Quantitative Data Collection & Analysis
Surveys
NACEPSurveyGuide.pdfNACEP has produced a survey guide to help institutions conduct concurrent enrollment evaluation surveys. The tools in this resource provide a good starting point to develop surveys that inform your evaluation.
PSRQuestionnaireTipSheet_0.pdfThis resource provides high-level guidance for developing survey items as well as some links to other resources to support survey development.
LikertScaleExamplesforSurveys.pdfThis resource offers examples of scales you can use for your surveys to indicate levels of agreement, value, importance, quality, and frequency. You may use a combination of these scales throughout your surveys, depending on the types of questions you are trying to answer.
25 Ways to Increase Survey Response Rates.pdfThis resource offers some basic tips to help increase survey response rates. For some more rigorous research on survey response, see the link here.
Analyzing Administrative Data
Some of your research questions may be answerable using data from your institutional research office about the courses students are taking, demographics of concurrent enrollment students, performance in courses, and progress in pathways toward credentials. Even if you do not have deep experience in dealing with course data, there are some features within programs you already have that can help to shape your data in a format that will allow you to monitor progress and answer your research questions. The resources below may help your analysis.
Intro to Descriptive Statistics - Towards Data Science.pdfThis resource offers a quick refresher on generating basic statistics to summarize your data. Calculating figures such as mean, standard deviation, and range allow you to summarize large data sets into simple tables or graphs for the consumers of your evaluation results.
Pivot tables are a powerful way to group data from raw output from a student information system. This tool, part of the standard Microsoft Excel program, can be used to easily find averages across courses, students, and time points.
Conditional formatting is another way to gain quick insight from raw data. Applying color scales or rules to highlight values (such as student course failures) can help you see patterns that influence your evaluation results.