Using SPSS and Stata for Data Analysis

A deep research requires you to apply statistics that help in revealing the observations and drawing precise and proof-based conclusions. In case of more complex statistical research, experts strongly advise to use software tools that are exclusively designed for analyzing data with the help of several tests. As a fact, researchers have diverse needs for data analysis, and that they possess different levels of statistical education or training. However, luckily, the statistical software is flexible enough to offer two chief products: SPSS and Stata.

SPSS is a famous statistical package that probably has the most user-friendly interface for writing programs. In fact, the interface is much similar like the Excel spreadsheet. Therefore, it is the best choice of the beginners. You can almost do everything you need in this package via its comprehensive interface but not all on your own. Therefore, you might need to learn programming in SPSS. This software package is the choice of those who have the power to minimize complex data management but do not need to perform cutting-edge statistical analysis.

SPSS is capable of performing the most general analyses such as regression, factor analysis, logistic regression, variance, survival analysis and multivariate analysis. The highest pros of SPSS are multivariate analysis such as discriminant and manova analyses and analysis of variance as it allows conducting various tests. The version of 11.5 has some features for analyzing mixed models. However, SPSS does not offer robust regression and survey data analysis in the basic pack.

Stata is an easier statistical tool to learn and comes with an option of a command-line interface along with the GUI. It is ideal to use with time-series data. The tool offers several survival analysis routines and enables you to write your own commands. Unlike SPSS, Stata is best for those who want to perform a cutting-edge research but do not possess data management needs that call for the use of SAS. Moreover, the tool is the choice of those who are developing or altering statistical procedures.

Stata performs almost all general statistical analyses. It specializes in the survey of data analysis, regression and logistic regression apart from a myriad of robust methods such as robust regression. However, there is no scope for analysis of variance and discriminant analysis. Further, the tool loads the complete dataset into memory, which can hinder your work in case the dataset is very large. However, this is a rare incident.

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