Comparison of R Studio vs STATA vs SPSS

R vs SPSS vs STATAAs a data analyst, either in practice or still a newbie, learning the ropes in data analysis, you have to know your tools of work. Data analysis is a whole process that could be as simple or as complex as you want it. It basically entails the systematic application of statistical and logical techniques to evaluate data. You go to work using a certain route and every day by half-past seven there is always traffic, then with this data, you are free to make a decision to choose another route after you take into consideration all that you know. This is the basic application of data analysis in day to day life. However, taking this up a notch, data analysis is used on a large scale to make strides in science, economics and pretty much any other aspect of life. However, analyzing large data need you to use the best tools there is.

If you are here then I bet that you have heard of STATA, R and SPSS and are probably wondering, what is the difference between them and what are the pros and cons behind each of them and even more importantly which is the best for me. Well, you are in the right place as I intend to give you all the answers that you seek. For starters, these are three different tools that are aimed at doing the same kind of work but have different specs to them and are tailored differently. They differ in user-friendliness, the scope of work they can accomplish, their capacity and many other differences that I will be sharing with you. To get a good insight into them, let’s step into their arena and get to learn about them more.


R came into play in the mid- 90s from Ross and Robert and this explains why they choose R as the name of their product. It gained popularity over time as a very user-friendly and well-tailored data analysis tool. It is very effective in designing statistical models to solve complex statistical problems. Notably, R is free open source software, therefore, it is compatible with any operating system. Besides a point and click user interface, the program has command line and savable files that gives it ability to engage matrices, vectors as well as delineate aesthetic graphs and make it easy for users to interact with them all thanks to its graphical library.

  • R is very good for data analysis
  • Complex for beginners
  • Facilitates interaction with databases
  • It supports a variety of extensions
  • Proper organization of data in columns and rows
  • Has a variety of tools such as scatterplot 3D and high charter or data science application
  • Constant update of the system
  • It is ideal for data wrangling
  • Steep learning curve
  • Free open source software


STATA is a statistical software used in data analysis that enables users to analyze, manage and graphically represent data in a more meaningful way that allows them to make inferences as well as draw conclusions. Among the many reasons why people prefer to use stats are ;

  • STATA is easy to learn for beginners and easy to use as well
  •  it is supported by a wide range of introductory textbooks
  •  it is well-controlled by STATA Corp so that one can have real faith in STATA ’s results
  •  using STATA really does help students to learn about statistics
  • it offers a wide range of statistical analyses
  •  it has a great degree of flexibility
  •  it presents results in a clear format

When dealing with a data analysis assignment one of the very first thoughts you will have is, which is the best software for me to use in data analysis? And truth be told, you will wonder whether or not you will be able to deliver on your project. Well, I cannot assure you that you will deliver but I know people who will, You can contact us for data analysis help using R, STATA or SPSS. These are the guys for the job. With years of experience handling project after project, then you can be sure that our writers have an up to date database on all that you may need. This cuts across the board, right from choosing the right topic ideas for you, having the right structure for your paper, driving a valid argument for your case and most importantly giving you an authentic, original project. Have I mentioned that we have a 99% success rate in our work and this is the kind of thing you should look for before you decide on who you want working with you?

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SPSS was created by SPSS Inc and later in 2009 was acquired by IBM Corporation and renamed IBM SPSS Statistics Package. SPSS is abbreviated from the statistical package for social sciences. The software contains a statistical analysis and toolpak for easier statistical analysis and open source integration. and SPSS has earned its spot in the market over time and has for the longest time dominated thanks to top-notch features.

  • Supports data from multiple sources
  • Ideal for large amounts of data
  • Better for multivariate analysis
  • Works better in social and medical sciences
  • Best for complex data sets
  • Has a direct generation of output for reports
  • Ideal for forecasting and decision trees
  • Advanced statistics and charting capabilities
  • Custom tables add on package

You are probably wondering, is there any specific reason as to why I have ranked them in this order? Well, yes there is and I will prove that to you on a head to head comparison of these statistical analysis programs.






Used in medical and social sciences areas


Used mainly in economics and econometrics


Mostly suitable for complex data sets It is not very good for complex data analysis

It is mostly used for small data and in its application



It has its application in large scale and cutting edge research

Performs muitivariate analysis for large dataset Performs normal statistical analysis
Generates outputs into reports Uses a command line and documentation features
Performs modeling on complex data Not suitable for complex analysis and modeling
Excellent for data management Ideal for researchers



Choosing the software to use is an uphill task more so if you are a newbie or student. Unfortunately you will need to interact with the software themselves, however, this overview will give you an idea of what you are looking for so as to look into it in depth. After all, the best way to know what you want is to get a personal feel of it.

I would recommend that social science students looking for a friendly program for statistical analysis for dissertation i do recommend SPSS.

If you are looking to advance in statistics and learn to perform complex statistical analysis and statistical modeling we recommend you learn R programming language. It is versatile and can perform complex statistical analysis on large datasets.

Economics researchers and econometrics students keen to learn on economic model development and testing should be keen to learn STATA.

Related Links



How To Choose Statistical Tests

How To Choose Statistical Tests To Use in your Dissertation or Capstone Project

Are you a nursing student worried how you will analyze data for your DNP capstone project or dissertation paper? We would like to present to you a comprehensive tutorial on how to choose statistical tests for your dissertation project. In case you get stuck feel free to consult our SPSS data analysis help service

Most people can handle descriptive statistics without issues. But when it comes to inferential statistics, few people exude total confidence. But with the right statistical tests , inferential statistics does get a little easier. This post explains how to choose statistical tests that deliver high-quality findings.

Statistical analysis and testing are somewhat complex but very useful processes. They help researchers and analysts convert tons of raw data into useful information. If you don’t have a grasp of these procedures, you likely won’t see great results.

Before you dive into how to choose statistical tests for your study, study the distribution of your data. You also should spend some time extracting descriptive statistics from the data. In other words, you should calculate the mode, median, and mean. Doing that does make drawing inferences from your data somewhat easier.

What Factors Determine Which Tests You Should Select?

There are a plethora of statistical tests out there. Unsurprisingly, choosing the most fitting statistical test (s) for your research is a daunting task.

Three factors determine the kind of statistical test(s) you should select. These are the nature and distribution of your data, the research design, and the number and type of variables.

Here’s a little general advice on picking statistical tests. If your data is “normally distributed,” it’s best to use parametric tests. But if your data isn’t normally distributed (“non-normal” data), you should go with non-parametric tests.

9 Common Statistical Tests and How to Pick the Right One

Before you pick any particular statistical test, ask yourself: what do I want to do with my data? What do I want to know? After that, pick the statistical test that’s designed specifically to support the execution of that action.

Here’s a list of common statistical tests and what they’re best for so you can pick the best bet for your analysis.

Sign tests

Use the sign statistical test to study the difference between two related variables. This statistical test pays little attention to the magnitude of change in the difference (if any). However, the test factors in the direction of the difference between the variables in question.

Wilcoxon rank sum test

The test focuses on the difference between two variables that are independent of each other. It takes into consideration both the direction and magnitude of the difference between the variables.

Wilcoxon sign rank test

The Wilcoxon sign rank test is used to test the difference between two related variables.


This statistical test helps data analysts to test the difference between group means. Note: You shouldn’t run ANOVA before you’ve accounted for all variances in the outcome variable.

Paired T-test

The Paired T-test helps analyze the difference between the means of two related variables.

Independent T-test

The Independent T-test is the exact opposite of the Paired T-test. This test deepens the analyst’s understanding of the difference between the means of two independent variables.

Pearson correlation test

The Pearson test is a correlational statistical test. It enables you to accurately determine if any statistical association exists between two continuous variables.

Spearman correlation Test

The test is similar to the Pearson correlation test in that it’s a correlational statistical test. The test helps you to describe the association existing between two ordinal variables.

Chi-Square test

The Chi-Square test measures the strength of the association that exists between two categorical variables.

Multiple regression test

The test tracks how change in multiple predictor variables (2 or more) predicts change in the outcome variable.

Single regression test

The test tracks how change in one variable (predictor variable) predicts change in another variable (outcome variable).

You now know what each of 11 common statistical tests measure. At this point, picking the right test for your study should be pretty easy.

Statistical Tests For Inferential Analysis

You’ve learned what various statistical tests do. Which means you can easily know how to choose statistical tests that are suitable for your research. But you probably haven’t mastered any of the tests, have you? Obviously, you can’t leverage the power of any of the statistical tests if you don’t know how they work. Also, you should learn how to use statistical analysis software packages such as SPSS and MATLAB work. That’s because statistical tests and statistical software work hand in hand.

Infographic on how to choose statistical tests

how to choose statistical tests

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Video on how to choose statistical tests

Source: https://youtu.be/rulIUAN0U3w