Expectation States Theory and Research - SAGE Journals

Expectation States Theory and Research New Observations From Meta-Analysis

Sociological Methods & Research Volume 35 Number 2 November 2006 219-249 Ó 2006 Sage Publications 10.1177/0049124106290311 http://smr.sagepub.com hosted at http://online.sagepub.com

Will Kalkhoff Kent State University, Ohio

Shane R. Thye University of South Carolina, Columbia

Over the past 50 years, the expectation states research program has generated a set of interrelated theories to explain the relation between performance expectations and social influence. Yet while the relationship between performance expectations and social influence is now axiomatic, the reported effects do differ in magnitude, sometimes widely. The authors present results from the first formal meta-analysis of expectation states research on social influence. Their findings indicate that theoretically unimportant study-level differences alter expectation states and the baseline propensity to accept or reject social influence. Data from 26 separate experiments reveal that protocol variations, including the use of video and computer technology, sample size, and the number of trials, have important but previously unrecognized effects. The authors close by discussing the more general implications of their research for future investigators. Keywords: analysis

status; expectation states; rewards; social influence; meta-

xpectation states theory is a leading explanation of social influence in sociological social psychology. According to the theory, individuals in newly formed problem-solving groups (e.g., committees and juries) form a shared set of performance expectations for one another. These expectations are nonconscious and taken-for-granted beliefs about

E

Authors’ Note: This research was presented at the 100th annual meeting of the American Sociological Association, Philadelphia, August 2005. We thank Joseph Berger for comments on an earlier draft. Correspondence may be directed to Will Kalkhoff, Department of Sociology, Kent State University, P.O. Box 5190, Kent, OH 44242-0001; e-mail: [email protected].

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how likely it is that each group member will contribute to success at the task. Important sources of performance expectations include differences in actual ability, unequal reward allocations, evaluations of task performances, and differences in culturally valued status characteristics, such as age or race. Once formed, performance expectations are theorized to determine differential rates of social influence in such groups. In essence, because they are perceived as more competent, the most influential members of a task group tend to be those who are (1) more able, (2) more highly rewarded for their participation, (3) more favorably evaluated, and (4) of higher status (Wagner and Berger 1993). Empirically, the relationship between performance expectations and social influence is robust, but reported effects do differ in magnitude. One recent laboratory study suggests that at least some of the variance in social influence may be due to protocol variations that unwittingly affect theoretical components (Troyer 2001). Recent statistical advances allow us to explore this possibility with exceptional rigor. While there are a few published articles that summarize findings from expectation states research on social influence (e.g., Balkwell 1991a; Fisek, Norman, and Nelson-Kilger 1992; Fox and Moore 1979), none to date has employed the formal tools of ‘‘meta-analysis’’ (Glass 1976; Hunter and Schmidt 1990; but see Jackson, Hunter, and Hodge 1995 for a related study). Meta-analysis represents a family of statistical models that allows researchers to determine the consistency of empirical findings within a given research program (Bryk and Raudenbush 1992), controlling for sampling bias and theoretically irrelevant sources of measurement error. The first step is to determine if any observed inconsistencies across studies reflect sampling error. If not, the second step is to formulate a model to account for systematic sources of bias, which may include measurement errors or other study artifacts. A useful procedure for partitioning variance in this manner is hierarchical linear modeling, or HLM (Bryk and Raudenbush 1992).1 While originally introduced by Lindley and Smith (1972) as a general statistical approach to nested data with complex error structures, ‘‘It is natural to apply hierarchical models to meta-analytic data because such data are hierarchically structured: subjects are ‘nested’ within studies. Models are needed to take into account variation at the individual and the study level’’ (Bryk and Raudenbush 1992:156). In what follows, we present the first formal meta-analysis of expectation states research on social influence. Using data from 26 distinct experiments, we employ HLM 5 (Raudenbush et al. 2000) to decompose the inconsistency of reported results into that associated with sampling error, performance expectations, and ‘‘Level 2’’ differences in study protocol

Kalkhoff, Thye / Expectation States Theory

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and methodology. Importantly, the latter are theoretically unimportant and thus should be treated as sources of error that complicate ‘‘true score’’ effects (Thye 2000). Such sources include the use of video versus computer technology, the number of task trials, and the number of study participants. Furthermore, because social influence and performance expectations are uniformly measured in this tradition, the original scaling can be retained (i.e., it is not necessary to transform the reported effects into standardized ‘‘effect sizes’’ prior to conducting the meta-analysis). As such, we also estimate the true score values of two parameters central to the expectation states research program. The first is m, which estimates the baseline tendency for a given population to reject social influence. The second is q, which estimates the linear effect of expectation advantage on influence. Future researchers can use these new estimates as a benchmark to assess the (ir)regularity of their own results.

Theoretical Background Systematic research surrounding the expectation states research program began with efforts to understand the emergence of influence and prestige gradients in small groups (for an overview, see Wagner and Berger 2002). Early investigators found that stable interactional differences emerged quickly among initially homogeneous actors working together on a group task (Bales 1950; Bales et al. 1951). These studies led to the development of expectation states theory, which introduced the core concept of a performance expectation (i.e., beliefs about an actor’s future task performance). Performance expectations are not necessarily conscious. Rather, they are ‘‘unaware hunches about whose suggestions are likely to be better’’ (Ridgeway and Walker 1995:288). Performance expectations are postulated to shape differentiation along an observable power and prestige order. Today, there are distinct lines of theory that explain how status characteristics, rewards, and external evaluations produce expectations and behavioral differences in groups (Balkwell 1991b; Berger, Cohen, and Zelditch 1972; Berger, Conner, and Fisek 1974; Berger, Wagner, and Zelditch 1985; Ridgeway, Berger, and Smith 1985). We now briefly review each line of work, respectively.

Status Characteristics Theory Status characteristics theory (SCT), one branch of the larger expectation states program, connects culturally specified beliefs to performance

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expectations and social influence. The theory defines two kinds of status characteristics. For example, gender operates as a diffuse status characteristic in a particular culture if and only if (1) one state (e.g., male) is more highly valued than the other state (female); (2) men are expected to be more competent at specific tasks, such as sports; and (3) men are generally expected to be more capable at a wide range of tasks. In contrast, a specific status characteristic is one that satisfies conditions (1) and (2) but not (3). For example, math skill is a specific status characteristic when being good at math is preferable to being bad at math and when one expects a math expert to be competent at other numerical tasks. The theory applies to two or more individuals working on a task constrained by four scope conditions (Berger et al. 1977:95): (1) Group members are motivated to successfully complete the task, (2) actors believe there is an instrumental characteristic required to complete the task, (3) the task is clearly evaluated in that outcomes are defined as a success or failure, and (4) the task is collective, meaning that the actors deem it both necessary and legitimate to take into account one another’s opinions in forming a task solution. Numerous tasks fall within these conditions, from officers who conduct police investigations (Gerber 2001) to committees charged with evaluating job applicants (Foschi 1996). Numerous other tasks have been created for laboratory investigations (see below for details). At the core of SCT are five logically connected assumptions that explain how status characteristics translate into power and prestige orders and associated behaviors (e.g., Webster and Foschi 1988): (1) A status characteristic becomes salient if it differentiates members of a task group or is directly related to the task at hand, (2) a salient status characteristic is relevant to the task unless explicitly disassociated from the task, (3) status information is incorporated and/or maintained according to the principles described in assumptions 1 and 2 above as individuals enter or leave the group, (4) relevant characteristics combine to form an aggregated performance expectation associated with each member, and (5) aggregated performance expectations give rise to observable differences in social interaction. Those with relatively higher performance expectations are predicted to receive more opportunities to perform, perform more often, be evaluated more positively, and have greater influence over the group’s decisions. Numerous tests and replications have supported the basic claims of the theory (Wagner and Berger 2002). SCT employs a graph-theoretic modeling procedure that illustrates how status characteristics produce performance expectations. Central to this formulation is the notion of a relevance bond, defined as an expected


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Figure 1 Graph-Theoretic Representations of a Status Situation P

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association between two elements in the graph (Freese and Cohen 1973). Relevance bonds are represented by lines that link elements in the situation such as status characteristics, expectations of competence, and task abilities. These lines form paths connecting actors to task outcomes. SCT graphs typically begin with actors (denoted P and O) who possess some number of specific status characteristics (C) or diffuse status characteristics (D). Graphs also include points to represent beliefs about the instrumental ability (C) required to complete the task (T ). SCT graphs use Greek letters to represent beliefs about generalized and specific performance expectations. Furthermore, each element in the graph has a valence (+/−) to denote the state of the element: high/desirable (+) or low/undesirable (−). All lines in a graph are assumed to be positive, except for dimensionality bonds that always connect oppositely valued states of a possessed status characteristic (D or C) and are always negative (see Berger et al. 1972). Figure 1 illustrates a graph involving two actors and one salient diffuse status characteristic, D. Notice that P possesses the high state of the characteristic (D +), which is relevant to beliefs regarding generalized competence ( +). Such beliefs, in turn, become relevant to the high state of the instrumental ability (C +) that is associated with task success (T +).2 By contrast, O possesses the low state of the diffuse characteristic (D −), which is associated with generalized incompetence ( −). In turn, these beliefs become relevant to the low state of task ability (C −), which is associated with an unfavorable task outcome, (T −). In this way, SCT uses a graph-theoretic model of a situation to connect actors and their status characteristics to positive or negative task outcomes. We now review how such graphs are used to calculate performance expectations from salient status characteristics.

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Note that P is connected to the positive task outcome by a path of length 4 (P— D + — + — C + — T +). Furthermore, P is connected to the negative task outcome by a path of length 5 through the dimensionality bond (P— D + — D − — – — C − — T −). Because the graph is symmetrical, the opposite is true for O, who is connected to (T −) by a path of length 4 and to (T +) by a path of length 5. The overall valence of a given path is determined by the product of the signs next to each line along the path and the sign of the task outcome, T ð+Þor T ð−Þ. Thus, the valences of the 4-path and 5-path linking P to the task outcomes are both positive, and the valences for O are both negative. Positive paths contribute to positive expectations for an actor, while negative paths detract from an actor’s performance expectations. SCT specifies an aggregated expectation advantage from path lengths and valences. The theory assumes that actors behave as if they combine all positively evaluated information from positive paths into subsets (denoted e+ p ) and all negatively evaluated information from negative paths into distinct subsets (denoted e− p ). This is done for each actor in the situation, including self (Berger et al. 1992). The theory presumes that actors combine the subsets they have formed for each actor, including self, into a single quantity representing aggregated expectations. For a given situation, P’s aggregated expectation value (ep Þ is calculated in accord with equations (1) through (3): e+ p = f1 − ½1 − f ðiÞ . . . ½1 − f ðnÞg;

ð1Þ

e− p

ð2Þ

= −f1 − ½1 − f ðiÞ . . . ½1 − f ðnÞg;

ep = e+ p

+ e− p ;

ð3Þ

− where e+ p = the subset of positive (desirable) information; ep = the subset of negative (undesirable) information; i; n = path lengths; f ðiÞ; f ðnÞ = monotonically decreasing functions of i and n; and ep = Ps aggregate expectation value. The theory assumes that all actors in the situation undergo the same status organizing process, and so the aggregate expectation for O is calculated in the same fashion. When P is higher status, the relative standing of P vis-a-vis O (termed ‘‘Ps expectation advantage over O’’) is simply calculated as the difference between the two values, ep − eo . In computing the expectation advantage, however, researchers have used various values for f ðiÞ through f ðnÞ. Berger et al. (1977) provide values estimated from empirical data, while Balkwell (1991a) and Fisek et al. (1992) provide


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theoretically derived values. Using Balkwell’s (1991a) values, we calculate Ps expectation advantage for the status structure shown in Figure 1.3 We do this by substituting the values (f ð4Þ = :1504 and f ð5Þ = :0498) into equation (1) and solve as e+ p = f1 − ½1 − :1504 · ½1 − :0498g = :1927:

ð4Þ

As actor P possesses no negative status information, the aggregate expectation value for P is simply ep = :1927 0 = :1927. Because the status structure shown in Figure 1 is symmetrical, Os aggregate expectation value is eo = −:1927. Thus, Ps expectation advantage is ep − eo = :1927 − ð−:1927Þ = :3854:

ð5Þ

Status structures more complex than those displayed in Figure 1 are conceivable, and in such cases, the calculation of the expectation advantage can be tedious, particularly as the number of characteristics grows large. However, Whitmeyer (1998) and Walker (1999) have developed programs for calculating expectations in complex status structures, and Balkwell (2000) provides an easy-to-use online tool for calculating expectations. Most recently, Whitmeyer (2003) has developed an analytic model that can be used without the need for graph-theoretic analysis.

Reward Expectations Theory Within the expectation states tradition, reward expectations theory (RET) focuses on how status characteristics, abilities, and task accomplishments correspond to expectations for rewards (Berger, Fisek, Norman, and Wagner 1985). The theory assumes that whenever rewards are distributed in a situation, an ‘‘ability referential structure’’ is activated wherein actors expect that more able group members will be more highly rewarded for their participation. Interestingly, RET posits that the process also works in reverse; that is, actors infer ability from reward allocations. For example, knowing only that one doctor earns $120,000 annually, while another earns $35,000, may suggest that the former is more capable. In general, the theory suggests that actors who receive greater rewards for their task contributions are expected to be more competent than less highly rewarded group members. A graph of this situation is shown in Figure 2. Figure 2 introduces two new elements: goal objects (GO) and rewards (R). States of goal objects (GO + or GO −) represent specific realizations of rewards in a given situation, whereas states of rewards (R + or R −)

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Figure 2 Graph-Theoretic Representation of the Relations Between the Allocation of Goal Objects and the Formation of Expectations P

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represent the more abstract state of reward levels. For instance, when P is more highly paid than O for her or his task performance, P possesses the positive state of a specific goal object (GO + ). States of pay (GO) are connected by relevance bonds to more abstract states of reward levels (R). Given the activation of an ability referential structure, states of reward levels (R) are expected to be associated with states of the instrumental task ability (C), which is relevant to the task outcome (T ).4 The theory employs the same path-counting procedures and mathematical formulas described in the previous section to calculate Ps expectation advantage relative to O. While some of the elements in this situation are different, the path lengths and valences for the graph shown in Figure 2 are the same as those found in Figure 1. Thus, Ps expectation advantage is the same in both graphs.

Evaluations and Expectations Theory Fisek and colleagues (Fisek, Berger, and Moore 2002; Fisek, Berger, and Norman 1995) have integrated several bodies of work within the expectation states tradition to describe how performance evaluations create expectations. In the first version of evaluations and expectations theory (EET), Fisek et al. (1995) combined expectation states theory and status characteristics theory with Webster and Sobieszek’s (1974) ‘‘source theory’’ to explain how expectations are created by evaluations from designated, external evaluators. In doing so, they introduced two new concepts into the vernacular of expectation states theory: valued role and imputed possession. An actor who occupies a valued role in a task situation has the ‘‘right and responsibility to evaluate the performances of the interacting actors in


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the situation and is a source for them’’ (Fisek et al. 1995:728). The evaluator role is represented by a new graph-theoretic element, Ve+ (see Fisek et al. 1995:736). The second new concept, imputed possession, is defined as follows: ‘‘An actor, P, has imputed possession of a status element if another actor, O, for whom P’s expectations are positive, communicates evaluations indicating that the actor P possesses the element’’ (Fisek et al. 1995:730). The concept of imputed possession implies that O will be a source of performance expectations for P only when P holds positive aggregated expectations for O. Thus, evaluations will affect Ps expectations only when O has an overall expectation advantage over P (i.e., taking into account all salient status information, including that O occupies a valued role). When an external evaluator communicates a positive evaluation of an actor’s task performance, this ‘‘imputes’’ to the interactant the high state of the ability instrumental to task success. If the evaluation is negative, then the imputed state of the instrumental ability is negative. Imputed possession is indicated by a dashed line rather than a solid line in the graphs (see Fisek et al. 1995:736). The strength of an imputed status element’s effect on expectations depends on the length of the possession line, where shorter lines have stronger effects. The idea of a variable-length possession line is a unique detail in EET. In other expectation states theories, all relations—including possession, relevance, and dimensionality—are represented by a line of maximum strength and length 1. However, because imputed possession from evaluations will most often be less strong than possession in the typical sense (and can never be stronger), the length of an imputed possession line cannot be less than 1 and will likely be longer. The exact length of the imputed possession line is represented as an inverse continuous function of the aggregated expectations held for the evaluator, as given by l = 1=eα ;

ð6Þ

where e is the aggregate expectation state value for the evaluator, and α is an empirical constant estimated to be .83 (see Fisek et al. 1995:734).5 A more recent version of EET (Fisek et al. 2002) incorporates ideas from Moore’s (1985) theory of ‘‘enactment and second-order expectations’’ and explains the formation of expectations in situations, where the sources of evaluations are not only designated evaluators but also other task interactants who may or may not be higher status than the actor receiving the evaluation. Even when evaluations come from an equal- or lower-status coactor who does not occupy a valued role (Ve+ ), they may still lead to the formation of performance expectations through ‘‘enactment.’’ The concept

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Figure 3 Graph-Theoretic Representation of a Behavior Interchange Pattern Between Two Actors P

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of enactment captures the idea that actors ‘‘become the roles they play’’ (Fisek et al. 2002:332). Thus, regardless of the status differences, two actors can develop evaluation-based expectations by acting toward one another in behavioral cycles that reflect evaluations. Fisek et al. (2002) argue that the concept of a behavior interchange pattern (BIP), introduced in an earlier publication (Fisek, Berger, and Norman 1991), can be used to represent enactment in graph-theoretic terms. Figure 3 shows the graph of a BIP between two actors, P and O. The new status element, b, embodies the idea that an actor will behave in such a manner as to ‘‘enact’’ role expectations (in this case, ‘‘evaluations’’) projected by another actor. States of behavior patterns become relevant to task outcomes through corresponding states of ‘‘status typifications,’’ (B +) and (B −). These are ‘‘abstract conceptions [held by actors] of what high- and low-status behaviors are like’’ (Fisek et al. 1991:118). Thus, if P has received positive feedback from O, P may behaviorally enact such an evaluation by being assertive and expecting O to change her or his decisions when the two disagree. Such typifications become relevant to corresponding states of abstract task ability, (Y +) and (Y −), which in turn become relevant to the equivalent states of task outcomes, (T +) and (T −). Once again, expectation advantage is calculated using the same path-counting procedures and mathematical formulas described previously.

Common Features of the Research Paradigm Dozens of studies have examined the effects of status, rewards, and evaluations on expectation formation and social influence. Most of these


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studies have made use of a common set of experimental procedures, referred to as the standardized experimental setting (SES). The SES comprises a standardized set of (1) instructions for research participants, including statements devised to minimize random measurement error and satisfy the theoretical scope conditions; (2) directions for introducing and experimentally manipulating sources of performance expectations (i.e., status, rewards, and evaluations); and (3) procedures for implementing one of several binary-choice tasks designed to measure social influence. Central to the SES are directions for introducing and experimentally manipulating sources of performance expectations (the key independent construct). Because individuals may infer performance expectations from physical appearance, demeanor, and nonverbal cues, they never come face-to-face with one another during the study. Instead, to manipulate ‘‘diffuse status’’ as a source of performance expectations, an experimenter may lead each research participant to believe that her or his partner possesses the higher or lower state of some characteristic, such as education or age. In this manner, researchers presume that only manipulated factors determine systematic differences in performance expectations across experimental conditions. Other features of the SES control specific sources of variance associated with the task. For instance, all participants are informed that they will be working with a partner on a collective task that involves joint decision making. To reduce preconceptions, participants are told that task ability is not correlated with other known abilities, such as skill in mathematics or language. The specific task used to measure social influence varies from project to project, but generally, there are four different tasks (e.g., contrast sensitivity, relational ability, spatial judgment, and meaning insight). Though subtly different, the tasks are similar in that each is ambiguous and used to establish the possibility that some instrumental ability exists and is needed for task success. Participants are always shown a series of binary-choice decision-making items and led to believe that there is only one correct response. However, in reality, there is no objectively ‘‘correct’’ response because the task is not veridical. The specifics of the task are as follows. Research participants are shown a stimulus panel and asked to decide on the correct answer. They begin by making an initial choice. Next, they are allowed to view what is presented as their partner’s initial choice. In reality, the experimenter controls the feedback that research participants receive. Typically, a task will involve 25 separate problems, 20 of which are staged as ‘‘disagreements’’ between a research participant and her or his ostensive partner. These

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disagreements are referred to as ‘‘critical trials’’ because they can be used to measure influence. After seeing the partner’s decision, the focal participant must then enter her or his final choice. If a research participant does not change her or his initial choice to match the partner’s choice on critical trials, she or he is said to have rejected the partner’s influence. Thus, the dependent variable, rejection of influence, is measured as the proportion of critical trials on which a research participant ‘‘stays’’ with her or his own initial response given disagreement from a partner. The proportion of stay responses, or PðSÞ, is calculated by dividing the number of times that the participant stays with her or his initial answer by the total number of critical trials (typically 20). This index can range from 0 to 1, where higher values reflect less susceptibility to social influence. The effect of performance expectations on PðSÞ is often given as a linear function, PðSÞ = m + qðep − eo Þ;

ð7Þ

where ep and eo represent the aggregate expectation values for actors P and O.6 The terms m and q are treated as empirical constants that are typically estimated from data. In principle, these values reflect (1) the baseline population tendency to reject social influence and (2) the effect of the expectation advantage on influence associated with different situations, respectively. Importantly, both m and q may be sensitive to differences in study-level factors caused by sampling error, random measurement error across settings, and other theoretically nonrelevant factors. Variation in these empirical constants may also reflect differences in the protocol itself. Such differences are important because, if shown to exist, they would systematically bias empirical findings and blur subsequent theoretical interpretation. At issue is whether such biases exist and, if so, the degrees to which such forces alter research findings across different settings.

SES Variations and Unintended Consequences Webster (2003) has identified three main versions of the SES: the Basic setting, the Video setting, and the Computer setting. In the Basic setting, communication between pairs of research participants is controlled by means of an interaction control machine (ICOM) or comparable system. With the ICOM, each research participant is provided with a console consisting of buttons and lights. Participants use the buttons to submit initial and final choices for task problems, and the lights indicate their own as well as their partners’ initial choices (i.e., the lights designate whether an


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agreement or a disagreement has occurred on a task problem). The system is set up to deliver agreements or disagreements between the participants according to a predetermined sequence. For example, if both participants make the same initial choice on a ‘‘critical trial’’ (i.e., a trial that calls for a disagreement), the experimenter uses the ICOM to inform each participant that her or his partner has initially made a different choice. The host panel then displays each participant’s final choice, and the experimenter records all initial and final choices. In the Video setting, research participants are led to believe that they will introduce themselves to one another via a closed-circuit television system. In reality, research participants are introduced to a fictitious (prerecorded, videotaped) partner, generally as part of the manipulation of expectations. For example, to study the effects of ‘‘age’’ as a diffuse status characteristic, research participants might view a standardized videotaped introduction of a fictitious partner who is demonstrably younger or older than themselves (in high-status and low-status conditions, respectively). The use of a standardized videotaped partner is designed to reduce random measurement error associated with the manipulation. However, as discussed below, the mere incorporation of video into the laboratory procedures may itself create a nonrandom source of variation that would be manifest in the empirical estimates of m and q. A third variant of the Basic setting delivers information via computer. The software that has most often been used in this setting was originally developed by Foschi and associates (1990). As discussed by Troyer (2001), Foschi and colleagues’ (1990) experimental procedures differ from the original SES in two major ways. First, the computerized instructions for research participants do not include information indicating that people working together on the task tend to outperform individuals working alone. Second, agreements and disagreements on task problems are computer simulated and stressed with prompts stating that the partner ‘‘agrees’’ or ‘‘disagrees’’ with the participant. Although at first glance, such protocol changes may seem unimportant, we suspect they nonetheless create important and yet unrecognized variations in research findings. For example, labeling the partner’s feedback as ‘‘agree’’ and ‘‘disagree’’ is a variation involving a fairly major change in the decision-making situation compared to the Basic setting. As such, participants may become more sensitive to the number of ‘‘staged’’ disagreements that occur (personal communication with Joseph Berger, September 6, 2005). Even so, scant past research has examined how protocol variations alter empirical findings. The exception is Troyer (2001), who demonstrated that

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abbreviating the discussion of individual versus group scores produced lower levels of task orientation, but it had no effect on collective orientation or PðSÞ values. She also found that accentuating initial choices with text stating that the (fictitious) partner ‘‘agrees’’ or ‘‘disagrees’’ increased collective orientation for higher-status participants and decreased PðSÞ for lowerstatus participants. Finally, Troyer suggested that audio/video media may generate increased task orientation, collective orientation, and/or salience of sources of performance expectations.

Hypotheses We investigate how protocol variations affect expectation states processes, but differ from Troyer (2001) by focusing meta-analytically on (1) whether participants’ baseline propensity to reject influence (as measured by the linear model constant, m) varies across experimental protocols and (2) whether the effect of the expectation advantage (as measured by the linear model regression coefficient, q) similarly varies. By focusing on these constants, our aim is to better understand how protocol differences produce systematic changes in influence tendencies. A number of specific hypotheses guide our investigation. With respect to m, various lines of research demonstrate that increased ‘‘social presence’’ or ‘‘immediacy’’ tends to produce a greater tendency to be influenced by others (Latane 1981; Latane et al. 1995; Latane and L’Herrou 1996; Short 1974). Thus, the personalization of fictitious task partners in the expectation states Video setting may lower participants’ baseline tendency to reject influence, resulting in a smaller value of m. Thus, Hypothesis 1: Compared to the Basic setting, participants in expectation states studies that employ the Video setting will exhibit a lowered baseline propensity to reject influence (i.e., smaller m values).

For comparable reasons, we expect to see lower rates of m in studies employing the Foschi-Computer setting. Moreover, the reduction in m is further suggested by previous research that finds that participants in this setting are less motivated to succeed at the task than participants in the Basic setting (Troyer 2001). The combination of these forces suggests the following hypothesis: Hypothesis 2: Compared to the Basic setting, participants in expectation states studies that employ the Foschi-Computer setting will exhibit a lowered baseline propensity to reject influence (smaller m values).


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Another potentially relevant factor is the number of task trials. Regardless of the fictitious ability being examined, the experimental task usually involves 25 trials, 20 of which are designated as disagreement or critical trials. However, studies occasionally include more trials, sometimes as many 62—almost two-and-a-half times the typical number. Given the ambiguity of the task, we suspect that subject fluctuations within the course of the study, such as fatigue, boredom, attentional drift, and so forth, will produce random response error as the number of task trials increases (Thye 2000). Moreover, other research finds that negative emotion/sentiment mediates the effect of expectations on power and prestige behaviors, such as social influence (Fisek and Berger 1998). If participants experience negative emotions such as boredom or dissatisfaction with more task trials, the effect is to increase the baseline propensity to reject influence (m). This occurs because m absorbs variance from all omitted study-level variables (i.e., it represents the intercept term in the linear regression). As such, we expect the number of task trials to predict variation in m, not q. Thus Hypothesis 3: As the number of task trials increases, participants’ baseline propensity to reject influence will increase (i.e., larger m values).

Next, with respect to q, Troyer (2001) has speculated that q differs across research settings for divergent reasons. First, the use of video media may increase, among other things, the salience of factors that produce and accentuate performance expectations. This suggests that q will be larger in such settings, as predicted by Hypothesis 4. In addition, Troyer further suggests that the abbreviated discussion of individual versus group performance in the Foschi-Computer setting may produce lowered task orientation and subsequently a ‘‘regression toward peer interaction’’ apart from any other differences in protocol. When this occurs, PðSÞvalues for higher-status participants are depressed, while PðSÞ values for lower-status participants are inflated. Statistically, this translates to a decreased effect of q in the FoschiComputer setting. Thus the final two hypotheses: Hypothesis 4: Compared to the Basic setting, expectation states studies that employ the Video setting will report greater effects of the expectation advantage on PðSÞ (i.e., larger q values). Hypothesis 5: Compared to the Basic setting, expectation states studies that employ the Foschi-Computer setting will report lowered effects of expectation advantage on PðSÞ (i.e., smaller q values).

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Method Hierarchical linear modeling (HLM) is a statistical method well suited for addressing the above hypotheses. The approach affords a number of key benefits. Specifically, it allows one to (1) determine whether inconsistency in ‘‘true effects’’ exists among a set of statistics, (2) explore study-level (or Level 2) models formulated to account for variation among true effect parameters, and (3) estimate the ‘‘average’’ effect of parameters across the series of studies. The analysis is divided into three main components. First, we determine whether there are differences in true effects among the empirical constants in our data set, m and q. Second, we test whether any observed heterogeneity among the empirical constants is attributable to Level 2 protocol variations, as predicted above. Finally, researchers sometimes use m and q (as estimated from previous studies) to make predictions for PðSÞ in a particular research setting (see Whitmeyer, Webster, and Rashotte 2005). In our view, a more robust procedure is to base future predictions on m and q values that represent the average true score effects of these parameters calculated across populations. Ordinarily, it would not be possible to directly derive such values from meta-analysis because study differences in variable measurement and/or model specification usually require that the statistics of interest (e.g., mean differences, correlations, Fs, etc.) be transformed into standardized effect sizes prior to the analysis. Wherever possible, however, original scaling should be retained (Kelley and Tran 2001). Because the dependent (PðSÞ) and independent (expectation advantage) variables are uniformly measured in expectation states research, m and q need not be transformed. This is propitious, as we can use the raw values to meta-analytically derive the average, cross-population estimates of these parameters. Future researchers can then use these values as a benchmark to assess the (ir)regularity of their own results and as a basis for future prediction.

Study Inclusion Criteria We used Webster’s (2003) Database of Status Experiments as a comprehensive listing of prospective studies for our meta-analysis. We then applied eight inclusion criteria to this listing to produce the studies in our data set. Specifically, we located all studies where the authors (1) presented the number or the proportion of stay responses; (2) presented the


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standard deviation or variance of the number/proportion of stay responses; (3) examined sources of performance expectations that are amenable to graph modeling (i.e., status, rewards, and evaluations) and thus calculation of expectation advantage; (4) randomly manipulated sources of performance expectations (thereby ruling out other extraneous sources); (5) reported the number of participants per condition; (6) used one of the three major variants of the SES described above (Basic, Video, or the FoschiComputer setting); (7) used contrast sensitivity, relational ability, spatial judgment ability, or meaning insight as the binary-choice decision-making task; and (8) reported both the number of ‘‘critical trials’’ and the total number of trials for the task. Application of these criteria to Webster’s database resulted in a final set of 26 experimental studies that we use to examine the above hypotheses (see Table 1).

Computation of Empirical Constants Expectation states theorists often specify a linear function relating expectation advantage to PðSÞ in terms of the empirical constants, m and q (see equation (7)). Typically, however, the estimates of m and q are not reported in studies of expectation states processes. Instead, researchers tend to present mean differences in PðSÞ between experimental conditions. For example, a researcher might show that ‘‘age’’ operates as a diffuse status characteristic by reporting a statistically greater PðSÞ for participants who believe that their partner is younger than themselves compared to participants who believe that their partner is older. However, provided that our study inclusion criteria are met, calculation of m and q is relatively straightforward using the formulas available in the appendix of Fox and Moore (1979). For a meta-analysis, though, one must have the statistics as well as their associated variances, which may be easily derived.7 Expressed using the terms defined in Fox and Moore, the formula for the variance of m (the y-intercept in the linear function) is given in equation (8) as " !# 1 e2 + P MSE ; ð8Þ N ni ðei − eÞ2 where MSE is the within sums of squares divided by the error degrees of freedom, N is the total number of participants in the experiment, ni is the number of participants in condition i, ei is the expectation advantage for participants in condition i, and e is the average expectation advantage in the study (see Fox and Moore 1979). Finally, the formula for the variance of

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Table 1 Experimental Studies of Expectation Advantage Effects on P(S) Study 1. Berger and Conner (1969)a 2. Berger and Fisek (1970) 3. Berger, Cohen, and Zelditch (1972) 4. Freese and Cohen (1973) 5. Webster and Sobieszek (1974) 6. Berger, Fisek, and Freese (1976) 7. Freese (1976) 8. Parcel and Cook (1977)b 9. Webster (1977) 10. Webster and Driskell (1978) 11. Harrod (1980) 12. Zelditch, Lauderdale, and Stublarec (1980) 13. Hembroff (1982)c 14. Wagner and Berger (1982) 15. Markovsky, Smith, and Berger (1984)d 16. Martin and Sell (1985)e 17. Moore (1985)e 18. Wagner, Ford, and Ford (1986) 19. Ilardi and McMahon (1988) 20. Stewart (1988)e 21. Stewart and Moore (1992) 22. Foschi (1996) 23. Lovaglia and Houser (1996)f 24. Driskell and Webster (1997)g 25. Foschi, Enns, and Lapointe (2001)e 26. Foschi and Lapointe (2002)e

SES

Trials

n

q

m

Basic Basic Basic Basic Basic Basic Basic Basic Basic Video Video Basic Video Basic Video Basic Basic Video Basic Basic Basic Foschi Foschi Video Foschi Foschi

25 25 40 40 25 25 24 25 25 23 42 25 40 25 25 62 25 25 24 25 25 25 25 25 25 25

120 76 180 120 254 85 88 98 171 63 34 124 325 99 81 71 54 123 278 161 57 129 50 114 92 43

.0953 .0968 .0602 .1314 .0921 .1592 .0790 .0904 .0953 .1533 .1427 .1432 .0979 .1150 .2201 .1025 .0699 .1139 .0576 .0753 .1339 .0441 .0574 .1518 .0760 –.0052

.6385 .6715 .7894 .6467 .6272 .6638 .6675 .6593 .5814 .6212 .6150 .5958 .6215 .5929 .5755 .7086 .7091 .5985 .6455 .6794 .6661 .5436 .5500 .6293 .5001 .5410

a. This study was inadvertently omitted from Webster’s (2003) Database of Status Experiments. We include it based on feedback received from Joseph Berger on an earlier version of this article. b. Estimates are for Study 1. Study 2 uses an unusual modification of the standardized experimental setting (SES) involving performance feedback at the end of each trial. c. Includes three conditions from Hembroff, Martin, and Sell (1981). Unlike Balkwell (1991a), we cannot include two conditions from Martin and Sell (1980) because the variances for P(S) are not reported therein. d. Estimates are for Task A. We modeled ‘‘ability’’ as a relevant specific status characteristic (personal communication with Joseph Berger, September 6, 2005). e. Estimates exclude ‘‘no salient status information’’ conditions (see Balkwell 1991a). f. Estimates are for the baseline conditions only because Fisek and Berger (1998) demonstrate that these data cannot be fit with any existing graphtheoretic model of the effects of emotions on expectation advantage. g. Estimates are for all conditions using Fisek and Berger’s (1998) arguments concerning the constituent effects of emotions on expectation advantage.


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q (the unstandardized regression coefficient in the linear function) is given in equation (9) as follows: P

MSE ni ½ðei − eÞ2

:

ð9Þ

Variables Pertinent to our hypotheses, we dummy coded each of the 26 included studies for their use of the Basic version of the SES, the Video version, or the Foschi-Computer setting. Furthermore, we created a variable reflecting the number of task trials for each study. Finally, because large studies estimate effects more precisely than small ones (Ferrer 1998), we added a variable to statistically control for the study sample size in the analyses reported below.

Results The distribution of the 26 values for m is normal, as is the distribution of the 26 values for q. For m, the statistics range from .5001 to .7894, with the mean being .6284 and the median being .6283. Results of a Kolmogorov-Smirnov test indicate that we fail to reject the null hypothesis of no difference between the distribution of m values and a normal one (Z = :453; p = :986). For q, the statistics range from −.0052 to .2201, with the mean being .1019 and the median being .0961. Here as well, results of a Kolmogorov-Smirnov test indicate that we fail to reject the null hypothesis of no difference between the distribution of q values and a normal one (Z = :570; p = :901). We used the variance-known procedure in HLM 5 (Raudenbush et al. 2000) to perform our meta-analyses. Table 2 shows the results for m, with the empirical constant representing the baseline propensity to reject influence. Model 1 does not contain any Level 2 (i.e., study-level) predictors. In this ‘‘unconditional’’ analysis, the true effect parameters are viewed as varying around the grand mean, γ 0 , plus Level 2 error. The estimated grand mean for the model is .6284, which indicates that across studies, participants have a tendency to stay with their own response, on average, about 63 percent of the time, all else being equal. However, the estimated variance of the true effect parameters (^τ = :0036Þis significant (p < .001), indicating that variation in m across studies is due to more than chance

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Table 2 Meta-Analysis of Baseline Propensity to Reject Influence (m) Model 1 Fixed Effect Intercept, γ 0 Video, γ 1 Foschi-Computer, γ 2 Trials, γ 3 Sample size, γ 4 Random effect True effect size, δj ^τ χ2 df χ2 (compared to preceding model) ∗

Model 2

Model 3

Model 4

.6284∗∗∗

.6583∗∗∗ –.0485∗ –.1251∗∗∗

.5969∗∗∗ –.0508∗ –.1167∗∗∗ .0021∗

.6021∗∗∗ –.0509∗ –.1187∗∗∗ .0021∗ –.0000

.0036∗∗∗ 585.4373 25

.0017∗∗∗ 293.2157 23 292.2216∗∗∗

.0014∗∗∗ 227.7321 22 65.4836∗∗∗

.0015∗∗∗ 229.0648 20 –1.3327

p < :05: ∗∗∗ p < :001.

differences alone. We next examine how much of this variation can be accounted for by differences in study protocol. Recall that Hypotheses 1 and 2 predicted smaller m values for the video and Foschi-Computer settings relative to the Basic setting. The data support both hypotheses. Model 2 adds the Level 2 dummies that capture the impact of the three different settings (Basic SES is the omitted category). Note that the estimated grand mean is .6583, which indicates that across studies, participants in the Basic setting have a tendency to stay with their own response, on average, about 66 percent of the time. Furthermore, the coefficients for the Video and Foschi-Computer versions of the SES are both significant. The estimated effect of Video is −.0485, indicating that across studies, participants in Video-based studies are significantly less likely, on average, to stay with their own response (61 percent in the Video setting vs. 66 percent in the Basic setting). The estimated effect of Foschi-Computer is even stronger, −.1251, indicating that participants in this setting are even less likely, on average, to stay with their own response (53 percent in the Foschi-Computer setting vs. 66 percent in the Basic setting). While Model 2 fits the data better than the unconstrained first model (χ2 = 292:2216; df = 2; p < :0001), the estimated variance of the true effect parameters remains highly significant (^τ = :0017; p < .0001). This implies that variation in m across studies is due to more than


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chance alone, even after taking into account the impact of differences across the three settings. Next, we explore if the remaining variation in m is explainable by the number of task trials, as predicted in Hypothesis 3. Model 3 adds the number of task trials. As predicted by our third hypothesis, increasing the number of task trials increases overall resistance to influence. Specifically, for each additional task trial, resistance to influence, as captured by m, is increased by a factor of .0021, net of all other factors in the model. Because the mean number of task trials across all studies is 28.65, the adjusted mean baseline propensity to reject influence (m) for the Basic setting is :5969 + :0021∗ ð28:65Þ, or .6571. At 62 trials (the maximum number in our data set), the predicted resistance to influence is increased to .7271. At 25 trials (the conventional number), the predicted resistance to influence is quite a bit lower, .6494 (11 percent less than .7271). While Model 3 represents a significant improvement in fit (χ2 = 65:484, df = 1; p < :0001), there remains room for improvement. Notice that the estimated variance of the true effect parameters (^τ = :0014Þis still highly significant (p < .0001). This implies that variation in m across studies is due to more than chance alone, even after taking into account both the SES version as well as the number of task trials. Furthermore, we added a variable to control for any spurious effects of sample size. The results are shown in Model 4. As shown there, adding the control for sample size has no appreciable effects. To summarize, we find support for our first, second, and third hypotheses regarding systematic variation in m across studies. Compared to studies employing the Basic version of the SES, studies using the Video and Foschi-Computer versions report significantly lower values for m. In the case of the video setting, this may reflect the increased social presence or immediacy of the task partners (Short 1974; Latane 1981). In the case of the Foschi-Computer setting, the implication is that diminished task orientation causes participants to be less resistant to influence (Troyer 2001). Furthermore, we find that the number of task trials—a factor normally treated in an arbitrary way—has important effects on the propensity to accept or reject social influence. The implication is that the number of task trials operates as a source of specific measurement error across settings (see Thye 2000). Next, we perform a comparable analysis for values of q, with the empirical constant representing the effect of expectation advantage on PðSÞ. The variance-known meta-analytic results for q are shown in Table 3. As in Table 2, Model 1 does not contain any Level 2 predictors. The

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Table 3 Meta-Analysis of Expectation Advantage Effects (q) Model 1 Fixed Effect Intercept, γ 0 Video, γ 1 Foschi-Computer, γ 2 Trials, γ 3 Sample size, γ 4 Random effect True effect size, δj ^τ χ2 df χ2 (compared to preceding model)a

.0985∗∗∗

Model 2

Model 3

Model 4

.0977∗∗∗ .0395∗ –.0435∗

.1086∗∗∗ .0402∗ –.0450∗ –.0004

.1260∗∗∗ .0396∗ –.0513∗∗ –.0002∗

.0010∗∗∗ 121.3136 25

.0006∗∗∗ 81.5592 23 39.7544∗∗∗

.0006∗∗∗ 81.4453 22 .1139

.0004∗∗∗ 60.7622 22 20.7970∗∗∗

a. Model 4 is compared to Model 2. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

estimated grand mean for this model is .0985, which indicates that across studies, PðSÞ increases by approximately .10 (on average) for each unit increment in expectation advantage. However, the estimated variance of the true effect parameters (^τ = :0010Þ is significant (p < :0001), indicating that variation in q across studies is due to more than chance differences alone. Here, the main question is the same as it was in the case of m: How much of the cross-study variation in q can be accounted for by major protocol variations? Model 2 in Table 3 adds the Level 2 dummies for the Video and Foschi-Computer versions of SES (Basic is again the omitted category). Here the estimated grand mean is .0977, which represents the effect of expectation advantage on PðSÞ for studies employing the Basic setting. Note, however, that the coefficients representing the study-level effects of the Video and Foschi-Computer versions of the SES are both significant. On one hand, the estimated effect of Video is .0395, indicating that studies employing this version of the SES tend to report a greater effect of expectation advantage on PðSÞ compared to studies that employ the Basic setting. In contrast, the estimated effect of Foschi-Computer is −.0435, indicating that studies employing this particular version of the SES tend to


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report a lesser effect of expectation advantage on PðSÞ vis-a-vis the Basic setting. These findings lend support to our fourth and fifth hypotheses, respectively. Yet while the conditional model including the dummy variables for Video and Foschi-Computer is statistically preferable to the nested unconditional model (χ2 = 39:7543; df = 2; p < :0001), the estimated variance of the true effect parameters (^τ = :0006Þ remains highly significant (p < :0001). This implies that variation in q across studies is due to more than chance alone, even after taking into account the SES version. Model 3 adds the number of task trials, but the effect is not statistically significant. Consistent with the reasoning producing Hypothesis 3, this finding confirms our speculation that varying the number of task trials would not alter the effect of expectation advantage on PðSÞ, as captured by q. As such, we omit the number of task trials in the subsequent model. Model 4 adds the control variable for study sample size. Somewhat surprisingly, we find that for each additional participant, the cross-study effect of expectation advantage on PðSÞ decreases by a factor of −.0002. In other words, studies with larger sample sizes tend to report weaker effects of expectation advantage. Because the mean sample size across studies is 118.85, the adjusted mean representing the effect of expectation advantage on PðSÞ for the Basic setting is :1260 + ð−:0002Þ∗ ð118:85Þ = :1022. At 325 (the greatest number of participants among the included studies), the predicted effect of expectation advantage for a study employing the Basic setting is approximately half of that, or .0610. Yet while the conditional model that takes into account the study sample size is preferable over the one in column 2, which includes only the dummies for the SES version (χ2 = 20:7971; df = 1; p < :0001), the estimated variance of the true effect parameters (^τ = :0004Þstill remains significant (p < :0001). This means that variation in q across studies is due to more than chance, even after controlling for both the SES version as well as the study sample size. To summarize, the data provide clear and consistent support for our fourth and fifth hypotheses. Compared to studies employing the Basic version of the SES, studies employing the Video version report significantly greater effects of the expectation advantage on PðSÞ, while studies employing the FoschiComputer version report significantly weaker effects. In line with Troyer’s (2001) suggestion, the greater effect of expectation advantage in Video studies may be due to the enhancing result of a/v media on the salience of factors that create performance expectations. In the case of the Foschi-Computer setting, diminished task orientation related to the abbreviated discussion of

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individual versus group performance (Troyer 2001) may produce a ‘‘regression toward peer interaction,’’ thereby accounting for the weaker effect of expectation advantage in these studies. Finally, while not hypothesized, we found that studies with larger sample sizes tend to report weaker effects of expectation advantage, net of SES version. This may reflect the fact that, in general, effects can be estimated with greater precision in studies with larger sample sizes.

True Score Values of m and q Expectation states researchers sometimes use m and q values estimated from a previous study conducted at the same data collection site to make predictions about PðSÞ differences for a subsequent study. A form of ‘‘overfitting the data’’ is one potential problem with this strategy (Freese and Sell 1980). That is, a researcher may estimate a model using normatively aberrant values for m and q that miss the ‘‘larger picture’’ but nonetheless result in a statistically good fit between the predictions and the odd empirical data. To avoid this problem, we suggest that future investigators use our meta-analytically derived values for m and q, which represent the ‘‘true score’’ values for these parameters across protocols, geographic regions, time periods, and so forth—along with the appropriate ‘‘correction’’ for study-level effects when appropriate. To illustrate, when introducing new protocols or protocol variations, researchers can use the results from the unconditional analyses (Model 1) found in Tables 2 and 3. Specifically, the appropriate value for m is .6284, and the appropriate value for q is .0985. When using familiar protocols, researchers can ‘‘correct’’ m for the specific research setting and the number of task trials by using the Model 3 estimates shown in Table 2. The value for q can be similarly corrected for setting and sample size using the Model 4 estimates shown in Table 3. For example, if a researcher collects data from 100 participants using the Foschi-Computer setting with 25 task trials, the value for m using Table 2 is γ 0 + γ 2 + γ 3 (number of trials) = .5969 + (−.1187) + .0021 (25) = 0.5327. The value for q using Table 3 is γ 0 + γ 2 + γ 4 (number of participants) = .1260 + (−.0513) + (−.0002) (100) = 0.0547. Importantly, these corrected values for m and q do not rely on the unique characteristics of specific data but instead embody the collective wisdom of 26 distinct studies. These values can then be plugged into the linear equation (above) along with expectation advantage to obtain predictions for PðSÞ.


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Discussion We have presented the first formal meta-analysis of expectation states research on social influence. We examined data from 26 separate experiments and focused on protocol variations, including the use of video and computer technology, the number of task trials, and sample size. Our findings reveal that study-level differences affect both the baseline propensity to reject influence (as captured by m) as well as the effect of expectations on social influence (as captured by q). With respect to m, our study produced three key findings. First, compared to participants in studies that employ the Basic setting, participants in studies incorporating video technology tend to exhibit a lower baseline propensity to reject influence. This conforms to psychological research showing that increased social presence or immediacy tends to increase social influence (Latane 1981; Short 1974). Second, compared to participants in the Basic version of the SES, participants exposed to Foschi and colleagues’ (1990) computerized version of the SES also tend to exhibit a lowered tendency to reject influence. This suggests that the latter setting may produce lowered task orientation (Troyer 2001). Finally, we found that increasing the number of trials (across studies) tends to increase participants’ baseline propensity to reject influence. Given the ambiguity and difficulty of the task problems, we suggest that fatigue, negative emotion, or other specific sources of error increase as the number of task trials increases. Thus, if negative sentiments emerge over increasing task trials, our third finding is consistent with research showing that negative emotion/sentiment seems to mediate the effect of expectations on power and prestige behaviors such as social influence (Fisek and Berger 1998). In terms of the parameter q, our study produced three additional findings. First, compared to expectation states studies that employ the ‘‘Basic’’ version of the SES, studies that employ video technology tend to report greater effects of expectation advantage on social influence. This finding is consistent with Troyer’s (2001) claim that audiovisual media may increase the salience of factors that produce performance expectations. Second, compared to the Basic setting, expectation states studies that use the Foschi-Computer version of the SES tend to report lowered effects of expectation advantage on PðSÞ. This finding is consistent with results from Troyer’s laboratory study showing that the abbreviated discussion of individual versus group performance in the Foschi-Computer setting seems to produce lowered task orientation and subsequently a ‘‘regression

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toward peer interaction’’ (i.e., a diminished effect of the expectation advantage on influence). And finally, while not predicted, we found that studies with larger samples sizes tend to report weaker effects of expectation advantage on PðSÞ, net of SES version. This may reflect that effects can be estimated with greater precision in studies with larger sample sizes. Taken together, the set of findings from our study provides additional evidence that protocol variations systematically affect expectation states processes in unanticipated ways. Whether this occurs by altering participants’ levels of task orientation and/or collective orientation (Troyer 2001) or from uncontrolled causes of variation in social influence such as the immediacy of the source (Latane 1981), protocol variations commonly employed by expectation states researchers appear to produce nonrandom fluctuation in m (the baseline propensity to reject influence) and q (the effect of expectations on influence). Because these parameters are used by expectation states researchers to make predictions, understanding how they are influenced by experimental procedures and other nontheorized factors is paramount. In closing, it is worth noting that our findings suggest new templates for future research. For instance, while the protocol variations we investigated do account for a significant amount of variation in m and q, the estimated variance associated with both parameters remained significant, even after taking such protocol variations into account. This suggests that other unrecognized factors produce nonrandom variation in m and q. While the precise sources of such variance may be too numerous or difficult to catalog ex post facto, one may still use the average, cross-population values of m (.6284) and q (.0985) to initiate new research. For instance, if future research finds that empirical PðSÞ values do not conform to those PðSÞ values predicted from our true score estimates of m and q, then researchers may begin to develop more refined models of expectation states processes that capture other methodological or population idiosyncrasies. With the current estimates of these parameters now in place, the stage is set for new theoretical advances in the expectation states program.

Notes 1. Other techniques of meta-analysis analyze the r statistic for correlational data and/or the d statistic for experimental data (Glass 1976; Hunter and Schmidt 1990). We choose hierarchical linear modeling (HLM) because our interest is in estimating study-level biases, in addition to the true score parameter values.


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2. If D had been explicitly dissociated from the task outcome, then the lines between the s and the C s would not be present. 3. In general, the three sets of values produce similar predictions. We prefer Balkwell’s (1991a) a priori values because they converge a bit more closely with the empirical estimates provided by Berger and associates (1977). 4. For details, see Berger, Fisek, Norman, and Wagner (1985). 5. Webster and Whitmeyer (1999) propose an alternative conception for determining the length of an imputed possession line. 6. This linear function is the most common interpretation of the fifth assumption of status characteristics theory (SCT), which provides a behavioral model that translates expectations into social influence, as measured by PðSÞ. Other models based on different interpretations of this assumption have been used for a variety of other purposes and behavioral phenomena (see, e.g., Fisek, Berger, and Norman 1991). For a more detailed review of this issue, see Balkwell (1991b). 7. To reduce errors, we developed a Web-based application to facilitate the computation and archiving of the empirical constants and their associated variances, along with other study information (Earnhart 2005).

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Will Kalkhoff is an assistant professor of sociology at Kent State University. His research interests include developing and testing structural social psychological theories, experimental methods, and deviant behavior. He is currently working on projects that explore methodological issues in expectation states research (with Lisa Troyer and C. Wesley Younts) and on developing a theoretical integration of status characteristics theory and social influence network theory. Shane R. Thye is an associate professor of sociology at the University of South Carolina and series coeditor of Advances in Group Processes. His primary research interests include small group processes and experimental methods. He is currently testing and refining his status value theory of power (American Sociological Review, 2000) and conducting research on the emergence of commitment and micro social order in groups (with colleagues Edward J. Lawler and Jeongkoo Yoon).