blog_23_07_2020

For a young researcher looking to engage in hardcore data analysis and other such statistical exercises, there are several statistical software for adoption. However, most of this software are expensive propositions requiring paid subscription or licensing, which makes it difficult for the researcher to select the right software that will serve its purpose in the long run for various types of data analysis.

While each such software has its own pros and cons and certain software is perhaps better suited for specific types of data analysis, the software that has proved to be most popular as an overall package with multi-purpose flexibility is SPSS.

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SPSS (Statistical Package for the Social Sciences) is the set of software programs that are combined together in a single package as a basic application to analyze scientific data. While this software can be used for quantitative data analysis like market research, surveys, data mining, etc, it is also a very handy tool to deal with qualitative data as well. Officially dubbed as IBM SPSS Statistics (after IBM acquired the parent company in 2009) most researchers still refer to it as SPSS given it has been in the market since 1968! The biggest proof of its benefits is its longevity as the world standard for social science data analysis and it is still widely coveted for its straightforward and English-like command language, ease of data management, better output organization, and a wide range of functionalities.

The core functionalities offered in the SPSS package for a researcher are

  • Statistics Programs for various quantitative data analysis
  • Modeler program that allows predictive modeling
  • Text Analysis for Survey programs that enable deriving insights from qualitative inputs from open-ended questionnaires
  • Visualization Designer that allows a researcher to use their data to create a wide variety of visual representation of data in the form of density charts, radial boxplots, etc.

While SPSS is widely used by researchers for hardcore data analysis like regression analysis, ANOVA, MANOVA, and T-tests, those analyses can be done with other software as well. The reason why a researcher often prefers SPSS is its additional benefits. For instance, SPSSallows a researcher to store a metadata dictionary as part of its data documentation. This metadata dictionary acts as a centralized repository of information pertaining to data such as meaning, relationships to other data, origin, usage, and format, and can serve as an easy reference fora researcher in course of complicated data analysis.

Another popular feature is the Modeler Program; a versatile data and text analytics workbench that offers a complete range of advanced analytical functions, including state-of-the-art algorithms and automated data preparation that allows a researcher to deploy models, predictions, and insight.  The recent IBM SPSS Modeler integrates with IBM Cognos 8 Business Intelligence software, as well as with a wide range of databases, spreadsheets, and flat files across different formats and on a wide range of platforms.

The versatility of SPSS makes it the easiest and safest bet for a researcher as a multipurpose tool for data analytics.

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