IBM SPSS Statistics

I, along with my supervised research student, used IBM SPSS Statistics compared to other software because of its simplicity and user-friendliness. A timeframe is a fundamental part of research work. Time is precious for both of us in terms of research work and using IBM SPSS Statistics. However, our research work was done efficiently in the shortest possible time.

Capabilities like effective data predictive analysis production, Cloud data visualization and the ability to handle a large amount of different business data at once using IBM SPSS Statistics is nice and also the collaboration functionalities are the best. The platform helps manage time and recover and data through Cloud services as well as fantastic data visualization solution from IBM.

The price of IBM SPSS and its quality-price ratio was one of the triggers for choosing the software over the competition. The ease of obtaining a demo of the product and the continuous training it presents was another of the key points in the decision making we made in the company.

IBM SPSS Statistics Logistic Regression's user-friendly interface is among its most important benefits. Without the need for sophisticated technical knowledge, users can navigate and analyze their data with ease. As a faculty member of a university, I used it using its numerous features for data analysis, which helped me publish research articles, journals, thesis supervision, etc.

In addition to IBM SPSS Statistics, we also use statistical programming languages such as R and Python to perform data manipulation and analysis.

In general, SPSS seems to be better for novice users, because it has a much more straightforward interface and an easier learning curve. It accommodates a broad array of statistical tests and procedures.

However, our more senior analysts tend to prefer R or Python. These are better at handling big data, and can perform various bespoke tasks that aren't available within SPSS. From an organisational perspective, it's also important to note that R and Python are free, whereas SPSS has quite a high licence cost.

Compared to Stata, python and MPlus, SPSS is more user friendly especially for beginners. It displays data and output in easily readable formats and makes statistics fun and easy. However, Stata, python and MPlus are more ideal for complex statistical methods like structural equation modeling, hierarchical linear models and writing algorithms.

We tend to shy away from open source where possible. with SPSS from our feeder university system for our co-op interns, this is a great transition and a low barrier to getting them working quickly.

Deriving outcomes using the statistical analysis is the major advantage over all the above tools.

For my own statistical analyses, I personally use R and MPlus. However, these tools have a steep learning curve and require dedicated time and a course on their own. In m yopinion, they are not useful for trying to quickly acclimate undergrads to the new world of stats and research and data. IBM SPSS Statistics is better suited for these needs (especially when provided by the university already).

We have also analyzed and used products such as Minitab, R, Matlab, Q, Statistica, SAS and Stata. SPSS compares very well to them and has strengths and weaknesses just as any other analysis software. For our work environment, SPSS is the standard.

We actually use both Q Research Software and IBM SPSS. We started using Q [Research Software] about 5 years ago and initially thought it would replace [IBM] SPSS. While you technically can view .sav files directly in Q [Research Software], we found that the two softwares are good at different things. [IBM] SPSS is better at viewing the raw data as a whole (e.g. in spreadsheet format) and looking at data on the respondent level. Q [Research Software] is great at running complex analyses very quickly and in a user-friendly format. We use the two programs in tandem - we start with [IBM] SPSS where we format, check, and run high-level analyses. We, then, import that data into Q [Research Software] where we can create crosstabs and run more detailed analyses.

I prefer Q for creating banners, but SPSS is a nice tool for doing advanced data runs or quick data checks.

- It's very user-friendly.
- It can be used from simple to advanced analytics.
- It runs faster and smoother than other software.
- It's a bit cheaper than the other software offered by competitors.
- The one thing that it's lacking is support for mobile versions to view the dashboards and also, the crossfunctional usage could be better.

Its better for quick tasks, Psychology, Sociology, may lack in complex models, AI, or business-decision-making models. It's better for things that you want to compare, correlate or detect influence of one on the other. It's worse that R for complex models, custom models, big data analysis, and much worse that pipline tools for realtime analysis.

I also use or have used Tableau, Excel, and R (wasn’t able to list R above). Tableau is better for visualizations, Excel works for generalized/more basic statistical analysis but lacks more complex features, and R has been difficult for me to master and lacks the UI and ease of use that SPSS has.

Cost was a leading factor

IBM SPSS Statistics beats the pants off of Minitab in every area except cost. Minitab has far cheaper entry-level costs, but the software is much more limited. With the versions of Minitab I have used, importing mapping data is a non-starter. With IBM SPSS Statistics, once the few basic steps of locating and importing a map file are complete, a simple dialog box yields attractive maps that present data in a geographic context.

I described this in a previous question. R is free and full of features, but time consuming to learn, especially since you have to download different libraries for whatever you're doing. I know IBM SPSS Statistics fairly well, and it has been worth the cost, but maybe not for much longer.

As a tech-lover, I used many software products, such as Stata, R Studio, Python, Orange, Jupyter Notebook, MatLab, Julia, Hadoop etc. Each software has its own specific features. However, I like open source software, such as, R Studio and Python, as these software provide me multiple options to do analysis, using in-built or external packages. One more thing, with the help of an open source software, one can develop additional packages for an advanced analysis. In comparison to open source software, IMB SPSS Statistics provides an easy in-built options. These options save time of the user like me at some extent for basic analyses.

[IBM] SPSS is by far the best of the statistics software applications in terms of functionality and accessibility, but its biggest drawback is price. SPSS is prohibitively expensive in comparison to the other competing statistics applications such as R and SAS, making the purchase of a license for an individual very expensive if not covered by employer.

Easier to build ensembles and also in forecasting than with Python. De trending and deseasonalizing data are far easier.

Tabright much easier to use.

I have used R when I didn't have access to SPSS. It takes me longer because I'm terrible at syntax but it is powerful and it can be enjoyable to only have to wrestle with syntax and not a difficult UI.

SPSS is much easier to learn even for people with basic knowledge of statistics.

SPSS is the original statistical package. It might not have all the bells and whistles as they newer statistical software programs that you see out there now, but it does the fundamentals. Many of the more cloud base packages you have to upload datasets, and can't really "manipulate" the data all that well. Also most survey data is either exported in SPSS or CSV files so you know SPSS is the standard.