Why Use Quantitative Analysis?
Here are some resources to consider if you are unsure what type of quantitative analysis you need:
Commonly Used Tools/Software Packages
Statistical Package for the Social Sciences (SPSS)
All Purpose quantitative analysis tool. Does just about everything (descriptive statistics, correlations, chi square, t-test, ANOVA and the like, Cronbach's alpha, factor analysis, regressions, and linear or path models.
R is an open source language and environment for quantitative data manipulation, calculation, and graphics.
Quantitative Analysis, like SPSS
Quantitative Analysis Database functions Data Visualization
Additional Resources for Quantitative Analysis
The 2015 EER Workshop Intro to Quantitative Research Methods by Tim Shipley included several recommendation for online tools useful for quantitative analysis. These are:
- Statistical tests: http://statpages.org/#Comparisons
- 2 sample test: http://www.evanmiller.org/ab-testing/t-test.html
- Correlation link 1: http://www.fon.hum.uva.nl/Service/CGI-Inline/HTML/Statistics/Correlation_coefficient.html
- Correlation link 2: http://scistatcalc.blogspot.co.uk/2013/10/pearson-correlation-calculator.html
- Chi-square: http://statpages.org/ctab2x2.html
- Sample size: http://www.sample-size.net/sample-size-means/
Rasch Analysis for providing added reliability and validity to ordinal data sets. Rasch analysis is a excellent tool for analyzing and type of ordinal data generated from Likert-type instruments. Rasch analysis is a form of one-way probabilistic modeling that converts ordinal data into values that are more readily accessed by parametric statistical tests. It is also an extremely powerful tool for instrument design and revision.
Construct Map is a tool for doing relatively simple Item Item Response Theory (IRT) analyses as described in the book Constructing Measures (Wilson, 2005). This comes from the BEAR group (Berkeley Evaluation and Assessment Research Group). I learned to use Construct Map through a 3-day workshop run by the BEAR group, which was enough to get me up to speed and able to use the tool.
- Python is a very easy to learn programming environment, with very powerful graphical display powers. It is highly customizable, with lots of packages for certain programming tasks freely available. Python is free, and there are free academic licenses for graphical interfaces:
- R is a free and powerful programming language.
- Matlab and IDI are useful but quite expensive, and have a steep learning curve if you are new to programming.