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Software for Digital Scholarship

Information about DiSC-supported software for the collection, processing, analysis or display of numeric, text, or geospatial data

Jamovi is a free, open-source, standalone software that offers a point-and-click interface for R. It is in rapid development with new features being added every few months, and was started by developers who worked on JASP. It offers the power of R, including advanced analyses such as mixed models and factor analysis, with an interface elements that SPSS-users will recognize. Functionality can be replicated within R through the jmv package.

It has all the advanced analyses that experienced researchers need. But, it is even great for students learning to do statistics because it dramatically cuts the number and length of the files that are generated:

  • The data, transformations, and output are all in a single file. 
  • The output is interactive, updating on-the-fly as the variables are added and options set, and as data is filtered or changed. 
  • Variable transformations (ex. compute and recode) are saved as rules to review and modify as needed
  • It can help you learn R, since you can see how the syntax changes as you check boxes and add elements. 

Access to Jamovi

Using Jamovi

More Resources

Jamovi maintains links to additional resources at Community Resources

Video Series

  • jamovi from (also through YouTube)
    • Over 4hrs of content with a detailed Table of Contents for navigation and datafiles
  • How do I... in Jamovi (Alexander Swann)
    • Over 3hrs of video on both data management and analysis.
  • Jamovi (Ross Avilla)
    • Over 4hrs of videos for an introductory data analysis class focusing on various analyses.

OER Textbook

Other Useful Resources


Part of the reason I think Jamovi is great for many people is the simplicity of it's interface coupled with the ability to do even advanced analyses and graphing. Most of what is left out is unnecessary, but if you are interested in switching, here is a list of things that it cannot do.

How does Jamovi compare to other Statistical Software:

Items that are crossed out indicate features that have been added in recent versions. 

  • Import/Export: Cannot import .xls formats, data export is only csv or direct to R  
  • Merge/Restructure: Cannot merge, append, transpose, aggregate, or reshape
  • Weighting: No support for weighting, except for a few individual analyses
  • Data Formats: No built-in support for dates or date/times, or variable-level precision
  • Saving Values: Cannot save residuals Only some analyses allow for saving of case-level values
  • Formulas: Missing many useful non-statistical functions like count, rank, and string manipulation
  • Graphing: Typically done through analyses with fewer options, but several nice-looking schemes 
  • Analysis: No partial correlation, SEM, classification/clustering, or explicit time series support
  • Options: Has the most common post-hoc tests and options, lacks some less common ones
  • Exploration: Not as good with huge files, no data-looking features or find-replace
  • Interface: No interface customization or re-organization

How does Jamovi compare to SPSS?

Jamovi does the vast majority of what SPSS does. It also does many things SPSS does not.

But, to be complete, in addition to the limitations described above:

(See also jamovi's comparison of analyses)

Missing Analyses:

  • No partial correlations
  • No Classify, Forecasting, Simulation, Spatial and Temporal Modeling, 
  • No case summaries, ratio statistics, or tests from summary data. 
  • No SEM, Distances, Multi-dimensional Scaling, or Weight and Curve Estimation.  

Missing Time-Saving Tools

  • No Multiple Response Sets/Analyses, Reports, or much of what is implemented in Python. 
  • No Missing Value Analysis, Split files, automatic recode, visual binning, or comparing datasets.

Offered differently or less completely:

  • Overall fewer regression options, but does support mixed models and all base R generalized linear models.
  • Only offers commonly used post-hoc tests, heteroskedasticity tests, extraction methods, etc. 
  • Survival Analysis is very differently organized and has fewer options.

Handouts from Data Services