IBM SPSS Statistics

Rapid and precise responses are provided by IBM SPSS Statistics, which is one of the basic requirements for me as a faculty member as well as a researcher. I got a suggestion from my colleagues to use it for my research work, and I found it suitable, so researchers may use it.

Using IBM SPSS Statistics is more productive, especially on multiple business data processing at the same time and data visualization features are excellent. Creating amazing data reports and the prediction capability through IBM SPSS Statistics is impressive. This IBM Cloud platform enables easy prediction of next next operations outcome and the machine learning ability is perfect.

Although the use of IBM SPSS statistics requires prior knowledge, its user interface is quite intuitive and easy to use, you do not need to write codes for daily use. It integrates with other tools without any problem. Use in hotel chains, for example, where there is a lot of customer information, makes this great tool essential.It may be less appropriate for companies without offices or few clients.

There are numerous statistical tests in it. Excel and some other files can be inserted into it, and they can perform some excellent statistical analysis for research easily. With so many languages available, it's the perfect accessibility for everybody, and thus it helps researchers, academicians, students, and others for efficient output.

In general, I would recommend an advanced data analyst should use R or Python, rather than IBM SPSS Statistics. However, not everybody needs the advanced functionality offered by R and Python, and many people find coding to be quite challenging and intimidating. For these people, I would say that IBM SPSS Statistics is perfect, because it offers an interface which is much easier to use whilst still accommodating the range of statistical tests and procedures that most users will most commonly require.

SPSS is perfect for running quick analyses like correlations, t-test, chi square, regressions and ANOVA. You can also test mediation and moderation models if you have enough skill. But if you want to do really complex modelling like HLM or latent growth curve modeling you might have to turn to stata, SAS, MPlus etc. I much prefer the SPSS interface so would love if these became options in the future.

Quick and standardized models can be adopted by co-op students to run these models or create new ones as this is widely used in university, so the learning curve is shallow.

It all depends on your overall usage and requirements. If you are a normal users who doesn't want to go deeper in how algorithms works and mathematical intuition behind them, they this is perfect statistical tool for you. But you are in technical side, want to learn how those algorithms and mathematics works, and have plenty of time to design your own algorithm using Python, R, Scala, Julia, or any other languages, then you can avoid this. Overall, it is for the users, who just want easy to use solution.

My rating may be biased because this tool was taught to me at college and my professors highly recommended it and again at my work, this was the only tool being used. But it is hard to imagine another software that does everything that IBM SPSS can. Well suited for academic research, and survey analysis. Less appropriate for big data.

I would recommend IBM SPSS Statistics to colleagues who are new to statistical analyses and have an introductory grasp of statistics and the types of statistical analyses. Ultimately, that is where IBM SPSS Statistics is well-suited. It is a great tool with a point-and-click system for getting acclimated to data analysis. Once you move into a realm of more advanced analytics (and thus the need for degrees of freedom in how analyses are conducted and the types of models that are built), I do not believe from my experience that is where IBM SPSS Statistics is suited. Furthermore, for collaborations across research teams, other products like R are better suited for this functionality.

IBM SPSS is well suited for statistical and data analysis and has features comparable or better than other statistical packages. You can also use it efficiently for building analysis and reporting systems. It is not as well suited for new/novice users doing basic statistics, as there are better/easier options out there.

Overall, I would recommend [IBM] SPSS because there is no other tool I'm aware of that allows you to view raw data and run a quick analysis as easily. It's an essential tool for any market research professional. That being said, there is a steep learning curve and you must have a strong understanding of statistics in order to really get a lot of use out of it. The licensing is also a nightmare and we have had issues with both purchasing new licenses and transferring existing ones to new computers. That being said, the program is so integral to our day-to-day work that I would still ultimately recommend it.

SPSS is a great tool to check survey data is working properly. It is also a strong tool for complex data analysis for more intricate studies. It is great for all levels of data analysis. Even with limited experience/training, SPSS can make a big impact to your data analytics immediately.

It is very user-friendly to use without any previous hands-on experience. With a click of a button, we can draw analysis from the given data. It also has various charts, which we can use to represent pictorially. Also, we can use it for simple to advanced analytical purposes. It is well recommended even to students as well.

Its well suited for:
• Speed of analysis
• Correlation and regression analysis
• Questionnaire testing
• Comparative analysis (parametric and non-parametric tests)
Its not well suited for:
• Comples machine learning models, AI etc.
• Real-time data processing
• Advanced visualization (the graphs look ugly)

I would personally use it in fields like HR, Psychology, Sociology etc. Less of in pipeline work.

SPSS is well-suited for the following: 1) User Behavior Analysis: SPSS handles large datasets to analyze user behavior data. 2) Customer Satisfaction / Foundational Surveys: SPSS facilitates analysis of quant data from satisfaction surveys, keeping us informed about customer needs and preferences. 3) A/B test analysis: SPSS statistical tools for A/B test analysis, which helps optimize user experience of our products.

Scenarios where SPSS are less appropriate: 1) Qualitative Data Analysis: I do not use SPSS for open-ended survey responses/qual data. 2) Live/in-vivo data analysis: SPSS is not ideal for real-time data processing. 3) Complex Data Integration: SPSS isn’t the best fit for complex data integration tasks

I have been a long-time user of SPSS. I am very, very satisfied with how well it helps me analyze my datasets. I never ran into any issues with SPSS when performing data analyses. I must purchase AMOS separately as SPSS doesn't run structural equation modeling (SEM). Having it as part of the SPSS standard package would be nice, but I understand that not all users need SEM.

Mostly we use it in Marketing research wheein it gives us a bigger about the upcoming business. With its Data visualization, the charts, the graphs we get that makes it easy to understand the trend. It's quite helpful when we need to either import or export from IBM SPSS Statistics

IBM SPSS Statistics works when looking to reduce the time it takes to take action on relevant data making it a key to your statistical strategy solution. For example, if using data gathered from psychologists the patients can benefit from statistical results to put together a wellness plan gather from actionable data from larger groups.

Overall, if you need something quick, SPSS is a great tool to use. If you really want to manipulate data or format it, using something like R would be better.

IBM SPSS Statistics absolutely rocks at complex analysis. When data need to be converted, or new variables created based on a calculation between variables, or results saved for individual tests, IBM SPSS Statistics really can't be beaten. In my opinion, where IBM SPSS Statistics falls down a bit is that its mainframe roots show through at times in some of the dialogue boxes. If one has been using the product since its mainframe days, this is no big barrier. If one is a brand new user with no mainframe experience, this can be a bit of a steep learning curve.

If someone is starting off new with statistics, I probably would recommend using R instead, even though I don't like it as well. I note that many of the features I now am using in IBM SPSS Statistics actually come from R, according to the most recent update. IBM SPSS Statistics seems like a classic luxury car when many individual users might be just as happy with an e-bike. It also seems that IBM is probably more interested in developing AI and machine learning and less interested in people who need to analyze survey data accurately and quickly for education or marketing, in my opinion.

IBM SPSS Statistics is good in producing basic and some specific analyses, such as regression modeling, pair plot, scatter plot, box plot etc. This software also provides an option to estimate 95% CI with box plot (easy clickable option). There are various features like converting complex text data into analysable data. The software can help a beginner with no special training in handling. However, the software also provides an option to use or write syntax for statistical analyses. But, the rules of syntax or commands are completely restricted, as these are pre-defined texts or commands for specific analyses. IBM SPSS Statistics lacks in, for producing results through an advanced statistical analysis (using complex data).

IBM's SPSS is suited for situations where you have to answer questions using either simple or advanced statistics. For instance, if I want to predict future academic achievement from a student's demographic characteristics lBM SPSS is perfect. A scenario in which it would be less well suited is when I have to run structural equation modeling, which is not a key feature of [IBM] SPSS.

Quick build of models