The folly of theorizing “A” but testing “B”

The Leadership Quarterly 12 (2001) 515 – 551

The folly of theorizing ‘‘A’’ but testing ‘‘B’’ A selective level-of-analysis review of the field and a detailed Leader–Member Exchange illustration Chester A. Schriesheima,*, Stephanie L. Castroa, Xiaohua (Tracy) Zhoua, Francis J. Yammarinob a

Department of Management, School of Business Administration, University of Miami, 414 Jenkins Building, Coral Gables, FL, USA b State University of New York at Binghamton, Binghamton, NY, USA

Abstract Leadership research has recently begun to emphasize the importance of examining the level of analysis (e.g., individual, dyad, group, organization) at which phenomena are hypothesized to occur. Unfortunately, however, it is still not commonplace for theory to clearly specify, and for investigations to directly test, expected and rival level-of-analysis effects. This article first selectively reviews a crosssection of theories, models, and approaches in leadership, showing generally poor alignment between theory and the level of analysis actually used in its testing. A multiple levels of analysis investigation of the Leader –Member Exchange (LMX) model is next presented. This theory has as its foundation the dyadic relationship between a supervisor and his or her subordinates. Yet, less than 10% of published LMX studies have examined level of analysis — and none has employed dyadic analysis. Using within- and between-entities analysis (WABA) and two different samples, four LMX level-ofanalysis representations are tested, which involve monosource data; three of these models are then tested using heterosource data. Overall, good support is found for the LMX approach at the withingroups and between-dyads levels. Implications for aligning theory with appropriate levels of analysis in future research are considered. D 2002 Elsevier Science Inc. All rights reserved.

* Corresponding author. Tel.: +1-305-284-3758; fax: +1-305-284-3655. E-mail address: [email protected] (C.A. Schriesheim). 1048-9843/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 1 0 4 8 - 9 8 4 3 ( 0 1 ) 0 0 0 9 5 - 9

516

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

1. Introduction In any scientific endeavor, periodic review is necessary to consolidate the gains of past research and highlight areas that need increased future scholarly attention. Typically, reviews of the leadership field and its various theories and approaches have focused on conceptual and/or theoretical concerns and have dealt with basic measurement and/or analysis issues only marginally or tangentially. Our Yearly Review article differs from this pattern by focusing on one important concern — level of analysis — that is both theoretical/conceptual and measurement/analytical but has usually been neglected in previous reviews of the field. Our examination is not encyclopedic and we will not cover all aspects related to level-of-analysis issues; other writings are meant to be more exhaustive and interested readers are referred to these works (cf. Dansereau & Yammarino, 1998a, 1998b). Additionally, we should note that our general review of the field focuses on representative samples of various approaches as they have evolved to their present states. We do this to demonstrate the generality of the issues that we raise and to highlight that levelof-analysis concerns are unlikely to be satisfactorily addressed unless scholars explicitly and intentionally do so. Finally, we focus on one leadership approach (Leader–Member Exchange, LMX; Graen & Uhl-Bien, 1995) in considerable detail and show how it may be more thoroughly tested with respect to critical level-of-analysis predictions. Two major reasons exist for selecting the LMX approach for detailed illustration. First, a large amount of research has been conducted on this approach (Gerstner & Day, 1997; Liden, Sparrowe, & Wayne, 1997). Second, and more importantly, while LMX theory focuses on leader – follower dyads, not one empirical study has explored dyadic representations of the LMX process (Schriesheim, Castro, & Cogliser, 1999). Thus, existing LMX research may be viewed as fundamentally uninformative about the theory — since it is a theory of dyadic (leader–member) interaction but its predictions have not been tested at the dyadic level of analysis. This theme is more fully developed later but we believe that the same issues exist concerning most other leadership approaches. The LMX approach simply provides a clear and straightforward example of how level-of-analysis alternatives may be formulated and tested and why it is important that the theory clearly and explicitly states its level-of-analysis assumptions so as to facilitate the conduct of appropriate research. The thesis of this article, then, can be simply stated: We believe that it is absolutely critical that scholars specify the level of analysis at which their hypotheses, frameworks, models, and/or theories hold so that they may be adequately tested. We also believe that it is absolutely necessary that tests of any hypothesis, framework, model, and/or theory be conducted at the proper level(s) of analysis and that tests explicitly rule out inappropriate or competing (rival) levels of analysis. Otherwise, Type I or II errors may occur (i.e., improperly rejecting or not rejecting a particular null hypothesis; Dansereau, Alutto, & Yammarino, 1984; cf. Schriesheim, Cogliser, & Neider, 1995), and we may wind up erecting theoretical skyscrapers on foundations of empirical jello.

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

517

2. Background Leadership and organizational studies may adopt any one or several different levels of analysis (e.g., individuals, dyads, work groups, organizations; Roberts, Hulin, & Rousseau, 1978), and a number of scholars have taken the position that theory and research must address level-of-analysis issues to be considered fully adequate (e.g., Dansereau et al., 1984; Dansereau & Yammarino, 1998a, 1998b; Glick & Roberts, 1984; House, 1991; House, Rousseau, & Thomas-Hunt, 1995; Klein, Dansereau, & Hall, 1994; Klein & Kozlowski, 2000; Mowday & Sutton, 1993; Roberts et al., 1978; Rousseau, 1985). Unfortunately, however, most scholars still do not address level-of-analysis issues in their work, perhaps because they do not realize why specifying and testing a phenomenon’s level of analysis is important. The result is that, while level-of-analysis concerns are increasingly being addressed in both theoretical (e.g., House, 1996) and empirical (e.g., Schriesheim, Neider, & Scandura, 1998) work, level of analysis is still left unspecified and unaddressed in the vast majority of leadership research (for recent notable exceptions, see Dansereau & Yammarino, 1998a, 1998b). For those having difficulty understanding why we are concerned with level of analysis in our research, one way of conceptualizing the levels issue is analogous to the problem of unmeasured variable(s) in structural equations modeling — where it is widely recognized that parameter estimates (and the conclusions based upon them) may be erroneous if important variables are omitted from an analysis (cf. James, Mulaik, & Brett, 1982). As a concrete example, individual subordinate descriptions of their leader’s perceived supportive behavior (the independent variable) have often been correlated with the supervisor’s evaluation of each individual follower’s performance (the dependent variable) (Bass, 1990). These ‘‘raw score’’ correlations have then been used to draw inferences about leader–follower interactions at the individual level of analysis (e.g., Schriesheim & Murphy, 1976). However, these raw score correlations may be seriously misleading as portrayals of the relationship between leaders and followers at the individual level of analysis because of correlated error in the independent, dependent, or both variables (cf. Schriesheim et al., 1995). It should be remembered from basic and advanced statistical theory that regression and correlation (and most other traditional analytic methods) assume that the error in our data is uncorrelated (cf. Cohen & Cohen, 1983). Correlated error may lead to serious biases in parameter estimates and it may arise from a number of sources, including variables that are not specified in our models and not included in our analyses. In particular, since most organizations are hierarchical and therefore inherently multilevel (involving individuals, work groups or units, branches/sites, departments, divisions, etc.), it is very likely that one or more unmeasured variables may be operative in our data, thereby distorting our analytic results and producing erroneous conclusions. For example, in the raw score analysis example briefly developed above, if two or more followers work under the leadership of each supervisor, an unmeasured variable exists — ‘‘work group or work unit supervisor.’’ This unmeasured variable may cause the uniquenesses or error terms of the independent variable (perceived supportive leadership) to be highly

518

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

correlated within clusters of subordinates because, while leaders probably act differentially toward individual subordinates, they are also likely to have a general ‘‘unit leadership style’’ (i.e., to behave similarly toward work unit members) — due to such factors as time constraints and concerns about perceived equity (Cummings, 1975). In other words, for some dimensions of interest, leaders may treat subordinates differently; for other dimensions, leaders may treat subordinates the same. Similar arguments could be made for the dependent variable (supervisor-rated performance) because of such things as generalized rating factors (e.g., an individual supervisor’s leniency or harshness). The obtained correlation between perceived supportive leadership and rated performance may therefore be partly or wholly attributable to the unmeasured unit or group membership variable. This example can be further illustrated mathematically by noting that for individuals (i) within work units or groups ( j), raw scores (Xij) can be partitioned into two components — a unit or group average component (X¯j) and an individuals-within-groups or deviation from group average component (Xij
X¯j) (i.e., Xij=[X¯j]+[Xij
X¯j]; Dansereau et al., 1984). This later component may arise from various social comparison processes that involve comparing each individual group member with the group. Finding a significant raw score correlation between two variables can thus be seen to be uninformative concerning the level of analysis at which the relationship holds. If the relationship is due only to supervisor- or group-level effects, the correlation between the group average component variables (X¯j) will be significant and equal to the raw score correlation; the correlation between the variables’ deviation scores (Xij
X¯j) will be zero. Similarly, the relationship can be labeled as reflective of an ‘‘individuals-within-groups’’ phenomenon if the correlation between the deviation scores equals the raw score correlation, while the correlation between the group average component scores is zero. Finally, if groups are completely irrelevant to the relationship (i.e., the relationship varies strictly as a function of individuals), both the group average correlation and the deviation from group average correlation will be equal and both will also equal the raw score correlation. Raw score correlations therefore cannot be unambiguously interpreted since they may be due to relationships at different levels of analysis (e.g., individuals, groups, or individuals within groups). Consequently, theory needs to specify and research needs to examine not only the level of analysis at which a phenomenon is hypothesized to occur, but also rival levels where obtained support would disconfirm a particular level-of-analysis hypothesis. (Appendix A of this paper further demonstrates the ambiguity of raw score analyses and provides technical information on the data-analytic approach that we employ to test for levels of analysis; this is discussed in greater detail in our Method section.)

3. A selective level-of-analysis review of the field Having illustrated why concern about level-of-analysis specification and testing is important for leadership theory and research, we now want to briefly show the pervasiveness of the problem as it currently exists in the leadership field. Since it would

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

519

be an impossible undertaking, we will not attempt to exhaustively catalog all of the problems that exist. Rather, to demonstrate that our concern is real and that problems are not limited to one approach or another, we will present significant examples from several of what Yukl (2002) and others (e.g., Bass, 1990) would consider to be major leadership theories, models, or approaches. Again, we note that these examples are only a small subset of those that could be offered in support of our thesis concerning the extensiveness and seriousness of the problem. 3.1. The Vroom–Yetton–Jago normative decision model Vroom and Jago (1988) and Vroom and Yetton (1973) proposed initial and revised normative leadership decision-making models (respectively). Both of these models conceptualize effective leadership as requiring the selection of an appropriate decision-making style from among those that may be conceptualized as varying along a continuum. The purpose of selecting a style is to obtain needed (a) decision technical quality and (b) acceptance of the decision by subordinates (to obtain effective decision implementation). Briefly, the leadership style choices considered in these models range from AI (the leader makes the decision using information at hand) to AII (the leader asks one or more individual subordinates for relevant information, then decides), CI (the leader individually consults with one or more subordinates, then decides), CII (the leader consults with subordinates as a group and then makes the decision), GI (one-on-one joint decision making), GII (the leader decides with the group, as an equal), and DI (the decision is delegated to one subordinate, who then decides). The models also distinguish between individual and group problems and suggest that each has certain leadership styles that are inappropriate for the other (e.g., group consultation is not appropriate for an individual problem; delegation to an individual is not appropriate for a group decision). Although some laboratory studies have been conducted (e.g., Field, 1982), research testing the normative model has typically used retrospective accounts of decisions and their effectiveness, comparing the effects of leadership styles that were either consistent or inconsistent with the model’s prescriptions (Yukl, 2002). The implicit theoretical model employed therefore uses leadership decision-making style as the independent variable, decision quality and individual or group acceptance as intervening variables, and effectiveness of the decision as the ultimate endogenous or dependent variable. Moderating the relationship between leadership style and the two intervening variables are situational variables, such as conflict among subordinates in their solution preferences and the amount of relevant information possessed by the leader for making the decision. As one can see from our summary of this approach, in normative decision model research, the analytic focus or unit of analysis is typically one decision. The level of analysis, however, is less clear. Level refers to locus of the phenomenon, and in modern organizational research, it is most often concerned with the individual, dyad, group, or organizational levels. However, it is not clear whether the individual-oriented model is really individual, or possibly dyadic, in nature. Additionally, in the model, the leader’s style may be strictly individual or dyadic (e.g., GI and DI), strictly group-oriented (e.g., CII and GII), or possibly

520

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

flexible and adaptable to individuals or dyads or groups (e.g., AI, AII, and CI). Decision quality and acceptance (the two intervening variables) may likewise be dyadic, individuallevel, or group-level phenomena. Some of the situational moderators can also be at any of several levels of analysis (e.g., leader possession of relevant information; subordinate acceptance of an autocratic decision), while some are clearly at the group level of analysis (e.g., conflict among subordinates in their choice preferences). The model attempts to ensure level-of-analysis consistency by eliminating cross-level combinations (e.g., subordinate conflict is not considered in individual- or dyadic-level decisions). Obtaining retrospective data from managers about leadership decision-making styles that led to effective and ineffective outcomes might appear, at first glance, to reduce or eliminate concerns about level-of-analysis inconsistencies. However, the model appears to have been most often tested using one person’s (the manager’s) self-reports of all the relevant variables, and Vroom and Jago’s (1988) and Vroom and Yetton’s (1973) specifications of the model have not clarified what level of analysis is appropriate for its tests (e.g., individual, dyadic, or group). Data analyses for tests of the model can thus be seen as problematic as virtually all involve the analysis of raw scores like those in the supportive leadership–subordinate performance illustration presented earlier. Thus, while tests of this approach may be based upon one individual’s raw score descriptions of individual, dyadic, or group-level phenomena, one must question the appropriateness of such tests from a level-of-analysis perspective. Consequently, all other methodological concerns aside, one cannot help but wonder about the level of valid support that actually exists for the normative decision model. While Yukl (2002) concludes that, ‘‘The normative decision model is probably the best supported of the contingency theories of effective leadership’’ (p. 94), this assessment is not based on the concerns we have raised above. Consequently, future research on this model that more carefully aligns theory, variable measurement, and data analysis, and begins to address level-of-analysis concerns is badly needed. For further discussion of level-of-analysis issues and the normative decision model, readers are directed to the work of Eden (1998), Yetton and Craig (1998), and Vroom and Jago (1998a, 1998b). 3.2. Influence tactics A number of studies have identified different influence tactics that are or can be used in organizations (e.g., Kipnis, Schmidt, & Wilkinson, 1980; Yukl & Falbe, 1990). Yukl (2002) summarizes these as falling into 11 distinct strategies: (a) rational persuasion, (b) inspirational appeals, (c) consultation, (d) collaboration, (e) appraising, (f) ingratiation, (g) exchange, (h) personal appeals, (i) coalitions, (j) legitimating, and (k) pressure. Research that has employed influence tactics as independent variables has most commonly used measures of commitment, compliance, and/or resistance as their dependent variables. Sometimes agent power or other contingency factors have also been used as situational moderators. The analytic focus in influence tactic research is usually the effectiveness of different influence tactics that are displayed over some period of time. However, while most influence tactics studied clearly concern just the influence agent and influence target (e.g., personal

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

521

appeals involve the agent asking the target to do something out of friendship or loyalty to the agent), some incorporate additional persons (e.g., coalitions involve ‘‘getting help from other people to influence the target person,’’ Yukl, 2002, p. 163). One concern with those tactics that involve only the influence agent and target is whether the level of analysis should be the individual or the dyad. For example, most of the survey questionnaire measures that have been used in this domain (e.g., Kipnis et al., 1980) do not use a dyadic referent (they ask the respondent to describe how she or he acts toward a target), so that the appropriate level of analysis can be argued (i.e., Is using an individual level of analysis appropriate or inappropriate?). However, the appropriateness of using an individual level of analysis is more debatable for the more group-oriented influence tactics. Question wording may somewhat reduce concern about this issue. Still, the question is begged about what the level of analysis should be, and we believe that this is a theoretical and empirical issue that needs to be further addressed in all future influence tactics research. Again, it does not make sense to theorize about individual, dyadic, or group processes and to then test them at some other level of analysis. 3.3. Fiedler’s contingency theory Fiedler’s (1967) contingency theory of leadership effectiveness was the first situational theory of leadership, and it was quite influential in turning the field away from universal approaches and toward situational or contingency models. Additionally, although the contingency theory has been supplanted by a successor approach, cognitive resources theory (Fiedler, 1986; Fiedler & Garcia, 1987), the contingency theory’s empirical base provides part of the evidence that supports cognitive resources theory. While there has been debate about the scientific validity and practical usefulness of this approach (e.g., Fiedler, 1977; Schriesheim & Kerr, 1977a, 1977b), the accumulated empirical evidence now appears to generally support it (Peters, Hartke, & Pohlmann, 1985; Schriesheim, Tepper, & Tetrault, 1994; Strube & Garcia, 1981). The original contingency theory posits that a leader’s score on Fiedler’s ‘‘Least Preferred Coworker’’ (LPC) scale is differentially related to the effectiveness of the leader, depending on the favorability of the situation for the leader to exert influence or control over the group. Three contingency factors have traditionally been considered in determining situational control or favorability: the leader’s relations with the members of the group, the degree to which the task performed by the leader’s group is structured, and the extent to which the leader possesses the ability to reward and/or punish subordinates. The apparent analytic focus of the contingency theory is the effectiveness of a leader (over some period of time). The level of analysis, however, appears mixed and therefore possibly confounded. The leader’s LPC score is just that: It is an individual-level variable. The leader’s relationship with the group and the leader’s position power could also be argued to be individual-level, but they appear more properly conceptualized as group-level variables. Task structure, on the other hand, has most commonly been treated as a group-level variable (its referent has been the task performed by the group) as has leader effectiveness (usually assessed by the performance of the leader’s work group).

522

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

When one examines how tests of the contingency model have typically been conducted, it can be seen that a crossed or mixed level of analysis is involved. An individual-level variable (LPC) is related to a group-level variable (group performance), with the relationship being conditioned (moderated) by a combination of group-level (task structure) and level-ambiguous (leader–member relations and position power) variables. Although a recent level-ofanalysis reinterpretation of the contingency model now treats position power and task structure as individual-level variables, this only reduces (and does not fully negate) concern about using variables conceptualized and measured at mixed or crossed levels of analysis (since leader–member relations and leader performance remain specified as group-level variables; Ayman, Chemers, & Fiedler, 1998). While contingency theory data have commonly been analyzed through bivariate correlation, multiple regression, and analysis of variance (ANOVA), the results cannot be clearly interpreted from a level-of-analysis perspective. Thus, although we believe that the accumulated empirical results of tests of the contingency model can be characterized as supporting it, the meaning of these results appears unclear. The recent writings of Ayman et al. (1998), Chemers, Ayman, and Fiedler (1998), Vecchio (1998), and Zaccaro (1998) assist in clarifying how future research might begin to untangle the contingency model’s level-of-analysis knot. However, additional theory and research appear needed to help us better understand this most basic underpinning of leadership knowledge. 3.4. Path–goal leadership theory Path–goal leadership theory was originally proposed by Evans (1968, 1970) and later made situational (House, 1971) and extended (House, 1996; House & Mitchell, 1974) to include additional leader behavior-independent variables and additional moderator variables. The earlier versions of the theory are currently covered in most basic textbooks (Hunt, 1996) and well over 100 studies have explored the theory’s scientific merits (Wofford & Liska, 1993; Yukl, 2002). Basically, the theory sees the motivational function of the leader as involving ‘‘increasing personal payoffs to subordinates for work–goal attainment and making the path to these payoffs easier to travel’’ (House, 1971, p. 324). This may be done by a variety of leader behaviors, including most notably instrumental and supportive leadership, and participative and achievement-oriented leadership (House & Mitchell, 1974). Additionally, according to House and Dessler (1974, p. 13), leader behavior will increase subordinate satisfaction ‘‘to the extent that the subordinates see such behavior as either an immediate source of satisfaction or as instrumental to future satisfaction.’’ Yukl (2002) notes that the most recent version of the theory (House, 1996) is quite complicated and we would add to this that it has not been tested so as to yield confidence in its predictions. These criticisms notwithstanding, it should be noted that the newest restatement of path–goal theory now clearly and explicitly states the theory’s appropriate level of analysis: It states that path – goal theory is an individually oriented theory. Additionally, ‘‘Path–goal theory . . . does not address the effect of leaders on groups or work units’’ (House, 1996, p. 325).

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

523

We believe that this was the focus of all versions of the theory — that they have all used the individual level of analysis. However, we also believe that there are virtually no direct tests of the theory’s level-of-analysis predictions. Path–goal theory research typically correlates leader behavior descriptions collected from individual subordinates with outcome measures such as individual subordinate self-reports of satisfaction, role clarity, or organizational commitment; oftentimes, moderators, such as task structure, are employed as perceived and reported by the subordinate (Wofford & Liska, 1993). Correlating the data collected in this manner yields a raw score analysis that cannot be unambiguously interpreted. As discussed and shown earlier, each raw score can be partitioned into two subcomponents: a unit average component that represents all subordinates with the same supervisor, and a deviation from the unit average that represents each individual subordinate relative to others within the unit. Since path–goal theory specifies that effects should be obtained at the individual level of analysis, this means that finding relationships to hold at only the between-units or between-groups level does not support the theory. Support for the theory can only be inferred when empirical relationships are shown to hold for either individuals within groups (i.e., using deviation scores) or to hold both within- and betweengroups (showing that the groups do not affect the relationship — that the relationship occurs regardless of groups). Since there have been no studies that have even attempted to test path– goal theory predictions with respect to level of analysis, we cannot help but conclude that support for the theory’s level of analysis is badly needed. This is particularly true since several of the leader behaviors that are central elements of the path–goal theory (instrumental and supportive leadership) have been shown to sometimes yield group-level relationships with such dependent variables as unit morale or unit performance (Bass, 1990; Kerr, Schriesheim, Murphy, & Stogdill, 1974). Additionally, some of the theory’s moderators (e.g., task structure) may describe or characterize groups of subordinates and therefore be more properly conceptualized as group-level variables. 3.5. Substitutes for leadership Kerr and Jermier (1978) developed a model to explain why leadership sometimes is not associated with various hypothesized dependent variables (such as performance and satisfaction), using what may be considered path–goal theory logic (Schriesheim, 1997). Basically, the model proposes two types of explanatory variables: leadership substitutes and neutralizers. Substitutes act in place of leadership and make leader behavior redundant and/or unnecessary. Neutralizers are constraints that prevent the leader from acting in a particular manner or that block the effects of the leader’s behaviors on the dependent variables. Research on the model (e.g., Podsakoff, Niehoff, MacKenzie, & Williams, 1993) has been mixed in its support, but many potential substitutes and neutralizers have not yet been studied. Although the model is meant to be broad and include a host of possible factors, substitutes and neutralizers have usually been conceptualized as falling into the three categories originally proposed by Kerr and Jermier (1978): subordinate characteristics (e.g., experience, ability, training), task characteristics (e.g., routine or structured tasks), and group and organizational characteristics (e.g., group cohesiveness, organizational rules and procedures).

524

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

Typically, the variables have been measured by asking individuals to provide self-reports, and tests of the model have then involved correlation and/or regression analysis. These analyses may therefore be arguably appropriate for some substitutes and neutralizers but are clearly less so for others. For example, in traditional manufacturing plants, task characteristics may be shared by many (or, perhaps, most) of the subordinates who report to the same supervisor. This would make them probably more properly classified as group-level variables and thus require both measurement and data analysis at the group level. The same is true for variables such as group cohesiveness and a leader’s formal position power. What this means, then, is that the leadership substitutes model is in clear need of further theoretical specification that clarifies its level-of-analysis predictions. Additionally, support for the model may be greater or less than we currently believe (because its implicit and explicit level-of-analysis assumptions have not been tested by empirical research), so that appropriate level-of-analysis testing of this approach is also needed. For further discussion of level-of-analysis issues with respect to substitutes for leadership, see Dionne, Yammarino, Atwater, and James (in press), Murry (1998), Podsakoff and MacKenzie (1998a, 1998b), and Tosi and Banning (1998). 3.6. Transformational leadership Another leadership approach considered among our examples is that of transformational leadership. There are a number of different theories and approaches that could be discussed under this label (e.g., Bass, 1985, 1997; Conger & Kanungo, 1988; House & Podsakoff, 1994; Podsakoff, MacKenzie, Mohrman, & Fetter, 1990). However, rather than going into great detail, we will simply note that some of the variables that are employed in these models are unclear as to their appropriate level of analysis (e.g., charisma). Other variables appear to be strictly individual-level (e.g., individualized consideration; Bass, 1985), while still others seem to be more group-level (e.g., leader behaviors that foster the acceptance of group goals; Podsakoff et al., 1990). Since most of the research in this domain has not explicitly formulated nor tested level-of-analysis predictions (for exceptions, see Yammarino & Dubinsky, 1994; Yammarino, Dubinsky, Comer, & Jolson, 1997; Yammarino, Dubinsky, & Spangler, 1998), it therefore seems apparent that much additional work is needed to first theoretically specify and then empirically test the level of analysis at which transformational leadership effects occur. By so doing, we will clearly gain additional knowledge concerning key transformational leadership processes. For further discussion of level-of-analysis issues and transformational leadership, readers are directed to Avolio and Bass (1998a, 1998b), Conger (1998), and Locke (1998). 3.7. LMX theory LMX theory examines the relationship that develops and evolves between leaders and followers as a result of exchange processes over time (Graen & Uhl-Bien, 1995). Briefly, subordinates may be roughly classified into those who have high- or low-quality exchange with their bosses (earlier presentations of the LMX approach labeled these as ‘‘in-group’’

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

525

and ‘‘out-group’’ exchanges, respectively). Subordinates with high LMX tend to invest increased levels of effort and personal loyalty in their relationship with the leader, thereby providing an enhanced contribution to the unit and leader’s performance. Leaders tend to reciprocate by giving such subordinates increased social support, organizational resources, and rewards. Conversely, employees with low LMX tend to rely more on the formal exchange parameters extant in the organization. They do not go beyond normal work expectations, and their leaders are therefore less likely to provide them with incremental resources or benefits in exchange. High-quality LMX has been shown to be associated with a number of positive workrelated outcomes, including enhanced subordinate performance, career progress, and job satisfaction (Gerstner & Day, 1997; Liden et al., 1997). Situational moderators of LMX have infrequently been proposed and studied; more typically, LMX and LMX–outcome relationships have been examined without considering various potential contingency factors (Cogliser & Schriesheim, 2000; Gerstner & Day, 1997). Although it seems clear that the unit of analysis is the leader–subordinate relationship (Graen & Uhl-Bien, 1995) and that the level of analysis should therefore be the dyad, no LMX research has employed this level (Schriesheim et al., 1999). Instead, data have typically been collected from either just the subordinate or just the boss; when data have been collected from both, they have not been used in any type of dyadic analysis. Consequently, we believe that all the extant research is fundamentally uninformative about the LMX process because it has not studied the exchange at the dyadic level of analysis (it has studied the relationship from one perspective or the other perspective, but not jointly). We know that boss–subordinate descriptions of the LMX process usually share little covariance (typically 10–20%; Gerstner & Day, 1997; Schriesheim et al., 1998). So, what are we examining when we study LMX at the individual (leader or follower) level of analysis? We clearly cannot be studying an exchange, since exchanges are, by their very nature, dyadic. This, of course, shows a very serious inconsistency between the theory and how it has been tested, and it suggests that we may not know as much as we think about the dyadic processes underlying LMX. Future research, and paying careful attention to aligning the theory and the level of analysis at which LMX predictions are tested therefore seem urgently needed (see Coleman, 1998; Rousseau, 1998; Schriesheim et al., 1999). The detailed illustration that we provide next is thus intended to show how such research might be conducted and to also provide a first set of rival level-appropriate LMX analyses.

4. A detailed LMX illustration As briefly mentioned above, the LMX model has been one of the most researched approaches to leadership in the last three decades. In fact, it has had approximately 150 examinations conducted to-date (Gerstner & Day, 1997; Graen & Uhl-Bien, 1995; Schriesheim et al., 1999). However, although the LMX model has been heavily researched, there are still a number of substantive areas where additional research is needed. One such area concerns variables that are likely to be part of the exchange process and that therefore should

526

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

be related to LMX quality. In fact, if we are to gain the broadest understanding of the LMX process and its effects, Dienesch and Liden (1986), Gerstner and Day (1997), Graen and Scandura (1987), Graen and Uhl-Bien (1995), and others suggest that fewer ‘‘outcomeoriented’’ LMX studies (with performance or satisfaction as dependent variables) are now needed and that more basic LMX correlates should be examined. The substantive question investigated in this study, how LMX is associated with the influence tactics or control that leaders and subordinates use on each other, is an attempt to be responsive to this suggestion for needed research. Besides helping advance our knowledge about basic or fundamental outcomes of leadership (such as performance and satisfaction), the LMX approach has also been responsible for reorienting leadership research away from the so-called ‘‘average leadership style’’ (ALS) model, which theoretically treated the leader’s behavior toward subordinates as being reasonably constant across subordinates (cf. Kerr & Schriesheim, 1974; Kerr et al., 1974). Empirically, to represent the leader’s behavior, ALS research most often used one of two approaches. Raw leadership description scores (typically descriptions of a leader’s behavior provided by individual subordinates) were sometimes used, despite their being inappropriate for testing an ALS. Also employed were unit-averaged leadership description scores (computed by averaging the leadership descriptions of all the subordinates within the same leader’s unit or group; this treatment is now called a ‘‘between-groups’’ level of analysis) (for reviews of the ALS approach, see Kerr & Schriesheim, 1974; Korman, 1966). Although even the earliest LMX writings argued that research on LMX should be focused on the vertical (leader–subordinate) dyad (Dansereau, Graen, & Haga, 1975; Graen & Cashman, 1975), subsequent empirical research has not employed a dyadic orientation and has instead either used raw scores (e.g., Graen, Novak, & Sommercamp, 1982) or group deviation scores (e.g., Graen, Liden, & Hoel, 1982) to test LMX hypotheses (in contemporary terminology, the group deviation treatment is now called an ‘‘individuals-within-groups’’ level of analysis). Most researchers have not tested LMX level-of-analysis predictions at all, and Gerstner and Day (1997) have concluded that, ‘‘much empirical research is needed to understand how the LMX model operates at different levels of analysis’’ (p. 839). In fact, Schriesheim et al. (1999) found no studies that examined LMX relationships at a dyadic level of analysis and that ‘‘. . . only 10 (of 137) empirical studies . . . can speak about support (or nonsupport) for LMX theory’’ at any particular level of analysis (p. 98). As Bass (1990, p. 338) notes, this failure to test predictions at the dyadic level of analysis is clearly discrepant with a very fundamental aspect of LMX theory. It is also discrepant with the field’s recently enunciated emphasis on first specifying and then testing hypotheses at an appropriate level of analysis (e.g., see House et al., 1995; Klein et al., 1994; Klein & Kozlowski, 2000; Mowday & Sutton, 1993; Yammarino, 1994). The reason for this emphasis on testing levels of analysis is simple: As illustrated in our earlier raw score analysis example, obtained results may be misleading or even artifacts if level-of-analysis testing is not conducted (Dansereau et al., 1984). Consequently, the current illustrative research is designed to examine multiple levels of analysis. In doing so, we extend previous LMX research by reporting the very first comparative test of basic LMX processes at several levels of analysis (including the dyadic) and also the first LMX level-of-analysis tests with what Gerstner and

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

527

Day (1997) and Graen and Uhl-Bien (1995) consider to be a fully adequate measure of LMX. We also illustrate how others who are not interested in the LMX approach may still test their theories, models, and/or hypotheses for effects at different levels of analysis. This is absolutely critical if we are to begin better aligning the theory that we propose with the level of analysis that we actually employed in its tests. 4.1. LMX and control One of the central tenets of the LMX approach is that high-quality LMX is characterized by shared influence and mutual trust, respect, and obligation between a leader and his or her subordinates (Graen & Scandura, 1987; Graen & Uhl-Bien, 1995). These characteristics develop over time, as informally negotiated exchanges between the leader and follower replace formally designated exchanges dictated by the organization. The movement toward a more egalitarian sharing of influence, which characterizes good LMX, suggests that a leader’s initially high (relative to the subordinate) power and more frequent use of strong influence tactics should diminish with good LMX. Reasons for these expectations include the fact that the more the boss feels that there is a good exchange relationship with a subordinate, the more likely the supervisor will trust the subordinate and therefore not feel the need to use strong and controlling influence tactics to force him/her to comply with supervisory requests. Additionally, as Yukl (1998) suggests, . . . the special relationship with in-group subordinates creates certain obligations and constraints for the leader. To maintain the relationship, the leader must . . . rely more on time-consuming influence methods such as persuasion and consultation. The leader cannot resort to coercion or heavy-handed use of authority without endangering the special relationship. (p. 151)

Correspondingly, the more movement toward an egalitarian relationship, the more the subordinate’s initially low power should increase. There are several possible reasons why subordinates’ use of stronger influence tactics may also increase. For one, the subordinate’s increased power and the supervisor’s reduced use of controlling influence tactics would allow the subordinate to more frequently use stronger influence tactics when dealing with the supervisor (Kipnis, 1984). Additionally, although research has produced inconsistent relationships between LMX and various influence tactics (e.g., Deluga & Perry, 1991; Dockery & Steiner, 1990; Farmer, Maslyn, Fedor, & Goodman, 1997; Krone, 1991), subordinates in good LMX relationships have been shown to be more likely to have better communication with their supervisors (Fairhurst, Rogers, & Sarr, 1987) and to have increased confidence that their supervisor likes them (Dockery & Steiner, 1990; Liden, Wayne, & Stilwell, 1993). Moreover, Scandura, Graen, and Novak (1986) found that LMX quality was significantly and positively correlated with the strength of subordinate decision influence, Keller and Dansereau (1995) found a significant positive LMX – empowerment relationship, and Schriesheim et al. (1998) found a significant positive relationship between both supervisor and subordinate reports of LMX and the leader’s use of delegation. All of these appear to support the idea that subordinates in high-quality LMX relationships are more likely to feel

528

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

empowered and therefore enabled to exert stronger control over the boss. Thus, the substantive hypothesis tested in this research is that high-quality LMX should be associated with lower use of strong influence tactics (‘‘control’’) by the supervisor toward the subordinate and higher subordinate use of these same tactics toward the supervisor. 4.2. Levels of analysis alternatives 4.2.1. Within- and between-entities analysis (WABA) The level of analysis at which our hypothesis should hold is now considered. Unfortunately, neither previous theory nor previous research speaks directly about the appropriate level of analysis for this prediction, although drawing upon the earliest (e.g., Dansereau et al., 1975; Graen & Cashman, 1975) and most basic underpinnings of LMX theory (e.g., Dienesch & Liden, 1986; Graen & Scandura, 1987) would suggest that a dyadic level of analysis approach would be most appropriate. However, a case can also be made for the hypothesized relationships to hold at the individuals-within-work groups level, especially when data from only one dyad member (e.g., supervisor or subordinate) are examined (cf. Dansereau, Alutto, Markham, & Dumas, 1982; Graen, Liden, & Hoel, 1982). This perspective will be developed below. However, before doing so, a brief conceptual overview of the methodology of WABA (Dansereau et al., 1984) may facilitate the subsequent discussion. WABA is a data-analytic approach that can be used to test a substantive hypothesis at several levels of analysis (cf. Dansereau et al., 1984; Markham & Halverson, in press; Yammarino, 1994). An entity is any analytic ‘‘whole.’’ For example, a supervisor’s work group might be an entity. A ‘‘part’’ is any component of an entity. For example, in a work group, it might be an individual subordinate. Data that are collected from parts within entities can consequently be represented at two levels. One is at the entity level and the other is at the part within entity level. For example, using work groups as entities and individual subordinates as parts, survey responses of each subordinate may be represented by a whole entity score (in this case, the average score of all subordinates within the same work group) and by a part within entity score (each subordinate’s score minus the work group average score). Mathematically, what is being done is that each subordinate’s score on a variable is being partitioned into a group average (whole entity) score and an individual deviation from group average (part within entity) score. What this score partitioning allows to be tested are conceptual specifications of wholes and parts effects. In the work group entity example used above, a wholes effect would imply that the supervisors treat all subordinates the same. More specifically, a wholes effect would exist if the subordinates’ group-averaged LMX scores were related to some corresponding group-averaged dependent variable (such as the average strength of the influence tactics that are used by subordinates toward the supervisor). A parts-withinentities effect would imply that the supervisors treat their subordinates differently and relative to one another. More specifically, a parts effect would be evidenced by obtaining a relationship between the subordinates’ LMX deviation scores and their corresponding influence strength deviation scores. Obtaining whole entity effects would enable one to conclude that the quality of the LMX with supervisors on average (a leadership ‘‘style’’

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

529

measure) is related to the averaged indicator of influence strength (an indicator of ‘‘subordinate controllingness’’). On the other hand, obtaining parts effects would enable one to conclude that the quality of leader exchange with individual subordinates, relative to the group as a whole, is related to the relative degree to which the leader uses controlling influence tactics on individual subordinates. The more complex technical details of conducting a WABA analysis are presented in Appendix A of this article, but the key conceptual elements have been summarized above. As mentioned earlier, while early writings clearly portrayed LMX as a dyadic process (and therefore would seem to imply a need for dyadic analyses), over 90% of LMX studies (127 of 137) have used only raw score analyses, with the remainder (10 of 137) using group deviations or within- and between-groups analyses; no studies have employed dyadic analyses to-date (Schriesheim et al., 1999). However, from the discussion above and Appendix A, it seems clear that those studies that have employed raw score analyses have used a questionable analytic approach. This is because raw scores can be conceptualized as composite variables that, as shown by Formula (1), may have very different correlations at the levels of wholes and parts. Raw score studies consequently have utility only in the very narrow sense of suggesting general support for hypotheses drawn from LMX theory; they cannot unambiguously support LMX theory because they are mute about the level(s) of analysis at which obtained results hold. Thus, below, we develop three alternative levels of analysis for testing the LMX–control tactics hypothesis. 4.2.2. Individuals within- and between-work groups analysis Rather than using just raw score analyses, using an individuals-within- and between-work groups level of analysis is more appropriate, since it allows the testing of whether LMX relationships are relative and differentiated from a typical or average relationship within the work group (i.e., within-groups) — or whether they are more universal and stylistic and therefore an ALS (between-groups) conceptualization makes more sense. Thus, finding support for within-work group relationships and no support for between-groups relationships diminishes the plausibility of the ALS approach to leadership and thereby increases the reasonableness of viewing leadership processes as being differentiated and relative — as determined by differences between each subordinate and the average of the work group. This within-groups framework implies that how subordinates react to their exchange relationship is a function of the nature of the exchange relationships that other subordinates have with the same supervisor. In other words, subordinates are engaging in a within-group social comparison process that employs the supervisor’s work group as the basis for judging the exchange and its correlates (cf. Jacobs, 1970). Of course, as noted above, this is the level-of-analysis portrayal that has been used in those few LMX studies that have looked at level-of-analysis issues at all (Schriesheim et al., 1999). Thus, we test our substantive hypothesis at the individuals within- and between-work groups level of analysis because of its reasonableness and to provide continuity with and comparability to the few extant LMX level-of-analysis studies. Previous LMX research has typically interpreted the finding of within-group relationships to support the LMX model, while between-groups relationships have been seen as supportive of the ALS approach

530

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

(Schriesheim et al., 1999). This seems reasonable and therefore we interpret within- and between-work group test results in the same manner. 4.2.3. Dyads within- and between-work groups analysis Although conducting tests of our substantive hypothesis at the individuals-within-and between-groups level of analysis seems reasonable, as we mentioned earlier, we believe that it is more appropriate to test the hypothesis using a dyadic perspective. Here, two alternatives seem most reasonable. The first is what we might call a dyads within- and between-work groups approach. Whereas the individuals-within- and between-groups approaches require data from only one source (e.g., supervisor or subordinate descriptions of the exchange), both dyadic approaches require that matching data be collected from each dyad member. For example, in the current study, subordinates were asked to describe the quality of their exchange with their supervisor (LMX) and to also provide a description of the tactics that they use to influence their boss. Correspondingly, the supervisor was asked to describe the exchange relationship with each subordinate (called ‘‘SLMX’’ below), along with indicating the influence tactics that he or she used on each. In a dyads-within- and between-groups analysis, the leader–subordinate dyad is the part and the supervisor’s work group is the whole. Thus, the corresponding data from each supervisor and one subordinate are averaged to represent each dyad (part) (e.g., matched LMX and SLMX scores are averaged to represent dyadic exchange quality or ‘‘DLMX’’). Then, since each supervisor has more than one subordinate (there is more than one dyad within each supervisor’s work group), dyads-between-groups (or wholes) scores are computed by averaging the parts scores for each work group (by averaging the DLMX scores for each supervisory unit). Finally, dyads-within-groups (parts within wholes) scores are calculated by subtracting the appropriate whole score (group average DLMX) from each parts score (DLMX). This type of analysis thus measures each dyad from both supervisor and subordinate viewpoints. The analytic levels that are examined are dyads-within- and betweengroups: (1) a whole dyads or dyads-between-groups effect would require finding a relationship between the average dyadic exchange quality generally shown by different supervisors and the supervisor’s general (average) use of controlling influence tactics, while (2) a parts-within-wholes or dyads-within-groups effect would be manifested by finding a relationship between relative dyadic exchange quality (relative to other dyads in the work group) and the supervisor’s relative use of controlling influence tactics (also relative to other dyads in the work group). Under this analytic approach, support for our hypothesis would be evidenced by finding dyads-within-groups effects. In this case, the social comparison process that is occurring involves the subordinate and the supervisor comparing a particular dyadic relationship with other dyadic relationships within the same work group. Finding dyadsbetween-groups effects would support a more stylistic or ALS conceptualization of LMX and therefore would not be congruent with key underpinnings of the theory. 4.2.4. Individuals within- and between-dyads analysis The second dyadic perspective that seems appropriate for testing the LMX–control substantive hypothesis is individuals-within- and between-dyads. In contrast to the dyads-

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

531

within- and between-groups perspective, each supervisor–subordinate relationship within the work group may actually be independent of others in the group, and the perceptions of each dyad’s supervisor and subordinate interrelated or not (Dansereau, 1995; Nachman, Dansereau, & Naughton, 1985). Unlike the previous two level-of-analysis conceptualizations, there is no social comparison process operating in this level-of-analysis conceptualization. Neither the supervisor nor the subordinate compares his/her dyadic relationship to any other relationship within the work group. This is what might be called the individuals-withinand between-dyads perspective. Here, the wholes or ‘‘between-entity’’ units of analysis are the supervisor–subordinate dyads; the parts or ‘‘within-entity’’ units are the descriptions of each dyad by the supervisor and by the subordinate. The individuals’ between-dyads (or wholes) scores are calculated by averaging the data from each supervisor and one subordinate. The individuals-within-dyads (or parts) scores are then computed by subtracting the betweendyad score from either the supervisor’s or the subordinate’s corresponding score. Simply put, if dyads are ‘‘truly’’ operative, it would seem reasonable to expect more differentiation between dyads than within dyads. Conversely, finding support for the individuals’ withindyads perspective would indicate relatively poor within-dyad agreement between supervisor and subordinate on fundamental LMX processes and would, therefore, be contrary to LMX theory predictions. 4.3. Methodological issues Since descriptions of LMX and control were obtained from both supervisors and subordinates, the individuals-within- and between-work groups analyses could be examined from the supervisors’ perspective and from the subordinates’ perspective. Results from these analysis might differ, since supervisor and subordinate descriptions of the LMX process are usually only weakly related — as mentioned earlier, generally no more than 10–20% of the variance is shared (see Schriesheim et al., 1998 for a brief review). In fact, a recent metaanalysis by Gerstner and Day (1997) found the mean sample-weighted correlation to be .29 (.37 corrected for unreliability), and previous research has found that support for LMX hypotheses may vary depending upon whether supervisor- or subordinate-provided LMX descriptions are employed (cf. Gerstner & Day, 1997; Schriesheim et al., 1998). Thus, we will refer below to the individuals-within- and between-groups approach as measured by all supervisor-provided data as ‘‘Model 1a,’’ while the same perspective examined by subordinate-provided data will be called ‘‘Model 1b.’’ For consistency with this nomenclature, we will call the dyads-within- and between-groups approach ‘‘Model 2,’’ and the individuals-withinand between-dyads perspective ‘‘Model 3.’’ In addition to examining the effect that data source has on obtained findings, it also seems desirable to examine the potentially biasing effects of using all same-source data. This is because one criticism that is often leveled toward organizational research concerns commonsource or ‘‘monosource’’ bias (cf. Podsakoff & Organ, 1986; Spector, 1987). That is, when data on both the independent and dependent variable(s) are collected from the same source (e.g., all from the supervisor or all from the subordinate), inflated relationships may be

532

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

obtained due to perceptual processes such as ‘‘cognition balancing’’ or ‘‘consistency seeking.’’ Although no direct evidence apparently exists about such effects in LMX research, Gerstner and Day (1997) found in their meta-analysis that supervisor-provided ratings of subordinate performance correlated .41 (.55 corrected for unreliability) with supervisor LMX descriptions (SLMX) but only .28 with subordinate LMX descriptions (.30 corrected). No additional evidence was presented but they concluded that, ‘‘more research is certainly needed . . . to understand the degree to which same-source bias inflates the correlation between LMX and performance ratings’’ (p. 836). If common-source bias can indeed be a serious threat to the validity of inferences drawn from some research (Crampton & Wagner, 1994), one way in which the relationships of Models 1a, 1b, and 3 above may be more rigorously tested is to use LMX reports from one source (supervisor or subordinate) and match these with control reports provided by the other member of the dyad (i.e., ‘‘heterosource’’ data). We will denote these models by a prime sign (e.g., Model 1a0), and this approach seems theoretically justifiable from a level-of-analysis perspective (see Avolio, Yammarino, & Bass, 1991). In fact, it seems reasonable to expect that, in an exchange relationship, perceptions of good exchange by the supervisor should be associated with the subordinate perceiving greater self-control (i.e., subordinate control) over the supervisor (Model 1a0). Also likely is that perceptions of good exchange by the subordinate should be associated with supervisor-perceived lower supervisory control (Model 1b0). Finally, combining these two cross-source approaches and the level of analysis employed in Model 3 yields Model 30. In Model 30, as in Model 3, the between-dyads level is assessed by the average of both dyad members’ reported LMX and by the average of both dyad members’ description of control directed toward the other dyad member. However, in Model 30, the within-dyads level is represented by deviation scores that (a) match the supervisor’s SLMX description with the subordinate’s description of controllingness directed toward the supervisor, and (b) match the subordinate’s LMX description with the supervisor’s description of controllingness directed toward the subordinate. These last three models (1a0, 1b0, and 30) thus allow the testing of level-of-analysis effects without the inflation of results due to monosource bias. The level-of-analysis expectations for these models are, of course, the same as for Models 1a, 1b, and 3 (respectively). In the individuals-within- and between-groups analyses (Models 1a, 1b, 1a0, and 1b0), LMX theory would be most supported by finding only within-groups effects (indicating differentiation from the group relative relationships). In the dyads-withinand between-groups analyses (Model 2), obtaining only within-groups relationships would again be most supportive of LMX theory. Finally, in the individuals-within- and betweendyads analyses (Models 3 and 30), finding only between-dyads relationships would lend the most support to the LMX approach. Although we believe that the dyadic models (Models 2, 3, and 30) constitute the most appropriate tests of LMX theory (e.g., Dienesch & Liden, 1986; Gerstner & Day, 1997; Graen & Scandura, 1987; Schriesheim et al., 1999), all of the alternative models are examined so that stronger inferences may be drawn than if a more limited set of rival level-of-analysis representations were examined. This is the approach to level-of-analysis testing that we advocate be adopted by future researchers in the field — as inclusive or exhaustive as possible.

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

533

5. Method Data were collected from two separate samples to increase confidence in the generalizability of obtained findings. In both samples, two subordinates were sampled per supervisor, so that the degrees of freedom within- and between-entities would be of equal sizes, preventing obtained findings from being artifacts due to one being larger than the other. Also, the use of equal -and small-sized groups for analysis purposes would be expected to eliminate all or virtually all of the biases in WABA I estimates, which may occur with largersized groups or with groups of varying sizes (Bliese & Halverson, 1998). 5.1. Samples and procedure 5.1.1. Survey Prior to conducting a short training program at a large western bank’s and a large aerospace engineering company’s headquarters, survey questionnaires were administered by the authors to a sample of 98 branch bank managers and 62 engineering managers. These surveys were conducted under joint university–company sponsorship, and the anonymity of the participants was guaranteed verbally and in writing. The surveys were sequentially numbered and names were not asked of the participants. All guidelines of the American Psychological Association and the Academy of Management for the protection of human subjects were followed. The survey given to both samples began with a joint university–company cover letter. For the bank managers, next came a section which asked them to: Please choose a subordinate (an individual who reports directly to you) who you consider to be one of the better performers that you supervise (but not necessarily your ‘‘best’’). We will refer to this person as ‘‘Subordinate 1.’’ Next, please choose another subordinate who you consider to be one of the poorer performers you supervise (but not necessarily your ‘‘worst’’). We will refer to this person below as ‘‘Subordinate 2.’’

A slightly different set of instructions was given to the engineering managers: Please assign a unique number to each of your subordinates (those individuals who report directly to you). Then, randomly select two of these subordinates to participate in this study and label them ‘‘Subordinate A’’ and ‘‘Subordinate B.’’ If you do not have easy access to a table of random numbers, use the business section of your newspaper to see today’s stock trading volume on either the NASDAQ or the New York Stock Exchange (NYSE). For example, if the NASDAQ volume is 1,863,247(000) shares, select the subordinate who was assigned the number ‘‘7’’ as ‘‘Subordinate A’’ and the subordinate who was assigned the number ‘‘4’’ as ‘‘Subordinate B.’’ If you do not have seven subordinates, skip the number ‘‘7’’ and select the subordinate who was assigned the number ‘‘4’’ as ‘‘Subordinate A’’ and the subordinate who was assigned the number ‘‘2’’ as ‘‘Subordinate B.’’

The managers were then requested to describe the quality of their interactions with each subordinate separately via a seven-item supervisor LMX scale for each subordinate. After doing this, they were asked to indicate the frequency with which they used 20 different

534

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

potential tactics to influence each of the two subordinates (separately) and to rank these 20 tactics in terms of how controlling or forceful they (the supervisor) felt they were when directed at subordinates. Attached to the managers’ surveys were two additional surveys, labeled ‘‘Subordinate 1 Survey’’ and ‘‘Subordinate 2 Survey’’ or ‘‘Subordinate A Survey’’ and ‘‘Subordinate B Survey’’ and numbered to correspond with each manager’s survey. The managers were instructed to give these to the appropriate subordinates and to ask them to complete the surveys and send them to the researchers in the prepaid return envelopes that were attached (these were addressed to the university). The employee surveys contained a similar cover letter, a demographics section, a 20-item measure indicating how they attempted to influence their supervisor to comply with their (the subordinates’) requests, a section asking them to rank the same 20 influence tactics with respect to their perceived forcefulness or controllingness when directed at the supervisor, and the seven-item LMX scale recommended by Gerstner and Day (1997) and Graen and Uhl-Bien (1995). Missing data from the bank managers reduced their number to 94, and missing data and nonresponses reduced the size of the bank subordinates’ subsample to 84 high performers and 75 low performers. Combining all of these data yielded a fully matched data set consisting of 75 bank managers describing their interactions with 150 subordinates (one a high and one a low performer) and these same 150 subordinates describing their interactions with their bosses. Missing data from the engineering managers reduced their number to 60, and nonresponses reduced the number of randomly selected subordinates to 58 pairs (116 total). 5.1.2. Sample demographics The final sample of 75 bank managers reported their average age to be 39.22 years old, 51 were male, and they averaged 4.13 years working in their current jobs. The managers’ racial composition was 53 Caucasians, 14 Hispanics, and 8 Blacks. All were high school graduates, 47 had 4-year college degrees, and 10 had at least some postgraduate work. The average age of the 150 subordinates was 34.30 years old, 83 were male, and they averaged 1.44 years working under their current supervisors and 2.72 years working at their current jobs. Racially, they consisted of 96 Caucasians, 33 Hispanics, 20 Blacks, and 1 ‘‘other.’’ Two had graduated from middle school, 61 had high school degrees, 64 had 4-year college degrees, and 23 reported at least some postgraduate work. The average age of the final sample of 58 engineering managers was 32.13 years old, 52 were male, and they averaged 6.47 years working in their current jobs. The managers’ racial composition was 46 Caucasians, 1 Hispanic, 1 Black, and 4 Asians. All had 4-year college degrees, and 36 (62%) had graduate degrees. The average age of the 116 subordinates was 27.09 years old, 95 were male, and they averaged 3.69 years working under their current supervisors. Their racial composition was 99 Caucasians, 6 Hispanics, 4 Blacks, and 7 Asians. All were college graduates and 47 had at least some postgraduate training. 5.1.3. Tests for nonresponse bias The number of nonrespondents in the engineering manager sample was too small to test for nonresponse bias and thus we did not consider sampling bias to be a serious concern in

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

535

this sample. For the bank managers, multiple analysis of variance (MANOVA) runs were conducted to test for differences between the 75 branch managers included in the final sample and the 19 who were omitted due to missing subordinate data, as well as for differences between the 75 high-performing subordinates included in the final sample and the 9 who were omitted due to missing data from their low-performance counterparts. No differences were found in demographic variables, attitudinal (LMX and control) variables, or both (combined). Thus, nonresponse bias does not appear likely to be problematic in either sample employed in this study. 5.2. Measures 5.2.1. Subordinate (LMX)- and supervisor (SLMX)-perceived LMX quality The LMX measure employed was the scale presented in Graen and Uhl-Bien (1995, p. 123) and advocated by Gerstner and Day (1997) and Graen and Uhl-Bien (1995) as the best available LMX scale. This measure consists of seven items (with five-point response scales) that ask for descriptions of LMX quality. Following conventional practice (cf. Graen & Scandura, 1987; Scandura et al., 1986), slight modifications were made in the original seven items so as to allow the supervisors to describe the quality of their exchanges with each subordinate (sample item: ‘‘How would you characterize your working relationship with Subordinate 1?’’). The coefficient a internal consistency reliability of this scale in the bank sample was .86 and .83 for the subordinate and supervisor descriptions, respectively, while they were .84 and .81 in the engineering sample. 5.2.2. Control The supervisors’ and subordinates’ reported use of strong or forceful control tactics was assessed with Tepper’s (1990) 20-item scale. This questionnaire measure is based on Tepper’s (1989) 20-category taxonomy of influence tactics and contains 20 items (one for each tactic) that ask respondents to indicate the frequency with which they use each tactic to affect the influence target (the five-point response scale ranges from Never to Usually). This measure was employed because of its successful previous use with midlevel bank and professional managers and because it overcomes a number of shortcomings inherent in other influence measures (such as inadequate representation of the domain of potential influence tactics; Tepper, 1990). Instead of unit-weighting the 20 items to form the measure of controllingness, Tepper’s procedure of using fractional weights was employed. These fractional weights were obtained by using Thurstone Case III scaling (Guilford, 1954) on the rankings provided by the supervisors and subordinates (each separately) of how controlling or forceful they felt each tactic was when it was directed at the referent (the subordinates for the supervisor and the supervisor for the subordinates). Thus, for example, each subordinate’s controllingness score was computed by multiplying his or her response to each of the 20 influence tactic items by the corresponding obtained Thurstone Case III scale value for that tactic. The same was done for the supervisors; however, their scores were reversed to indicate low controllingness. This was done to prevent effects from ‘‘washing out’’ each other (i.e., the expected positive correlations between LMX and subordinate control might cancel the expected

536

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

negative correlations between SLMX and supervisor control when these two relationships were simultaneously examined in Models 2, 3, and 30). In the bank sample, the final obtained measure had coefficient a internal consistency reliabilities of .78 for the measure of supervisor control of the subordinate and .77 for the measure of subordinate control of the supervisor. In the engineering sample, the corresponding coefficient a values were computed to be .76 and .75, respectively. 5.3. Analyses The rival LMX level-of-analysis representations discussed above were assessed by standard WABA (Dansereau et al., 1984; Yammarino & Markham, 1992). Appendix A presents computational and analytic details and, for additional discussion, see Dansereau et al. (1984), Markham and McKee (1991), Schriesheim et al. (1998), Yammarino and Dubinsky (1992), and Yammarino and Markham (1992). The Thurstone Case III rank-order scaling employed Bradford and Schriesheim’s (1990) computer program to compute controllingness scale values for each of the 20 influence tactics. Thurstone scaling is based upon the assumption that one stimulus, which is psychologically equal to another, will be chosen by respondents 50% of the time over the other stimulus. Thus, proportion of choice can be used as a way of scaling stimuli or assigning numbers that represent their degree of some attribute (such as strength or controllingness). Thurstone Case III rank-order scaling involves having stimuli ranked by respondents. Since rank-order data contain all preference orderings among a set of stimuli (e.g., a rank of ‘‘1’’ means the stimulus is preferred over all others, a rank of ‘‘2’’ means preference over all but the stimulus ranked ‘‘1’’, etc.), these rankings are then converted to a matrix of proportional choices. The proportional choices are next totalled and averaged and then converted to standard scores for each stimulus. Finally, the scores are adjusted by an estimate of choice dispersion, which yields the final Case III scale values (for further details and computational specifics, see Guilford, 1954).

6. Results 6.1. Raw score correlations Table 1 presents the variable means, standard deviations, and zero-order intercorrelations. As mentioned earlier, all of the reliabilities were acceptable in both samples (above .7; Nunnally & Bernstein, 1994). As shown in Table 1, most of the variable intercorrelations are statistically significant ( P < .05) for both samples. However, as would be expected due to monosource bias, the correlation between supervisor-perceived exchange (SLMX) and supervisor-perceived control (SCONT) is higher than the correlation between SLMX and subordinate-perceived control (CONT) (correlations of .53 versus .34 for the bankers, and .49 versus .35 for the engineers). The same pattern exists for subordinate-perceived exchange (LMX), subordinate-perceived control (CONT), and supervisor-perceived control (SCONT) (r values of .44 versus .16 for the bankers, and .47 versus .30 for the engineers). This

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

537

Table 1 Variable means, standard deviations, and raw score correlations Measure Mean S.D. Supervisor-perceived exchange (SLMX) Subordinate-perceived exchange (LMX) Supervisor-perceived control (SCONT) Subordinate-perceived control (CONT)

Mean

S.D.

1

2

26.32 4.68 –

26.08 4.97 0.41*

– – 27.33

– – 4.37

27.25

5.60

0.27*

132.91

24.66

93.46

16.18

3

4 28.31 26.22 0.49*

97.56 18.43 0.35*

–

0.30*

0.47*

0.53*

0.16*

–


0.06

0.34*

0.44*


0.09

–

Entries below the main diagonal are for the bank sample (N = 150); entries above the main diagonal are for the engineer sample (N = 116). * P < .05.

obviously supports the desirability of examining Models 1a0, 1b0, and 30, where potential same-source bias effects are eliminated. 6.2. WABA I results 6.2.1. Bank sample In the bank sample, the WABA I tests of statistical ( F test) and practical (E test) significance shown in Table 2 indicate that the variance for most of the variables in the models is stronger between entities than within entities (see the ‘‘Inference’’ column). The sole exception to this generalization is for the control variable in Models 3 and 30, which statistically and practically shows more variance within- than between dyads. 6.2.2. Engineer sample The WABA I results for the engineer sample are similar in pattern to that of the bank sample, but several notable differences exist. Again, most of the variance is stronger between entities than within entities. However, none of these differences is practically or statistically significant. Also, for Models 3 and 30, there is more within-than between-dyad variance for the LMX variable and the control variable, although the first difference is very small (.01) and neither difference shows practical or statistical significance. These results therefore support an inference of ‘‘both,’’ indicating that there is variance within- and between-entities for all of the engineer sample models. 6.3. WABA II results 6.3.1. Bank sample In contrast to the WABA I results, the WABA II findings presented in Table 3 show that the hypothesized LMX – control relationships of Models 1a, 1b, 2, and 1a0 are best

538

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

Table 2 WABA I results Etas

Tests

Model and variable (measure)

Within

Between

E ratio

F value

WABA I inference

Bank sample (1a) Exchange (SLMX) Control (SCONT) (1b) Exchange (LMX) Control (CONT) (2) Exchange ([SLMX + LMX]/2) Control ([SCONT + CONT]/2) (3) Exchange (SLMX and LMX) Control (SCONT and CONT) (1a0) Exchange (SLMX) Control (CONT) (1b0) Exchange (LMX) Control (SCONT) (30) Exchange (SLMX and LMX) Control (CONT and SCONT)

.55 .56 .50 .47 .51 .56 .61 .87 .55 .47 .50 .56 .61 .87

.83 .83 .86 .88 .86 .83 .79 .49 .83 .88 .86 .83 .79 .49

1.51y 1.48y 1.72y 1.87y 1.69y 1.48y 1.30y 0.56y 1.51y 1.87y 1.72y 1.48y 1.30y 0.56y

2.31* 2.23* 3.00* 3.55* 2.88* 2.23* 1.69 * 3.13a,* 2.31* 3.55* 3.00* 2.23* 1.69* 3.13a,*

Between Between Between Between Between Between Between Within Between Between Between Between Between Within

Engineer sample (1a) Exchange (SLMX) Control (SCONT) (1b) Exchange (LMX) Control (CONT) (2) Exchange ([SLMX + LMX]/2) Control ([SCONT + CONT]/2) (3) Exchange (SLMX and LMX) Control (SCONT and CONT) (1a0) Exchange (SLMX) Control (CONT) (1b0) Exchange (LMX) Control (SCONT) (30) Exchange (SLMX and LMX) Control (CONT and SCONT)

.68 .66 .66 .64 .66 .65 .71 .78 .68 .64 .66 .66 .71 .78

.73 .75 .75 .77 .75 .76 .70 .63 .73 .77 .75 .75 .70 .63

1.07 1.14 1.14 1.20 1.14 1.17 0.99 0.81 1.07 1.20 1.14 1.14 0.99 0.81

1.17 1.31 1.31 1.47 1.31 1.39 1.00a 1.51a 1.17 1.47 1.31 1.31 1.00a 1.51a

Both Both Both Both Both Both Both Both Both Both Both Both Both Both

SLMX refers to supervisor-perceived LMX, while LMX refers to subordinate-perceived LMX. SCONT refers to supervisor-perceived control, while CONT refers to subordinate-perceived control. In the bank sample, for Models 1a, 1b, 2, 1a0, and 1b0, N = 150 and K = 75 (K is the number of entities, wholes, or groups); for Models 3 and 30, N = 300 and K = 150. In the engineer sample, for Models 1a, 1b, 2, 1a0, and 1b0, N = 116 and K = 58; for Models 3 and 30, N = 232 and K = 116. a F value shown is from an inverted F test (Dansereau et al., 1984). * P < .05. y E test significant at 15.

characterized as both within- and between-groups for the bank sample. This is because the within- and between-groups correlations are both statistically (t test) and practically (R test) significant, and neither correlation is either statistically (Z test) or practically (A test) larger than the other. For Model 1b0, the relationship is inferred to be ‘‘null,’’ or neither within- nor

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

539

Table 3 WABA II and overall WABA results Correlations Model

Raw

Bank sample 1a .53* 1b .44* 2 .64* 3 .37*

Within

Differences Between

A

WABA II inference

Z

Components Within

Between

.48*,y .31*,y .59*,y .23*

.55*,y .49*,y .66*,y .64*,y

0.08 0.20 0.09 0.46z

0.57 1.30 0.69 4.52*

Both Both Both Between

.15 .07 .17 .12

.38 .37 .47 .25

1a0 1b0 30

.34* .16* .13*

.31*,y .25*
.23*

.35*,y .13 .64*,y

0.04
0.12 0.46z

0.27
0.75 4.52*

Both Neither Between

.08 .07
.12

.26 .09 .25

Engineer 1a 1b 2 3 1a0 1b0 30

sample .49* .63*,y .47* .59*,y .49* .72*,y .43* .24 .35* .47*,y .30* .41* .17*
.24*

.39*,y .38*,y .31*,y .67*,y .27*,y .22 .67*,y


0.28z
0.25
0.49z 0.49z
0.22
0.18 0.49z


2.27*
1.47
3.11* 4.29*
1.21
1.16 4.29*

Within Both Within Between Both Neither Between

.28 .25 .31 .13 .20 .18
.13

.21 .22 .18 .30 .15 .12 .30

Overall inference Both Both Both Moderately Between Both Neither Moderately Between

Within Both Within Between Both Neither Between

See Table 2 and the text for the specific measures involved in each model. In the bank sample, for Models 1a, 1b, 2, 1a0, and 1b0, N = 150 and K = 75; for Models 3 and 30, N = 300 and K = 150. In the engineer sample, for Models 1a, 1b, 2, 1a0, and 1b0, N = 116 and K = 58; for Models 3 and 30, N = 232 and K = 116. Moderately Between = moderately strong between conclusion because one of the variables is ‘‘within’’ in WABA I. * P < .05. y R test significant at 15. z A test significant at 15.

between-groups, because (a) only the within-groups correlation is statistically significant and neither correlation is practically significant, and (b) neither correlation is statistically or practically larger than the other. Bank sample Models 3 and 30 show their hypothesized relationship to be best characterized as between-dyads. Here, the between-dyads correlations are both statistically and practically significant, while the within-dyads correlations are only statistically significant. Additionally, the between-dyads correlations are both statistically and practically larger than the withindyads correlations. 6.3.2. Engineer sample The engineer sample WABA II results support a within-entities inference for Models 1a and 2 because both the tests of practical and statistical significance show the correlations within-groups to be larger than the correlations between-groups (even though both sets of correlations are statistically and practically significant). The results for Models 1b and 1a0 are best characterized as both within- and between-groups. Here, the differences between

540

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

the within- and between-groups correlations are neither practically nor statistically significant and all of these correlations are statistically and practically significant. As in the bank sample, the results for Model 1b0 are null (for the exact same reasons). Finally, the Models 3 and 30 results strongly support a between-dyads inference. This is because only the between-dyads correlations are both practically and statistically significant, and the differences between the within -and between-dyads correlations are both practically and statistically significant. 6.4. Combined WABA I and II results 6.4.1. Bank sample In the bank sample, combining the WABA I and II results (as suggested by Dansereau et al., 1984; Yammarino & Markham, 1992) lead to an inference that both a within- and a between-groups relationship exists between LMX and control in Models 1a, 1b, 2, and 1a0. However, although the WABA II results support an inference of both, the WABA I findings, coupled with the higher between- than within-groups correlation components (see Table 3), suggest that this inference be viewed as somewhat weak. Simply put, the relationship is ‘‘both,’’ but it appears to lean more toward being between-groups than within-groups. For Model 1b0, the overall conclusion is that no relationship exists, either within- or between-groups. This is in contrast to the inference which would be drawn using the raw score correlation alone (r =.16; P < .05), and it serves to further illustrate the somewhat conservative nature of the WABA inference process (see Dansereau et al., 1984 for a further discussion of this). The conclusion drawn for Models 3 and 30, on the other hand, would be that the LMX–control relationship is moderately strong between-dyads. This inference again contrasts with the weak relationship that is portrayed by the raw score correlation, and it is based upon the (a) ‘‘mixed’’ WABA I results (showing both between- and within-dyads variance), (b) reasonably strong WABA II between-dyads findings, and (c) higher betweenthan within-dyads correlation components. 6.4.2. Engineer sample In the engineer sample, the WABA I results support an inference of ‘‘both,’’ without exception. The WABA II results also support an inference of both within- and betweengroups relationships for Models 1b and 1a0, and neither of the correlation components is notably larger than the other for either model. Thus, for Models 1b and 1a0, the most reasonable conclusion appears to be that the exchange–control relationship is both withinand between-groups. For Model 1b0, a combined null inference is appropriate due to the strongly null WABA II results. However, for Models 1a and 2, a within-entities inference seems most reasonable. This is based upon the WABA I ‘‘both’’ inference, the WABA II ‘‘within’’ inference, and the fact that both within-entities correlation components are greater than their corresponding between-entities components (.28 versus .21 for Model 1a, and .31 versus .18 for Model 2). In Models 3 and 30, a between-dyads inference seems most supported. This is due to the WABA I inference of ‘‘both,’’ the WABA II inference of between-dyads, and the between-

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

541

dyads correlation components being higher than the corresponding within-dyads components (.30 versus .13 for Model 3, and .30 versus
.13 for Model 30). 6.5. Overall (combined sample) results Looking at the entire set of results for both samples together, no support is evidenced for the LMX–control hypothesis in Model 1b0, since a null relationship is obtained in the both samples. In contrast, mixed support is obtained in Models 1b and 1a0. This is because the results in both samples support an inference that the relationship holds both within- and between-groups, while LMX theory predicts a stronger within-groups effect (i.e., it predicts differentiated relationships that are relative to the others in the group). Stronger support for the hypothesis is seen in Models 1a and 2. Here, the results in the bank sample support both within- and between-groups effects for both Models 1a and 2. However, more in accord with LMX predictions, the engineer sample supports the LMX– control hypothesis at the within-groups level of analysis in both models. Finally, the strongest support for the LMX–control hypothesis is provided by Models 3 and 30. The results in both samples support a between-dyads perspective, suggesting the presence of differentiated dyads consistent with LMX theory.

7. Discussion The thesis of this article, stated at its beginning, has been that it is absolutely critical that (1) scholars specify the level of analysis at which their hypotheses, frameworks, models, and/ or theories hold, so that they may be adequately tested; (2) tests of all hypotheses, frameworks, models, and/or theories be conducted at the proper level(s) of analysis; and (3) such tests explicitly rule out inappropriate or competing (rival) levels of analysis. Our earlier review of selected theories, models, and approaches in the leadership field demonstrated that, as the field has evolved, much misalignment has occurred between what has been theorized, how it has been measured, and how it has tested. Most leadership approaches may be characterized as theorizing one thing (‘‘A’’), while tests have typically examined something else (‘‘B’’). In fact, in many instances, a more accurate characterization might be that theory has been unclear or confused or has left the phenomenon’s level of analysis unspecified (‘‘?’’), and that subsequent tests have ignored level of analysis altogether or have employed confounded or inappropriate levels tests (‘‘?’’ — but not the same ‘‘?’’ as for the theories). It seems increasingly accepted that good theory includes a clear and complete treatment of its level-of-analysis implications and that good empirical research measures and tests a theory at its appropriate level(s) of analysis (e.g., Dansereau et al., 1984; Dansereau & Yammarino, 1998a, 1998b; Klein & Kozlowski, 2000). Thus, it seems obvious that future leadership research must much more carefully align what is theorized and what is tested. As an example of the misalignment problem and of how we might begin to correct it, we presented a detailed illustration of a multiple levels of analysis test of the LMX approach to the study of leadership. The LMX approach is predicated upon the assumption that leaders

542

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

have different exchange relationships with different subordinates (Graen & Uhl-Bien, 1995). Unfortunately, however, over 90% of LMX studies have not even attempted to test fundamental level-of-analysis issues and none has examined LMX as a dyadic phenomenon (Schriesheim et al., 1999). This is a particularly critical shortcoming because embedded within the LMX approach is the implicit, yet testable, assumption that exchange relationships will be most strongly manifested at the individuals-within-group, dyads-within-groups, and between-dyads levels of analysis (see our earlier development of rival LMX levels models for further details). To the extent that support does not exist for the LMX approach at one or more of these levels of analysis, the validity of the LMX approach may be questioned. The illustrative LMX part of this article tested a substantive hypothesis that appears fundamental to the LMX approach, namely, that good leader–member relations should be associated with reduced supervisory control of the subordinate and increased subordinate control of the supervisor. This prediction was based upon the idea that good leader– subordinate exchange should be associated with indicators of a more egalitarian relationship, including a more even balance of influence and control between the supervisor and the subordinate. Finding support for this prediction suggests that managers who are interested in using prescriptions that are derived from LMX theory for managing their subordinates (cf. Graen & Scandura, 1987; Graen & Uhl-Bien, 1995) may have to tolerate operating in a more egalitarian manner and accept a reduction in their control of subordinates (and an increase in subordinates’ control of the manager). Not everyone is likely to be able to accept or adapt to such changes, so that future LMX research may be useful to more fully explore the implications of this finding for applying LMX theory in work organizations. The results of our illustrative study showed only mixed support for the hypothesized LMX–control relationship at the appropriate level of analysis in monosource Model 1b and in heterosource Model 1a0. No support was obtained in heterosource Model 1b0. Stronger support was seen in monosource Model 1a and in heterosource Model 2. Finally, monosource Model 3 and its heterosource counterpart, Model 30, showed very good support for viewing the hypothesized LMX–control relationship as operating at the between-dyads level of analysis. As a whole, then, these results thus show relatively good support for both the substantive hypothesis and for the level-of-analysis predictions that were drawn concerning LMX theory. The particularly strong dyadic findings (for Models 2, 3, and 30) clearly lend support to the assertions made earlier by Dansereau et al. (1975), Graen and Cashman (1975), and others, that LMX should be viewed as a dyadic process. However, the mixed support that was obtained for the individuals within-groups level (in Models 1a, 1b, and 1a0) also indicates that it may not be invalid to view LMX in terms of relationships that are differentiated from and relative to others in the supervisor’s work group. While the results of this study support the LMX approach in general and the substantive hypothesis that was tested in particular, they also show how critical it is that substantive tests of theory measure variables appropriately and suitably test level-ofanalysis alternatives. With respect to LMX research in particular, our study further reinforces the suggestion that matching data need to be collected from both members

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

543

of the dyad (Gerstner & Day, 1997; Scandura et al., 1986; Schriesheim et al., 1998) and that tests of the LMX model must examine dyads and other competing level-of-analysis alternatives. One clear insight coming from this research is that support for the LMX approach may be understated if dyadic testing of hypotheses is not conducted. Also, using monosource data in within- and between-groups designs may lead to inflated relationships, as illustrated with monosource Model 1b — which showed null results in both samples when tested using heterosource data (Model 1b0). Finally, as shown in Table 3, withinand between-dyads relationships are apparently not inflated by monosource bias (compare the results for Models 3 and 30), while within- and between-groups relationships are (contrast Models 1a and 1a0 and Models 1b and 1b0). Thus, this research suggests that high-quality support for the LMX model may be more likely to be forthcoming from dyadic tests and that data source bias is not likely to be problematic in such tests. We suspect that similar findings will be uncovered in tests of other leadership approaches, once the field turns its attention to more completely clarifying and examining theoretical and empirical level-of-analysis issues. As a final comment before concluding, we should perhaps note that the strength of WABA as a data-analytic system is illustrated in our LMX results. Scholars interested in exploring level of analysis in their research should therefore give WABA very serious consideration as a data-analytic approach. In support of this assertion, it might be reiterated that the conclusion drawn from WABA about Model 1b0 is more conservative than the conclusion that would be drawn from a traditional zero-order correlation analysis. Additionally, if one interprets the raw score correlations for Model 30, one would conclude that these relationships, although statistically significant ( P < .05), are practically trivial. However, the within- and betweendyads correlations show much more substantial relationships that are opposite in sign and, consequently, somewhat cancel each other out when combined into the raw score correlation. This ‘‘washout’’ effect, and the fact that the signs of the two correlations are opposite, clearly show the utility of WABA in teasing out relationships from a data set. It also supports our earlier assertion that raw score correlations (especially low correlations) can be highly misleading as well. In summary and conclusion, this article has endeavored to highlight that much more additional work is needed if we are to begin to accumulate theory and research that properly deals with levels of analysis concerns. We apologize if we appear to have singled out any theory or any author for undue criticism, since that was not our intent. We simply wanted to make what we believe is a critical point and to show its applicability for leadership research in general. With respect to LMX theory, our illustrative study supported a fundamental LMX prediction at multiple levels of analysis. Since there is little LMX research that looks at levels, and virtually none which has dyadically examined LMX relationships, more investigations like the one reported herein are needed. We believe that the LMX approach has merit. However, given the importance of level-of-analysis issues to the field in general (Dansereau & Yammarino, 1998a, 1998b; Klein et al., 1994; Klein & Kozlowski, 2000), and to LMX theory in particular (Gerstner & Day, 1977; Schriesheim et al., 1999), much more research on and in support of level-of-analysis predictions appears critical to future advances in our knowledge about fundamental leadership processes.

544

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

Appendix A. WABA A.1. The fundamental WABA equation WABA was developed by Dansereau et al. (1984). In WABA, within- and betweenentity indicators are computed and compared to each other by tests of statistical and practical significance. Drawing upon the logic of ANOVA, data are divided into withincells (deviation from cell average) and between-cells (cell average) components, with the cells representing analytic entities such as work groups or other organizational units. The relationships that result from these calculations are summarized in the basic WABA equation as: r

Txy ¼h Bhx By r Bxy þhWxh Wy r Wxy ;

ð1Þ

where rTxy is the total (raw score) correlation of variables x and y, hBx and hBy are the between-entity etas for variables x and y, hWx and hWy are the corresponding within-entity etas, and rBxy and rWxy are the corresponding between-entity and within-entity correlations of variables x and y. The within-entity etas, hWx and hWy, are computed by correlating the raw scores ([xnk] or [ ynk]) with the appropriate within-entity deviation scores ([xnk
x¯k] or [ ynk
y¯k]) for n parts (e.g., the 1 to n respondents) within k entities (e.g., the 1 to K work groups); the between-entity etas, hBx and hBy, are computed by correlating the raw scores of the n parts (xnk or ynk) with their between-entity scores (i.e., the appropriate [x¯k] or [y¯k] for the entity within which each part is situated). The within-entity correlation (rWxy) is calculated by correlating the within-entity deviation scores (i.e., [xnk
x¯k] and [ ynk
y¯k]) for the n parts, while the between-entity correlation (rBxy) is calculated by assigning each part its appropriate between-entity scores ([x¯k] and [y¯k]) and then correlating these across the parts. As shown in Eq. (1), raw score correlations can be partitioned into two separate components — a between-entity (hBxhByrBxy) and a within-entity component (hWxhWyrWxy); both are the products of multiplying their appropriate etas and component correlations. A.2. WABA I WABA I examines each variable’s variance by partitioning the original raw score (e.g., [xnk]) into within-and between-entity component scores (e.g., [xnk
x¯k] and [x¯k]); these component scores are then correlated with the original raw score to yield within-entity (hW) and between-entity (hB) etas. Finally, the etas are tested (relative to each other) with F tests of statistical significance and E tests of practical significance. The traditional F tests of statistical significance have K
1 and N
K degrees of freedom for the between- and within-entity etas, respectively, where K is the number of entities and N is the total number of parts within entities. When a between-entities eta exceeds its corresponding within-entities eta, a traditional F test is used. However, when the within-

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

545

entities eta is larger, a corrected F test, which is simply the inverse of the traditional F test, is employed (Dansereau et al., 1984; Haggard, 1958) (Eq. (2)): Corrected F ¼ ½hw =ðN
KÞ =½hB =ðK
1Þ ¼ 1=F:

ð2Þ

The E (eta ratio) tests assess the magnitude of within- versus between-effects relative to each other; they are geometrically based, not dependent upon degrees of freedom, and are calculated as (Eq. (3)): E ¼ hB =hW :

ð3Þ

A.3. WABA II WABA II examines relationships among variables by first computing within- and betweenentities correlations (using all within- or all between-entity scores for the n parts). The magnitude of these correlations is then tested for statistical significance (using traditional t tests) and for practical significance (using newly developed R tests). The t tests have K
2 and N
K
1 degrees of freedom for the between- and within-entity correlations, respectively. The geometrically based R (correlation) tests of practical significance are not dependent upon degrees of freedom and are computed as (Eqs. (4) and (5)): RB ¼ rB =ð1
rÞ1=2

ð4Þ

RW ¼ rW =ð1
rÞ1=2 :

ð5Þ

and

Finally, differences between within- and between-entity correlations, which involve the same variables, are tested using Fisher Z transformation tests of statistical significance (with K
3 and N
K
2 degrees of freedom for the between- and within-entity correlations, respectively), and A (angular) tests of practical significance (for the Z and A tests, only differences in absolute magnitudes are tested). The A tests are geometrically based and not dependent upon degrees of freedom, and are computed as (Eq. (6)): A ¼ qW
qB

ð6Þ

where qW and qB are the angles associated with the within- and between-entity correlations (respectively). A.4. Drawing inferences Inferences are drawn using the .05 and .01 levels of statistical significance for the F, Z, and t tests and the 15 and 30 levels of practical significance for the E, R, and A tests. The 15 and 30 angle criteria arise from the fact that a 90 angle represents unrelated variables, while a 0 angle represents a perfect relationship; smaller angles thus represent stronger relation-

546

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

ships (in the R tests), while angular ratios different from 1.0 or larger differences between angles (in the E and A tests, respectively) indicate more meaningful differences in the magnitudes of relationships (see Dansereau et al., 1984, for additional details). Decomposed raw score correlation components are also computed by multiplying the product of the WABA I (within- or between-entities) etas with their WABA II (within- or between-entities) correlations; these results can then be examined by the A test to determine whether one component is meaningfully greater than the other. Finally, the WABA I and II results and the decomposed correlations are examined and an overall conclusion drawn concerning the phenomenon under investigation. Dansereau et al. (1984, pp. 183–185) present guidelines for integrating WABA I and II results; for excellent yet brief descriptions of WABA I and II procedures, see Markham and McKee (1991), Schriesheim et al. (1998), Yammarino and Dubinsky (1992), and Yammarino and Markham (1992).

References Avolio, B. J., & Bass, B. M. (1998a). Individual consideration viewed at multiple levels of analysis: a multi-level framework for examining the diffusion of transformational leadership. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 53 – 74). Stamford, CT: JAI Press. Avolio, B. J., & Bass, B. M. (1998b). Unlocking the essence of individualized consideration: a view from the inside out. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 93 – 102). Stamford, CT: JAI Press. Avolio, B. J., Yammarino, F. J., & Bass, B. M. (1991). Identifying common methods variance with data collected from a single source: an unresolved sticky issue. Journal of Management, 17, 571 – 587. Ayman, R., Chemers, M. M., & Fiedler, F. E. (1998). The contingency model of leadership effectiveness: its levels of analysis. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 73 – 95). Stamford, CT: JAI Press. Bass, B. M. (1985). Leadership and performance beyond expectations. New York: Free Press. Bass, B. M. (1990). Bass and Stogdill’s handbook of leadership (3rd ed.). New York: Free Press. Bass, B. M. (1997). Does the transactional – transformational leadership paradigm transcend organizational and national boundaries? American Psychologist, 52, 130 – 139. Bliese, P. D., & Halverson, R. R. (1998). Group consensus and psychological well-being: a large field study. Journal of Applied Social Psychology, 28, 563 – 580. Bradford, J. W., & Schriesheim, C. A. (1990). Thurscal: a program for interval scaling using paired comparisons or paired comparison treatment of complete ranks under case III assumptions. Educational and Psychological Measurement, 50, 849 – 851. Chemers, M. M., Ayman, R., & Fiedler, F. E. (1998). The contingency model of leadership effectiveness: back to the future. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 135 – 143). Stamford, CT: JAI Press. Cogliser, C. C., & Schriesheim, C. A. (2000). Exploring work unit context and leader – member exchange: a multilevel perspective. Journal of Organizational Behavior, 21, 487 – 511. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. Coleman, D. F. (1998). An alternative ‘‘limited domain’’ view of leader – member exchange. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 137 – 148). Stamford, CT: JAI Press. Conger, J. A. (1998). The folly of knowing an elephant by its tail: why the leadership field needs multiple levels of

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

547

analysis. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 77 – 83). Stamford, CT: JAI Press. Conger, J. A., & Kanungo, R. S. (1988). Behavioral dimensions of charismatic leadership. In: J. A. Conger, & R. N. Kanungo (Eds.), Charismatic leadership: the elusive factor in organizational effectiveness ( pp. 78 – 97). San Francisco, CA: Jossey-Bass. Crampton, S. M., & Wagner, J. A. III (1994). Percept – percept inflation in microorganizational research: an investigation of prevalence and effect. Journal of Applied Psychology, 79, 67 – 76. Cummings, L. L. (1975). Discussion of Graen and Cashman’s research and comparing it with other approaches. In: J. G. Hunt, & L. L. Larson (Eds.), Leadership frontiers ( pp. 181 – 185). Carbondale, IL: Southern Illinois University Press. Dansereau, F. (1995). A dyadic approach to leadership: creating and nurturing this approach under fire. Leadership Quarterly, 6, 479 – 490. Dansereau, F., Alutto, J. A., Markham, S. E., & Dumas, M. (1982). Multiplexed supervision and leadership: an application of within and between analysis. In: J. G. Hunt, U. Sekaran, & C. A. Schriesheim (Eds.), Leadership: beyond establishment views ( pp. 81 – 103). Carbondale, IL: Southern Illinois University Press. Dansereau, F., Alutto, J. A., & Yammarino, F. J. (1984). Theory testing in organizational behavior: the variant approach. Englewood Cliffs, NJ: Prentice-Hall. Dansereau, F., Graen Jr., G., & Haga, W. J. (1975). A vertical dyad linkage approach to leadership within formal organizations: a longitudinal investigation of the role-making process. Organizational Behavior and Human Performance, 13, 46 – 78. Dansereau, F., & Yammarino, F. J. (Eds.) (1998a). Leadership: the multiple-level approaches: Part A. Classical and new wave. Stamford, CT: JAI Press. Dansereau, F., & Yammarino, F. J. (Eds.) (1998b). Leadership: the multiple-level approaches: Part B. Contemporary and alternative. Stamford, CT: JAI Press. Deluga, R. J., & Perry, J. T. (1991). The relationship of subordinate upward influencing behaviour, satisfaction and perceived superior effectiveness with leader – member exchanges. Journal of Occupational Psychology, 64, 239 – 252. Dienesch, R. M., & Liden, R. C. (1986). Leader – member exchange model of leadership: a critique and further development. Academy of Management Review, 11, 618 – 634. Dionne, S. D., Yammarino, F. J., Atwater, L. E., & James, L. R. (2002). Neutralizing substitutes for leadership theory: leadership effects and common-source bias. Journal of Applied Psychology, 87 (forthcoming). Dockery, T. M., & Steiner, D. D. (1990). The role of the initial interaction in leader – member exchange. Group and Organization Studies, 15, 395 – 413. Eden, D. (1998). Multiple levels as a self-fulfilling prophecy: one sees what one expects to see. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 163 – 172). Stamford, CT: JAI Press. Evans, M. G. (1968). The effects of supervisory behavior upon worker perception of their path – goal relationships. Unpublished doctoral dissertation, Yale University. Evans, M. G. (1970). The effects of supervisory behavior on the path – goal relationship. Organizational Behavior and Human Performance, 5, 277 – 298. Fairhurst, G. T., Rogers, L. E., & Sarr, R. A. (1987). Manager – subordinate control patterns and judgments about the relationship. Communication Yearbook, 10, 395 – 415. Farmer, S. M., Maslyn, J. M., Fedor, D. B., & Goodman, J. S. (1997). Putting upward influence strategies in context. Journal of Organizational Behavior, 18, 17 – 42. Fiedler, F. E. (1967). A theory of leadership effectiveness. New York: McGraw-Hill. Fiedler, F. E. (1977). A rejoinder to Schriesheim and Kerr’s premature obituary of the contingency model. In: J. G. Hunt, & L. L. Larson (Eds.), Leadership: the cutting edge ( pp. 45 – 51). Carbondale, IL: Southern Illinois University Press. Fiedler, F. E. (1986). The contribution of cognitive resources to leadership performance. Journal of Applied Psychology, 16, 532 – 548.

548

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

Fiedler, F. E., & Garcia, J. E. (1987). New approaches to leadership: cognitive resources and organizational performance. New York: Wiley. Field, R. H. G. (1982). A test of the Vroom – Yetton normative model of leadership. Journal of Applied Psychology, 67, 523 – 532. Gerstner, C. R., & Day, D. V. (1997). Meta-analytic review of leader – member exchange theory: correlates and construct issues. Journal of Applied Psychology, 82, 827 – 844. Glick, W. H., & Roberts, K. H. (1984). Hypothesized interdependence, assumed independence. Academy of Management Review, 9, 722 – 735. Graen, G., & Cashman, J. F. (1975). A role-making model of leadership in formal organizations: a developmental approach. In: J. G. Hunt, & L. L. Larson (Eds.), Leadership frontiers ( pp. 143 – 165). Kent, OH: Kent State University Press. Graen, G., Liden, R. C., & Hoel, W. (1982). The role of leadership in the employee withdrawal process. Journal of Applied Psychology, 67, 868 – 872. Graen, G., Novak, M. A., & Sommerkamp, P. (1982). The effects of leader – member exchange and job design on productivity and job satisfaction: testing a dual attachment model. Organizational Behavior and Human Performance, 30, 109 – 131. Graen, G. B., & Scandura, T. A. (1987). Toward a psychology of dyadic organizing. Research in Organizational Behavior, 9, 175 – 208. Graen, G., & Uhl-Bien, M. (1995). Relationship-based approach to leadership: development of leader – member exchange (LMX) theory of leadership over 25 years: applying a multi-level multi-domain perspective. Leadership Quarterly, 6, 219 – 247. Guilford, J. P. (1954). Psychometric methods (2nd ed.). New York: McGraw-Hill. Haggard, E. (1958). Intraclass correlation and the analysis of variance. New York: Dryden Press. House, R. J. (1971). A path – goal theory of leader effectiveness. Administrative Science Quarterly, 16, 321 – 328. House, R. J. (1991). The distribution and exercise of power in complex organizations: a meso theory. Leadership Quarterly, 2, 23 – 58. House, R. J. (1996). Path – goal theory of leadership: lessons, legacy, and a reformulated theory. Leadership Quarterly, 7, 323 – 352. House, R. J., & Dessler, G. (1974). The path – goal theory of leadership: some post hoc and a priori tests. In: J. Hunt, & L. Larson (Eds.), Contingency approaches to leadership ( pp. 29 – 55). Carbondale, IL: Southern Illinois University Press. House, R. J., & Mitchell, T. R. (1974). Path – goal theory of leadership. Journal of Contemporary Business, 3, 81 – 97. House, R. J., & Podsakoff, P. M. (1994). Leadership effectiveness: past perspectives and future directions for research. In: J. Greenberg (Ed.), Organizational behavior: the state of the science ( pp. 45 – 82). Hillsdale, NJ: Lawrence Erlbaum Associates. House, R. J., Rousseau, D. M., & Thomas-Hunt, M. (1995). The meso paradigm: a framework for the integration of micro and macro organizational behavior. Research in Organizational Behavior, 17, 71 – 114. Hunt, J. G. (1996). Citation classics. Leadership Quarterly, 7, 303 – 304. Jacobs, T. O. (1970). Leadership and exchange in formal organizations. Alexandria, VA: Human Resources Research Organization (HumRRO). James, L. R., Mulaik, S. A., & Brett, J. M. (1982). Causal analysis: assumptions, models, and data. Beverly Hills, CA: SAGE Publications. Keller, T., & Dansereau, F. (1995). Leadership and empowerment: a social exchange perspective. Human Relations, 48, 127 – 145. Kerr, S., & Jermier, J. M. (1978). Substitutes for leadership: the meaning and measurement. Organizational Behavior and Human Performance, 22, 375 – 403. Kerr, S., & Schriesheim, C. A. (1974). Consideration, initiating structure, and organizational criteria — an update of Korman’s 1966 review. Personnel Psychology, 27, 555 – 568. Kerr, S., Schriesheim, C. A., Murphy, C. J., & Stogdill, R. M. (1974). Toward a contingency theory of

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

549

leadership based upon the consideration and initiating structure literature. Organizational Behavior and Human Performance, 12, 62 – 82. Kipnis, D. (1984). The use of power in organizations and in interpersonal settings. Applied Social Psychology Annual, 5, 179 – 210. Kipnis, D., Schmidt, S. M., & Wilkinson, I. (1980). Intraorganizational influence tactics: explorations in getting one’s way. Journal of Applied Psychology, 65, 440 – 452. Klein, K. J., Dansereau, F., & Hall, R. J. (1994). Levels issues in theory development, data collection and analysis. Academy of Management Review, 19, 195 – 229. Klein, K. J., & Kozlowski, S. W. (Eds.) (2000). Multilevel theory, research, and methods in organizations. San Francisco, CA: Jossey-Bass. Korman, A. K. (1966). ‘‘Consideration,’’ ‘‘initiating structure,’’ and organizational criteria — a review. Personnel Psychology, 19, 349 – 361. Krone, K. J. (1991). Effects of leader – member exchange on subordinates’ upward influence attempts. Communication Research Reports, 8, 9 – 18. Liden, R. C., Sparrowe, R. T., & Wayne, S. J. (1997). Leader – member exchange theory: the past and potential for the future. Research in Personnel and Human Resource Management, 15, 47 – 119. Liden, R. C., Wayne, S. J., & Stilwell, D. (1993). A longitudinal study on the early development of leader – member exchanges. Journal of Applied Psychology, 78, 662 – 674. Locke, E. A. (1998). The role of individual consideration, vision, and egoism in effective leadership. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 85 – 102). Stamford, CT: JAI Press. Markham, S. E., & Halverson, R. R. (2002). Within and between-entity analysis in multilevel research: a leadership example using single level analysis (SLA) and boundary conditions (MRA). Leadership Quarterly, 13 (in press). Markham, S. E., & McKee, G. H. (1991). Declining organizational size and increasing unemployment rates: predicting employee absenteeism from within- and between-plant perspectives. Academy of Management Journal, 34, 952 – 965. Mowday, R. T., & Sutton, R. I. (1993). Organizational behavior: linking individuals and groups to organizational contexts. Annual Review of Psychology, 44, 195 – 229. Murry, W. D. (1998). A re-examination of substitutes for leadership within a levels-of-analysis framework. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 261 – 270). Stamford, CT: JAI Press. Nachman, S., Dansereau, F., & Naughton, T. J. (1985). Levels of analysis and the vertical dyad linkage approach to leadership. Psychological Reports, 57, 661 – 662. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Peters, L. H., Hartke, D. D., & Pohlmann, J. T. (1985). Fiedler’s contingency theory of leadership: an application of the meta-analysis procedures of Schmidt and Hunter. Psychological Bulletin, 97, 274 – 285. Podsakoff, P. M., & MacKenzie, S. B. (1998a). An examination of substitutes for leadership within a levels-ofanalysis framework. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 215 – 253). Stamford, CT: JAI Press. Podsakoff, P. M., & MacKenzie, S. B. (1998b). Are there effects of substitutes for leadership at different levels of analysis? In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 277 – 284). Stamford, CT: JAI Press. Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their effects on followers’ trust in leader, satisfaction, and organizational citizenship behaviors. Leadership Quarterly, 1, 107 – 142. Podsakoff, P. M., Niehoff, B. P., MacKenzie, S. B., & Williams, M. L. (1993). Do substitutes for leadership really substitute for leadership? An empirical examination of Kerr and Jermier’s situational leadership model. Organizational Behavior and Human Decision Processes, 54, 1 – 44. Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: problems and prospects. Journal of Management, 12, 531 – 544.

550

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

Roberts, K. H., Hulin, C. L., & Rousseau, D. M. (1978). Developing an interdisciplinary science of organizations. San Francisco, CA: Jossey-Bass. Rousseau, D. M. (1985). Issues of level in organizational research: multi-level and cross-level perspectives. Research in Organizational Behavior, 7, 1 – 37. Rousseau, D. M. (1998). LMX meets the psychological contract: looking inside the black box of leader – member exchange. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 149 – 154). Stamford, CT: JAI Press. Scandura, T. A., & Graen, G. B. (1984). Moderating effects of initial leader – member exchange status on the effects of a leadership intervention. Journal of Applied Psychology, 69, 428 – 436. Scandura, T. A., Graen, G. B., & Novak, M. A. (1986). When managers decide not to decide autocratically: an investigation of leader – member exchange and decision influence. Journal of Applied Psychology, 71, 579 – 584. Schriesheim, C. A. (1997). Substitutes-for-leadership theory: development and basic concepts. Leadership Quarterly, 8, 103 – 108. Schriesheim, C. A., Castro, S. L., & Cogliser, C. C. (1999). Leader – member exchange (LMX) research: a comprehensive review of theory, measurement, and data-analytic practices. Leadership Quarterly, 10, 63 – 113. Schriesheim, C. A., Cogliser, C. C., & Neider, L. L. (1995). ‘‘Is it trustworthy?’’ A multiple levels of-analysis reexamination of an Ohio State leadership study, with implications for future research. Leadership Quarterly, 6, 111 – 145. Schriesheim, C. A., & Kerr, S. (1977a). R.I.P. LPC: a reply to Fiedler. In: J. G. Hunt, & L. L. Larson (Eds.), Leadership: the cutting edge ( pp. 51 – 56). Carbondale, IL: Southern Illinois University Press. Schriesheim, C. A., & Kerr, S. (1977b). Theories and measures of leadership: a critical appraisal. In: J. G. Larson, & L. L. Larson (Eds.), Leadership: the cutting edge ( pp. 9 – 45). Carbondale, IL: Southern Illinois University Press. Schriesheim, C. A., & Murphy, C. J. (1976). Relationships between leader behavior and subordinate satisfaction and performance: a test of some situational moderators. Journal of Applied Psychology, 61, 634 – 641. Schriesheim, C. A., Neider, L. L., & Scandura, T. A. (1998). Delegation and leader – member exchange: main effects, moderators, and measurement issues. Academy of Management Journal, 41, 298 – 318. Schriesheim, C. A., Tepper, B. J., & Tetrault, L. A. (1994). The validity of drawing across — octant conclusions from the contingency model of leadership: critique and analysis. Journal of Applied Psychology, 79, 561 – 573. Spector, P. E. (1987). Method variance as an artifact in self-reported affect and perceptions at work: myth or significant problem. Journal of Applied Psychology, 72, 438 – 443. Strube, M. J., & Garcia, J. E. (1981). A meta-analytic investigation of Fiedler’s contingency model of leadership effectiveness. Psychological Bulletin, 90, 307 – 321. Tepper, B. J. (1989). A replication and extension of the inductive strategy used by Kipnis, Schmidt, and Wilkinson (1980) to derive tactics of organizational influence. Unpublished MS Thesis. Department of Psychology, University of Miami. Tepper, B. J. (1990). Influence tactics employed in charismatic and noncharismatic leader – follower interactions. Unpublished PhD Dissertation. Department of Psychology, University of Miami. Tosi, H. L., & Banning, K. (1998). A need to reconceptualize ‘‘substitutes for leadership’’. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part B. Contemporary and alternative ( pp. 271 – 276). Stamford, CT: JAI Press. Vecchio, R. P. (1998). Some continuing challenges for the contingency model of leadership. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 115 – 124). Stamford, CT: JAI Press. Vroom, V. H., & Jago, A. G. (1988). The new leadership: managing participation in organizations. Englewood Cliffs, NJ: Prentice-Hall. Vroom, V. H., & Jago, A. G. (1998a). On interdependence and levels of analysis. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 183 – 189). Stamford, CT: JAI Press.

C.A. Schriesheim et al. / The Leadership Quarterly 12 (2001) 515–551

551

Vroom, V. H., & Jago, A. G. (1998b). Situation effects and levels of analysis in the study of leader participation. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 145 – 159). Stamford, CT: JAI Press. Vroom, V. H., & Yetton, P. W. (1973). Leadership and decision-making. Pittsburgh: University of Pittsburgh Press. Wofford, J. C., & Liska, L. Z. (1993). Path – goal theories of leadership: a meta-analysis. Journal of Management, 19, 857 – 876. Yammarino, F. J. (1994). Indirect leadership: transformational leadership at a distance. In: B. M. Bass, & B. J. Avolio (Eds.), Improving organizational effectiveness through transformational leadership ( pp. 26 – 47). Thousand Oaks, CA: SAGE Publications. Yammarino, F. J., & Dubinsky, A. J. (1992). Superior – subordinate relationships: a multiple levels of analysis approach. Human Relations, 45, 575 – 600. Yammarino, F. J., & Dubinsky, A. J. (1994). Transformational leadership theory: using levels of analysis to determine boundary conditions. Personnel Psychology, 47, 787 – 811. Yammarino, F. J., Dubinsky, A. J., Comer, L. B., & Jolson, M. A. (1997). Women and transformational and contingent reward leadership: a multiple levels of analysis perspective. Academy of Management Journal, 40, 205 – 222. Yammarino, F. J., Dubinsky, A. J., & Spangler, W. D. (1998). Transformational and contingent reward leadership: individual, dyad, and group levels of analysis. Leadership Quarterly, 9, 27 – 54. Yammarino, F. J., & Markham, S. E. (1992). On the application of within and between analysis: are absence and affect really group-based phenomena? Journal of Applied Psychology, 77, 168 – 176. Yetton, P. W., & Craig, J. F. (1998). Integrating across levels of analysis. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 173 – 182). Stamford, CT: JAI Press. Yukl, G. A. (1998). Leadership in organizations (4th ed.). Englewood Cliffs, NJ: Prentice-Hall. Yukl, G. A. (2002). Leadership in organizations (5th ed.). Englewood Cliffs, NJ: Prentice-Hall. Yukl, G. A., & Fable, C. M. (1990). Influence tactics and objectives in upward, downward, and lateral influence attempts. Journal of Applied Psychology, 75, 132 – 140. Zaccaro, S. J. (1998). The contingency model and executive leadership. In: F. Dansereau, & F. J. Yammarino (Eds.), Leadership: the multiple-level approaches: Part A. Classical and new wave ( pp. 125 – 133). Stamford, CT: JAI Press.