University Libraries offers research software consulting for Kent State University faculty, staff, and students at no charge. This guide contains information about the services we offer, as well as online support for SAS, SPSS, JMP, NVivo, and Qualtrics.
Statistical and Qualitative Software Information
If you use statistical software for data analysis in your research, it is appropriate give the name and version of the software program you used, and where possible, the procedure used for analysis.
If you are using R/RStudio and are using one or more R packages in your research, it is appropriate (and strongly encouraged) that you cite the packages used. The
citation() function can be used to find the author information.
We've just added a new guide with how-tos for Qualtrics! Check out our Qualtrics LibGuide.
- NIST/SEMATECH e-Handbook of Statistics
Thorough and well-organized coverage of both descriptive and inferential statistical methods. Example problems are drawn from the fields of science and engineering. Content focuses on understanding statistical methods and their assumptions rather than software.
- Institute for Digital Research (IDRE) at UCLA
Contains a wealth of tutorials and worked examples using SAS, SPSS, Stata, MPlus, and R. Topics covered include (but are not limited to): regression, generalized linear models, MANOVA, factor analysis, power analysis, and data management.
- Research Analytics Group at Indiana University - Tutorials
The Research Analytics Group at Indiana University offers introductory guides to SAS, SPSS, R, Stata, Minitab, SigmaPlot, and Matlab. This link goes directly to their tutorial page.
- Duke University - Introduction to Data Visualization
Duke University LibGuide on data visualization. Resources and tutorials on types of data visualizations and tips for creating better graphs. A must-read.
- Coursera.org - Data Science Specialization
The Data Science Specialization series on Coursera.org consists of nine free courses on data analysis using the statistical software R. It is useful for those who are new to R, or those who are new to data analysis entirely.