IBM SPSS Statistics

Good for doing simple runs like freqs and correlations.

If you aren't comfortable programming and you need to do statistics, SPSS is a great choice. Psychology undergrads, for example, have to take statistics. Their difficulty with the programming portion cuts into their ability to learn the actual analyses, so SPSS is good for that. I would love to see IBM SPSS invest more in their UI/UX and increase the usability of this tool because it could be so much better. Their iconography is a mess, there's very little feedback, and don't even get me started on that last "big update" that basically truncated all functionality. The biggest thing I would like to see is to have the "Analyze" function broken into categories, or other buttons, or be able to customize it in some way. Having to repeatedly go through drop down menus to run analysis after analysis is a huge time suck, and very error prone (easy to click on the wrong thing). Lastly, one of the biggest challenges for beginning (and even experienced!) statisticians is interpreting output. More tool tips, feedback, integrated coaching, or something like that to help people understand more about what they are seeing could be so beneficial.

Perfect for small or medium businesses or non-profits with medium and large data sets. Might not be as effective in the clinical/medical field where you are dealing with massive data sets.

It is cheap, reliable and very easy to install on almost any laptop or desktop with minimum specifications.

SPSS is well suited when you are dealing with respondent level data and need to clean, recode, or make changes. It's one of the only softwares that I always go back to using as it is the foundation of statistical software programs.
It might not be very useful when looking at BIG data, but it works for reviewing survey data.

IBM SPSS is most suited for analysis of survey data, wherein you have large datasets to perform data analysis of, while deriving relationships between several variables and function, meanwhile interaction is with multiple fields and tabular on nature, i.e., uses a lot of tables, and various relationships are derived on comparison of several fields with several others

SPSS is most suited for most researchers, although for power size and other uses will require further purchases or use of other programs to account for the limitations that SPSS might have. It can handle heavy data and there should not be any limitations for multiple variables. It may not be suited for those looking for a more robust and programmable option. Also, for those on a budget, other sources like R-Statistics might be better suited.

IBM SPSS has worked out very well for us. However, some of the more popular modules are not easily obtained due to pricing. The licensing structure should be simpler as well.

While SPSS is not perfect, there is no other tool as powerful for working with statistics. This tool is perfect for universities and students who are pursuing research. Honestly, most PhD students could probably not graduate without SPSS as their statistics tool. I think that this tool could also be useful for any corporate data that needed to be crunched.

If you are interested in pulling data, cleaning the data, transforming the data, and creating statistical studies and viewing the results in tabular and graphical format in a quickly and easy manner then you found it.

SPSS is great for people starting out with statistical analysis because it is easy to use. SPSS may not be so great for people with a coding background who are more inclined to write codes instead of click on buttons.

IBM SSPS is one of the best geospatial analysis tools on the market, possibly only exceeded by ArcGIS. It is very good with handling large and complicated datasets and manipulating these for statistical purposes. Unfortunately it can be a little non-user friendly and takes quite a bit of time to get used to.

SPSS is well suited to our survey data and analysis needs (but not data collection). It also works well for our data mining needs. Most of the staff I hire were exposed to SPSS in college, but either way, there is a learning period to understand how to document your work using Syntax (even if pasted from the menus) and I utilize mostly online resources to help them get up to speed.

SPSS is well-suited for academic environments, given the strong foothold it has in the educational and research institutions of the world; it helps if your institution has a licensing agreement as it is not free to use indefinitely (although a 14-day free trial is available). As it is a powerful and complex statistical analysis package, it is a good fit for large and otherwise unwieldy datasets and analytical projects.

SPSS is probably not a great fit for individual users doing simple statistical analysis, as much of this level of work can be accomplished at no cost using Google Sheets and an add-on such as the XLMiner package.

Overall, I can see how IBM SPSS can be used by teams with simple objectives when it comes to analytics. It is similarly very appropriate for teaching students about basic notions of applied statistics. However, compared with other statistical software and programming languages, it does not come with great advantages in terms of computational capabilities and customization.

Overall, it is best suited for determining the value of our products and services, and determining how the value of those products will hold. It is also great, in that respect, for decision making. We rely on it a lot for the organization of our statistical data. Like I have mentioned before, we do not rely on it for visual representations of our data.

I would say if you are not proficient in other statistical software packages and only want to use point and click, SPSS is the product for you. If you want to do more complex analysis, SPSS may not meet your needs.

I think other than those two things SPSS is well suited and you can use SPSS in all scenarios

SPSS is suited for entry-level analysts and even managers who prefer to get a little more technical. It is not suitable for sophisticated stats analysis with a large data set.

Great for large datasets and running advance stats. Once you learn how to use it, it is nimble. It doesn't produce the best viz and is difficult to get into viz programs like Tableau.

I would say that it is an ideal integral program for the application of descriptive and inferential statistical methods and for the systematization, analysis, evaluation and organization of data for decision making.

It offers multiple options for analyzing results that allows individuals or organizations to understand or present a report of a certain situation.

I would tell you if you want to make quality charts and easy to understand report look for another program.

It contains a multifunctional easy-to-use and understandable spreadsheet that offers the possibility of working with any database, as well as options that allow a lot of content to be left in rows or columns and perform the respective statistical analyzes.

I recommend inferential statistics tools because they are very complete, which allows people or organizations to obtain complex results ideal for making strategic decisions.

IBM SPSS is well suited for quantitative analysis of academic research. In doing qualitative research, it falls rather short. It has great tools and functionality to meet the needs of both students and professors (across all skill levels) when doing statistical analysis. It works great with Qualtrics which allows you to download your data as an SPSS save file.

SPSS is useful in analyzing quantitative data to evaluate relationships among variables or groups of variables, to evaluate group differences in individual variables or groups of variables, to evaluate the psychometric properties of a measure or item, to manipulate and process data for use in reports and articles, to document analytical processes, and creating easily digestible analysis output. I use it in studies with thousands of participants responding to hundreds of questions. It enhances my ability to manage and use data.

Ad hoc analysis when you need a quick answer as to whether a particular pathway is worthy of pursuing further. It can provide quick assessments of various multiple paths for analytical considerations -- many may prove to not be worthy of further pursuit in depth. This allows the user to provide rapid feedback and avoid additional downstream costs.

It is suitable for analyzing statistical data and formulating statistical quality analyzes. Suitable for generating charts and graphs that represent or summarize the statistical study. Highly suitable for performing complex statistical studies. Inadequate to perform simple tasks, this software is specialized and to make the most of it, it is best to use it in studies that require statistical precision.

I can't think of any scenario where SPSS isn't appropriate. Each tool has a different function, and this platform covers a lot of tools.