IBM SPSS Statistics vs R++ Comparison

When I started my project at our institution I had to use a lot of statistical techniques and to do so, I was told that I had to be fluent in a programming language like R or Python which require significant amount of time to learn multivariate data analysis on them. I was so overwhelmed by the difficulties of R and got stressed until one of my professors introduced me to SPSS. SPSS saved my life. One of the greatest things about SPSS is that you can do a lot of advanced data analysis without knowing a lot of coding.

I do not find the price of SPSS very affordable. Due to the fact that other alternatives like R is free, I invested a lot of time to learn coding and now I am about to completely switch to R. The other thing that I think SPSS fails to compete with R is Machine Learning techniques.

Great out the box capabilities, easy integration with other solutions, like Oracle DB, MS Sql server, etc.

Expensive solution. Their are others open solution with the sale features like R

SPSS feels very intuitive. I'm used to programs like R that are mucu more complicated to navigate.

There was a bit of a learning curve, but everything went smoothly once I got the hang of it!

I like most, that it is quite easy-to-use and powerful. During the analysis steps the software guides you quite well, even if you don't have to much experience.

The pricing point, especially if you compare it with alternatives, that are open-source (such as R)

SPSS allows me to work in point and click and also in syntax to run analyses to solve our research questions. I typically run linear regressions, ANOVAs, ANCOVAs, and descriptives/frequencies in SPSS. My favorite part of this software is how easy it is to use in comparison to its competitors.

I really dislike the price of a license. My company covers the cost of the license so I do not have to worry about it, but I would be hesitant to use SPSS if I had to purchase a license on my own dime.

It is very well built software. It has all the statistical functionality you would normally need. Custom functions also available.

With the emergence of R and Python as leaders in the data science community, spss is somewhat losing it’s sway.

It is great platform for descriptive analysis. SPSS easy can handle large datasets. You can save your codes inside SPSS to use it later.

It's too expensive. UI looks boring and outdated. Many advanced analytical procedures are not available and it's not as fast as R programming.

I really enjoy the fact that SPSS is really easy to use for my data analytical needs. The user interface is very user friendly and it is easy to navigate.

Some of the analysis I have run the point and click is not user friendly for. For example, ANOVA testing does not show you the P-value for each of the predicting variables. You have to run it manually through the syntax if you want to get those P values.

I like the user-friendly interface so that anyone who is or is not a statistics professional can use it to crunch statistics. This is very good software for beginners to learning statistics.

The scripting language is limited and this software is not as expandable and customizable as R or Python. The software is also expensive for some organizations.

Well I like it a lot because it is a very used statistical software easy to use allows a lot of data to process and manage works with java modules and can be used in Microsoft Windows, Apple Mac or Linux operating systems in the market for example if we compare it with other statistical statistical software such as the SAS this requires commands for its operation which creates a certain level of complexity or operabilit

Although it is a software widely used everywhere the user must have knowledge of statistics and have some training in the use of this software and some programming knowledge

Easy way to use a statistical tool without focusing on coding. Simple interface, good first program to use for someone who only used Excel before.

I believe the program offers less options and is less flexibile than more advanced options, such as R or Stata, which are probably be preferred for any serious project.

I m totally new to this app, but leant in 30 days. its very easy to learn and user friendly unlike R software. it is well organized and mostly beneficial for statistical analysis.

there is nothing to say about it. I never had any trouble so far, so no least as per my review

SPSS is the go to software for my field. We train our majors and graduate students how to analyze data using SPSS. I use it to analyze my own data. It has the capability of running a large number of analyses, including most using the GLM, but many others as well. It is really easy to use, with drop down menus and boxes to check. It is easy to demonstrate its use and easy to teach with.

It doesn't handle missing data particularly well. It also doesn't do many types of modeling. Thus, m-plus and other types of data analytic software are needed to complement SPSS for some analyses.

What I like the most about IBM SPSS is its friendly interface, where users like me who are not that familiar with advanced statistical techniques can easily deploy their computation without the hassle.

For users who are really into statistics, you might want to use other software (e.g., STATA, R) if you wish to deploy normal analysis procedures, such as estimating econometric models among others.

I learnt statistics using SPSS, and for that it will always be a product that has personal significance for me. It is very user friendly and the UI and ribbon, although dated, allows inexperienced users to quickly understand and navigate its functionality. For simple statistical operations, SPSS is great.

For statisticians who are more experienced, SPSS will likely feel sluggish. It does not have the kind of customisation that other packages or programming languages have, e.g. R, and it can feel slow.

The custom tables are a great add on feature

The cost. It’s really frustrating that their new pricing model is so pricey. When there are free options like R and cheap options like Stata, it’s hard to justify the cost of SPSS

SPSS is very easy to use and covers a wide variety of fundamental statistical analysis. The visualization tools are great and there are tutorials for everything regarding SPSS on the web.

Compared to other statistical analysis, SPSS is a bit limited in terms of tools that it provides. It is not open source and thus there are not many tools developed by users (Like R).

Probably the easiest software to start doing heavy sampling and statistical analysis (logistic regression, chi-sq).

I wouldn't say this is the best software for handling messy data, or data with unconventional structures. If you need to manipulate your data before analysis, you should take a look at SAS, R, or Python.

I have used quite a few statistical softwares that include SPSS, SAS, R. SPSS was very useful for the climate data that I was working with. I felt it was easier than SAS as SAS involved a lot of coding

It did take some time to get accustomed with SPSS but that would be true for most softwares out there

SPSS - via the drop down menus - is one of the easiest tools to use when it comes to analyzing data. There are a host of options and the process is quick to learn. The use of syntax also makes the sharing of analysis easy.

Price - similar data analysis is possible on free-to-download platforms such as R. Also, because of the price, I have found that my collaborators are unlikely to use SPSS and more likely to use other software.

I liked how IBM SPSS allowed me to perform data analysis on large databases of data. I work in trading, where there are lots of numbers to crunch, different sources of data to manage, and scalability/performance is critical. It generates intuitive simple reports for our traders to view and trade with.

Compared to Python and R, which are common data analysis languages with lots of library support, SPSS definitely has less features. However, this isn't necessarily a downside since traders are typically less technical, and this is simple to use for them.

I liked learning this back when I was an undergraduate because I thought it was empowering to know these kinds of software. It was also a major statistical software to use for the Social Sciences it made conducting calculations much easier.

I think what I like least about it is that it is no longer in use. Everyone uses Python and R for statistical analysis. I can't think of anyone today, especially those in the Big Data trend who would fall back on SPSS.

SPSS is a versatile data analysis tool with a wide range of statistical analysis tools. One can carry out simple descriptive analysis as well as pre-process/clean, visualise and model data using GUI as well as code. The interface of SPSS makes it easier to learn as compared to SAS. Data management capabilities of SPSS are a big plus.

SPSS does not have any major drawbacks other than its price tag. R as a competitor is free and open source. I also found SPSS coding to be less intuitive and graphs/plots rendered to be less 'attractive' than some other analysis packages available.

There is no way to know 'all' of SPSS. It is an extremely rich and broad tool to perform statistical analyses (and more). You can import variety types of data files, transform data, and analyze it. It is more powerful than Excel, but you have to get used to the interface and unique menu system.

It is fairly expensive. I do not think that a small business could afford it. It probably does not have a good value. The learning curve for SPSS would be steep for a novice.

The graphical user interface is very simple (as it has a familiar tabular look) but offers a lot of data analytics functionality

If you are dealing with moderate amount of data with moderate level of complexity SPSS is the best choice. However, as your dataset gets larger/more complex, you might be better of using more advanced software or just moving everything to your hand and use R.