Analysis

We used a comparative case study design to explore how Willow and Elm organized to implement inclusion. Our analyses identified and compared formal and informal organizational structures that each school utilized to enact their inclusion programs.

2.3.4.1 School organizational structures

We performed thematic, qualitative analysis of interview transcripts in order to understand the formal structures in each school that organize resources for inclusion, including two rounds of coding. Our first round of codes were developed inductively as well as deductively, reflecting established themes from previous special education literature as well as emergent categories and themes identified through an initial read of interview transcripts. We systematically applied the coding scheme to transcripts from interviews with district and school level administrators, general educators, counselors, assistants, and special educators at Willow (n = 29) and Elm (n = 18). After coding all transcripts, we retrieved coded text using NVivo and then organized coded text around emergent, second round codes that revealed how resources were organized for

inclusion (e.g. allocation of special educators, special educators’ schedules, physical spaces). From these second round codes, we created a detailed case summary of the way that resources for inclusion were organized and embedded in the structures of either school. From these detailed case summaries, formal organizational structures emerged as participants described the structures that shape how they spend their time and how other resources for inclusion are allocated.

2.3.4.2 Teacher interaction networks

We sought to understand the structure and composition of educators’ special education-related interactions in each school. We employed social network analysis using UCINET software (Borgatti, Everett, & Freeman, 2002) to calculate properties of Willow and Elm’s special education interaction networks, drawing on survey data. Density is calculated as the proportion of connections relative to the number of possible ties. UCINET measures centralization by calculating how central each individual is in the network (i.e. how many ties they have) and then summing the difference between each individual’s centrality score and the score of the network’s most central node. Both centralization and density scores range from 0 to 1, with a score of 1 signifying that a network is maximally dense (every member is connected to every other member) and completely centralized (all connections flow through a central member). These measures control for the total number of ties in a network, which makes it possible to compare networks of different sizes (Borgatti, Everett, & Johnson, 2013).

2.3.4.3 Organizational routines

In the next part of our analysis, we utilized interview transcripts, observational field notes, and survey data in order to achieve triangulation (Yin, 2013) in identifying dominant routines for

inclusion. Our analysis involved five phases, outlined in detail in Table 3. We identified potential routines as they were described in the interviews, and then verified their existence in practice with observational data. The interviews and observations led us to hypotheses regarding which routines were dominant in the daily work of special educators, which we were able to test utilizing the social network data. Specifically, we used this network data as another data point for triangulation in order to compare the patterns of interaction described in each routine to those reported by staff members in they survey and strengthen our claims about the routines that guide the implementation of inclusion. We isolated the “ego networks” of the special educators, comprised of all survey participants who report interacting with each special educator.5 Using the UCINET software, we generated descriptive statistics and visualizations of ego networks (Borgatti et al., 2002). We compared those patterns of interaction to the patterns of interaction described in each dominant routine. In the final phase we reviewed codes capturing the enactment of each routine in order to conceptualize the type of support offered to students.

2.3.4.4 Limitations

Our approach has several limitations and unique affordances. We seek to understand the daily work and routines of educators with interviews, observations, and social network data from a single time point. Although none of these sources alone can tell us about practice over time, our triangulation of these sources provides a unique perspective on teachers’ daily work. Additionally, while we know that successful inclusion relies upon the participation of both general and special educators, we chose to emphasize the role of special educators in routines for inclusion. During initial interviews, staff members in both schools made clear that special educators were central actors in the inclusion program. Furthermore, our analysis of the social

networks surveys confirmed that most of the interaction related to special education in both 33

schools flowed through special educators. Therefore, we are confident that this analytic decision is true to the way teachers conceptualized and enacted inclusion in Willow and Elm. Still, we sought to incorporate general educator perspectives based upon interviews, their survey responses, and field notes from shadowing special educators. Lastly, our analytic decision to shadow special educators in their daily tasks did not end up providing an opportunity to observe general education instruction in Willow. While we anticipated that special educators would spend time inside of general education classrooms, only Elm’s special educators spent substantial time inside of general education classes. While our observations from Willow do not provide much insight into general education instructional practice, they reveal a lot about the extent to which special educators supported what goes on inside general education classrooms.

2.3.4.5 Trustworthiness

We took several measures to ensure the trustworthiness and reliability of this study. First, we drew upon data from a variety of participants as well as data sources (e.g. interviews, observations, social network survey, and artifacts) in order to search for convergence in determining the major themes in our findings. Second, we systematically looked for disconfirming evidence throughout the analysis process in order to strengthen our case that there were not competing themes emergent in the data. For example, we attended to differences in teacher enactment of the potential organizational routines for inclusion. In several cases, we found that not all special educators utilized a particular routine, and so excluded those potential routines from subsequent analyses. Third, our data analysis process was collaborative, involving researchers who were directly responsible for collecting the data as well as one researcher who was not involved at the time. Throughout the analysis process, we created memos to capture emergent themes and held ongoing meetings to engage in discussion and arrive at consensus

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when needed (Brantlinger, Jimenez, Klingner, Pugach, & Richardson, 2005; Miles, Huberman, & Saldana, 2013).