A collection of projects completed for DHUM730 Spring 2025.

Introduction

In pondering my quantified self, I can’t help but reflect upon my work schedule as a full-time employee.

When I began this full-time position as a Research Assistant at the CUNY Dominican Studies Institute in 2023, I was unaware of what being a research assistant looked like and how it would impact my day-to-day life. Like any young professional aware of the demands of capitalist production, from the very beginning I was concerned about my work-life balance. However, at the time my fears were out weighed as I was excited and motivated by the prospects of the world of academia. Thus, I worked hard, demonstrating that I was eager and accommodating to the needs of the job.

The CUNY Dominican Studies Institute (CUNY DSI) is a fast-paced, productive environment that brings in millions of dollars in grants and capital funding to the City College of New York (CCNY). The CUNY DSI is a research institute that produces and disseminates knowledge on the study of people of Dominican descent in the United States and abroad. The Institute houses the Dominican Archives and the Dominican Library, the first and only institutions in the United States collecting primary and secondary source material about people of Dominican descent. In 2023, I began working within the Research Unit under the Director of the Institute, Dr. Ramona Hernandez.

At the time I began, I was one of three research assistants, and I maily worked on the one research project I was contracted under. As the weeks went on, I was given responsibilities that began to fill the need for a Director’s assistant. And, thus, I began working as an assistant to the Director; meaning that her work schedule became my work schedule. During this transition, I was unaware of the long-hours and late nights that would soon become a weekly norm. As I reflect upon 2024, I have the impression that I frequently worked overtime hours—or, as my workplace refers to it, compensation hours (that are to be saved and used at leisure). Enough so that those close to me were shocked about how long my workdays were.

Thus, I ask myself: In 2024, what were my work patterns as a Research Assistant at the CUNY DSI? When did I work longer hours, and is there a reason why? I have formed these research questions to investigate my time-commitment as a Research Assistant at the CUNY DSI and to learn insights that I can apply to my work-life, moving forward.

The data for this research project is collected from my employee timesheets. In its original form this is a daily, handwritten record of when I arrive to and leave from work. However, I personally used these timesheets to keep track of the amount of compensation hours (i.e. regular-pay overtime hours) that I was accumulating for later use.

For this research project I used data spanning the lengths of 5 months, June through October of 2024. It is important to note that there are two distinct periods within this dataset, classified by two types of work schedules. From June through August 23^rd, 2024, I was scheduled to work from 10:00am to 6:00pm, Monday through Fridays. In this period–Period A–I worked 7.0 hours per day, as contracted. Then, from August 26^th through October 2024, I was enrolled in a graduate program and was scheduled to work from 10:15am to 8:00pm Tuesday through Friday. In this period–Period B–I worked 8.75 hours per day, as I arranged my schedule. This schedule change (for Period B) was permissible because (1) my workplace values and accommodates educational pursuits, and (2) as I function as an assistant to the Director, later work hours are more convenient for the tasks of my job.

The dataset was prepared with the transcribed handwritten data inside the tables and along the margins of my employee timesheets, onto an excel sheet. Each row in the data set represents a weekday, whether I was scheduled to work or not. I created a list of variables that describe whether I worked, when I worked, the work-benefits used (i.e., sick and annual leave), and compensation hours worked and used. The dataset includes the following variables:  

• Activity
• Activity Descriptor
• Date
• Weekday  
• Masters Day (i.e. “yes” or “no”) 
• Expected Time-In
• Expected Time-Out
• Actual Time-In
• Actual Time-Out
• Lunch Duration
• Work from Home (i.e. “yes” or “no”) 
• Total Hours Worked
• Sick day (i.e. “yes” or “no”) 
• Used Sick Leave Hours
• Scheduled Holiday (i.e. “yes” or “no”)  
• Used Annual Leave Hours
• Worked Compensation Hours
• Used Compensation Hours

To investigate the patterns, present within my 2024 work-life, I have analyzed my work behaviors from June through October. These two months consider work patterns in different seasons, during different programming, during different schedules and an increase in responsibilities. During the summertime (June-August) I went from co-leading to solo-leading the Institute’s summer research internship program with 15 interns. And, after late August I adapted to the Director’s late work schedule to both accommodate my academic schedule and to adapt to the needs of the job. Thus, taking on additional responsibilities that came with working later into the day.

Results

The first three bar charts explore the total hours I worked daily over the span of 5 months.

To Graph 1, I have added a third-degree polynomial trend line to visualize the change in total hours worked daily. And a short-intermittent, constant line to highlight when my work schedule changed, and distinguish between the pattern on the left and right of the line.

The 7.699 hours I worked on average during Period A (June-August) was greater than the 7.0 hours I was expected to work daily. In Graph 2, the standard deviation highlights that I often worked anywhere from 30-minutes to 1 hour 30 minutes over time on any given day. The two exceptions to this overtime are July 20^th through July 26^th when I took annual leave, and August 6^th to August 9^th when my boss was abroad at a seminar. Excluding these two scenarios, the average amount of hours I worked daily increases to 7.779 hours.   

In comparison, in Period B (August-October), the 8.492 hours I ended up working was less than the 8.75 hours I was expected to work. In Graph 3, the standard deviation highlights about 68% of my workdays in Period B. On average, I left work 15 minutes earlier from work.

It is important to note that I proposed to work 8.75 hours a day from Tuesday through Friday to meet my 35.0 hours per week obligation. Thus, in the beginning of this new schedule (September and October) I clocked out when my boss clocked out. So, if her day ended at 7:00pm, so did mine. Mainly because, I served as her direct assistant. This accounts for most of the variation in when I clocked out during Period B.

However, there are outlier cases like the scheduled early-leave from 4:00pm to 6:00pm where I pre-requested early leave and compensated for the missed work hours by using sick leave, annual leave, or compensation hours.

The following set of graphs illustrate the average amount of hours I worked per weekday, separated by Period A (June-August) and Period B (August-October).

When aggregated by weekday, the data clearly shows that regardless of my work schedule (i.e., Period A or B) I work the most overtime hours on Wednesdays. For Period A, there is not much of a difference between the average amount of hours worked on Mondays, Tuesday and Wednesdays. Indicating that the beginning of the week carried the greater workload. In Period B, Fridays were a close runner up with an 8.667 average amount of hours, almost the 8.75 hours I was scheduled to work.

Overall, I often work closer to 8.0 hours a day regardless of the work schedule at hand.

The following sets of graphs display sick leave, annual leave, and compensation hours.

Altogether, these graphs begin to contextualize my work patterns even deeper. Graph 8 tells us that often, I usually take my full 1.0-hour unpaid, lunch break. However, when the average duration of my lunch by weekday is calculated, accounting for the days I was did not end up taking the full hour, we the data portrays that I am more prone to take a shorter lunch break on Thursdays, regardless of the work schedule. And, on Wednesdays, my longest work days on average, I end up taking most of my lunch break.

Graph 9 compare the dates on which I worked and used compensation hours. The graph illustrates that I often worked overtime and would either use it to work an hour or so less the next day, or I’d accumulate them to take a whole 7.0-hour day off.*

And, Graph 10 represents my usage of my sick and annual leave. It is important to note that this pattern is significant because I earn these hours as I work and am entitled to using them on my discretion. Thus, they are important to me in maintaining a work life balance. We saw earlier that in this 5-month period I took one-week long vacation in which I used my annual leave and compensation hours. Excluding the vacation period (July 22-July 26), I often end up using this bank of hours (sick, annual and compensation) to work shorter work days. There are days in which I will take a scheduled sick day to attend medical appointments and tend to my health needs. And, there are days I take off with my compensation hours because I can and I should.

*This is a payroll-technicality that I have to adhere to.

Moving Forward

From this analysis, I have learned about the trend of my work as a Research Assistant at the CUNY DSI, and I have learned about the impacts of longer work-days.

The analysis concluded that I work an average of 8.0 hours per work day, and that Wednesdays are my longest work days with the most overtime on average when compared to other weekdays. This trend of work rings true as I would often schedule meetings for Wednesday afternoons, leaving all other administrative tasks to be done in the late afternoon and early evening. And, it makes sense that I worked at least an hour overtime on average because most of the workers in my office clocked out at 6:00pm, leaving me behind to tidy up the loose ends of the day. Mainly, I am glad to be able to prove that I did indeed frequently work overtime in 2024.

As an assistant to the Director, I am still learning the balance between optimal performance for my boss and myself. As an assistant, I have independent tasks to perform, and I have to work alongside my boss on the day-to-day while she is writing emails, taking meetings, and planning project developments. I spend the mornings working on my independent tasks, and the afternoon and evenings work alongside the Director. In Period A, this resulted in me working at least an hour of overtime everyday. In Period B, this resulted in me leaving work between 7:00pm and 8:00pm. As an employee who is only contracted for 35.0 hours a week, this trend begs the question: what would be the most optimal work schedule, where I do not have to constantly work overtime?

Should I begin my work day later, to accommodate for the fact that I often end up leaving later than I am scheduled to? In the future, I would like to continue this research by answering this question. This would require me to include the details of the tasks that I am performing at work and how my time is being distributed. Additionally, it would also be interesting to an explanation for tasks that were performed during overtime hours, and whether or not other employees were working overtime, as well. These factors will allow me to create a more complex portrait of my work-life, and hopefully it will allow me to work smarter.