Chapter 4 revolves around a detailed content analysis of organizational mission statements. The first step was to identify key movement frames (i.e., those used by a reasonable portion of the sample of TFNs), with particular attention to the different ways in which the frames have been used.37
I conducted a content analysis with frames as the primary objects of interest. A content analysis typically focuses primarily on words or word combinations and enables quantification of words or sets of words, if the researcher so desires.” The results of this detailed content analysis are presented in chapter 4, and are used to construct the
dependent variables for the qualitative comparative analysis in chapter 5.
I used Atlas.ti, a qualitative data analysis software, to code all organizational texts for the presence of collective action frames. Qualitative software presents several
advantages over more traditional forms of analyzing qualitative data. Perhaps most importantly, it enhances efficiency, easily keeps electronic records of notes, memos, and comments, and enables the researcher to perform queries and counts quickly.
Furthermore, having engaged in the analysis of texts both with and without software, I
have found that the visual organization of the software gives me greater confidence in the consistency of my coding. In instances where I was unsure about the category in which a frame belonged, I could easily and quickly re-visit other previously coded passages (in a sense, conferring with myself); with the click of a mouse, I was immediately taken to another screen containing the information I needed. This kind of procedure, although certainly possible with more conventional hand-coding practices, would be more
laborious without the use of software. One final advantage worth mentioning is comment and memo functions, which enabled me to keep a codebook that could easily be added to and modified right there within the application.
I consider texts as holistic constructs; that is, during each phase of the coding process I was able to review entire statements38 rather than simply the phrase or
sentence(s) containing the frames of interest. Qualitative software is particularly helpful in this regard because I could look at each complete text during each round of coding; I never coded from only a partial passage. Recall that one document per TFN was
imported into Atlas.ti, and that each document contains statements of mission and values for the corresponding TFN. Statements, in their entirety, ranged from 2-3 short
paragraphs to 6+ pages long; most were around 3 single-spaced pages. I first approached the texts using a procedure commonly referred to as “open coding” (Strauss 1987; Strauss and Corbin 1990). At a basic level, coding is the process by which the researcher begins to extract meaning by identifying and providing labels for pieces, or “chunks” of the data (Hesse-Biber and Leavy 2005). Open coding is often the first step in analyzing
38 I use the terms “document,” “entire text,” and “complete/entire statement” interchangeably to refer to the individual documents I created to represent each TFN‟s mission, vision, values, and goals; so, there are 31 statements, documents, or texts. Later, when referring to “passages” of text, I mean to convey the passages (within a text) that constitute a collective action frame.
qualitative data, and consists of “breaking down, examining, comparing, conceptualizing, and categorizing data” (Strauss and Corbin, 1990: 61).
I first carefully scrutinized the texts for the presence of collective action frames by considering whether passages engaged in one of the core framing tasks of diagnosis or prognosis (Snow and Benford 1988).39 I also noted instances where organizations made a statement about their collective identity, or engaged in meaning work that attempted to construct boundaries of the group (Silver 1997). This first pass through the data was followed by numerous coding sessions through which the coding scheme was refined repeatedly. Although I anticipated the presence of frames such as human rights and democracy based on my pre-existing knowledge of the movement, I did not begin the coding process with a formal list of codes; rather, I allowed codes to emerge from the data (Charmaz 2006). I attempted to code the text of one TFN in its entirety before moving on to the next one. Each time I identified a new frame, I re-visited the previously coded text of TFNs in order to assess whether I had overlooked the presence of that frame. This procedure entailed multiple examinations of each text.40 Ultimately, my coding scheme accounted for differences on several dimensions of the frames:
problem/enemy definition, solution articulation, and identity/boundary work.
Although I coded multiple uses of the same frame (when they existed), ultimately I am not concerned with how many times each organization used a particular frame, but rather, how many organizations use each type of frame. As I will discuss later, the
39 Snow and Benford also identify a third core framing task, motivation. It is often the case that
motivational frames overlap heavily with diagnostic and prognostic frames, and the purposes are difficult to disentangle in the process of operationalization. Therefore, I did not code specifically for motivational frames; following Benford‟s (1993) model, I focus on variation in diagnoses and prognoses.
40 I did not keep a count of how many times I visited each document, as there was no theoretical or methodological rationale for doing so. However, I can report that I consulted each text no fewer than 10
primary outcome of interest in the causal analysis is simply the presence or absence of frames in the mission/vision statement of each TFN.
Here I provide only a brief overview of the categories of collective action frames present in the texts of the TFNs. I provide a much more thorough description in the following chapter. Table 3.2 contains a list of the frames as well as the percentage of TFNs (n=31) utilizing each frame. As shown in the table, frames were categorized as diagnostic, prognostic, or identity-based. TFNs could simultaneously employ diagnostic, prognostic, and identity frames, and many did so. However, not all TFNs used each of these three types of frames in their statements. Furthermore, individual TFNs could and did utilize multiple forms of diagnostic, prognostic, and identity frames at the same time.
Passages of text that identified a problem or enemy were categorized as
diagnostic. Passages that identified a specific solution or goal were coded as prognostic.
The most common diagnostic frames identified the key problems facing women as either economic, or systemic/institutional. The prognostic frames offered by TFNs proposed a wide range of solutions to combat problems; these frames fell into one of six broad categories: institutional, economic liberal political, rights-based, capacity-building, and movement process. Finally, in instances where the TFN made a statement about its identity, the passage was coded as an identity frame. Examples of this included a group‟s self-identification as “feminist,” and also as women of the Third World or global South.
Table 3.2. Overview of Collective Action Frames of TFNs
Frame Category *Percentage of TFNs Using the Frame (Total number of TFNs = 31)
Economic Diagnosis
Neoliberal globalization/capitalism First world consumption
General economic inequality Systemic/Institutional Diagnosis Discrimination
Militarization & war Violence
35.5%
12.9%
12.9%
19.4%
38.7%
29%
22.6%
22.6%
Institutional Prognosis Gender mainstreaming Legislative/policy change
Education of leaders and the public Economic Prognosis
Economic redistribution Economic development Liberal Political Prognosis Equality
Democracy Justice
Rights-based Prognosis Human Rights
Women‟s Rights
Capacity-building Prognosis Leadership/empowerment
Education/knowledge (for women) Movement-building/networking Movement Process Prognosis Inclusivity/diversity
Internal democracy
48.4%
16.1%
35.5%
29.0%
41.9%
12.9%
35.5%
51.6%
35.5%
19.4%
25.8%
58.1%
41.9%
32.3%
74.2%
32.3%
12.9%
61.3%
45.2%
32.3%
19.4%
Identity Feminist
Third World/Global South
35.5%
35.5%
9.7%
* Percentages in each cell do not add up to 100 because it was possible for organizations to offer more than
Stage 2: Meso-Level Influences on the Framing Strategies of TFNs. I make