If you’ve been following the doctoral journey posts, you have learned the experiences I had to prepare for my dissertation writing, including the collection of data. In this entry, I will share how I evaluated and analyzed the data collected from my surveys in preparation for writing those sections of my dissertation articles.
My survey had two purposes. First, many of the questions contributed to my quantitative study with respondents sharing their perceptions of organizational support by scoring Likert-based questions. The remaining questions allowed for open-ended responses that allowed respondents to share their first-hand experiences receiving organizational support in their jobs. That data contributed to my qualitative study.
For my quantitative study, I extracted the Qualtrics results to an Excel file. Portions of that data were loaded into SPSS, a software tool that allows for multiple types of data analysis. I chose to provide insights through a descriptive analysis that explored my research questions and inferential data analysis that sought to determine if key characteristics affected an individual’s perceptions of organizational support for onboarding and development as a remote worker. The descriptive analysis allowed me to see the overall responses to each survey question collectively and identify if responses were more positive, negative, or neutral. The inferential analysis allowed me to take those same questions and dig deeper into the data by seeing if the answers changed based on someone’s role, gender, and their competency levels related to technology use. I felt that SPSS was a helpful tool in my data analysis and offered several ways to represent the data graphically which gave me a few choices for how I wanted my results represented in my dissertation.
The data for my qualitative study consisted of verbal responses to the open-ended questions included in the Qualtrics survey. To analyze this data, I used an online tool called Delve. It allows you to load your qualitative responses and apply codes to the text to identify trends. The tool also has AI functionality to simplify the process, but I did not use this in my analysis. For each code, I included definitions to help guide coding. After the results were coded, I had a list to review and determined which codes had more responses. This brought out trends and themes where I could share commonalities in the experiences of the respondents based on their narrative. In addition, I was able to create a transcript of the coding that I added to the appendix of my qualitative paper. This data helps tell the story of the experiences a group of remote workers had in getting the support they needed from their company, leaders, and peers.
With the data analyzed, I was ready to move forward with the completion of my dissertation and get closer to earning my doctoral degree. In my next post, I’ll share about the finishing steps of completing my dissertation writing and preparing for my defense.
Which type of data do you find more useful when you are researching or hearing a presentation with data? Share your comments!
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