SPSS 19.0

The leader in the market for statistical analysis of data
3.7  (1,326 votes)
26.0 (See all)

SPSS is a professional commercial-grade software suite for statistical analysis of data. Initially designed for social science research, over the years the suite developed into one of the most recognized analytical tools in higher education, business, and scientific worlds.

The suite can handle just about any statistical analysis you throw at it: from the simple descriptive statistics, such as mean, median, deviation, to the complex studies of variances and covariances, such as ANAOVA, ANCOVA. Correlations and regression models and many others can also be tested on your data sets.

The suite also helps you to make sure that your collected data is entered correctly and all the variables are properly defined. There are extensive help files within the program to help you choose the best scale for your variables. There are also tests available to check for anomalies in your test results to exclude the obviously erroneous data.

The program is great for evaluating and analyzing any data available at hand. For example, a business may want to evaluate effectiveness of their marketing campaign using their collected survey data. SPSS already provides a special plug-in to do just that. Or a business may want to find a correlation between the price of their services and the growth of the customer base. The possibilities here are endless.

The program works well. However, a word of caution should be addressed to those who interpret the results. The only error that this program may produce is the human error (incorrect conclusions about the data), therefore only the experienced professionals should be allowed to interpret the results of the tests made in SPSS.

Joshua Wrightwood
Editor rating:

Review summary


  • All softs of statistical analysis
  • Social science and business (marketing) applications
  • There are third-party add-ons available for functionality that are not included in the base packet (Crystal Ball, for example)


  • Requires substantial resources when dealing with large data sets - both in terms of memory and processor power
  • Requires a lot of patience and a higher education degree for advanced analysis
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