Data Management and Analysis

All four methods of data collection were organized individually, so management and analysis techniques are discussed separately.

For the online survey, Survey Monkey^TM software used for the web survey allowed the researcher to create the survey on custom templates and distribute the web link for participants to complete. This survey service compiles data into a variety of summary tables and generates descriptive statistics and graphic information. To test the accuracy of preliminary summary tables, survey results were downloaded into excel spreadsheets and reviewed individually, by municipality, and in aggregate.

Participation across all seven municipalities was not even, which made it difficult to summarize findings in aggregate. For example, initial results for “barriers to reurbanization” across all seven municipalities were skewed by the results from municipalities with high response rates. As a result, average responses from each municipality for each question were calculated in Microsoft Excel and were used to replace the summary tables provided by Survey Monkey. This was an important step in the data management/analysis procedure as raw responses could not be used to summarize findings. As such, findings from each municipality have been assigned equal weight in the summary calculations. As the

sample size was rather small and not intended to be statistically significant, results were not further analysed through statistical software.

For key informant interviews, interview transcripts were the major unit of analysis for this study and several steps were taken to carefully analyze the results. Following the interviews, I transcribed the audio recording using Express Scribe™ software. Interview transcripts were reviewed several times throughout data collection to ensure accuracy and to continually measure data saturation. Additionally, to achieve construct validity (Yin, 2014), the researcher used member checking (Creswell, 2009) during the interviews, which involved recapping the participant’s thoughts and asking for confirmation to increase the accuracy of the researcher’s interpretations. Reviewing transcripts prior to future interviews helped improve question probes and capture new information. The objective here was to obtain a general sense of the information, which lead to a better understanding of differences and similarities in responses across interview participants and to evaluate the credibility of information.

The second stage of analysis involved a more detailed and rigorous coding procedure. Coding is considered by many to be a best practice interpretive analysis method that is used to derive chains of evidence in qualitative research (Bhattacherjee, 2012). Creswell (2009, p. 186) describes coding as “the process of organizing the material into chunks or segments of text before bringing meaning to

information.” Coding technique such as open coding, axial coding, and selective coding are frequently used in Grounded Theory research (Bhattacherjee, 2012; Corbin & Strauss, 1990, 1998); however, similar coding techniques are also useful for research that utilizes theoretical frameworks and evidence from the literature to guide data collection (McCracken, 1988). Codes can be key words or phrases used by interview participants (called an in vivo term) or labels assigned by the researcher (Creswell, 2009).

Before information was grouped into categories or codes, transcripts were reviewed line by line to with an open mind looking for all possible connections to the phenomenon at study. This technique is referred to as open coding as the researcher remains open to information beyond the confines of the

structured interview questions, which helps detect themes or patterns that may have otherwise gone undetected (Bhattacherjee, 2012).

Next, text was grouped into the following three codes: “success factors,” “opportunities for investment,” and “barriers.” Findings naturally fell under these three categories due to the use of semi-structured interview questions. Transcripts from each interview were fractured and reorganized so that all key themes, ideas, and terms expressed by participants were categorized under one of the three codes. The frequency of key codes in each category was recorded to measure data saturation and inter-participant agreement. Codes that were mentioned frequently within and across interview transcripts were identified as important research findings. Narrative passages from interview transcripts are frequently used to explain important research findings from participants’ first hand perspectives.

4.6 Summary

This chapter has described the methods and procedure used to explore key research objectives and analyze findings. This thesis employs qualitative research methods such as key informant interviews, an online survey, a literature review, and direct observation to assess the opportunities for and constraints to reurbanization in the Region of Waterloo and other mid-sized cities in the Greater Golden Horseshoe. The primary research focus is on the Region of Waterloo, which is explored through a case study with

embedded units of analysis while the online survey is used capture an environmental scan of similar issues in other mid-sized cities. Overall, this research is descriptive in nature and seeks to capture the beliefs, attitudes, and opinions of planners and real estate developers to better understand the policy and practice of redeveloping under-utilized property. The results of this study are intended to establish what Yin (2012) calls analytic generalization where logic is identified that might be applicable to other situations rather than statistical generalization where results are directly inferred or applied to the larger population. The next chapter highlights key findings from both the online survey and the case study from

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