Quantitative analysis can take many forms. The analysis of descriptive statistics is very common. Examples include identifying the number of votes in an election and the geographical distribution of those votes. They describe the basic features of data. Inferential statistics, on the other hand, seeks to use quantitative data to help explain or predict certain phenomena. For instance, we may combine data about votes with data we have about the demographic characteristics of a population to make inferences. We may want to know whether older voters vote more conservatively than younger voters, or be able to predict how gender may influence a future election. While spreadsheet software–such as Microsoft Excel–can be used for both forms of analysis, for inferential statistics it often is useful to make use of more advanced software programs such as those described below.
Using Statistical Software Packages
Social scientists who adopt quantitative methods often turn to professional software packages to collect, archive, interpret, analyze, and project their data. These packages include but are not limited to STATA, SPSS, SAS, and R. The following are a list of resources on the techniques and codes of four major programs – R, STATA, SPSS, and SAS:
“R” is a free statistical analysis program. The learning curve for most people is steeper than the others, but it is a powerful program once you get the hang of it. For more information, go to R Project and R Studio.
UCLA Academic Technology Services – UCLA’s Statistical Computing website had help, examples and annotated output for Stata, SAS and SPSS.
Data and Information Services Resource Center – University of Wisconsin Madison’s collection of online resources for Stata, SAS and SPSS.
Statalist – The independently operated Stata listserver. Hosted at the Harvard School of Public Health, Statalist is an email listserver where over 2,500 Stata users from experts to neophytes maintain a lively dialogue about all things statistical and Stata.
Stanford University’s Statistical Analysis Resources
Professor Lisa Dierker of Wesleyan’s Psychology Department on Data Architecture, Data Sets, and Analysis:
Statistician Nate Silver using statistics to estimate the predictive value of race on politics. (courtesy of TED.com)
Peter Donnelly of Oxford University on the precariousness of statistical evidence. (courtesy of TED.com)
@ Monmouth College
coming soon: information on quantitative analysis resources at Monmouth College
updated August 3, 2017 – MN