Join Prakesh for a high-level overview of the three analysis-ready (AR) files available in the Postsecondary Data Partnership (cohort-level, course-level, and financial aid), differences between the files, and potential questions that the AR files can answer.

Prakesh is our institution’s Director of Institutional Effectiveness and has been at the institution less than a year.

Recently, Prakesh attended an introductory Postsecondary Data Partnership webinar and discovered that an institution’s PDP administrator, or other authorized personnel, can download “analysis-ready files” or “AR files” for short.

Prakesh is excited to learn that the analysis ready files are powerful student-identified files. They can enhance the insights gained in the dashboards in a variety of ways like:

  • identifying a subset of students found in the dashboards for additional contact
  • digging deeper into questions about the student experience
  • and linking the AR files to other data sets the institution already has on hand.

Prakesh downloads the analysis-ready files from the Clearinghouse’s secure FTP site and explores the Cohort-Level Analysis-Ready File.

He learns:

  • The Cohort-Level Analysis-Ready File is a csv file of student-level data.
  • Each row of data is one student’s record, and each column contains data elements and calculated outcomes.
  • Data are derived from the PDP course and cohort files that are uploaded by the institution, Clearinghouse enrollment and degree data, and derived metrics from the dashboards
  • There are many variables contained in the Cohort-Level Analysis-Ready File. Those include student identifying information and characteristics like student ID, gender, race and ethnicity, first-generation status, and Pell recipient status. The file also contains information like cohort year, enrollment type, math or English placement, developmental education attempted and completed, credits attempted and completed after the first year and gateway courses attempted in the first year.

Given the institution’s focus on gateway course completion, this file can be used to answer questions like:

  • What is the profile of students who successfully complete their gateway math in their first year of college? Or the profile of those who complete English?
  • Of first-year students placed into math and/or English developmental courses, what percentage completed a gateway math and/or English course in their first year of college?
  • Are students, who complete their required gateway courses in their first year of college, more likely to retain in the following term compared to those who do not complete their required gateway courses?

Next, Prakesh explores the Course-Level Analysis-Ready File.

He learns the following:

  • Unlike the Cohort Analysis-Ready File, which contains one row per student, this Course-Level Analysis-Ready file contains one row per student per course record, allowing for a granular view of courses and students. It also contains additional data elements not available in the dashboards such as course modality.

    For example, i addition to the course-level information, this file contains an indicator of whether a student is enrolled at any other institution.

  • With this information, users can better understand whether certain student groups, like certain age groups or different enrollment intensities, are enrolling at other institutions and whether enrollment at another institution impacts student outcomes at this institution, like course grades or credit accumulation rate).
  • Users can also use these data to better understand student outcomes at the course/section level and whether factors like course type or level impact those outcomes.

This file can be used to answer questions like:

  • What percentage of first-year students are co-enrolled at another institution?
  • What percentage of students earned a DFWI in at least one course?
  • Which courses are more likely to be taken multiple times before a student receives a satisfactory grade?
  • How do success rates in various math gateway courses compare?
  • Are there patterns that emerge when looking at section level/modality?

Finally, Prakesh explores the Financial Aid Analysis-Ready File.

He learns the following:

  • The Financial Aid Analysis-Ready file is a CSV file of student-level data where each row of data is one student’s record for a given academic year.
  • Columns contain data elements and calculated outcomes.
  • Data are derived from the PDP Financial Aid upload and Clearinghouse enrollment and degree data.
  • The file is only available to institutions who upload student financial aid data.
  • There are many variables contained in this file, including identifying information similar to the other two AR files, like student ID, gender, race and ethnicity, and first-generation status; and variables that appear on the financial aid dashboard, like unmet financial need and income information; and variables not found on the financial aid dashboard, like a breakdown of grants by type.

This file can be used to answer questions like:

  • Are students with unmet financial need more likely to live on or off campus?
  • What is the average amount of institutional, state, or federal grants awarded to students with unmet financial need?
  • Is there a relationship between dependency status and unmet financial need?
  • Has the average unmet financial need changed over time?

Prakesh notices that all three analysis-ready files contain student identifiers which means that these files can be merged with each other and with other institutional data to create a comprehensive data file for research.
Prakesh makes a note to discuss these files, and potential questions they could answer, with the Student Success Council at their next meeting.

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