Heidi Saucier

Doctoral Journey part 4 – Data Analysis

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!

Heidi Saucier

Doctoral Journey part 3 – the dissertation experience begins

The time finally came for me to start my dissertation. One of the first things I had to do was get approval to do my research. Because my study included human subjects, this involved submitting a request to the university’s Institutional Review Board, or IRB.  The request included the goals of my study, a description of the sample, and how I would gather data. The group reviews requests and determines if the study is ethical and follows the university’s policies. My original plan was to ask volunteers to complete an online survey for my quantitative paper, then ask for leaders of remote or hybrid teams to participate in interviews about their experiences for my qualitative paper. I received feedback from the IRB looking for clarification and additional things I would need to do for the interviews, which posed some unexpected challenges. Due to some deadlines I needed to meet, I discussed alternative options with my adviser. We agreed that I could still do a qualitative study using open-ended questions in the survey I was creating for my quantitative research. I adjusted my IRB submission and resubmitted it to the board. Shortly after that submission, I received an approval. I could now move forward with sending out my survey and collecting data.

There are various types of samples that can be used for research. There’s random sampling, where individuals are selected unbiasedly like a lottery drawing. Convenience sampling allows the researcher to choose individuals based on their ability to participate and tend to be people that are more easily accessible. Another one of the methods is snowball sampling, which involves people referring others to participate. I chose a convenience sample by soliciting volunteers to complete my survey. I posted on Facebook, LinkedIn, and X asking for people to participate. In each post, there was a link to the survey that included a letter explaining the survey and confirming their participation and that no harm would come to their physical or mental health. I gave participants three weeks to complete the survey. While I was waiting for my data to come in, I worked on literature reviews and sections of my articles that did not rely on my research results. I was excited to see the responses that individuals with experience working from home had and how they related to prior research and my research questions.

Next time, I will share how I evaluated my data and used it to move forward with the writing of two of my three artifacts.

I hope you are enjoying hearing about this journey. I’d love to hear any a-ha moments you’ve had along the way!