Analyzing high school or college transcripts is an effective way to measure student achievement outcomes. Data from transcripts can provide detailed information on students' progress toward a degree, their course-level and overall performance (GPA), and the kinds of courses taken.

I. Suggested Uses of Transcript Analysis:

  • To monitor student progress
  • To provide additional student outcome data
  • To identify trends and patterns in student academic performance

II. Limitations of Transcript Analysis

  • If done manually, time-consuming data input and clean up process
  • Requires some degree of skills with numerical databases

III. Data Collection or Data Entry:

A. Direct ELECTRONIC upload from student transcript computer system.

  •  Representatives of the high school and college should first meet to discuss specific variables to be uploaded, student academic years of interest, and anticipated timeframe. Transcript data collection/analysis once a year is probably most manageable.
  • Basic variables from college transcript system to upload should include:
    • High school code (colleges typically have a variable for this)
    • Student last name
    • Student first name
    • Student middle name
    • Student date of birth
    • Student college ID number
    • Cumulative GPA (fall through to summer term)
    • Cumulative credits attempted (fall through to summer term)
    • Cumulative credits earned (fall through to summer term)
    • Cumulative credits earned (fall through to summer term)
    • Term (fall, spring, winter, summer)
    • Year (2008, 2009, 2010)
    • Course name (ENG 101: English Composition)
    • Course ID (some systems list ID & name separately, ENG 101; English Comp
    • Course credits attempted (not always available; use college catalog for reference)
    • Course credits earned
    • Course grade
    • Other student demographic variables typically not included on college transcripts (e.g., grade level, gender, ethnicity).
  • Provide the college with a document variable list to avoid any confusion; ask for a "cumulative upload," meaning all college coursework data for given school year of students (e.g., all 2009-10 high school students who enrolled in college courses). The data spreadsheet should be in PER COURSE listing format, meaning that any given student may have multiple rows of course information. This allows for easy adding of future coursework to your database and easier analyses of the data.
  • Ask that data be uploaded/imported into Microsoft Excel or other statistical program and then saved to a CD disk.

B. Manual data entry from PAPER transcripts

  • Request cumulative paper transcripts from the college for given school year of students (e.g, 2009-10 high school students who enrolled in college courses).
  • Enter the specified transcript variables listed above into an Excel spreadsheet. Data should be input as a PER COURSE listing format, meaning that any given student may have multiple rows of course information. This makes the process of adding, organizing, and analyzing future coursework to your database easier.
  • TIP: For variables such as student name, GPA, and credits--those that will be the same value for a given student--only input the data value once. You can later group those variable columns together and do a mass copy/paste into subsequent cells.

IV. Organizing and Analyzing the Data:

  • Cleaning up the data and preparing for analysis (this will take some time and is an ongoing process). Suggestions for reviewing the data upload or data entry for accuracy:
  • Depending on the number of students in the database, check ALL the information entered in the database against the paper transcript for every tenth or so student.
  • Make corrections as necessary.
  • Sort individual variables to clean up any values as needed.
  • Use the sort function (in Excel or a statistical software programs, such as SPSS) to check the data-entry values for each of the variables. For example, if you sort by GPA, the range should only fall between 0 and 4, and anything higher would mean a data entry error. Also with student course names, you should be able to see cases in which the names were misspelled or entered in correction.
  • Using statistical software programs, such as SPSS, for this clean-up process is most efficient, You can run basic frequencies and check for unexpected items in the output.
  • Aggregate the data.
  • To make the data easy to use for analysis, aggregating A PER COURSE database and creating an additional PER STUDENT database and PER SCHOOL YEAR database is important.
  • The PER COURSE database (the original database up to this point) will be used to extract specific course and grade information. It lists multiple rows of course information for any given student.
  • The PER STUDENT database should be aggregated/filtered from the PER COURSE database. This PER STUDENT database will list one row of information for any given student. This is the database you want to use when running overall averages/means.

USING MICROSOFT EXCEL. Use the "advanced filter" function.

USING STATISTICAL SOFTWARE. With software such as SPSS, you can also use the "aggregate" function.

V. Transcript Analysis Reporting:

  • Data points that may be of interest for inclusion in a report include:
    • Summary college course data such as GPA, credits, and pass rate by grade cohort, which may be calculated overall, by grade cohort, by gender, by racial/ethnic group, by lunch status, etc.
      • Grade cohort snapshots
      • Average college credits per student
      • College course grade results
      • Distribution of course grades
    • Number of course enrollments by subject area
    • All students
      • College course pass rate, cumulative
      • Number of course enrollments and earned credits by subject area
      • List of college courses by subject area, cumulative
  • Visual data representations
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