Anshika, Anna, Liv, Veronika, Essy
July 29, 2023
Tools Used and Why
We have employed a diverse array of tools and software to effectively gather, process, and present our project. Notably, we utilized Excel, Google Sheets, ArcGIS Story Maps, and RAWGraphs to exhibit the scope of our project. These tools were appropriate for our project because they made data presentation easy as well as clear and concise for the viewers.
For instance, Excel and Google Sheets played pivotal roles in the initial stages of our data collection process. We collected information related to universities in the United States from the year 2021 all the way back to 1968. The data was sourced from reputable databases such as the Integrated Postsecondary Education Data System (IPEDS) and its predecessor, the Higher Education General Information Survey (HEGIS). These platforms offered the convenience of downloading data in CSV format, which facilitated seamless integration with Excel for data processing and manipulation. Excel’s powerful functionalities allowed us to conduct various calculations and transformations on the raw data. Furthermore, Google Sheets served as an invaluable tool for data sharing and collaboration within our group.
Apart from Excel and Google Sheets, we also leveraged ArcGIS Story Maps, which served as a dynamic and interactive platform to present our findings visually. ArcGIS Story Maps will allow us to create engaging narratives, incorporating geographical data and interactive maps to enhance the audience’s understanding and engagement with the information.
Methods
Our main approach to gathering data for our institutions was reliant on individual research of our respective institutions; each group member browsed internet databases and institutional archives to learn more about the history of co-education at their university. The archives, digital collections, and web pages dedicated to this topic provided the context for the development of these now co-educational institutions. We also did our best to document responses to the co-ed status of the colleges such as with students, alumni, administration, and the general public. Most of this information is presented textually, so for project purposes, we employ text analysis as one of the methods. Instruments like Voyant Tools are very useful for gathering the general idea of people’s perception of co-education by collecting commonly used words and phrases.
Tim Sherratt’s project, The real face of White Australia, is a reading that is helpful in maintaining mindfulness of the power that the framing of our research has on the information we produce. For example, Sherratt developed technology that used physical cues to identify the faces of Australians who were not white. As a result, it is possible that mixed-race individuals or non-white individuals who are white-passing did not fit the criteria that the AI technology was equipped to identify. This is relevant to our own project, as we are producing data that is directly guided by social constructions: gender and race. In many frameworks, such as the American census, groups of people can be excluded or misrepresented by their legal race or gender.
Consequently, we decided it was important that we were intentional with the labels we used to reference gender and race (such as the ones specified in the table below). Additionally, because gender inclusion is the focus of our project, and the institutions we are representing were originally developed using a gender binary framework, it was important we did not perpetuate this incorrect and dated model in the presentation of our current research.
Data Modifications
Due to the extensive time span of over 60 years covered in our data analysis, we encountered numerous variations in the way data was recorded. To ensure consistency and comparability across the dataset, we devised our own standardized version, carefully considering these historical fluctuations.
Despite our best efforts, we must acknowledge that this standardization process has certain limitations. One noteworthy limitation is the presence of blank entries in certain columns, such as the “Institution Size Category,” particularly in earlier years. These gaps exist due to the unavailability of data during those periods.
In the calculation of percentages for student groups, we followed a standardized approach. IPEDS rounded the percentage to the nearest whole number. Consequently, for each year that required percentage calculations, we consistently applied this rounding method to maintain uniformity.
Our data analysis spans a substantial timeframe, prompting the need for a standardized version to account for the evolving data recording practices. VLOOKUP and extensive filtering were used to fill in any missing data.
| UnitID | IPEDs ID for each college |
| Institution Name | Name of the Institution |
| Year | The year the row of data is from |
| Longitude location of institution | Longitude of the institution |
| Latitude location of institution | Latitude of the institution |
| Status of institution | An indicator of whether the university was active, closed or simply unrecord for that year |
| Grand Total | The total number of students (graduate, undergraduate, full and part time) |
| Coed, All-Mens or All-Womens | A derived column from Percent Women that indicates if there are 100% women (All-Women’s college), 0% women (All-Men’s) or somewhere in between (Coed). |
| Percent Women | The percentage of the student body who are women. It is unclear if this is a self-identified value or based on sex. |
| Percent American Indian or Alaska Native | The percentage of the student body who are American Indian or Alaska Native. |
| Percent Asian/Native Hawaiian/Pacific Islander | The percentage of the student body who are Asian, Native Hawaiian, or Pacific Islander. Some years reported Asian separately from Native Hawaiian or Pacific Islander, so these values were combined for simplicity. |
| Percent Black | The percentage of the student body who are Black. |
| Percent Hispanic | The percentage of the student body who are Hispanic. |
| Percent White | The percentage of the student body who are White. |
| Percent Two or More Races | The percentage of the student body who are two or more races. |
| Percent Race/Ethnicity Unknown | The percentage of the student body whose race or ethnicity is unknown. |
| Percent Nonresident Alien | The percentage of the student body who are are nonresident alien. This means any individual who doesn’t qualify for a green card, so for example international students who have lived in the US for at least 5 years. |
| Tuition and Fees | The average cost for students. |
| Institution Size Category | Indicates a standardized range in which the institution falls into |
| Historically Black College or University | Indicates whether the institution is a Historically Black College or University |
| Tribal college | Indicates whether the institution is a historically tribal college, in other words, if it has primarily served Native Americans. |
| Degree of urbanization | Indicates how urban or rural the area around the institution is. |
