Data Analysis pt. 2

Priya V -

Hello again!

Unfortunately I don’t have much in the way of updates this week. I’ve continued meeting with providers from MHC Healthcare and learning about their experiences with COVID-19 working in community health, and the general agreement seems to be overall positivity and appreciation for the resources available at MHC Healthcare for providers. However, one interviewee mentioned feeling like gaining access to those resources tended to be a long and arduous process, so people in emergent need may not be able to have that need met quickly. 

Beyond interviews, data analysis continues, albeit slowly. I’ve finished Pearson’s correlations for all of my variables, and I’ve found a few interesting things. Emotional consonance (reacting to the emotions of others around you) shows an overall negative correlation with almost every other variable, indicating that it may be most commonly seen in providers with overall lower rates of burnout. Additionally, surface acting (outwardly faking the “expected” emotions for your position) shows some of the strongest positive correlations with other variables, including burnout and all 4 of its subscales. This indicated that providers with poor mental health often find themselves faking positive emotions towards patients, possibly in order to hide their emotional state. I plan to explore the data that I’ve collected from my interviews to determine why exactly these correlations are so strong.

I’m also beginning to find Spearman’s correlations for all of my variables, which turns out to be about double the work of the Pearson’s. Spearman’s is a “rank-order” correlation, meaning that instead of correlating the actual values of all of my variables, I instead correlate how high the number is compared to the rest of the values. For example, my dataset has 53 responses. If the highest burnout value of those responses was 4.9, for a Pearson’s correlation, I would just use 4.9 in my correlation calculations. For Spearman’s, however, the calculation would instead use the number 1, as 4.9 is the largest burnout value, and therefore falls first in the rank-order of values in the burnout dataset. This means that before I can even start correlating values, I have to calculate rank-order for each variable individually (all 11 of them!) and then calculate correlations for those newly created dataset.

Here’s an update on my spreadsheet!

– Priya <3

More Posts

Comments:

All viewpoints are welcome but profane, threatening, disrespectful, or harassing comments will not be tolerated and are subject to moderation up to, and including, full deletion.

    colin_k
    Hi Priya, when you mentioned that interviewees with a higher emotional consonance have a more negative correlation with the other variables would that be like if an interviewee has a high emotional consonance they won't have to try as hard or even surface act at all leading to less burnout?
    priya_v
    Hi Colin! I'm not totally sure as to qualitatively what causes this, but providers who answered the survey who showed lower levels of burnout and COVID-19 anxiety tended to show higher levels of emotional consonance. However, the negative correlations weren't particularly strong either. I plan to go through the interview transcriptions to see if the providers who noted that they tended to react naturally to patients' emotions can provide any further insight on why this is.

Leave a Reply

Your email address will not be published. Required fields are marked *