Social Psychology of Education 2: 237–261, 1999. © 1999 Kluwer Academic Publishers. Printed in the Netherlands.
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Attitude Change During College: Normative or Informational Social Influence? SERGE GUIMOND? Université Blaise Pascal, Clermont-Ferrand
Abstract. Students from a small, tightly knit military college participated in a longitudinal study which assessed attitudes toward sociopolitical issues as well as military attitudes at entrance and three years later. A college-wide change in a conservative direction was predicted and observed (p < .001). While a normative influence explanation argues that peer group pressure is responsible for such attitude change, an explanation based on informational influence would argue that the knowledge communicated by faculty also plays a role. The results confirmed the existence of peer group influence on measures of military attitudes but not on measures of sociopolitical attitudes. Rather, and consistent with a process of informational influence, the academic major pursued by the students emerged as a significant predictor of change in sociopolitical attitudes, regardless of reference group identification.
The social psychology of social influence has emphasized two general mechanisms through which social groups exert an influence on their members: a social process of normative influence, and a cognitive process of informational influence (Crano & Hannula-Bral, 1994; Deutsch & Gerard, 1955; Eagly & Chaiken, 1993; Leyens & Yzerbyt, 1997; Van Avermaet, 1996; Wood, Lundgren, Ouellette, Busceme, & Blackstone, 1994). Whether one considers research in the area of conformity, group polarization, attitude change, minority influence, or power, “the single most important theme that emerges,” writes Turner (1991, p. 143), “is the widespread acceptance of a distinction between normative and informational processes of influence.” In this article, I present a longitudinal study of attitude change that examines the role of these two types of influence in the explanation of the effects of college on students’ attitudes. EARLY ADULTHOOD AS THE IMPRESSIONABLE YEARS
Research carried out over the last three decades suggests that studying the socialpsychological effects of college may be central to an understanding of the role of attitudes and beliefs in social life. First, studies of the relations between age and ? This research was supported by a grant from the Social Science and Humanities Re-
search Council of Canada. Correspondence concerning this article should be sent to Serge Guimond, Universit´e Blaise Pascal, Laboratoire de psychologie sociale de la cognition, 34 avenue Carnot, 63000 Clermont-Ferrand, France. Tel: 4-73-40-62-51; Fax: 4-73-40-61-14; E-mail:
[email protected]
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attitude change have identified the young adult years as a period in life during which changes in sociopolitical attitudes are most likely to occur – the impressionable years (Krosnick & Alwin, 1989; Muxel, 1992; Sears, 1987). Second, this finding, however, does not necessarily mean that young people are inherently more impressionable than older adults. Rather, research indicates that older people may be equally open to change but less likely than young adults to encounter changeinducing events (Guimond, 1995a; Tyler & Schuller, 1991). Going to college has been suggested as a major change-inducing event that could explain why people are more likely to change between the ages of 18 and 25 (Krosnick & Alwin, 1989). Third, there is considerable evidence showing not only that attitudes do change during college but also that such changes are likely to be long lasting (Fendrich, 1974; Hyman & Wright, 1979; Hyman, Wright, & Reed, 1975; Pascarella & Terenzini, 1991). In fact, Newcomb’s Bennington study reveals that the effects of college on sociopolitical orientation can persist for the remaining of people’s lives, that is, up to 50 years after graduation (Alwin, Cohen, & Newcomb, 1991; Newcomb, 1943; Newcomb, Koening, Flackts, & Warwick, 1967). Given such long-term consequences, questions relating to the socio-psychological processes that can explain attitude formation and change during the college years emerge as critical ones. Yet, little research has been undertaken to illuminate this issue. As Eagly and Chaiken (1993) noted in concluding their extensive review, one of the main omissions in current attitude theory and research is “the lack of attention to the developmental issue of how attitudes are formed and become strong” (p. 681). They argue that naturalistic studies of the Bennington-type, “carried out in settings in which people develop strong attitudes are badly needed to understand how attitudes crystallize and become strong “ (p. 681).
EXPLAINING ATTITUDE CHANGE DURING COLLEGE
The explanation of college impact currently dominant is based on a process of normative influence (Altemeyer, 1988; Feldman & Newcomb, 1969; Pascarella & Terenzini, 1991; Thistlethwaite, 1973). For example, Newcomb (1958) argued that the liberalization of attitudes that was observed at Bennington was best explained by considering which groups the students used as a reference for their attitudes and values. Analysis of interviews with Bennington students were taken to suggest that those who took the college community as a positive reference group became more liberal, while those who kept their family and friends outside the college as a reference group did not change in this way. Thus, Newcomb et al. (1967) stated that “one of the most important factors in attitude change is the individual’s relationship with ‘reference groups’ ... For the college students, two of the most important reference groups seem to be the family and campus peers, especially friends” (pp. 162–163). Note that college professors are not mentioned. In their review of research carried out between 1970 and 1990 on college impact, Pascarella
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and Terenzini (1991) similarly emphasized the importance of the peer group in order to explain attitude change among students. Such accounts of the effects of college clearly stress the role of normative influence as opposed to informational influence. Students would change their social and political views to conform to the standards set by their peers, the change being motivated presumably by a desire for acceptance and social approval – the need to be liked (Insko, Drenan, Solomon, Smith, & Wade, 1983). While this account is certainly plausible, it is surprising that so little attention has been given to an alternative, equally plausible, explanation based on informational influence. In this view, students would change because of the information to which they are exposed, and their motivation would be a concern with being right (Insko et al., 1983). As Krech, Crutchfield, and Ballachey (1962) observed: ... the political-economic liberalism of the majority of the Bennington College upperclassmen should not be interpreted as due solely to their strivings for group acceptance and status. The faculty provided the students with new information about political and economic issues and events. ... Undoubtedly, the attitudes of many students changed because of changes in their cognitions. (pp. 252–253) This statement suggests that the academic program itself, that is the various faculty members and courses to which students are exposed, may represent an important source of influence. However, researchers have given little attention to the role of these variables in a setting such as the Bennington College, and most interpretations of Newcomb’s results continue to focus almost exclusively on normative influence of the peer group (Aebisher & Oberlé, 1990; Brown, 1988; Forsyth, 1990; Mackie, Worth, & Asuncion, 1990; Ross & Nisbett, 1991; Turner, 1991; Zimbardo, Ebbesen, & Maslach, 1977).
REEXAMINING THE ROLE OF REFERENCE GROUP IDENTIFICATION
In order to test these two different accounts of attitude change, a longitudinal study was carried out in a college similar in several respects to Bennington College. Both were small size institutions (less than 800 students), “self-sufficient and selfcontained,” as Newcomb puts it, in the sense that students lived on the college grounds, shared every activity and every meal, and were relatively isolated from the external community. However, instead of being a liberal arts college, the setting of the present study was a military college whose central aim was to train and educate individuals who would fulfill the role of officers in the Canadian Armed Forces. Thus, while there was a liberal norm at Bennington, one could expect to find a more conservative orientation within the college studied here (Kriesi, 1989). Indeed, past research consistently indicates that military students are more conservative in their sociopolitical views than civilian university students (Dorman, 1976; Goertzet & Hengst, 1971; Palmer, Guimond, Baker, & Bégin, 1989). My
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first hypothesis (Hypothesis 1), then, was that attitude change within this college would be in the direction of greater conservatism. A second important aim of the present study was to reexamine the reference group explanation of attitude change proposed by Newcomb (1943, 1958) which, theoretically, should apply as well to change in a conservative direction as it applied to change in a liberal direction. No direct test of the link between identification with a reference group and attitude change can be found in the original research (Newcomb, 1943; Urry, 1973). In contrast, participants in the present investigation were asked to indicate the degree of their identification with their peers at the college, with their family and friends outside the college, and with several other groups. Friendship patterns were assessed as well as the extent to which students were active within the college life. Consistent with an explanation based on normative influence, I expected (Hypothesis 2) that the more students identified with their peer group and the more they were integrated within the college life, the more their attitudes would be likely to change in a conservative direction. ACADEMIC TRAINING AS A PROCESS OF INFORMATIONAL SOCIAL INFLUENCE
If, in the first place, the rather definite attitude changes of Bennington students are primarily the result of certain kinds of academic interests or of college work pursued, e.g., Social Studies, the significance of those changes is quite different from what it would be if the changes were a community-wide phenomenon. (Newcomb, 1943, p. 38) I have stressed the importance of informational influence in order to account for attitude change among students. Consistent with this perspective, I hypothesized (Hypothesis 3) that attitude change would differ according to the academic program pursued by the students. While most military students major in engineering, a minority of them study humanities/social science (economics, political science, English literature and history) and, thus, are more likely to be exposed to information about political and economic issues. Consequently, I predicted that engineering students would change in a conservative direction, while humanities/social science students would change in a more liberal direction. Research among students from larger civilian universities have shown that the social sciences have a liberal effect on attitudes and social cognitions (Feldman & Newcomb, 1969; Guimond, 1992, 1995b; Guimond, Bégin, & Palmer, 1989; Guimond & Palmer, 1990; Guimond, Palmer, & Bégin, 1989). However, whether such results could be obtained in the present study was of considerable theoretical interest because a normative influence perspective predicted little or no effect of academic majors in a small college such as the one I studied here. From that theoretical viewpoint, group size is a key variable that affects group cohesion and ultimately determines the extent to which the peer group is able to exert influence (Newcomb, 1962). Thus, in large institutions where there may be considerable social or even geographic distance between departments, a student is
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likely to come into contact mainly with other students from the same department and “may not be familiar with any academic environment save that of his own major department” (Feldman & Newcomb, 1969, p. 190). In those conditions, a normative influence explanation would predict that attitudes do change as a function of these academic divisions because these divisions entail a segmentation of social relations. In contrast, in small institutions, such as Bennington or the setting of the present study where students live on the college ground and interact continuously together as officer cadets of a particular squadron all wearing the same military uniform, attitude changes are expected to be a community- or college-wide phenomenon (Astin, 1986; Bereiter & Freedman, 1962; Feldman & Newcomb, 1969; Newcomb, 1943; Thistlethwaite, 1973; Webster, Freedman, & Heist, 1962). As Bereiter and Freedman (1962) put it: “in a relatively small, tightly knit college (...) the effect of the total college experience greatly outweighs the influence of particular subjects or departments” (p. 585). Developed on the basis of field research, such a position is clearly supported by experimental research on group cohesion which demonstrates that group size has an effect on group cohesion and that group cohesion increases conformity to the group’s norms (Baron, Byrne, & Watson, 1995; Forsyth, 1990; Guimond, 1994; Kiesler & Kiesler, 1969; Newcomb & Wilson, 1966; Schachter, 1951). It follows, then, that from the classic normative influence perspective, the college peer group will be more cohesive and will exert stronger pressures in a small college as opposed to a large one leading to the prediction of college-wide changes in a small institution and little effect of academic major. In fact, Newcomb (1943) considered whether attitudes differed according to the academic major pursued at Bennington. He concluded (chapter 6) that these differences were slight and that the changes occurring were college-wide, providing empirical justifications for theoretical arguments such as those briefly summarized above. However, from the perspective of a process of informational social influence, group size and group cohesion have little relevance and do not lead to differing predictions. The argument put forward here is that, if the effects of academic majors are seen as a form of informational influence reflecting the impact of the differing courses and faculty members to which students are exposed, then these effects should occur regardless of institutional size. In other words, to the extent that these effects are due to the content of the program of study, then they should occur even within a small military college, as the academic programs offered within such a setting are equivalent to the programs offered at any other larger civilian institutions (in terms of textbooks, faculty who are almost exclusively civilians, or course content). This position implies that the academic program should also have played a role at Bennington, and I will argue that it did. In the last chapter of his book, Newcomb (1943) suggested that the knowledge communicated to the students played an important role at Bennington. He explains that the
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... degree of liberalization which occurs there is to be traced, originally at least, to faculty attitudes (...) in the sense that faculty were concerned to make students aware of their contemporary world (...) The writer would put great emphasis upon this point. The stress upon the contemporary world was, during the years of this study, marked in the areas of literature and the arts as well as in social studies. (p. 175) This conclusion about “the stress upon the contemporary world” that was “marked” in certain fields of study is clearly at variance with the rest of the book which emphasizes the role of status, prestige, and reference groups in accounting for the change. But it is consistent with the hypothesis that academic major did play a role at Bennington. Indeed, on the main measure of conservatism that he used, Newcomb’s major findings represent a change of 11.8 points between mean scores of students as freshmen (M = 74.2) and as seniors (M = 62.4), where a high score indicates greater conservatism. Yet, the spread in mean scores that he reported between juniors and seniors (combined) according to field of study was even more impressive, with students in science having a mean score of 74 and students in literature having a mean score of 58.4 for a difference of 15.6. Clearly, the social and political attitudes of students in science, even after three or four years at Bennington, were as conservative as the average entering freshmen, while the attitudes of students in literature were as nonconservative if not more so than the average students leaving Bennington. As indicated by the beginning quote of this section, the significance of these differences, although well understood by Newcomb, were never taken into account in subsequent analysis. The aim of the present study is to provide a test of this alternative hypothesis. If the college peer group is the main source of attitude change in a small college, then one should observe college-wide changes and little effect of academic major. This prediction should translate in a two-way interaction of peer group identification by time. If, to the contrary, the effects of academic major are not reducible to a peer group effect, then one should also observe – consistent with Hypothesis 3 – a two-way interaction of academic major by time, suggesting that attitude change is also a function of academic major.
Method PARTICIPANTS
English-speaking students from a Canadian military college served as participants in the research. The study was carried out in two phases. Phase 1 occurred during the first week of class and tested all entering first-year students (n = 123). The testing occurred in a classroom setting involving 20 to 25 students at a time. Phase 2 occurred two-and-a-half years later and tested, through an internal mail survey, 81 third-year students who had participated in first year (65% of the initial sample).
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QUESTIONNAIRES AND PROCEDURE
Students were asked to participate in a survey by filling out a questionnaire as part of a program of longitudinal research. Written instructions indicated that the study was designed to investigate the attitudes of young people towards a large variety of social issues. Emphasis was put on the fact that all answers were confidential and that individual information was not going to be released to any civilian or military authorities. In addition to background information, the questionnaire used in Phase 1 contained six attitude scales representing the basic dependent variables of the study that were also part of the questionnaire used in Phase 2. One scale concerned military attitudes and five scales dealt with attitudes and beliefs toward various issues (homosexuality, unemployment, poverty, criminality) that had been defined as central to the concept of sociopolitical orientation (Alwin et al., 1991; Kerlinger, 1984). These five scales are referred to here, collectively, as measures of liberalism or sociopolitical attitudes, and I describe each of them in turn. Sociopolitical Attitudes and Beliefs First, respondents completed a 16-item measure of attitudes towards homosexuals adapted from Bégin, Tremblay, and Lavoie (1981) who presented evidence demonstrating its reliability and validity. Items such as “Acceptance of homosexuality is a sign of the moral decadence of our society” were rated from 1 (strongly disagree) to 5 (strongly agree). Ratings on this and seven other negatively worded items were reversed so that a high score indicated a more liberal response. Average Cronbach’s alpha for Phase 1 and 2 was .92. Second, a further 16 items, also rated from 1 (strongly disagree) to 5 (strongly agree), measured attitudes towards convicts and ex-convicts (Bégin & Couture, 1980). There were two subscales: a 10-item measure of willingness to interact with ex-convicts, and a six-item measure of punitiveness/leniency (see Palmer et al., 1989). While the former subscale dealt with behavioral intentions (e.g., “I would give an ex-convict a job”), the latter concerned how severe should be the punishment for those who engage in criminal activities (e.g., “I want to see a return to the death penalty”). Extensive testing of the reliability and validity of these scales have also been carried out (see Bégin & Couture, 1980; Palmer et al., 1989). In the present study, average Alpha for the “interact” subscale was .85 and .76 for the “punish” subscale. Again, negatively worded items were reversed so that a high score indicated a more liberal attitude (or less punitiveness). Finally, two scales were used to measure respondents’ causal attributions for poverty and unemployment, based on previous research (Guimond & Palmer, 1990; Guimond, Bégin & Palmer, 1989). Students were asked to rate the importance of various factors as explanations of unemployment and poverty. A scale of individualistic explanation (or internal attribution) was constructed from the importance given by respondents to four items describing characteristics of the poor or the unemployed (e.g., “Poor people do not try hard enough”). Average Alpha for this
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scale was .64. A scale of situational explanation (or external attribution) was derived from the importance accorded to social, economic and political conditions (5 items, average Alpha of .50). While these scales have less than optimum reliability, they have been shown in the past to be significant predictors of policy preferences and actual behavior, such as signing a petition (see Guimond & Palmer, 1996a). The higher the score on these attributional scales, the more respondents believe that dispositional and/or situational factors are important to explain poverty and unemployment. Consequently, a liberal attitude here corresponded to giving greater importance to situational factors (Zucker & Weiner, 1993). Military Attitudes The final scale, the military ethos scale (Cotton, 1981), had a somewhat different conceptual status in that it did not relate directly to conventional definition of liberalism/conservatism, and for this reason, it was treated separately in the data analysis. It was composed of six items dealing with professional military values (e.g., “Military personnel should perform their operational duties regardless of personal and family consequences”). The higher the score on a five-point agree-disagree format, the more respondents shared the military ethos in that they defined military service as a special vocation calling for a pervasive commitment. Average Alpha for this scale was .62. Previous research indicated that scores on this scale were reliable predictors of relevant behavioral intentions among officer cadets (Guimond, 1995a). Additional Measures The questionnaire used in Phase 2 contained several measures intended to help in the interpretation of the nature of the expected attitude change. In terms of normative influence, the measures taken had several distinct formats. First, respondents were presented with a list of 17 group labels. They were instructed to go over the list carefully and then to indicate on a five-point scale (1 = not at all, 5 = very strongly) how strongly they identified or felt a closeness towards each group. Figure 1 presents the mean ratings given for each group. Consistent with Newcomb et al. (1967), the two groups receiving the highest ratings, and presumably important reference groups, were family and friends at the college. In the sample as a whole, identification with friends outside of the college or with professional role models (e.g., “Canadian Forces officers”) appeared less important. A scale of peer group identification was constructed using identification ratings given to four of these groups (“college friends,” “my squadron,” “officer cadets,” and “members of the Canadian Forces”) and the answers provided to three further items which were found to be highly interrelated. These three items were: (a) the extent to which respondents feel they have things in common with other students at the college (1 = very little, 5 = very much); (b) how strong respondents believe
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Figure 1. Strength of identification with 17 group labels among third-year military students.
their ties were with other students at the college (1 = very weak, 5 = very strong); and (c) how often they attended or participated in college activities (1 = never; 5 = very often). Alpha for this scale of peer group identification composed of seven items was .78, indicating a satisfactory level of internal consistency. Finally, respondents reported how many of their friends, from “none” (1) to “almost all” (5), were officer cadets in engineering, officer cadets in other areas, civilian students in engineering, civilian students in other areas, or nonstudents. In terms of informational social influence, the students were asked to specify their academic major and to name what they considered the most important course in their area of study. Also, four items sought to measure how students evaluated their program of study by asking them to rate the quality of course content and teaching in their area of study, from “very poor” (1) to “excellent” (5); respect felt for professors teaching these courses from “very little respect” (1) to “the utmost respect” (5); and level of interest in their courses from “not interested at all” (1) to “extremely interested” (5). Reliability Alpha for this measure was .75. Finally, the questionnaire in Phase 2 measured the respondents’ own beliefs about the “power” of five agents of influence. On a scale from “very little” (1) to “very much” (5), students were asked to indicate how much their ideas have been influenced (a) “by the content of the courses you have taken”; (b) “by the professors teaching those courses”; (c) “by contact with your fellow students”; (d) “by information obtained through the media or extra-curricular reading”; and (e) “by contact with officers of the college.” Items (a) and (b) were relevant to a
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Table I. Mean Scores on Measures of Peer Group Cohesion and Perceived Influence as a Function of Institutional Size
Measures Things in common with peers Ties with peers Campus activities Perceived influence peers Perceived influence courses Perceived influence professors Perceived influence other info. Age
Size of institution Small college Large university 3.4 3.9 3.9 3.5 2.8 2.6 3.3 21.8 years N = 78
3.2 3.2 3.5 3.2 2.7 2.5 3.3 21.7 years N = 287
F 4.45∗ 33.69∗∗∗ 13.87∗∗∗ 9.49∗∗ 1.33 2.21 0.03 0.57
Note. For all measures except age, means can vary from 1 to 5.
∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.
process of informational influence and, as expected, they correlated positively with students’ evaluations of the quality of their academic program as measured by the 4-item scale described above (r = .28, p < .01, r = .30, p < .01, respectively). Item (c), perceived influence of fellow students, was relevant to the classic normative influence explanation and, as expected, it correlated positively with the scale of identification with the peer group (r = .35, p < .01). Items (d) and (e) were relevant to other potential sources of influence and did not correlate with either identification with peers or the evaluation of the academic program. Peer Group Identification and Institutional Size An important argument of the present analysis is that the peer group at the college studied, like other small size institution, is likely to represent a more cohesive unit and, thus, a more powerful source of normative influence than is the case in larger institutions. In order to validate that assumption, civilian students in a neighboring university 10 times larger in terms of its student population were asked to answer a questionnaire similar to the one answered during Phase 2 of the present study. Table I presents a comparison of these two populations on identical measures taken at the same time period, that is, toward the end of the third academic year. It can be seen that students in the present study (at a small college) felt they had more in common with other students than did those who came from a larger institution. They also felt that their ties with their peers were stronger, that their fellow students were a more important source of influence, and that they were more active in campus events than students from a large university. In contrast, there were no significant differences between the two groups in terms of age, perceived influence
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of course content, professors, or extracurricular readings. Thus, the assumption that the peer group is a more cohesive unit in a small institution was validated.
Results Because this study is concerned with attitude formation and change, the analysis of the results focuses on the longitudinal participants. However, students who took part only once were used to examine two potential problems associated with longitudinal research. The first problem concerns the question of attrition (Campbell & Stanley, 1966). Some students participated in the first year but not in the third year because of nonresponse, because they left the college, or for other reasons. If they differed in systematic ways from the longitudinal participants, then the results may have been biased in a particular manner. To estimate the extent to which such attrition effects were present, first-year once-only respondents (n = 38) were compared to the longitudinal respondents (n = 74) on their first-year attitude scores. No significant differences were found, suggesting that longitudinal participants were similar to their peers who dropped out of the study. A second potential problem concerns the effects of testing. Answering the same questionnaire several times might sensitize the respondents and lead them to give different answers in the second testing as compared to the first (Cook & Campbell, 1979). To examine this possibility, I collected data from fourth-year students, some of whom were participating in the study for the first time (n = 69), while others had participated in the first and third year (n = 72). No significant differences were found between these two groups of respondents on the measures of sociopolitical attitudes, suggesting that test-retest is not a factor affecting the response of longitudinal participants. However, on the military ethos scale, fourth-year students longitudinal respondents were found to score higher (M = 2.86) than their peers participating for the first time, M = 2,63, F (1, 139) = 8.15, p < .01). The remaining analyses, carried out using longitudinal participants only, are presented in three sections. The first two sections use repeated-measures MANOVA to establish the significance of change over time and to test the three main hypotheses of this study. This procedure has the advantage of allowing one to test the effect of time and other variables on several dependent variables simultaneously. However, because of the small size of this academic institution, some programs of study concerned very few students (less than five). Testing the effects of academic major thus required dropping these students. On the other hand, all students answered the peer group identification measure, and so, analyses using this measure are presented in the first section, without considering academic major, in order to use the full longitudinal sample. The second section then examines the role of academic major, contrasting the response of engineering students to those in humanities/social science and leaving aside eight respondents with other majors. The third and final section uses regression analyses to examine the relative importance of various predictors of attitude change (e.g., peer group identification
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Table II. College-wide Attitude Change Between the Beginning of the First Year and the Third Year in College (n = 68)
Attitudes
Mean scores First year Third year
F
Homosexuals Convicts-interact Convicts-punitiveness/leniency Individualistic explanation Situational explanation
2.85 3.54 2.73 2.98 3.37
0.09 5.03∗ 19.04∗∗ 1.85 19.83∗∗
2.88 3.42 2.44 3.10 3.02
Note. Univariate F-tests with (1,66) D.F. (exclusion of missing data account for the reduced n). All mean scores can range from 1 to 5, with a higher number indicating a more positive attitude or greater attribution. ∗ p < .05, ∗∗ p < .001.
vs. academic major) on the reduced sample using as dependent measures only the attitude scales on which significant changes over time were obtained in previous analyses.
ATTITUDE CHANGE AND PEER GROUP IDENTIFICATION
The first hypothesis about overall attitude change in a conservative direction and the second hypothesis about peer group effects were tested by means of a 2 × 2 MANOVA for a repeated measures design, with level of peer group identification as a first factor (low vs. high identification based on a median-split) and time (first year vs. third year) as the second factor. Hypothesis 1 suggested that a main effect of time should be obtained while Hypothesis 2 implied that this main effect should be qualified by an interaction effect of peer group identification by time. Using attitudes towards homosexuals, attitudes towards convicts and ex-convicts, and the scales of individualistic and situational explanations as dependent variables, this analysis indicates no overall effect of peer group identification, F(5, 61) = 0.59, ns, no interaction between peers and time, F(6, 62) = 1.22, ns, but a significant main effect of time, F(5, 62) = 6.59, p < .001. Table II presents the mean attitude scores at Time 1 and at Time 2 and univariate tests of significance. Consistent with Hypothesis 1, students became less liberal or more conservative from Time 1 to Time 2. More specifically, Table II shows that attitudes towards convicts became less favorable. In particular, scores on the scale of leniency decreased indicating a more punitive orientation in the third year as compared to the first year. Scores on the scale of situational explanation also decreased significantly indicating that students were less likely to blame the system for unemployment and poverty in the third year than they were in the first year. In contrast, no change appeared on attitudes towards homosexuals and on individualistic explanation.
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Figure 2. Change in military professional attitudes by level of identification with college peers.
As indicated above, because of a different conceptual status, results on the military ethos scale were analyzed separately in a peer group identification (low vs. high) × time (first year vs. third year) repeated-measures ANOVA. A significant main effect of peer group identification was observed, F(1, 75) = 6.35, p < .01. The effect of time was marginally significant, F(1, 75) = 3.48, p < .10, and the peer group identification by time interaction was significant, F(1, 75) = 4.82, p < .05. Figure 2 presents this interaction. It can be seen that little change appeared on the military ethos scale among those who did not identify with their peers (Ms = 3.26 at Time 1 and 3.24 at Time 2; t(37) = 0.25, ns). In contrast, students who did identify strongly with other students at the college were found to become more committed to the military ethos from the first (M = 3.41) to the third year (M = 3.68, t(38) = -2.69, p < .01). Moreover, cross-sectional analysis indicates that in the first year, military ethos scores of low and high identification groups did not differ significantly (F (1,75) = 1.02, ns) while a clear difference was found in the third year (F(1,75) = 13.06, p < .001). Support for Hypothesis 2 predicting that peer group identification would explain attitude change was found then on the measure of military orientation but not on the measures of sociopolitical orientation.
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Figure 3. Change in leniency toward convicts, by academic major.
ATTITUDE CHANGE AND ACADEMIC MAJOR
Hypothesis 3 argued that attitude change is a function of the academic major pursued. To examine this hypothesis, subjects were classified as majoring in humanities/social science (n = 18) or engineering (n = 52) on the basis of their reported academic major in the third year. The measure asking students for the name of their “most important course” served to check on the validity of this classification, and no major inconsistency emerged. However, eight students having other majors (three in strategic studies and five in commerce), being too few in numbers, were dropped from this and other analyses using academic majors (thus accounting for the reduced number of degrees of freedom in the analysis of academic major). Ratings on the five scales of liberalism were analyzed in a Major × Time repeated-measures MANOVA, paralleling the analysis of the effect of peer group identification reported above. The multivariate main effect for academic major, F(5, 55) = 1.38, ns, and for time, F(5, 55) = 1.94, ns, were found to be nonsignificant. But a significant multivariate interaction was observed, F(5, 55) = 2.86, p < .05. Univariate tests indicated that this interaction was significant for the measure of leniency towards convicts, F(1,59) = 12.04, p < .001, and for the scale of situational explanation, F(1,59) = 4.21, p < .05 – the same two variables on which college-wide change was observed in the preceding analysis (see Table II). As Figure 3 indicates, engineering students became more punitive from the first (M = 2.71) to the third year (M = 2.33, t = 5.75, df = 50, p < .001) while
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Figure 4. Change in the importance given to situational causes of poverty and unemployment, by academic major.
humanities/social science students showed a nonsignificant tendency to become more lenient (Ms = 2.78 and 2.89 in the first and the third year, respectively, t = -.82, df = 18, ns). Similarly, Figure 4 shows that humanities/social science students maintained their belief in a situational explanation of poverty and unemployment from Time 1 (M = 3.38) to Time 2 (M = 3.34, t = .40, df = 17, ns), but engineering students became less likely to believe in the importance of situational factors from their first (M = 3.31) to their third year of study (M = 2.87, t = 4.65, df = 47, p < .001). Cross-sectional analyses indicated that in the first year, humanities/social science students did not differ significantly from engineering students on the measure of punitiveness (F = 0.02, ns) or on the measure of situational explanation (F = 0.08, ns). However, because of a differential change over time, in the third year, humanities/social science students were significantly less punitive (F = 5.81, p < .05) and more likely to blame the system (F = 6.79, p < .01) than engineering students. First- to third-year mean change scores can mask a variety of patterns of individual changes (Feldman & Newcomb, 1969; Guimond & Palmer, 1994). Consequently, I did further analyses to compare the number of students within each field of study who became more conservative (e.g., negative change score) to those who did not (e.g., neutral or positive change score). On the measure of situational explanation, I found that the scores of most engineering students decreased over
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Table III. Mean Scores of Humanities/Social Science and Engineering Students on Measures of Peer Group Identification and Informational Influence
Measures
Peer group identification Academic program Perceived influence peers Perceived influence courses Perceived influence professors Perceived influence other info. Perceived influence officers
Academic major Humanities/ social science Engineering
F
3.76 4.12 3.61 3.33 3.33 3.39 2.28
0.02 7.13∗∗ 0.03 3.91∗ 9.13∗∗ 0.44 0.97
3.72 3.67 3.42 2.60 2.37 3.21 2.12
Note. Means can vary from 1 to 5.
∗ p < .05, ∗∗ p < .01.
time (67%) while only a minority of them maintained or increased their ratings on that scale (33%). Within humanities/social science, the reverse was observed with most students (61%) maintaining or increasing their score and only a small proportion showing a decrease (39%; X2 = 4.14, df = 1, p < .05). Similar significant differences were found on the measure of punitiveness. Within engineering, most students (76.5%) showed a decrease in score (23.5% either maintained or increased their scores), while among humanities/social science, in contrast, most students (61%) maintained or increased their scores from Time 1 to Time 2, and only 39% showed a negative change score (X2 = 8.13, df = 1, p < .01). In contrast to the results on sociopolitical attitudes, there was no significant effect of academic major on military ethos, F(1, 67) = 1.75, ns, no effect of time, F(1, 68) = 2.67, ns, and no major by time interaction on this scale, F(1, 68) = 0.08, ns. Support for Hypothesis 3 was found, then, on measures of sociopolitical orientation but not on the measure of military orientation.
ACCOUNTING FOR ATTITUDE CHANGE BETWEEN FIRST AND THIRD YEAR
Having established the independent effect of peer group identification on the one hand and academic major on the other, I now consider their relative importance and the extent to which one may account for the other. If the effects of academic majors are explainable in terms of a process of normative social influence, then engineering students should identify more strongly with their peers, and humanities/social science students may feel more isolated from the rest of the college community. On the other hand, if a process of informational influence is at play, then humanities/social science students should be more likely to perceive their courses and their professors as a significant source of influence on their own ideas.
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Table III shows that there is little evidence consistent with the role of normative influence. Humanities/social science students appeared to be as well integrated within the college community as engineering students. They identified strongly with their peers, and they perceived them as an important source of influence, as did engineering students. In contrast, support for the role of informational influence was found in that humanities/social science students perceived their academic program as being of higher quality, and they attributed greater influence to course content and faculty compared to engineers. No differences were found on the other perceived influence measures. A series of regression analysis were performed in order to provide a more direct test of the relative importance of normative and informational influence. The three scales on which change was observed in previous analyses were selected as dependent variables – that is, the third-year scores on the scales of leniency, situational explanation, and military ethos, respectively, were used as criteria. Firstyear scores on the relevant dependent variable were entered in the equation in a first step followed by the peer group identification scale in a second step. Academic major (humanities/social science coded 1 and engineering coded 2) was entered in a third and last step. By entering first-year attitude in the initial step, this hierarchical method of analysis allowed for an estimation of the extent to which subsequent variables entered in the regression could explain variations in third-year attitudes over and above the initial first-year attitudes (thus accounting for attitude change). Moreover, by entering peer group identification in a second step, these analyses capitalized on the continuous nature of this variable and tested whether peer group identification, on its own, could account for attitude change. Finally, by including academic major in the last step, it was possible to discover the increase in explanatory power, if any, afforded by this variable over and above any contribution made by peer group identification. Table IV presents the results. All F change scores in the table represent the unique contribution of the effect above and beyond that which was accounted for by variables in the previous step (Cohen & Cohen, 1983). On the military ethos scale, the top panel of Table IV indicates that the initial first-year scores on this measure accounted for 26% of the variance in third-year scores. More importantly, it reveals that peer group identification accounted for an additional 16% of variance. Thus, peer group identification was a highly significant predictor of change in military attitudes (ß = .41, p < .001), but academic major was not (ß = .12, ns). Entering academic major in a third step does not improve prediction. The middle panel of Table IV indicates that first-year scores in punitiveness accounts for 48% of variance in third year scores on this measure. In contrast to the results for military ethos, entering peer group identification in a second step did not improve prediction, while entering academic major in a third step increased significantly the amount of variance explained by 9%. Thus, academic major was a significant predictor of change in punitiveness (ß = -.31, p < .001).
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Table IV. Predictors of Attitude Change Between First and Third Year Regression steps
R
R2 change
F change
[Dependent variable = Military ethos at time 2] 1. Military ethos at time 1 .51 .26 24.39∗∗∗ 2. Peer group identification .65 .16 19.24∗∗∗ 3. Academic major .66 .01 1.79 [Dependent variable = Punitiveness at time 2] 1. Punitiveness at time 1 .69 .48 63.06∗∗∗ 2. Peer group identification .70 .01 2.11 3. Academic major .77 .09 15.90∗∗∗ [Dependent variable = Situational explanation at time 2] 1. Situational explanation at time 1 .37 .14 10.29∗∗ 2. Peer group identification .37 .00 0.15 3. Academic major .50 .11 9.43∗∗ Note. Probabilities are for test of increment to variance accounted for at each step, that is, when variables enter the equation. ∗∗ p < .01, ∗∗∗ p < .00.
The last panel in Table IV shows that similar results are obtained when change in situational explanation is examined. Again, peer group identification, on its own, did not account for a significant proportion of variance over that accounted for by the first-year scores on the measure while academic major accounted for an additional 11% (ß = -.34, p < .01). These results clearly confirm and extend those obtained using the MANOVA procedure. Even when peer group identification is taken into account, academic major does predict change in sociopolitical orientation. Discussion Consistent with my first hypothesis, overall attitude change in a conservative direction was observed. Students at this military college were found to become increasingly favorable toward using punitive measures to deal with criminals. They also became less likely to attribute fault to the system in order to explain poverty and unemployment. These results confirm previous research revealing the existence of attitude change during the young adult years and suggest that if students in a liberal college become more liberal, those in a more conservative institution, such as a military academy, are likely to change in a conservative direction. No change was observed on attitudes towards homosexuals and on individualistic explanation. These results should be interpreted with care. Evidence of change among the sample as a whole cannot be attributed unambiguously to the college experience, and evidence of no change does not necessarily mean that college has no impact,
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a point to which I return below (see Feldman & Newcomb, 1969; Guimond & Palmer, 1994; Guimond & Palmer, 1996a). The main findings of the present study relate to the variables which can account for the formation of attitudes. The theoretical basis for my second hypothesis, postulating the existence of peer group effects, was perhaps the strongest. A long tradition of research in social psychology suggests that social groups have considerable power over their members and that cohesive groups exert greater influence than noncohesive groups (Forsyth, 1990; Schachter, 1951; Stephan & Stephan, 1990). In terms of military professional orientation, I found relatively strong evidence in support of such a process. Students who identified with their peers in the sense that they participated actively in college life and felt a closeness towards other students at the college were more likely to internalize the “military” point of view. Because Newcomb’s study did not include direct measures of identification with a reference group (Urry, 1973), these results provide an important confirmation of his explanation of attitude change. Moreover, given that research other than Newcomb’s purporting to show the effect of a reference group on attitudes have tended to use retrospective measurement for both reference group identification and attitudes (Singer, 1981), my use of a longitudinal design gives stronger support to the hypothesis. The fact that first-year scores on the military ethos scale did not predict later peer group identification provides important information consistent with the hypothesis that reference group identification leads to attitude change rather than the reverse. Since Newcomb’s research was carried out 60 years ago, such confirmation of his work using more objective evidence is remarkable given the claim that research findings in social psychology are unstable or dependent on particular historical contexts (Gergen, 1973). On the other hand, I also found evidence suggesting possible limitations with such analysis. The results also suggest that peer group identification does not explain changes in sociopolitical orientation. One of the crucial findings of the present study is that taking into account academic major does lead, as expected, to a rather different pattern of attitude change. The two variables on which overall change was observed in a conservative direction (Table II) turned out to be two variables on which differential effects of academic majors were observed. Thus, what appeared to be a college-wide change in a conservative direction was, in fact, a change occurring within the dominant field of study at this institution – engineering. There is no evidence suggesting that humanities/social science students became more conservative. The results, then, are consistent with my analysis of the original Bennington study and contrary to the interpretation of Newcomb’s results that is usually proposed. As suggested in the introduction, the clear prediction that can be made from a normative influence interpretation of Newcomb’s findings is that in a small, tightly knit community, college-wide attitude change should be observed with little effect of academic major. Since college peers are identified as the main source of change, it follows
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that attitude change should be a community-wide phenomenon in such a setting; and Newcomb clearly emphasised that this is what occurred at Bennington. On the other hand, if the main source of change, consistent with some form of informational social influence, is identified as college faculty and courses, then one can expect that attitude change will not be a community wide phenomenon but will vary according to academic major. Consistent with this analysis, I have shown that, even though the college peer group is indeed more cohesive in a smaller institution and that it does exert influence, sociopolitical attitudes are, nevertheless, changing as a function of academic major and regardless of this cohesiveness. In short, the first point to note about these results is that they confirm the need to reexamine certain classic findings in social psychology. Since academic major is obviously related to attitude change at this college, “the significance of those changes is quite different from what it would be if the changes were a community-wide phenomenon” as Newcomb (1943, p. 38) himself pointed out. A striking feature is selectivity in the observed effects: peer group identification had a significant influence on military orientation, not on sociopolitical attitudes, while academic major was related to sociopolitical orientation, not to military attitudes. These results suggest that two different processes may have been at work, one (normative) affecting military attitudes, and another (informational) affecting sociopolitical attitudes and beliefs. It may be argued, however, that while engineering students became significantly more conservative, humanities/social science students showed no significant attitude change whatsoever. Taken at face value, these results suggest that the engineering program had a strong effect in a conservative direction, while the humanities/social science program had little effect. I suggest that this apparently straightforward conclusion may be misleading, and that an alternative interpretation, more consistent with past research, is that the lack of change among humanities/social science students nevertheless reflects an impact of this program of study (see Guimond & Palmer, 1996a, 1996b). In discussing theoretical and methodological problems associated with studying the psychological impact of education, Feldman and Newcomb (1969) have distinguished between six different types of attitudinal change that can occur as effects of education. They range from the “conversion” of an attitude to the “neutralization” of an attitude. Particularly relevant here is the “maintenance” of an attitude, the case where there is no observed change on the attitude continuum. Feldman and Newcomb (1969) state that a finding of no change on the continuum “does not necessarily mean that nothing has happened, or that the college as a whole or some parts of it had no impact” (p. 55). Rather, they argued that the attitude may have a firmer basis in the student’s consciousness, or it may have become more strongly related to other attitudes and beliefs. When this occurs, attitude maintenance really reflects what Feldman and Newcomb (1969, p. 55) called “reinforcement of an attitude” (emphasis in original). Similarly, it can be argued that, given the general change in a conservative direction that is occurring at this college (see Table II), the absence of such a change
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among humanities/social science students may reflect a strong countervailing effect specific to that field. Whether or not the lack of change in terms of mean scores represents a field effect, the different proportion of students within each field whose change score is negative suggests that an educational effect has occurred. Within engineering, more than 76% of the students showed a negative change score on the scale of leniency/punitiveness, but among humanities/social science students, only 39% did the same. My ability to document this process is nevertheless limited in part because these humanities/social science students constituted a small sample, while the engineering group was almost three times larger. But research carried out in other settings where larger samples of students in social science existed have also revealed attitude maintenance that, on closer inspection, turned out to be attitude reinforcement (see Guimond & Palmer, 1994, 1996a, 1996b). Students who did not follow the liberal trend at Bennington College were shown by Newcomb to be less well integrated within the college community. No such evidence was found here to account for the fact that humanities/social science students did not follow the conservative norm. Engineering students did not differ from humanities/social science students in their level of peer group identification or in the perception of the importance of their fellow students as a source of influence. But they differed strongly in their appreciation of their academic program and in the perception of the importance of course content and professors as sources of influence. As a rule, humanities/social science students are more likely than others to rely on their faculty and their courses as epistemic authority (Bar-Tal, Raviv, Raviv & Brosh, 1991; Kruglanski, 1989). These relations, however, are clearly not unique to the context of the present study. In a longitudinal study carried out among commerce and social science students from a larger and more typical civilian university, Guimond and Palmer (1996a) also found evidence of the impact of academic major on sociopolitical attitudes and attributions. Moreover, they reported that the perceived influence of course content is the single main variable that can account for such effect. For example, in a way somewhat analogous to the behavior of humanities/social science students in the present study, they found that commerce students did not change significantly from the first to the third year in their tendency to explain unemployment and poverty by internal causes. Yet, concluding that the program of study has no effect on causal attributions would have missed a fundamental point because two subgroups within this sample of commerce students, differentiated by the extent to which they reported being influenced by course content, were observed to change in the opposite direction on this measure. Those who perceived course content to be influential were significantly more likely to blame the poor and the unemployed in the third year than they were in the first year, and a change in the opposite direction was also significant for those who reported course content to be less influential. Moreover, as in the present study, the perceived influence of other students could not account for the effect of academic major on attitude change. While it can be argued that the
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peer group in the context of the Guimond and Palmer (1996a) study was lacking cohesion and, thus, could not be expected to exert a strong influence, such is not the case here. Thus, the significance of the present results derives from the fact that they were obtained in a setting which could be expected to favor normative social influence, where the college peer group was cohesive and did exert influence. A theory of normative influence would seem unable to account for the fact that the perceived influence of course content relates to attitude and belief change. However, because I did not fully control any of those variables, experimental research will be needed to test some of the claims that I have put forward. For example, the scale that I used to measure the evaluation of the academic program contained items referring to courses but also referring to those who teach the courses. Because faculty provide knowledge, their influence can be seen as informational. Indeed, research showing that children are more likely to conform to their peers than to their professors were widely seen as revealing a form of normative influence (peers) being stronger than a form of informational influence (professors) (see Kiesler & Kiesler, 1969). But of course, professors also provide rewards and sanctions. Their influence can, thus, be of a normative nature. My results raise this problem without suggesting any definite answers. Future experiments could vary influence attempts by professors and by peers in order to discover their effects. It is clear, however, that if Newcomb, after the Bennington study, concentrated his interest on peer group influence, not on the influence of faculty or course content (see Newcomb, 1962; Newcomb & Wilson, 1966), there are now grounds to argue that research be carried out on the role of these other sources of influence of a more academic nature. In particular, the role of knowledge needs to be reconsidered, and experiments need to examine the effects of course content on attitudes and cognitive processes. This would perhaps help to reinforce the argument of Asch (1952/1987) who also thought that at Bennington “the students were responding to events, to knowledge and information” (p. 600).
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Biographical Note Serge Guimond is a professor of social psychology at the Université Blaise Pascal in Clermont-Ferrand, France. He received his Ph.D. in social psychology from the Université de Montréal, Canada. His research interests include the effects of higher education on attitudes and beliefs and, more generally, the social psychology of intergroup and intercultural relations.
