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Non-triviality of the results of the field experiment conducted on Stanley Milgram’s methodology in the New York and Moscow subway have been studied. The statistical significance of the difference between empirical and predicted results has been taken as the non-triviality criterion. 208 respondents (psychologists and students studying psychology) were asked to predict an experimental result in dependence on an experimenter’s and subject’s gender, a subject’s age, and a city where the experiment was carried out. The obtained results have confirmed our hypothesis on non-triviality of the experiments in subways: it has been showed that there is a statistically significant difference between real behavior of subway passengers (in New York and in Moscow) and predictions made by Moscow and Tashkent respondents. Practically in most cases the predicted probability that a subject gives a seat after request of experimenter (young woman or young man) is much less than in reality. The structural equation modeling (SEM) has been used to analyze the data by constructing the model taking account of all factors mentioned above. The model fit the experimental data well (CFI = 0.919). It has been found that predicted results depend not only on gender, age, and residence of a respondent but also on the degree of familiarity with the research. The obtained data give an important material for a further study of the role of situational (an experiment design) and individual (respondent characteristics) factors in predicted results; they contribute to further understanding of the problem of creation and support of non-formal social norms in various cultures and show new aspects of research carried out on experimental methodology of Stanley Milgram.

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Introduction In the field experiment in the New York subway (Takooshian, 1972; Milgram, Sabini, 1978; Milgram, 2001; Luo, 2004; Milgram, 2010) healthy looking psychology students (experimenters) asked random passengers (subjects) to give up their seat in a jam-packed car. The first original research was carried out by student Harold Takooshian (Takooshian, 1972) under supervising by his professor Stanley Milgram. In the experimental situation “Without motivation” the experimenters did not explain the subjects why they wanted to take the seat breaking informal social norms of behavior (Cialdini, Kallgren, and Reno, 1991; Bicchieri, 2006) in a subway. The result was unexpected for the scientists: more than half of the subjects gave up their seats. Unbearable stressful responses of experimenters during collection of empirical data were also unpredictable (Takooshian, 1972; Milgram, Sabini, 1978; Luo, 2004; Cliffs, Schultz, Cialdini, Goldstein, Griskevicius, 2007). All of the experimenters complained about unexpectedly high intensity of negative emotions experienced during this experiment. During 2008-2010 these results were confirmed by experiments in Moscow and Sankt- Petersburg subways (Al’-Batal, 2009; Voronov, Al’-Batal, 2010; Voronov, Mitina, Al’- Batal, Gorbunova, 2011; Mitina, Voronov, 2012). The unexpectedness of results of all these experiments in the subway allowed us to propose a hypothesis on non-triviality of the scientific fact under consideration. The important criterion of a true value of the obtained scientific knowledge is the statistically significant difference between the experimental result and its prediction. This criterion was suggested and justified (Mitina, Petrovskii, 2001). The respondents predicting the result may be people: - who are not experts in the field and they did not hear anything about such experiments; - who are experts and they know about such experiments (while they studied psychology), but they do not remember results; - who are experts in the field and say that they remember the results. In the first part of this paper the results of analysis of an accuracy of predicted results made by psychologists for subway experiments are presented. Our independent respondents were citizens of Moscow and Tashkent. They predicted results of experiments carried out in New York, Moscow, and Tashkent. In truth the real experiment was not conducted in Tashkent but the respondents did not know about it (they were told that the experiment took place in Tashkent also). It was studied how much the place of an experiment conducting, the gender of an experimenter, the gender and age of a subject, the gender, age and place of residence of a respondent and his (or her) awareness about subway studies affect such predictions. Unfortunately, we were not able to use for comparison the predicted results obtained in the USA by Harold Takooshian (Takooshian, 1972; Milgram, Sabini, 1978) because of undifferentiatedness (these predicted results were not separated according mentioned parameters; however, we showed that these parameters influence significantly on the prognoses). In the second part of our paper we used SEM for cross-cultural analysis of the data obtained in Moscow and Tashkent. We have constructed the model taking into account all possible combinations of the factors mentioned above and their influence on predictions. The model fits data with CFI = 0,919. We have identified the statistical significance of 1) factor loadings, 2) correlations between latent variables determining an experimental scheme, 3) correlations between latent variables corresponding to the situations in the experimental design and characteristics of our respondents. The obtained information is important both for the study of non-triviality of empirical facts and as an additional material for understanding how non-formal social norms in various cultures are formed and maintained (Scott, 1971). Procedure and methods of research The respondents (psychologists and students studying psychology) from Tashkent (April, 2011) and Moscow (October, 2011 - end of June, 2012) filled in the prognostic questionnaire in university classrooms during their lectures or seminars. Here we quote the introductory part of the questionnaire: “In 1972 a famous American psychologist Stanley Milgram carried out experiments in New York subway. A healthy looking young man or a woman asked a passenger to give him (or her) a seat in a subway car full of people without explaining why. In 2008-2010 such an experiment was carried out in Moscow and Tashkent subways. The results of all these experiments were subjected to statistical analysis. The subjects-passengers were divided in four groups (men at the age before and after 40 and women at the age before and after 40). What do you think: How many of 100 passengers chosen randomly in each gender-age group gave their seat to a young man and how many to a young woman in New York, Moscow, and Tashkent? While answering these questions, in the end of each from 24 lines (after the colon) write down a number of passengers (a range of numbers is not allowed!)” Following this introduction there were 24 lines for 24 different experimental situations (for 24 predicting answers) which consists of 8 similar situations in 3 cities: 2×2×2, i.e. an experimenter’s gender multiplied by a subject’s gender multiplied by a subject’s age (younger or older 40). Table 1 presents the results of real experiments in Moscow and New York. Table 1 The results of experiments in New York (1972) and Moscow (2008-2010) City Gender of Age of subject The number of subjects (= the number of experiments) %% of subjects having given a seat* experimenter subject New York Male Male < 40 years 10 60.0 > 40 years 9 44.4 Female < 40 years 8 50.0 > 40 years 7 42.9 Female Male < 40 years 6 83.3 > 40 years 5 80.0 Female < 40 years 8 87.5 > 40 years 3 66.7 Moscow Male Male < 40 years 12 83.3 > 40 years 10 30.0 Female < 40 years 11 72.7 > 40 years 13 53.9 Female Male < 40 years 21 95.2 > 40 years 21 66.7 Female < 40 years 21 90.5 > 40 years 17 70.6 Our database was supplemented by the US data as professor Takooshian kindly gave us “Sheets” of manual recordings made by experimenters and observers during experimental study in New York City (Takooshian, 1972). In 1972 the experiment in New York was carried out in 4 variations. Table 1 presents the summarized results only for 2 of them, i.e. with “Verbal request without motivation” and with “Written request without motivation”; there were no statistically significant differences between the results only of these two variations (Milgram, Sabini, 1978). Thus, all 56 cases (subjects-passengers) of these two variations in the New York City experiment (having analyzed the experiments, we excluded several ambiguously interpreted cases of subject’s behavior from the US data) may be considered as one homogeneous group of data obtained in the version “Without motivation”. In Moscow (and according to the experiment legend in Tashkent) all experimenters worked in the version “Verbal request without motivation”; Moscow experimenters asked 126 subjects-passengers. Each experimenter in Moscow and New York asked several subjects-passengers. Table 2 presents the gender and age of experimenters. All experimenters were students getting their master or doctoral degree of about the same age. Therefore, it was not possible to differentiate experimental situation in dependence on their age. Table 2 Description of the experimenters’ group in New York (1972) and Moscow (2008-2010) City Number of experimenters Females from them Average age New York 10 4 24.0 Moscow 15 9 21.1 Table 3 presents the description of respondents’ group. They are mainly Psychology students or the people who have already received Ph.D. in Psychology. All the respondents got an additional question: how much do they know about these experiments? Among 175 Moscow respondents only 48 gave a positive answer but they added that they did not remember exact figures and they gave wrong prognosis very often. Table 3 Description of the respondents’ group in Tashkent (2011) and Moscow (2012) Respondents Moscow Tashkent (uninformed about the experiment) Total Uninformed about the experiment Informed about the experiment Total 127 48 55 230 Males 29 (23%) 10 (21%) 10 (18%) 49 (21%) Females 98 (77%) 38 (79%) 45 (82%) 181 (79%) 28 years old and younger 110 (87%) 44 (91%) 54 (98%) 208 (90%) We have compared these two respondents groups (familiar and not familiar with the experiments) and found out that those who knew something about experiments predicted higher percentage of getting seats in a subway car than those who did not know anything about experiments. The difference was significant. Thus, we have revealed a degree of latent knowledge. Recall that the experimental results have shown suddenly high percent of those who gave their seats. In Tashkent the respondents were Psychology students of the 2nd and 3rd year of training when they did not yet take the course which would describe these experiments and were not familiar with them. So we had three groups of respondents: one group (in Moscow) who knew about experiments and two groups (in Moscow and Tashkent) did not know about them. 87% of Moscow respondents and 98% of Tashkent respondents were not older than 28. At this age people do not fully understand that standing in a public transport could be a physical problem. For them, the resolution of the situation realized in the experiment is most likely determined by the force of the informal norms of behavior in a subway. To provide the homogeneity we restricted the respondent selection by this age. Thus we had the final sample of 208 subjects: 1) 110 Moscow respondents and 54 Tashkent respondents who were not familiar with experiments and 2) 44 Moscow respondents who knew about them. Cross-tabulation analysis with calculation χ 2 statistics and Gilford φ-coefficient showed the balance of respondents among each group according gender (p = .408). There were about in 4 times more females than males in both groups. Results and Discussion Study of non-triviality of the results of Milgram field experiment in the subway of New York and Moscow In the Table 4 all prognoses which were made for all experimental situations differentiated by city (New York/Moscow/Tashkent), by gender of an experimenter and a subject, and by the subject’s age are presented (24 situations in total). Because all respondents giving prognoses were differentiated on the subgroups according their gender, place of living (Moscow/Tashkent) and a prior familiarity with the experiment (6 subgroups in total) the table 4 contains 24× 6 = 144 prognoses numbers and 16 real results of experiments (8 in Moscow plus 8 in New York; as it was said in Tashkent a real experiment was not conducted). So only 96 prognoses from 144 can be compared with the real results. Table 4 shows that all predictions of positive response were lower than real results and these differences were significant for uninformed respondents and partly for respondents who had known about the experiment beforehand in the part concerning Moscow (because information about the experiments in Moscow is not included in traditional courses of social psychology yet). This is most likely due to the powerful informal social norm of passenger’s behavior in the subway rooted in the minds of respondents (even informed respondents!). Perhaps, the respondents, being psychology students, put themselves in the place of experimenters (also psychology students) (Scott, 1971). They were fearful of misconduct. The strength of this norm can manifest itself (from respondent’s point of view) in low percent of positive behavioral responses made by all kinds of subjects on the experimenter request. There are only 3 cases of prognosis which are higher than the experimental results. All these 3 cases are statistically insignificant and belong to group of informed respondents. In these three cases an experimenter asked an old man to give (to him or her) a seat. And male respondents overestimated the situation concerning a male experimenter (in New York and Moscow) and female respondent overestimated the situation concerning a female experimenter (in Moscow). Because of insignificance of differences in these cases we will not to discuss the possible reasons of such results. Table 4 Comparison of experimental results with the results of predictions (% of subjects who gave their seats) City of the experiment Experimenter’s gender Subject’s gender Subject’s age Real results of the experiments Prognoses of the experiment’s results Respondents uniformed about the experiment Respondents informed about the experiment Moscow Tashkent Moscow Respondent’s gender Respondent’s gender Respondent’s gender Male Female Male Female Male Female New York Male Male < 40 years old 60.00 28.29** 26.82** 32.00* 15.33** 36.86 35.89 > 40 years old 44.44 24.46 18.59** 24.56 11.07** 44.71 32.58 Female < 40 years old 50.00 22.75* 25.31 20.22** 14.14** 37.71 33.92 > 40 years old 42.86 16.96* 15.58** 18.33* 9.28** 19.71 24.36 Female Male < 40 years old 83.33 64.71 67.27 53.33 27.53** 76.14 73.64 > 40 years old 80.00 54.42 49.65 39.67* 22.28** 52.86 62.47 Female < 40 years old 87.50 29.92** 34.33** 39.44** 15.02** 38.14** 43.53** > 40 years old 66.67 17.42** 23.66* 23.67* 14.00** 36.29 33.53 Moscow Male Male < 40 years old 83.33 21.54** 19.41** 24.22** 13.33** 36.29** 33.22** > 40 years old 30.00 16.58 14.67 18.44 10.70** 31.14 27.17 Female < 40 years old 72.73 17.08** 21,25** 19.33** 10.93** 25.57** 33.72** > 40 years old 53.85 12.33** 15.39** 16.78** 8.49** 19.43** 24.92** Female Male < 40 years old 95.24 66.63** 66.90** 53.44** 32.74** 73.57** 70.17** > 40 years old 66.67 54.54 51.55 37.33** 23.81** 60.86 70.28 Female < 40 years old 90.48 28.08** 30.82** 33.44** 15.63** 34.71** 48.92** > 40 years old 70.59 16.33** 20.41** 23.44** 11.98** 31.00** 37.53** Tashkent Male Male < 40 years old The experiment was not conducted 25.58 25.57 24.44 29.44 26.86 45.56 > 40 years old 21.83 16.42 26.67 22.30 20.57 35.11 Female < 40 years old 29.25 26.90 29.22 27.44 40.43 35.58 > 40 years old 15.42 18.35 16.67 19.47 31.43 27.58 Female Male < 40 years old 64.04 66.80 65.22 58.19 55.00 74.06 > 40 years old 48.58 53.89 47.44 42.12 42.43 64.64 Female < 40 years old 37.58 33.34 41.89 33.86 33.57 47.50 > 40 years old 23.25 23.08 30.78 25.33 15.71 34.53 Note. Statistical significance of prognoses differences from real results was tested using binomial statistics: * .05 > p > .01; ** p < .01; all other prognoses are As it has been expected, the most distant from reality results came out in the group of uninformed respondents: 79.7% (51 of 64) of their prognoses differ significantly from experimental results; the levels of significance of these differences tested by using binomial statistics (here and below “binomial statistics” just will be designated in abbreviated form as “binst”) were p < .05 for 7 of 51 (13.7%) predictions and p < .01 for 44 of 51 (86.3%) ones; young women from Tashkent (subgroup of uninformed respondents), in contrast to all of other five subgroups, made wrong predictions for all 16 positions (8 in New York and 8 in Moscow) with the lowest p-values < .01 (binst). Each of these 16 predictions was the lowest among all 6 subgroups of respondents. At the same time their analogous 8 predictions for Tashkent are considerably higher than for Moscow and New York, i.e. for these respondents the location of the experiment was the most important factor. For comparison, in the group of informed respondents we have only 43. 8% (14 of 32) of predictions differing significantly from experimental results; all (14 of 14) of the levels of significance of these differences had p < .01 (binst). We haven’t found distinctions between gender subgroups of informed respondents concerning distribution of statistically significant and non-significant differences between all of predictions and corresponding frequencies of positive responses of subjects in all of 16 experimental situations. The level of significance of the total distinction in prognoses exactness between uninformed respondents and informed ones is p = .004 (Mann-Whitney U Test). Perhaps, during thinking about their prognostic decisions respondents identify themselves not only with experimenters but with subjects as well. One can think about this kind of identification in the cases when gender, age, and residence of respondent coincide with the corresponding characteristics of a subject. We call it complete identification. In the case of partial coincidence of these characteristics we could talk about partial identification of a respondent with a subject. In these two cases (complete and partial identification of a respondent with a subject) we could expect higher predictions as compared with predictions made by respondents free from identification with a subject: the phenomenon of the fundamental error of attribution (I am more personally oriented than other typical person of my gender, age, place of living etc.). This effect must be more pronounced when identification is more complete. Our data confirm these suggestions in 8 of 12 cases of complete identification and in 34 of 60 cases of partial identification. The situation when a young man (experimenter) asks a woman (subject-passenger) over 40 to yield him a seat is the strongest misconduct in a subway. Such a situation is the most stressful and therefore it is the most difficult for the participants (for the young male experimenter and for the female subject-passenger). If our respondents thought similarly while making their predictions then we could assume that the minimal predicted frequencies of passengers’ positive responses should be for such a situation; our assumption was confirmed by all 6 groups of respondents for New York subway and by 5 from 6 groups for Moscow subway. In the real experiment this minimum of passenger response in such a situation was observed only in New York - 42.9%. In Moscow, such a minimum is 30.0% occurred in another “scene” when the young male experimenter made his unexpected request not to a female passenger over 40 years of age, but to a male passenger over 40 years of age. Such an experimental result in Moscow can be explained by the fact that men older than 40 years are the least healthy part of the Moscow population; another explanation can be revealed from breaking within gender “concurrent” subordination (analogically it was expected that older women will not yield a seat to younger female experimenters, but really it did not happen). Only the subgroup of uninformed (!) female respondents from Moscow (i.e., only one of all 6 subgroups of our respondents) correctly predicted the result of this “real Moscow minimum”. In a group of 8 real Moscow experiments the result (53.9%) of the theoretically minimal in passengers’ response to the experimental situation (a young male experimenter and a female passenger over 40 years of age) follows immediately after 30% response in the situation of the “real Moscow minimum”. In New York, 44.4% of the response of male passengers over the age of 40 to the request of the young male experimenter (the “real Moscow minimum” situation) follows immediately after the theoretical and practical minimum of the American passengers’ responses (see 42.9% above). Now let us discuss the predictions about experiments in Tashkent (which has never been carried out) made by the same 6 subgroups of respondents. The respondents of 4 subgroups predicted minimal number of positive responses for the situation when an experimenter is a young man and a subject is a woman older than 40 years old. Almost all respondents (except informed women from Moscow) predicted sufficiently low percent positive results for the situation when the experimenter is a young man and the subject is a man older than 40. Some gender aspects of the predictions of the results obtained in subway experiments In 70 of total 72 prognoses (6 subgroups of respondents × 3 cities × 4 categories of subjects-passengers) of the frequencies of positive reactions on the request to give a seat to the young female experimenter these frequencies are higher than the analogical prognostic frequencies but related to the young male experimenter. This result can be explained by the fact that a such behavior of a young woman (an experimenter) breaks informal social norms of behavior much less than a similar request made by a young man (such a request of a young man is a challenge to accepted norms of behavior of men in a public place). One can think that this gender effect reveals itself when a respondent makes predictions. Really (in the experiments), this gender effect manifests in both cities (Moscow and New York) for all 4 categories of subjects-passengers. Uninformed respondents predicted the behavior of female subjects less accurately than of male subjects: the group of these respondents gave 31 predictions statistically significantly differing from reality from total 32 predictions of behavior of female subjects (compare similar values for male subjects: 20 from 32). The level of significance of this gender distinction is p < .001 (φ*-criterion with φ* emp = 3.86). The same gender phenomenon was also observed for the group of informed respondents: these respondents gave 10 predictions statistically significantly differing from reality from total 16 predictions of behavior of female subjects in comparison with 4 from 16 for male subjects; the level of significance is p = .014 (φ*-criterion with φ* emp = 2.195). This second gender effect (the first one was described above) may be probably explained by unexpectedly high frequencies of real positive responses of female subjects after the experimenter’s request. The cross-cultural aspect of the accuracy of prediction of the results of subway experiments The integrated sample of all 6 subgroups of respondents gave more precise predictions of results of the New York experiments than of the Moscow ones. They gave (from total 48 for each town) 22 (45. 8%) and 9 (18. 8%) predictions non-significantly statistically differing from reality for New York and for Moscow, respectively. The level of significance of this cross-cultural distinction is p < .001 (φ*-criterion with φ* emp = 2.89). This cross-cultural difference between the accuracy of prognoses data (New York vs. Moscow) was more obviously [p < .001 (φ*-criterion with φ* emp = 3.881)] for the group of informed respondents: they gave 14 (87.5%) and 4 (25%) predictions (from total 16 for each town) statistically non-significantly differing from reality for New York and for Moscow, respectively; probably the awareness of this group was based only on the published US data [for example, in English (Milgram & Sabini, 1978) and in Russian (Milgram, 2001)]. We found no distinctions between gender subgroups of the group of informed respondents concerning distribution of statistically significant and non-significant prognoses. Concretely: in each of these gender subgroups statistically significant distinctions of predicted results from real ones were noticed in the identical 1 of 8 (12.5%) and in the identical 6 of 8 (75%) cases for New York and Moscow, respectively; statistical significance of each of these 14 (7+7) distinctions had also the same level: p < .01 (bins). The group of uninformed respondents gave 8 (25%) and 5 (15.6%) predictions (from total 32 for each town) statistically non-significantly differing from reality for New York and for Moscow, respectively. However, this cross-cultural effect was statistically nonsignificant: p > .1 (φ*-criterion with φ* emp = .94). The distinction between cross-cultural (Moscow vs. Tashkent) subgroups of uninformed respondents concerning analogous distribution of statistically significant (10 vs. 3) and non-significant (22 vs. 29) differences amongst all of their 64 predictions (32 for Moscow respondents plus 32 for Tashkent ones) was at the level of statistical significance p = .012 (φ*-criterion with φ*emp = 2.256). The using SEM for analyzing all of the results Using SEM (Bentler, 2000) allows us to summarize separate findings made in previsions section, to bring together all differences and build a complete picture of individual fragments just as a large mosaic is made. The predictions made under various combinations of factors determining an experimental situation were considered observable variables. The latent dummy variables were created corresponding to each possible value of all variables determining the experimental situations. For variable city latent dummy variables Moscow, New York, Tashkent were created, for variable experimenter gender latent dummy variables Female and Male were created and so on. According to the theoretical model, each latent dummy variable, being the level of realization of the factor, determines observable variables (situations) with a corresponding level of realization of this factor (see Fig. 1). Fig. 1. The theoretical model describing the influence of all possible combinations of levels of realization of factors determining an experiment on the results of predictions Thus, all measured variables (the predictions about behavior of subjects) in New York are determined by the latent variable “New York”. All predictions about behavior of subjects with respect to an experimenter being a young man are determined by the latent variable “young man” and so on. Since each situation is determined by four factors, in the full model there are four arrows from any four latent variables entering in each rectangle (a dependent variable). The calculated model can differ from the full model because some determinations can be insignificant. The analysis will allow us to talk about convergent influence of the latent dummy variable corresponding to this or that value of each variable deterring experimental situation if the majority of predictions corresponding to this latent variable is confirmed. At the same time a “non-relevant” latent variable for this or that measured variable should not give significant predictions. Apart from this, the variables should not be completely synonymous or antonymous (i.e. the absolute value of the coefficient of correlation between them has to be less than 1). The structural model includes correlations between all latent dummy variables: characteristics of experimental situations, characteristics of respondents making predictions (their gender, age, residence). Having used the SEM we have found out: - which of the factor loadings are significant; - which of the correlations between factors determining an experimental situation are significant; - which of the correlations between factors determining an experimental situation and characteristics of respondents are significant. We should note that there are no cases when any loading should not be according the theoretical model but they are according to the data. The model is good (CFI = .919). If a latent variable loads a measured variable significantly, this means that the corresponding information (the value of this latent variable) affects significantly the corresponding prediction. The analysis of Table 5 allows us to make the following conclusions. The predictions are mostly dependent on the information about a city where an experiment was carried out. However, in Tashkent the most important information was the fact that the experimenter was a young man and the place of the experiment affected the predictions much less. Therefore, we can assert that there are convergent and divergent impacts of these latent variables. First, almost all measured variables were loaded by corresponding latent variables. Second, the latent variables corresponding to the cities where the experiments were carried out correlate between themselves, however, this correlation differs from 1 significantly (See Table 6). The information that the experimenter is a young man affects significantly the predictions in Tashkent, while in New York the predictions are affected significantly by the fact that the experimenter is a young woman. The latent variable “a subject is younger than 40” affects the predictions significantly in Moscow and New York, while the latent variable “a subject is older than 40” is very important for predictions in Tashkent. Significant factor loadings for each latent variable according to the experimental data with p-values < ,05 City Experimenter Subject City Experimenter Subject’s gender Subject’s age Gender Age New York Moscow Tashkent Young man Young woman Male Female Younger 40 Older 40 New York Young man Male Younger 40 .624 .254 .477 Older 40 .682 .119 .477 Female Younger 40 .771 .293 Older 40 .921 Young woman Male Younger 40 .672 .669 .223 Older 40 .779 .660 .102 Female Younger 40 .676 .146 .444 Older 40 .712 .310 .264 Moscow Young man Male Younger 40 .616 .244 .508 Older 40 .729 .527 Female Younger 40 .843 .329 Older 40 .896 -.164 Young woman Male Younger 40 .664 .654 .148 Older 40 .867 .688 Female Younger 40 .682 .682 Older 40 .628 .346 .384 Tashkent Young man Male Younger 40 .737 .464 Older 40 .548 .125 .205 Female Younger 40 .985 Older 40 .671 .417 Young woman Male Younger 40 .757 .648 Older 40 .825 .627 .130 Female Younger 40 .705 .513 Older 40 .577 .241 .520 Notes. 1. If, according to the theoretical model (Fig. 1), loading of a latent dummy variable on the measured variable has to exist and according to calculation on the experimental data this loading is significant we put the value of the loading in the corresponding cell of the table on the white background [all these loadings “on the white background” are positive as were expected; the only exclusion is significantly negative factor loading by female gender of the subject in the situation when young man asks older woman to give him a sit in Moscow subway (the value of this unique negative factor loading is highlighted in the corresponding cell in bold italics);the answer the question why in this situation the factor played opposite role is the subject of the future study]. 2. If the corresponding factor loading should not exist in the theoretical model and it is not significant according to the experimental data the corresponding cell is empty and white. 3. If the loading should be significant (according to theoretical model) but is not such according the empirical model the corresponding cell is grey color Correlation of independent variables (of the factors fixing the experiment and of respondent’s characteristics) City Experimenter Subject Respondent New York Moscow Tashkent Young man Young woman Male Female < 40 From Tashkent Female Age Prior knowing about the experiment City New York -.284 Moscow .832 -.266 .129 .092 Tashkent .698 .713 -.106 .088 Experimenter Male .634 .654 .616 Female -.304 -.324 -.310 -.384 -.394 Subject Male -.270 -.117 .249 Female .100 .352 -.126 .132 < 40 -.083 .237 .185 .463 .370 -.134 .118 > 40 .288 .335 .261 .225 -.147 -.307 Note. All correlations are significant: p-value < .05. It is worth mentioning that the information about a subject’s gender positively affects predictions if the experimenter’s gender is the same as the subject’s gender: 5 times from 6 possible cases for men and 6 times for 6 possible cases for women. Perhaps, it is a manifestation of gender solidarity. The positive response was predicted more often for young subjects in Moscow and New York (6 from 8), while in Tashkent for older subjects (3 from 4). The first part of Table 6 (the correlations of latent variables determining the experiment design) allows us to find correlations between attitudes to give a positive prediction. It is possible to assume that there is a general attitude to think that people will give a seat in a subway car if one is asked. This could happen in any city and in a majority of situations. So it reflects the attitude that people can break social norms and behave as persons (not members of a group). The latent variable “the experimenter is a young woman” stands out from this pattern. The correlations of this variable with all other latent variables are negative. Most probably this fact can be interpreted as a manifestation of a general custom to give a seat to a woman, i.e. we met the situation when respondent predicted the giving a seat not because of breaking social norm, but because of substitution of one social norm by another. Respondents thinking so give much higher predictions when the experimenter is a young woman. The right-hand side of Table 6 presents correlations between respondent’s characteristics and their attitudes toward results. We can say that respondents in Moscow are more likely to give positive prognoses (that a subject will give a seat) than respondents in Tashkent. And if to say about Tashkent’s respondents only that thinking about people who live in the same city with them better than about people from other cities. Female respondents expect more positive subject’s reactions in Tashkent. Relatively older respondents consider Moscow situations positively. But they are wary of men and younger subjects. Prior knowledge of the experiment makes predictions more accurate. Conclusion The results of the study confirmed our hypothesis about the non-triviality of the Milgram field experiment in the subway and indicated significant scientific value of this experiment. It is so surprising for the common understanding that even knowing about it can be implicitly corrected by an informed respondent (in these cases prognoses are lower than real result). Our assumption that respondents put themselves in the experimenter’s place needs to be checked. However, let us recall that the experimenters (according to their reports) felt uneasily to ask a subject to give them a seat. In some way it can explain the situation with understating results by respondents. As usual we think about someone sitting in subway as about the crowd and cannot image that asking him or her personally reveal personality attitudes and behavior (not group ones). Two gender differences were revealed and discussed during the analysis of the prognostic assessments: 1) prognostic frequencies of positive reactions on the request to give a seat to the young female experimenter are higher than the analogical prognostic frequencies but related to the young male experimenter, 2) this finding is less trivial one: our respondents (especially, uninformed ones) predicted the behavior of female-subjects in subway experiment less accurately than of male-subjects. The main cross-cultural aspect of our analysis (many informed respondents gave more precisely predictions of results of the New York experiments than of the Moscow ones) may be explained by the awareness of these respondents based only on the publications of US experiments. The using of SEM allowed us to summarize separate findings obtained in the course of the test analysis of our hypothesis of non-triviality to build a complete picture of interrelationships between all situational and individual characteristics of the survey and of the experiment. The data got during the survey gives material for future studying of the role of situational (experimental design) and individual (respondents’ variables) factors in prognosis estimations of empirical results of the experiments, makes impact on the understanding of the problem of forming and maintaining of informal social norms in different cultures and highlights new aspects of studies of these phenomena. Acknowledgments We thank Professor H. Takooshian (Fordham University, NYC, USA) for unpublished data reports of experimenters and observers who took part in the research in New York in 1972 (Takooshian, 1972). Also we thank Moscow psychologists G. Larina (Higher School of Economics - National Research University), I. Novikova (Peoples’ Friendship University of Russia), and V. Spivakovskiy (Lomonosov Moscow State University) for recruiting respondents into prognostic survey. The study was supported by grant Russian National Fond “Psychosemantic analysis of consciousness and the unconscious (on the material of general psychology, political psychology, psychology of art)” No. 17-18-01610.

About the authors

Olga V Mitina

Lomonosov Moscow State University

Ph.D. in Psychology, leading scientific fellow of Faculty of Psychology of Lomonosov Moscow State University (Moscow, Russia) Mokhovaya str., 11 (Bld 9), Moscow, Russia, 125009

Alexander Ya Voronov

State Academic University for the Humanities

Ph.D. in Biology, associate professor of Faculty of Psychology of State Academic University for the Humanities (Moscow, Russia) Yaroslavskaya str., 13, Moscow, Russia, 129366


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Copyright (c) 2017 Mitina O.V., Voronov A.Y.

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