	,838
Subjectivity	499,711	2	249,855	21,138	,000	,017
Error	29243,757	2474	11,820

Table 10 Estimated Marginal Means

Subjectivity	Mean	Std. Error	95% Confidence Interval
			Lower Bound	Upper Bound
Intersubjective	5,331	,085	5,164	5,497
Objective	5,975	,085	5,808	6,142
Subjective	5,272	,084	5,108	5,436

Table 12 Estimated Marginal Means

type	Mean	Std. Error	95% Confidence Interval
			Lower Bound	Upper Bound
Acceptability	6,358	,056	6,248	6,467
Honesty	4,694	,055	4,587	4,801



Table 13 Box's Test of Equality of Covariance Matricesa

Box's M	44,492
F	1,054
df1	42
df2	7629197,536
Sig.	,376

Table 14 Multivariate Testsa

Effect	Value	F	Hypothesis df	Error df	Sig.	Partial Eta Squared	Noncent. Parameter	Observed Powerc
type	Pillai's Trace	,225	682,838b	1,000	2357,000	,000	,225	682,838	1,000
	Wilks' Lambda	,775	682,838b	1,000	2357,000	,000	,225	682,838	1,000
	Hotelling's Trace	,290	682,838b	1,000	2357,000	,000	,225	682,838	1,000
	Roy's Largest Root	,290	682,838b	1,000	2357,000	,000	,225	682,838	1,000
type * Gender_f	Pillai's Trace	,000	,237b	1,000	2357,000	,626	,000	,237	,078
	Wilks' Lambda	1,000	,237b	1,000	2357,000	,626	,000	,237	,078
	Hotelling's Trace	,000	,237b	1,000	2357,000	,626	,000	,237	,078
	Roy's Largest Root	,000	,237b	1,000	2357,000	,626	,000	,237	,078
type * language_f	Pillai's Trace	,001	2,780b	1,000	2357,000	,096	,001	2,780	,385
	Wilks' Lambda	,999	2,780b	1,000	2357,000	,096	,001	2,780	,385
	Hotelling's Trace	,001	2,780b	1,000	2357,000	,096	,001	2,780	,385
	Roy's Largest Root	,001	2,780b	1,000	2357,000	,096	,001	2,780	,385
type * social_origins_f	Pillai's Trace	,002	4,362b	1,000	2357,000	,037	,002	4,362	,551
	Wilks' Lambda	,998	4,362b	1,000	2357,000	,037	,002	4,362	,551
	Hotelling's Trace	,002	4,362b	1,000	2357,000	,037	,002	4,362	,551
	Roy's Largest Root	,002	4,362b	1,000	2357,000	,037	,002	4,362	,551
type * geografical_origins_f	Pillai's Trace	,000	,531b	1,000	2357,000	,466	,000	,531	,113
	Wilks' Lambda	1,000	,531b	1,000	2357,000	,466	,000	,531	,113
	Hotelling's Trace	,000	,531b	1,000	2357,000	,466	,000	,531	,113
	Roy's Largest Root	,000	,531b	1,000	2357,000	,466	,000	,531	,113
type * Gender_f * language_f	Pillai's Trace	,002	4,025b	1,000	2357,000	,045	,002	4,025	,518
	Wilks' Lambda	,998	4,025b	1,000	2357,000	,045	,002	4,025	,518
	Hotelling's Trace	,002	4,025b	1,000	2357,000	,045	,002	4,025	,518
	Roy's Largest Root	,002	4,025b	1,000	2357,000	,045	,002	4,025	,518
type * Gender_f * social_origins_f	Pillai's Trace	,001	1,603b	1,000	2357,000	,206	,001	1,603	,244
	Wilks' Lambda	,999	1,603b	1,000	2357,000	,206	,001	1,603	,244
	Hotelling's Trace	,001	1,603b	1,000	2357,000	,206	,001	1,603	,244
	Roy's Largest Root	,001	1,603b	1,000	2357,000	,206	,001	1,603	,244
type * Gender_f * geografical_origins_f	Pillai's Trace	,000	,466b	1,000	2357,000	,495	,000	,466	,105
	Wilks' Lambda	1,000	,466b	1,000	2357,000	,495	,000	,466	,105
	Hotelling's Trace	,000	,466b	1,000	2357,000	,495	,000	,466	,105
	Roy's Largest Root	,000	,466b	1,000	2357,000	,495	,000	,466	,105
type * language_f * social_origins_f	Pillai's Trace	,000	,480b	1,000	2357,000	,489	,000	,480	,106
	Wilks' Lambda	1,000	,480b	1,000	2357,000	,489	,000	,480	,106
	Hotelling's Trace	,000	,480b	1,000	2357,000	,489	,000	,480	,106
	Roy's Largest Root	,000	,480b	1,000	2357,000	,489	,000	,480	,106
type * language_f * geografical_origins_f	Pillai's Trace	,000	,002b	1,000	2357,000	,964	,000	,002	,050
	Wilks' Lambda	1,000	,002b	1,000	2357,000	,964	,000	,002	,050
	Hotelling's Trace	,000	,002b	1,000	2357,000	,964	,000	,002	,050
	Roy's Largest Root	,000	,002b	1,000	2357,000	,964	,000	,002	,050
type * social_origins_f * geografical_origins_f	Pillai's Trace	,000	,740b	1,000	2357,000	,390	,000	,740	,138
	Wilks' Lambda	1,000	,740b	1,000	2357,000	,390	,000	,740	,138
	Hotelling's Trace	,000	,740b	1,000	2357,000	,390	,000	,740	,138
	Roy's Largest Root	,000	,740b	1,000	2357,000	,390	,000	,740	,138
type * Gender_f * language_f * social_origins_f	Pillai's Trace	,001	2,568b	1,000	2357,000	,109	,001	2,568	,360
	Wilks' Lambda	,999	2,568b	1,000	2357,000	,109	,001	2,568	,360
	Hotelling's Trace	,001	2,568b	1,000	2357,000	,109	,001	2,568	,360
	Roy's Largest Root	,001	2,568b	1,000	2357,000	,109	,001	2,568	,360
type * Gender_f * language_f * geografical_origins_f	Pillai's Trace	,000	,881b	1,000	2357,000	,348	,000	,881	,155
	Wilks' Lambda	1,000	,881b	1,000	2357,000	,348	,000	,881	,155
	Hotelling's Trace	,000	,881b	1,000	2357,000	,348	,000	,881	,155
	Roy's Largest Root	,000	,881b	1,000	2357,000	,348	,000	,881	,155
type * Gender_f * social_origins_f * geografical_origins_f	Pillai's Trace	,005	10,782b	1,000	2357,000	,001	,005	10,782	,907
	Wilks' Lambda	,995	10,782b	1,000	2357,000	,001	,005	10,782	,907
	Hotelling's Trace	,005	10,782b	1,000	2357,000	,001	,005	10,782	,907
	Roy's Largest Root	,005	10,782b	1,000	2357,000	,001	,005	10,782	,907
type * language_f * social_origins_f * geografical_origins_f	Pillai's Trace	,001	1,518b	1,000	2357,000	,218	,001	1,518	,234
	Wilks' Lambda	,999	1,518b	1,000	2357,000	,218	,001	1,518	,234
	Hotelling's Trace	,001	1,518b	1,000	2357,000	,218	,001	1,518	,234
	Roy's Largest Root	,001	1,518b	1,000	2357,000	,218	,001	1,518	,234
type * Gender_f * language_f * social_origins_f * geografical_origins_f	Pillai's Trace	,000	.b	,000	,000	.	.	.	.
	Wilks' Lambda	1,000	.b	,000	2357,000	.	.	.	.
	Hotelling's Trace	,000	.b	,000	2,000	.	.	.	.
	Roy's Largest Root	,000	,000b	1,000	2356,000	1,000	,000	,000	,050

Table 15 Tests of Within-Subjects Effects

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared	Noncent. Parameter	Observed Powera
type	Sphericity Assumed	1872,181	1	1872,181	682,838	,000	,225	682,838	1,000
	Greenhouse-Geisser	1872,181	1,000	1872,181	682,838	,000	,225	682,838	1,000
	Huynh-Feldt	1872,181	1,000	1872,181	682,838	,000	,225	682,838	1,000
	Lower-bound	1872,181	1,000	1872,181	682,838	,000	,225	682,838	1,000
type * Gender_f	Sphericity Assumed	,650	1	,650	,237	,626	,000	,237	,078
	Greenhouse-Geisser	,650	1,000	,650	,237	,626	,000	,237	,078
	Huynh-Feldt	,650	1,000	,650	,237	,626	,000	,237	,078
	Lower-bound	,650	1,000	,650	,237	,626	,000	,237	,078
type * language_f	Sphericity Assumed	7,621	1	7,621	2,780	,096	,001	2,780	,385
	Greenhouse-Geisser	7,621	1,000	7,621	2,780	,096	,001	2,780	,385
	Huynh-Feldt	7,621	1,000	7,621	2,780	,096	,001	2,780	,385
	Lower-bound	7,621	1,000	7,621	2,780	,096	,001	2,780	,385
type * social_origins_f	Sphericity Assumed	11,958	1	11,958	4,362	,037	,002	4,362	,551
	Greenhouse-Geisser	11,958	1,000	11,958	4,362	,037	,002	4,362	,551
	Huynh-Feldt	11,958	1,000	11,958	4,362	,037	,002	4,362	,551
	Lower-bound	11,958	1,000	11,958	4,362	,037	,002	4,362	,551
type * geografical_origins_f	Sphericity Assumed	1,457	1	1,457	,531	,466	,000	,531	,113
	Greenhouse-Geisser	1,457	1,000	1,457	,531	,466	,000	,531	,113
	Huynh-Feldt	1,457	1,000	1,457	,531	,466	,000	,531	,113
	Lower-bound	1,457	1,000	1,457	,531	,466	,000	,531	,113
type * Gender_f * language_f	Sphericity Assumed	11,034	1	11,034	4,025	,045	,002	4,025	,518
	Greenhouse-Geisser	11,034	1,000	11,034	4,025	,045	,002	4,025	,518
	Huynh-Feldt	11,034	1,000	11,034	4,025	,045	,002	4,025	,518
	Lower-bound	11,034	1,000	11,034	4,025	,045	,002	4,025	,518
type * Gender_f * social_origins_f	Sphericity Assumed	4,396	1	4,396	1,603	,206	,001	1,603	,244
	Greenhouse-Geisser	4,396	1,000	4,396	1,603	,206	,001	1,603	,244
	Huynh-Feldt	4,396	1,000	4,396	1,603	,206	,001	1,603	,244
	Lower-bound	4,396	1,000	4,396	1,603	,206	,001	1,603	,244
type * Gender_f * geografical_origins_f	Sphericity Assumed	1,277	1	1,277	,466	,495	,000	,466	,105
	Greenhouse-Geisser	1,277	1,000	1,277	,466	,495	,000	,466	,105
	Huynh-Feldt	1,277	1,000	1,277	,466	,495	,000	,466	,105
	Lower-bound	1,277	1,000	1,277	,466	,495	,000	,466	,105
type * language_f * social_origins_f	Sphericity Assumed	1,315	1	1,315	,480	,489	,000	,480	,106
	Greenhouse-Geisser	1,315	1,000	1,315	,480	,489	,000	,480	,106
	Huynh-Feldt	1,315	1,000	1,315	,480	,489	,000	,480	,106
	Lower-bound	1,315	1,000	1,315	,480	,489	,000	,480	,106
type * language_f * geografical_origins_f	Sphericity Assumed	,006	1	,006	,002	,964	,000	,002	,050
	Greenhouse-Geisser	,006	1,000	,006	,002	,964	,000	,002	,050
	Huynh-Feldt	,006	1,000	,006	,002	,964	,000	,002	,050
	Lower-bound	,006	1,000	,006	,002	,964	,000	,002	,050
type * social_origins_f * geografical_origins_f	Sphericity Assumed	2,028	1	2,028	,740	,390	,000	,740	,138
	Greenhouse-Geisser	2,028	1,000	2,028	,740	,390	,000	,740	,138
	Huynh-Feldt	2,028	1,000	2,028	,740	,390	,000	,740	,138
	Lower-bound	2,028	1,000	2,028	,740	,390	,000	,740	,138
type * Gender_f * language_f * social_origins_f	Sphericity Assumed	7,042	1	7,042	2,568	,109	,001	2,568	,360
	Greenhouse-Geisser	7,042	1,000	7,042	2,568	,109	,001	2,568	,360
	Huynh-Feldt	7,042	1,000	7,042	2,568	,109	,001	2,568	,360
	Lower-bound	7,042	1,000	7,042	2,568	,109	,001	2,568	,360
type * Gender_f * language_f * geografical_origins_f	Sphericity Assumed	2,417	1	2,417	,881	,348	,000	,881	,155
	Greenhouse-Geisser	2,417	1,000	2,417	,881	,348	,000	,881	,155
	Huynh-Feldt	2,417	1,000	2,417	,881	,348	,000	,881	,155
	Lower-bound	2,417	1,000	2,417	,881	,348	,000	,881	,155
type * Gender_f * social_origins_f * geografical_origins_f	Sphericity Assumed	29,563	1	29,563	10,782	,001	,005	10,782	,907
	Greenhouse-Geisser	29,563	1,000	29,563	10,782	,001	,005	10,782	,907
	Huynh-Feldt	29,563	1,000	29,563	10,782	,001	,005	10,782	,907
	Lower-bound	29,563	1,000	29,563	10,782	,001	,005	10,782	,907
type * language_f * social_origins_f * geografical_origins_f	Sphericity Assumed	4,162	1	4,162	1,518	,218	,001	1,518	,234
	Greenhouse-Geisser	4,162	1,000	4,162	1,518	,218	,001	1,518	,234
	Huynh-Feldt	4,162	1,000	4,162	1,518	,218	,001	1,518	,234
	Lower-bound	4,162	1,000	4,162	1,518	,218	,001	1,518	,234
type * Gender_f * language_f * social_origins_f * geografical_origins_f	Sphericity Assumed	,000	0	.	.	.	,000	,000	.
	Greenhouse-Geisser	,000	,000	.	.	.	,000	,000	.
	Huynh-Feldt	,000	,000	.	.	.	,000	,000	.
	Lower-bound	,000	,000	.	.	.	,000	,000	.
Error(type)	Sphericity Assumed	6462,340	2357	2,742
	Greenhouse-Geisser	6462,340	2357,000	2,742
	Huynh-Feldt	6462,340	2357,000	2,742
	Lower-bound	6462,340	2357,000	2,742

a. Design: Intercept + Gender_f + language_f + social_origins_f + geografical_origins_f + Gender_f * language_f + Gender_f * social_origins_f + Gender_f * geografical_origins_f + language_f * social_origins_f + language_f * geografical_origins_f + social_origins_f * geografical_origins_f + Gender_f * language_f * social_origins_f + Gender_f * language_f * geografical_origins_f + Gender_f * social_origins_f * geografical_origins_f + language_f * social_origins_f * geografical_origins_f + Gender_f * language_f * social_origins_f * geografical_origins_f Within Subjects Design: type b. Exact statistic

Table 16 Tests of Within-Subjects Effects

Source	type	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
type	Linear	1872,181	1	1872,181	682,838	,000	,225
type * Gender_f	Linear	,650	1	,650	,237	,626	,000
type * language_f	Linear	7,621	1	7,621	2,780	,096	,001
type * social_origins_f	Linear	11,958	1	11,958	4,362	,037	,002
type * geografical_origins_f	Linear	1,457	1	1,457	,531	,466	,000
type * Gender_f * language_f	Linear	11,034	1	11,034	4,025	,045	,002
type * Gender_f * social_origins_f	Linear	4,396	1	4,396	1,603	,206	,001
type * Gender_f * geografical_origins_f	Linear	1,277	1	1,277	,466	,495	,000
type * language_f * social_origins_f	Linear	1,315	1	1,315	,480	,489	,000
type * language_f * geografical_origins_f	Linear	,006	1	,006	,002	,964	,000
type * social_origins_f * geografical_origins_f	Linear	2,028	1	2,028	,740	,390	,000
type * Gender_f * language_f * social_origins_f	Linear	7,042	1	7,042	2,568	,109	,001
type * Gender_f * language_f * geografical_origins_f	Linear	2,417	1	2,417	,881	,348	,000
type * Gender_f * social_origins_f * geografical_origins_f	Linear	29,563	1	29,563	10,782	,001	,005
type * language_f * social_origins_f * geografical_origins_f	Linear	4,162	1	4,162	1,518	,218	,001
type * Gender_f * language_f * social_origins_f * geografical_origins_f	Linear	,000	0	.	.	.	,000
Error(type)	Linear	6462,340	2357	2,742

Table 17 Tests of Within-Subjects Effects

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared	Noncent. Parameter
Intercept	95841,786	1	95841,786	7280,020	,000	,755	7280,020
Gender_f	8,085	1	8,085	,614	,433	,000	,614
language_f	23,759	1	23,759	1,805	,179	,001	1,805
social_origins_f	95,396	1	95,396	7,246	,007	,003	7,246
geografical_origins_f	2,525	1	2,525	,192	,661	,000	,192
Gender_f * language_f	14,749	1	14,749	1,120	,290	,000	1,120
Gender_f * social_origins_f	6,346	1	6,346	,482	,488	,000	,482
Gender_f * geografical_origins_f	61,722	1	61,722	4,688	,030	,002	4,688
language_f * social_origins_f	,744	1	,744	,057	,812	,000	,057
language_f * geografical_origins_f	5,068	1	5,068	,385	,535	,000	,385
social_origins_f * geografical_origins_f	100,124	1	100,124	7,605	,006	,003	7,605
Gender_f * language_f * social_origins_f	18,434	1	18,434	1,400	,237	,001	1,400
Gender_f * language_f * geografical_origins_f	5,607	1	5,607	,426	,514	,000	,426
Gender_f * social_origins_f * geografical_origins_f	21,358	1	21,358	1,622	,203	,001	1,622
language_f * social_origins_f * geografical_origins_f	40,400	1	40,400	3,069	,080	,001	3,069
Gender_f * language_f * social_origins_f * geografical_origins_f	,000	0	.	.	.	,000	,000
Error	31030,009	2357	13,165

Table 19 Estimates

social origins	Mean	Std. Error	95% Confidence Interval
			Lower Bound	Upper Bound
manual	4,824	,075	4,678	4,970
non-manual	4,579a	,077	4,428	4,729



Table 21 Estimates

social origins	geographical origins	Mean	Std. Error	95% Confidence Interval
				Lower Bound	Upper Bound
Low	rural	5,003	,105	4,796	5,209
	urban	4,646	,105	4,439	4,852
High	rural	4,494a	,111	4,277	4,711
	urban	4,642	,106	4,435	4,850

Table 22 Estimates

type	Mean	Std. Error	95% Confidence Interval
			Lower Bound	Upper Bound
acceptability	5,375a	,061	5,254	5,495
honesty	4,045a	,056	3,935	4,155

Final survey (in Russian)

Приветственная страница

Спасибо за согласие принять участие в нашем исследовании. Оно посвящено Вашему восприятию повседневных ситуаций в стенах вуза. Задание очень короткое - нужно оценить всего пять ситуаций. Это займет не более 3-4 минут, поэтому просим Вас постараться выполнить его за один раз, не отвлекаясь ни на что другое. Ситуации могут показаться довольно похожими, но они различаются в деталях, поэтому мы просим Вас читать их внимательно.

При оценке опирайтесь только на ту информацию, которая дана в описании ситуации, и на свои представления о допустимости и честности.

Переходя на следующую страницу, Вы подтверждаете, что прочитали прилагаемую информацию о проекте, также Вы даете согласие на сбор и последующую обработку результатов анкеты. В свою очередь мы гарантируем защиту полученных данных от третьих лиц путем их обработки в обобщенном виде. Анкета заполняется анонимно, результаты будут использованы исключительно в научных целях.

Предварительные вопросы

ЯВЛЯЕТЕСЬ ЛИ ВЫ СТУДЕНТОМ ПЕРВОГО КУРСА БАКАЛАВРИАТА НИУ ВШЭ?

Да;

Нет.

Study 2 (респондент выполняет один случайный вариант)

Вариант 1

Артем учится на первом курсе одного московского университета. Он считает, что все вокруг часто списывают на экзаменах и переписывают домашние задания друг у друга. Сам Артем думает, что самое главное в обучении - хорошие оценки. На одном из курсов ему кажется, что преподаватель плохо готовится к парам и в целом не разбирается в предмете. На экзамене по этой дисциплине, к которому Артем не успевает подготовиться, он подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Артема допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Артем поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

Вариант 2

Артем учится на первом курсе одного московского университета. Его однокурсники считают, что все вокруг часто списывают на экзаменах и переписывают домашние задания друг у друга. Они думают, что для Артема самое главное в обучении- хорошие оценки. На одном из курсов многим студентам кажется, что преподаватель плохо готовится к парам и в целом не разбирается в предмете. На экзамене по этой дисциплине, к которому Артем не успевает подготовиться, он подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Артема допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Артем поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

Вариант 3

Артем учится на первом курсе одного московского университета. Однокурсники Артема часто списывают на экзаменах и переписывают домашние задания друг у друга. Для Артем самое главное в обучении - хорошие оценки. На одном из курсов преподаватель плохо готовится к парам и в целом не разбирается в предмете. На экзамене по этой дисциплине, к которому Артем не успевает подготовиться, он подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Артема допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Артем поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

Study 1 (респондент выполняет одну случайную виньетку)

A1B1C1D1. Андрей родился и закончил школу в Москве. Мать Андрея занята ручным трудом на текстильной фабрике, а отец - на сталелитейном заводе. Сейчас Андрей учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на русском языке, он не успевает подготовиться. На этом экзамене Андрей подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Андрея допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Андрей поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A1B2C1D1. Борис родился и закончил школу в Москве. Мать Бориса занята ручным трудом на текстильной фабрике, а отец - на сталелитейном заводе. Сейчас Борис учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на английском языке, он не успевает подготовиться. На этом экзамене Борис подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Бориса допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Борис поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A1B1C1D2. Геннадий родился и закончил школу в Рязанской области. Мать Геннадия занята ручным трудом на текстильной фабрике, а отец - на сталелитейном заводе. Сейчас Геннадий учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на русском языке, он не успевает подготовиться. На этом экзамене Геннадий подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Геннадия допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Геннадий поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A1B2C1D2. Евгений родился и закончил школу в Рязанской области. Мать Евгения занята ручным трудом на текстильной фабрике, а отец - на сталелитейном заводе. Сейчас Евгений учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на английском языке, он не успевает подготовиться. На этом экзамене Евгений подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Евгения допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Евгений поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A1B1C2D1. Владимир родился и закончил школу в Москве. Мать Владимира занимает высокую должность в банковской сфере, а отец - в энергетической промышленности. Сейчас Владимир учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на русском языке, он не успевает подготовиться. На этом экзамене Владимир подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Владимира допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Владимир поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A1B2C2D1. Дмитрий родился и закончил школу в Москве. Мать Дмитрия занимает высокую должность в банковской сфере, а отец - в энергетической промышленности. Сейчас Дмитрий учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на английском языке, он не успевает подготовиться. На этом экзамене Дмитрий подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Дмитрия допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Дмитрий поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A1B1C2D2. Игорь родился и закончил школу в Рязанской области. Мать Игоря занимает высокую должность в банковской сфере, а отец - в энергетической промышленности. Сейчас Игорь учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на русском языке, он не успевает подготовиться. На этом экзамене Игорь подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Игоря допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Игорь поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A1B2C2D2. Кирилл родился и закончил школу в Рязанской области. Мать Кирилла занимает высокую должность в банковской сфере, а отец - в энергетической промышленности. Сейчас Кирилл учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на английском языке, он не успевает подготовиться. На этом экзамене Кирилл подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Кирилла допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Кирилл поступил честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)

A2B1C1D1. Анна родилась и закончила школу в Москве. Мать Анны занята ручным трудом на текстильной фабрике, а отец - на сталелитейном заводе. Сейчас Анна учится на первом курсе одного московского университета. К экзамену по одной из дисциплин, преподаваемой на русском языке, она не успевает подготовиться. На этом экзамене Анна подглядывает ответы у соседа по парте и копирует их.

Считаете ли Вы, что действия Анна допустимы в описанной ситуации, и если да, то в какой степени? (шкала от 0 до 10, где 0 - полностью недопустимы, а 10 - абсолютно допустимы)

Считаете ли Вы, что Анна поступила честно или нечестно в этой ситуации (шкала от 0 до 10, где 0 - абсолютно нечестно, а 10 - абсолютно честно)