NSSME> Public Release Data

Public Release Data

The National Survey of Science and Mathematics Education (NSSME) has periodically provided data about the K-12 science and mathematics education system since 1977. The 2018 iteration of the study also collected data about computer science education, predominantly at the high school level. These data allow researchers to examine status and trends in the areas of course offerings, school programs and practices, teacher background and experience, curriculum and instruction, and the availability and use of instructional resources. As part of dissemination, de-identified datasets from the 2000, 2012, and 2018 NSSME are available for secondary analysis.

The NSSME sampling involved stratification, clustering, and unequal probabilities of selection, all of which must be reflected in standard error calculations so that the results of analyses are representative of all teachers in the U.S. Based on the sample design, jackknife 2 replicate weights were created for calculating standard errors for school, teacher, and class estimates.

For access to the data from one or multiple years, complete the form linked below. All fields are required.

Data Request Form

After receiving the completed form, we will contact you via email with instructions for accessing the data from the requested years. The data are available in either SPSS or tab-delimited text format. Accompanying each year’s datasets will be:

  • •An HTML data dictionary for each dataset
  • •The NSSME instruments
  • •The NSSME Public Release Datasets User Manual
  • •Tutorials on how to analyze NSSME data using WesVar.
Note: Although use of the weights in the public release dataset will provide correct parameter estimates in programs like SPSS, standard errors will be substantially underestimated, greatly increasing the likelihood of “false positives” from statistical tests. A number of methods exist for adjusting standard errors to account for sampling weights. One method is to use a statistical program designed for analyzing data from complex samples, such as WesVar, SUDAAN, Stata, or the survey procedures in SAS v9.