NSSE SPSS Syntax Library
The SPSS syntax files linked below assist you in conducting your own analyses. The syntax has been tested under SPSS 15. If you have any questions or feedback about the syntax library, please contact NSSE at NSSE@indiana.edu.
Syntax for Creating Student-Level Benchmark Scores and Institutional Benchmarks
NSSE revised its benchmark calculations in 2004 to provide participating schools with both institution-level and student-level benchmark scores. (Only institution-level benchmark scores were available before 2004.) With student-level scores in hand, NSSE users are able to examine student engagement differences at the college, department, and program level, as well as by student sub-groups, such as gender and academic major. The following syntax files reproduce the student-level benchmark scores.
Student-level benchmark scores (and associated weighting variables) have been included in institutional datasets since 2005. Student-level benchmark scores were not included in the institutional datasets in 2004 and prior years. You can create student-level benchmark scores with the provided syntax files, or you contact your NSSE Project Associate team and we will produce the benchmark scores for you.
Notes:
- These syntax files allow institutions to calculate their own student-level benchmark scores and to gain a better understanding of their derivation. To properly run the syntax, you simply need to change the name of the data file referenced in the syntax file to the name of your institution's data file for any particular year.
- The files listed under "NSSE Syntax" are the actual syntax files NSSE used to calculate institutional benchmarks. They are intended to make the benchmark calculation process as transparent as possible.
- Weighting variables were not included in the SPSS datasets received by institutions in 2004 and prior years. If you have requested weighing variables from NSSE, this syntax file can help you merge the weighting variables with your original NSSE dataset.
- These syntax files assume that weights have been added to your data file.
Dichotomize engagement variables
If you want to dichotomize the NSSE variables (i.e., collapse response options with two categories for the purpose of descriptive analysis) and present your analyses in the dichotomized version, here we provided two ways of dichotomizing: The first one combines often and very often a measure we sometimes label "frequently", and the second one uses only never.
Scales and scalelets
In addition to the benchmarks, NSSE items can be grouped in various formats. The links below provide code creating several useful scales and scalelets:
- Deep Learning scales:
For more information on Deep Learning scales click here. These scales can only be computed using data from 2005 and later
- Higher Order Thinking
- Integrative Learning
- Reflective Learning
- Satisfaction scales:
- Gains scales:
(2001, 2002-2003, 2004-2009)
- Gains in Personal & Social Development
- Gains in Practical Competence
- Gains in General Education
- Pike's scalelets:
(2001, 2002, 2003, 2004-2009)
These scalelets were created by Gary Pike (2006). Click here for more information.
- Course Challenge
- Writing
- Active Learning
- Collaborative Learning
- Course-Related Interactions with Faculty
- Out-of-Class Interaction with Faculty
- Use of Information Technology
- Emphasis on Diversity
- Varied Educational Experiences
- Support for Student Success
- Interpersonal Environment
Recoding and creating new variables
- Time on task:
(2001-2004, 2005+)
This syntax recodes time on task variables (e.g. question 9 on the NSSE instrument) and assigns mid-point values to each category. The results are values that approximate students' actual hours per week.
- Majors: Majors are recoded in various forms as below. This syntax file will only work with data from 2003 and later.
- Dummy code majors: This syntax recodes the major category into dummy variables.
- STEM: Science, Technology, Engineering and Math majors are coded in this syntax. You may want to look at student engagement in STEM fields and non-STEM fields.
- Biglan's major coding: Biglan (1973) groups majors into Pure-Applied and Soft-Hard categories.
- Holland's major coding: Holland (1985) defines majors into realistic, investigative, artistic, social, etc.
- Dummy code demographic and other variables:
(2001-2003, 2004, 2005-2009)
- Age, class, race, gender, enrollment, living on campus, etc.
- Parents' education: This syntax file will only work with data from 2003 and later.
- First generation (version 1). Defined as neither parent with a baccalaureate degree.
- First generation (version 2). According to NCES, first generation students are defined as those whose parents' highest level of education is a high school diploma or less. Note that either or both parents might have attended some college in this definition.
- First generation (version 3). Defined as neither parent attended college.
- Total parents' education (in years): Estimate of parents' total years of higher education completed after high school.
Average Number of Written Pages
The code below can be adapted to estimate the average number of pages that students write in a given school year:
Click here for the SPSS code.
Voluntary System of Accountability
This syntax will help you quickly gather your institution's NSSE data to input into the College Portrait template of the Voluntary System of Accountability (VSA).
Click here for the SPSS code.
NSSE Majors and CIP Codes