Increasingly, theoretical and empirical studies have shown that the teaching staff plays an important role in school improvement and in fostering student learning, since regulations, guidelines, and the decisions on the system level and on the level of the school management (school leader) have to be re-contextualized by the teaching staff and individual teachers to exert their influence on student learning and student outcomes (Fend, 2005, 2008; Hallinger & Heck, 1998). To deal with such processes, multilevel analysis has proven to be the standard in empirical school research (Luyten & Sammons, 2010). In this contribution, the multilevel approach is expanded to include a theoretical and methodological focus on the double character of group levels in organizations, on composition effects on a group level, and on position effects on an individual level.
Multilevel models allow depiction of hierarchically structured phenomena, such as schools or classes. For example, separate students are gathered in a single classroom, which is often assigned to a specific teacher. Separate teachers, in turn, form a teaching staff and a school, and separate schools are administrated by a school board in a municipality. Finally, schools are part of a geographical entity.
Analysing this nested or clustered structure as a multilevel model is a methodological necessity for two reasons. First, it considers the fact that observations of the same unit are not independent. Thus, it counteracts overestimation of statistical findings, as observations that belong to the same unit on a higher level are interdependent. It also allows determination of the contribution of the different levels regarding the overall variance of an interesting feature on the lowest level (Luyten & Sammons, 2010). Therefore, differences in student achievement, for example, can be attributed in a more differentiated manner to influences of the separate students, teachers, school management, the school, and possibly also to city districts.
But the way that nested structures are usually considered and calculated by multilevel models indicates a limited understanding of what non-independence of observations within a unit or a group means. This becomes clear by the fact that measures of agreement, such as the intraclass correlation (ICC), is usually used to determine the necessity of a multilevel model. Intraclass correlation (ICC) represents the ratio of the variance between units to the total variance, and it is interpreted as a measurement of agreement or similarity among observations within a unit (LeBreton & Senter, 2007). Therefore, when non-independence is conceived of only as the presence of a significant ICC value, the non-independence is simply defined by an over-proportional similarity of observations within a unit. But non-independence can mean more than converging observations, such as, for example, same shared attitudes among teachers or the same teaching staff. Non-independence in nested structures can be defined more generally by simply acknowledging that observations are influenced by the unit that they are in, and thus, by the shared context, and the unit’s influence can manifest itself in various forms. For teachers on a teaching staff, for example, the shared unit does not have to lead to shared attitudes. The same shared unit can also result in different attitudes because the teaching staff serves as an umbrella under which teachers have to interact. In this sense, non-independence means that every teacher refers to the other teachers within the same teaching staff. Thus, each teaching staff can be described by a specific composition and pattern that are a result of non-independence of the teachers.
This problem of too simplified group-level conceptions and non-independence has also been criticized in research on small groups and in organizational research by Kozlowski and Klein (2000). They also point out that research often simply aggregates lower-level individual characteristics to the next higher group level by averaging, without considering that groups can also be described by the specific composition of the individual characteristics. They suggest that groups and, thus, every higher level in nested data can be described by global properties, shared properties, and configural properties. We can adopt these aspects in our criticism of school research above. Global properties are located at the group level, or the higher level, respectively; they manifest only on that level, and their measurement does not depend on lower-level characteristics and are thus non-controversial. Therefore, global properties of a group serve as a shared context for lower level individuals. Furthermore, because they serve as a context for the individuals on lower level, global properties initiate a top-down process (Kozlowski, 2012). Collective characteristics of the lower level, which describe how similar or dissimilar group members are, can be generally described by group composition (Kozlowski, 2012; Lau & Murnighan, 1998; Mathieu, Maynard, Rapp, & Gilson, 2008; Schudel, 2012). According to Kozlowski and Klein (2000), the composition of a group can be described by shared properties or by configural properties. Shared properties are those characteristics of individuals that converge within the group and represent the homogeneity thereof. Configural properties are those characteristics of individuals that diverge within the group and represent the heterogeneity of a group.
In the case of school research, the neglect of group composition may be connected to the double character that group levels in school environment usually possess. The entities on a higher level – such as schools or classrooms – can be described by either separate characteristics on that higher level – the global properties – or by collective characteristics on a lower level – the group composition. Global properties can be an area of responsibility of a single individual on the higher level or a shared higher-level context. However, collective characteristics on a group level can only be described by the interplay of multiple individuals on the lower subordinate level. They emerge from the lower level by interaction but manifest themselves at the group level; thus, group composition refers to the fact that what develops in a group is more than just the simple sum of the individuals (Kozlowski & Klein, 2000). Therefore, the information about the global properties of a group can be obtained from that group level, and the information about group composition can only be gathered from the multiple lower level entities. For instance, if we are interested in the school level, we can describe and measure the global properties by separate characteristics of the responsible school principal or of the school, such as leadership quality and budget. But we can also describe and measure the composition of the school by collective characteristics of the cluster of teachers working at the school, the shared and configural properties of the teaching staff, such as shared beliefs of the teachers, but also as diverging subjective perspectives. The same holds true for the classroom level: We can describe and measure the global properties by separate characteristics of the responsible class teacher or of the classroom infrastructure, such as teaching quality and the number of computers available. We can also describe and measure the classroom composition by collective characteristics of the cluster of students that form a class, e.g. the average school achievement of the students as a shared property, when we assume that students in a class tend to have a similar learning progress – or e.g. different educational family backgrounds as a configural property.
In conclusion, although multilevel models in school research acknowledge that a group level always constitutes a combination of entities of a lower level (e.g. teaching staff as an association of teachers), the underlying assumption usually is that the shared group context leads to homogeneous entities. Therefore, research often focuses solely on shared properties, which is represented by the calculation of a group mean. However, the explanations above show that non-independence and shared group context do not preclude the possibility that the lower-level entities or individuals are different. Therefore, multilevel models in school research have to consider the double character of groups, consisting of global group properties emerging from the group level, and group composition emerging from the individual lower level. Further, they have to consider the possibility of both shared properties and configural properties of group compositions.
Disentangling those two characteristics of a group or a higher level entity is also crucial because it allows us to depict the re-contextualization processes in the school environment (Fend, 2005, 2008). If we separated global properties from group composition, we could make it visible that global properties – such as a responsible person or an existing infrastructure – serve as an opportunity and that individuals on the lower level make use of that opportunity by their specific group composition. Kozlowski (2012) analogously observes that a group is finally the result of top-down effects of global properties and bottom-up effects emerging from the group composition. That what we measure on a specific unit level, therefore, is mostly a result of the interactions between a responsible separate person, or a shared context characteristic, and a subordinate collective as shown in Fig. 6.1.
As composition and configural properties in particular are often missing in research, we can assume that research reduces unit levels to areas of responsibility rather than also take their collective character of associations into account. Therefore, contrary to the theoretically acknowledged fact that diversity of the teaching staff has an influence on school improvement processes, research has placed too little emphasis on the compositional characteristics and composition effects of the teaching staff in study designs and analyses.
At class level, the well-known ‘little-fish-big-pond effect’ can be taken as an example: A student’s self-concept is affected not only by his or her own achievements, but also by the aggregated average performance index of the classroom (the entity one level above the student). Accordingly, the school class acts as a frame of reference, through social comparison, for students’ self-concepts (Marsh et al., 2008). This is a phenomenon at the classroom level, and it has also been understood as a composition effect.
Further, pertaining to the level of the teachers, the literature on school improvement capacity or professional learning communities points to the importance of group composition. Mitchell and Sackney (2000), for example, emphasize the relevance of interpersonal capacities to learning communities. This relevance becomes apparent in shared properties, such as shared norms, expectations, and knowledge, or in communication patterns, among other things. For group climate to be effective, each group member’s contributions should be explicitly acknowledged. As a consequence, Mitchell and Sackney (2000) also observed problems in schools with high configural properties, thus, with group compositions, in which dominant excluding subgroups were formed that isolated and marginalized other members. Also, Louis, Marks, and Kruse (1996) showed that diverse subgroups within the teaching staff can have negative effects on the successful achievement of joint objectives. They assume that subgroups can emerge particularly in large schools, alongside discipline demarcations. However, despite the relevance of the composition and structure of teaching staff, there are (still) no studies examining these composition effects differentially.
Based on diversity research, we will first elaborate on how composition can be theorized in school improvement research, particularly at the teaching staff level. In a second step, the Group Actor-Partner Interdependence Model (GAPIM) approach is introduced as a methodological tool. The GAPIM allows analysis of composition effects on the individual level and takes the particular position of the teachers on staff into consideration. We then apply the model to an existing data set (Maag Merki, 2012) as an example.Footnote 1 We will illustrate the analysis of the main effects and composition effects of the teaching staff and positioning effects of the separate teachers on the teaching staff regarding the effects of teachers’ individual and collective self-efficacy on teachers’ individual job satisfaction. Since in the existing study, teachers at 37 secondary schools completed a standardized survey on various aspects, the data set is suitable to discuss strengths and weaknesses of the GAPIM for school improvement research.