iNCUMS‘SYENT STATUS QHARACTEMSTICS AND \NFLUENCE PROCESSES: _ A REPUCAT‘OM AND REFORMULMIOM “We-"sis for the Dams M M A. meme»: STATE UNWERSETY PAUL t-L mess ' x972 ' ...... c . . , LI B R A R y V Michigan State University . ABSTRACT INCONSISTENT STATUS CHARACTERISTICS AND INFLUENCE PROCESSES: A REPLICATION AND REFORMULATION By Paul H. Tress Theoretical and experimental social psychology in recent years has concentrated on how status variables affect the power and pres- tige orders of small groups. The chief finding of such investigations is that individuals who possess the higher state of a characteristic that differentiates members of a group are less prone to influence than individuals who possess low states of the characteriStic. Until recently, work has concentrated on the case of one char- acteristic, showing that the differences in acceptance of influence among group members is related to the status differential existing among the group members. Berger and Fisek (1970) have conducted an experiment with the intent of generalizing such a finding to the case of multiple characteristics. More specifically, Berger and Fisek used two characteristics in a dyad where the characteristics were relevant to the group's task. Our work was initially concerned with issues raised by the Berger and Fisek paper and resulted in a reformulation of the theory. In order to do this we first show the relationship between status characteristics and the pattern of interaction a group undergoes in a two-step decision task. If one member of the group changes his decision between the first and second step, the member is said to be influenced. Paul H. Tress Influence is related to possession of different states of status characteristics relevant to the group's task. Individuals can possess similar or dissimilar states of characteristics, that is, they can evaluate themselves according to whether or not they are univalent and possess the same state for each characteristic. We agree with Berger and Fisek that individuals who are univalent in the high state will be less prone to influence than individuals who are univalent in the low state. However, our major concern is the direction and nature of univalence in the multivalent or non-univalent case. Whereas Berger and Fisek argue an individual in such a case will combine the two char- acteristics or cognitively balance them, our opinion differs. Berger and Fisek fail to realize combining or balancing implies some univalent process that needs to be specified. Such a specification takes the form of inquiring into how information individuals have about each other on their differentiating characteristics is related to or maps onto some expected level of perfbrmance on the group's task. An experiment was conducted to force disagreement among subjects in a problem-solving task. The only information subjects had about each other were the states of the characteristics they and their partner in the dyad process. The resolution of such disagreement provides an indirect measure of influence. Despite some differences in the design of our experiment and Berger and Fisek's, the results are almost similar. Statistical analysis gives little insight into the nature of the univalent process. However, the process appears to be an independent trials process and reaches stability quite rapidly. INCONSISTENT STATUS CHARACTERISTICS AND INFLUENCE PROCESSES: A REPLICATION AND REFORMULATION By 3“" (I. L}. Paul HVWTress A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF ARTS Department of Sociology 1971 ACKNOWLEDGMENTS The ideas developed in this thesis have evolved over the past two years due to continual interaction with the members of the thesis committee. I am especially indebted to Dr. Thomas L. Conner, the chairman of the committee. Continual debate and dialogue with Dr. Conner resulted in an almost periodic re-evaluation of the intent and meaning of the thesis. The time spent in discussion with Dr. Conner was matched with time spent with other members of the committee. I would like to thank these members for their guidance and advice. These individuals are Drs. Bo Anderson, Santo F. Camilleri, and Hans B. Lee. The research reported in this thesis was sponsored by a Social Science Research Training Grant, N.I.H.M.H. lIUIO—02,administered by Dr. Camilleri. ii TABLE OF CONTENTS LIST OF TABLES . . . . . . . . . . . . . . . . . . LIST OF FIGURES . . . . . . . . . . . . . . . . . . . INTRODUCTION . . . . . . . . . . . . . . . . . . . . . GENERAL THEORETICAL CONSIDERATIONS . . . . . PROCESSUAL CONSIDERATIONS . . . . . . . . . THE EXPERIMENT . . . . . . . . . . . . . . . . . . . . Phase I O O 0 O I O O O O O O O O O O O O O O I 0 Phase II 0 I O O O O O O O O O O O O O O O O O O 0 RESULTS . . . . . . . . . . . . . . . . . . . . . . Subjects . . . . . . . . . . . . . . . . . . . Comparison of the Original Experiment and the Replication . . . . . . . . . . . . The Nature of Univalence . . . . . . . Processual Observations . . . . . . . . . . . . . SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . BIBLIOGRAPHY O O O O O O O O O O O O O O I O O C . APPENDIX A: INSTRUCTIONS GIVEN TO SUBJECTS . . . . . . Phase I . . . . . . . . . . . . . . . . . . Phase II . . . . . . . . . . . . Boardman: Part II . . . . . . . . . . . . . . . . APPENDIX B: INTERVIEW SCHEDULE . . . . . . . . . . Phase I . . . . . . . . . . . . . . . . . . . Phase II . . . . . . . . . . . . . . . . . . Debriefing . . . . . . . . . . . . . . APPENDIX C: REASONS FOR ELIMINATING SUBJECTS FROM SAMPLE O O O O O O O O 0 O O O O O C I O I 0 APPENDIX D: BERGER—FISEK HOST PROCEDURE: PHASE II . . iii iv 19 2O 22 27 27 27 29 3M ui as 1+7 1+7 53 58 60 so 61 62 64 66 LIST OF TABLES P(S), Mean, and Variance for Inconsistent Symmetric Studies 0 I O O O O O O O O O O O O O O O O . Tests for Combining of Data . . . . . . . . . . . . Previous Studies of Specific Characteristics . Distribution Vector and Observed Values for Critical Trials until Theoretical Stability . . . iv 28 29 30 37 10. 11. LIST OF FIGURES The Influence Process Arising from Initial Disagreement . . . . . . . P's Evaluation of Alternatives P's Evaluation and Selection of Alternatives Initial Evaluation and Selection of Alternatives Subsequent Evaluation of Self and Other . . . . . Observed Frequency of Stay Re8ponses Observed and Expected Frequency of Stay Responses, Given Two Parameters . . . Processual Matrices . . . . . Observed and Predicted Values of P(S) for Each Critical Trial . . . . . . Plot of Trial Blocks . . . Detail of Plot of Trial Blocks 10 10 11 ll 31 32 36 38 39 #0 INTRODUCTION Within the last decade, researchers have shown how experimentally induced status variables affect the power and prestige order of small groups. This work has attempted to formalize well-known influence processes in human interaction. The main finding of these studies is that individuals who possess the higher of two different states of a status characteristic will be less prone to influence than indi- viduals with a lower state of the characteristic.1 This process occurs whether the status characteristic is diffuse or specific. Diffuse characteristics are those that are not initially relevant to the group's task. As a diffuse characteristic, income level is not related to chess-playing ability. However, one would base his expectations of an individual's chess-playing ability on income level if no other basis of differentiation were present (Berger, Cohen, 8 Zelditch, 1966). Specific characteristics are those that are germane to the group's task throughout the task-focused activity of the group. High verbal ability would result in a high expectation for an individual possessing this ability, if the group's task were some type of word game, and if the relevance between the level of verbal 1This interpretation slightly contrasts with the statement that high status members exercise greater influence on the task than low status members. Our statement is more amenable to empirical verifi— cation; (See Berger 8 Fisek, 1970, p. 287, for an example of the above. ability and the expected performance on the word game were made explicit. The details of such influence processes have been investigated. However, such studies have concentrated on only one differentiating dimension, i.e., either one diffuse or one Specific status character- istic. A recent paper has tried to investigate such influence processes in the case of more than one characteristic. The work of Berger and Fisek (1970) was concerned with two characteristics in a dyad.2 Berger and Fisek were concerned with two specific types of arrays of the char- acteristics: (a) a consistent state of exact opposites, with one person having a high state of each characteristic and the other person having a low state of each characteristic; and (b) an inconsistent state of polarized mirror images. (One person is high on one characteristic and low on the other, while the other person is respectively low and high on these characteristics.) In the inconsistent case, each individual is faced with dissonant or incongruent information about himself and the other individual. Such inconsistency must be resolved. The bulk of the Berger and Fisek work was a concern with the resolution of such dissonance or incongruity. Our study has two concerns, a specification of the resolution process and an attempt to replicate Berger and Fisek's experiment. We will first discuss general theoretical formulations and then detail 2Most of the work done on the relationship of status character- istics and influence processes is in the dyad. Theoretical and methodological considerations with other size groups increase at an exponential rate. This is a limitation of work done on the formalization of influence processes. the resolution of the influence process in the inconsistent case. This will be followed by a description of our experiment. The experi- ment was designed to replicate Berger and Pisek's work and to give us some insight into the nature of the resolution process.3 Following this, our data analysis is given and conclusions about the nature of the influence process are drawn. 3Our replication is not concerned with other possible arrays. Included in this is the "status edge case" where one individual has consistent and the other inconsistent states of the characteristics. In this case, the characteristics are not in a symmetric array as in the inconsistent case (Berger 8 Fisek, 1970, p. 301). Berger and Fisek neglect to investigate two other cases: individuals may be exactly the same and consistent or inconsistent. Our replication is concerned only with the inconsistent polarized mirror image case. GENERAL THEORETICAL CONSIDERATIONS A brief discussion of theoretical considerations common to the relationship of status characteristics and influence processes is needed before we detail the nature of the influence process in the multiple characteristic case. In all cases we assume interaction takes place in a dyad. Such interaction is seen through the eyes of one actor whom we designate as p. The other actor is designated as o. P and o possess states of Ci’ a status characteristic. This will be denoted as Cl and C2 in the case of two characteristics. We will assume that interaction between p and o is task-focused and that p and o are oriented to each other. The interaction is task- focused in that p and o are oriented to successful completion of the task. The orientation of p and o to each other means that p and o evaluate each other's task performance and have expectations as to each other's future performance. At the initial part of the interaction process p and 0 have no prior knowledge of which Ci is instrumental to successful completion of the group's task. However, we assume p and 0 know that possession of some state of some specific characteristic is necessary to success— ful completion of the group's task. In order for some differentiating attribute of individuals to be considered a specific Ci’ three conditions must hold: 4 (1) Ci must have different states or degrees which are recog- nized by p and o. (2) P and 0 must associate each of these differentiated states with certain levels of expectations as to the future performance of himself and other on the task if task performance is related and rele— vant to possession of some state of Ci' (3) Knowledge of the states of C1 possessed by p and o generates general expectations to p and 0 about the personal qualities of indi- viduals who possess such states of Ci' For simplicity of analysis, we further assume each Ci in the case of multiple Cis is equally relevant to the group's task and that the Cis are differentiated in a dichotomized sense of high and low degrees. These degrees are designated by H and L respectively. Thus, X:ab designates person X's state of C and C . For example, p:HL, 1 2 o:LL means p is high on C1 and low on C2 respectively. PzHH, o:LL and p:HL, o:LH are examples of consistent and inconsistent symmetric cases respectively. Our major theoretical concern is the relationship between the Cis and influence processes within the group. It has been found that the rate at which p and o reject influence from each other in task performance activities is related to possession of high states of Ci’ if Ci is relevant to the group's task (Berger, Conner, 8 McKeown, 1969; Berger 8 Conner, 1970; Moore, 1969). To paraphrase Moore, the argument in its most general form thus asserts that the influence differential between S and 0 is a direct function of the status dif- ferential existing between S and 0 (Moore, 1969, p. 1M7; Moore's S is our p). In order to understand the nature of this "direct function," we need to detail the influence process. PROCESSUAL CONSIDERATIONS Our analysis of the process will be seen through the eyes of p. Let us assume that the group's task involves a Choice among a set of alternatives. More specifically, the task is binary with two alter- natives, A and B. With this basis we state the following: Assumption 1: An alternative positively evaluated by p (or 0) will be selected by p (or o). A negatively evaluated alternative will be rejected. Let us further assume that p and o are differentiated on some C18 and that these Cis are relevant to the group's task. The Cis are specific and the only information p and 0 have about each other. If p gives a performance output to o in the form of an attempted solution to some task problem, and if 0 has a positive reaction to p's performance output (or vice versa), we say that p and 0 have agreed on a solution to a task problem. If one member of the dyad has a negative reaction to the performance output of another, the members of the dyad are said to be in disagreement. If the solution to a problem involves an initial and final choice of alternatives by p and 0, initial and final agreement and disagreement are possible. Obviously, we are interested in the case of initial disagreement. If one member's selection is in initial disagreement with another member's selection, a performance output has been given from one member to the other in the form of a negative reaction to selection of alternatives. This may result in an influence process, since p 7 and 0 may change their evaluations of each other's initial perform- ance output. Figure 1 illustrates this process.” X(+R)X O‘1 X(PO)X X(IB)X 31 X(-R)X ”(130)? B2 Yp>.05 p:LH, o:HL 103 58 161 .640 .360 55 31 86 .640 .360 2u7 x2 = 0.0183 N = 13 n s.* pooled 208 128 336 .619 .381 120 57 . 177 .678 .322 513 x2 = 1.u99 N = 27 n.s. *Not Significant. Fig. 8.--Processual matrices. On the first trial, 92.6 per cent of the subjects made stay responses. That is, “0 = (.926, .074). By post-multiplying powers of our pooled transition matrix, the aggregate stochastic matrix, by no, we can predict P(S) for any trial of the process. In general, Wk gives the predicted distribution of P(S) and l-P(S) on the k+l 37 trial such that "k = Pkno, where P is the pooled transition matrix. Table 4 presents our predicted and observed values fer P(S). TABLE 4 DISTRIBUTION VECTOR AND OBSERVED VALUES FOR CRITICAL TRIALS UNTIL THEORETICAL STABILITY _k *1: ______j= Distribution Critical Trial Vector Observed P(S) 1 (.926, .074) .926 2 (.623, .377) .630 3 (.641, .359) .630 4 (.640, .360) .630 4+ (.640, .360) -- P, the pooled transition matrix, reaches a limit of (.640, .360). That is, um, the equilibrium vector, is (.640, .360). However, Table 4 indicates that this value occurs on the fourth and subsequent trials. Theoretically, this means the influence process stabilizes quite rapidly, with a stable value of P(S) = .640. In general, how does this projected stable value of P(S) = .640 compare to our observations? We can compare our sets of data by using a chi-square goodness of fit test. 'After combining categories we end up with a chi-square of 1 degree of freedom equal to 4.175. This is significant at the .05 level. This implies our observed data do not fit our expected distribution. This may indicate that the influence process is ngt_a process with l P(S), more specifically a P(S) of .640. However, this conclusion is not necessarily warranted, since the sample size is small. The value of .640 is not significantly 38 different from the pooled Tress value of P(S) = .654. Comparison of the two P(S)s via a critical ratio gives a Z-score of .4750. If we plot the observed values of P(S) on each of the critical trials, we can see if the process tends to stabilize as time goes on. This curve is plotted in Figure 9. The dotted line in Figure 9 represents the values for the distribution vector presented in Table 4. P(S) 1'0 P observed ' ____predicted .8 r 6 L - P(S) = .640 A A )4 n j l a A e A A r A A A A l I A 0 1 3 5 7 9 11 13 15 17 19 Critical Trial 20 Fig. 9.—-Observed and predicted values of P(S) for each critical trial. Since our sample is small, Figure 9's plot of P(S) on a trial by trial basis yields too great a variance. As Moore indicates, a plot of a cumulative P(S) curve will obscure trends, especially if they occur towards the end of the process (Moore, 1969, p. 149). Following Moore, we reach the following compromise: P(S) is plotted in blocks of seven trials. This is presented in Figures 10 and 11. Specifically, each point in the curve represents the proportion of 39 stay responses made by subjects in a sequence of seven trials. For example, the first point represents trials 1 to 7; the second, trials 2 to 8; the ith, trials i-6 to i; and the last point, trials 14 to 20.17 1.00