Both primary and secondary data were collected and analyzed as a part of this study. The sources for the primary data were the key informant interviews and case studies and the analysis of resulting themes, patterns, similarities and differences. The secondary data analysis consisted of a comprehensive literature review as well as both publicly available
documents regarding university and industry partnerships and procedural, organizational or institutional documents which are acquired as a result of the key informant interviews. An overview of this data analysis may be depicted as follows in Table 21:
Table 21: Process for data analysis
After the key informant interviews were conducted, audio recordings of the interviews were reviewed and transcriptions of the interview sessions were analyzed for accuracy. The investigator conducted a thematic analysis using the notes, memos, transcriptions and digital recordings in order to identify differences and similarities. The transcriptions were analyzed using coding to identify pertinent themes, patterns, ideas, concepts, behaviors, interactions, incidents, terminology or phrases used. The analysis was used to compare and contrast responses from the various interview sessions.
Charmaz (2006, 42) states that coding works to “disassemble and reassemble data” and the codes serve to “summarize, synthesize and sort the many observations made of the
LIterature review Review of publicly available information regarding selected partners Analysis and comparison of policies, organizations, partnership types Key informant interviews & case perspectiv e analysis Analysis of interviews & case studies Review documents provided by key informant interview participants Analysis of overall findings (trends, differences, similarities)
data.” Coding becomes the fundamental means of developing the analysis. The analysis of interview data and identification of codes is an iterative, progressive and non-linear process in which categories may need to be adjusted or new categories added to accommodate data that do not fit existing codes. The coding process consisted of a combination of a priori codes, which were developed before analysis of the data, and inductive codes, which were developed as the coding was performed. Main categories of data were analyzed into smaller, more defined categories which allowed for greater discrimination and differentiation,
allowing for the identification of patterns and more meaningful analysis of the responses. In large part, the assessment itself of the relative importance of different themes and the recognition of subtle variations can potentially be an instructive aspect of the analysis.
The coding of thematic categories indicates that some themes occur consistently across the data, which will help to explain the “why” in certain successful, or unsuccessful, partnerships. The analysis sought to inform how these things relate, what data support the interpretation and what additional factors may be contributing factors. Likewise, the analysis sought to understand examples or events that run counter to prevailing themes and what may be suggested by these countervailing responses. It is significant in the coding portion of the analysis to understand items that do not fit the categorization system as those that fit clearly into prescribed categories.
Data coding was accomplished utilizing MAXQDA, a qualitative data analysis software program developed in Germany in 1989 by VERBI GmbH. MAXQDA was developed as a method of finding deep patterns in qualitative or mixed methods research data, and to provide insight into the complexity of the research data by enabling the researcher a method of systematically evaluating and interpreting text.
Analytical coding was employed in the data analysis, in which new categories were created based on concepts that emerged as a result of further reflection on the data. Line-by- line coding was initially conducted with each of the interview transcripts, which identified over forty codes. The themes that were included in the coding process represented the collective knowledge, perceptions and experiences of the researcher as well as the key informants, allowing for a robust analysis of the research questions. Appendix E lists the codes and number of statements per theme that were included in the analysis. Many of the passages cited by informants had more than one code assigned to them.
Once the coding of the data was completed, the analysis was used to consolidate the data employing the emergent themes, trends and overarching connections to explain the findings. In some cases, responses were quantified and the themes were coded and weighted, either by relative importance, through frequency of responses or the number of unique respondents who refer to certain themes; or through the identification of common topics, themes, observations or comments. After the interviews were coded, the interviews were read a second time and excerpts were extracted that were thought to be exemplary of the various codes that had been established. These quotations and excerpts from the interview transcripts were recorded and were grouped by category using an Excel spreadsheet. This method of theoretical sorting to classify the categories, connect categories to one other and support codes with dialogue and quotations provided grounding to the categories produced. This process of theoretical sorting “gives you logic for organizing your analysis and a way of creating and refining the theoretical links that prompt you to make comparisons between categories” (Charmaz, 2006, 115).
The data were then analyzed by asking the following questions, using the conceptual framework depicted in Table 2 as a theoretical guide and lens for analysis:
1. How do the categories fit together and relate to each other? 2. What data seem to be more important?
3. Are there exceptions or critical cases that do not seem to fit? Are there alternative explanations?
4. What paradoxical information, conflicting themes or other evidence may exist that might challenge or contradict the interpretations?
The interpretation of the coding data brings meaning and signification to the analysis by answering the following questions:
1. What are the key ideas being expressed within the category?
2. What are the similarities and differences in the way interviewees responded, including the subtle variations in comments?
3. What are the major lessons identified in the comments?
4. What comments have application to other settings, studies or situations?
5. What will those who read the results of this research be most interested in knowing?
At the beginning of the research project, it was anticipated that the information which was obtained from a review of the literature would be reinforced and substantiated by key informant interviews and the review of existing policy manuals and publicly available information regarding academic-industry partnerships from the university and industry participants. The data analysis did not show inconsistencies to the literature review, but did
offer a greater depth of understanding of the current thought process by industry and academic experts as to the future strategic direction of these partnerships.