Yes, there are several alternatives to SPSS for statistical analysis that you might find useful. Some popular options include R, which is a free and open-source programming language for statistical computing, and Python, which is another free and open-source programming language that has many libraries for data analysis. Other options include SAS, Stata, and JMP. It’s worth noting that the best option for you will depend on your specific needs and preferences.

Adding: R is free, you get it from here: https://cran.r-project.org/

You can also find old releases (already compiled for Windows), e.g. for 32bit machines (the current versions are 64bit only, and if your device is very old, as you said, it might still have a 32bit processor).

Hello Muhammad Syafiq Mohd Nasarudin. Readers may be better able to help you if you provide a little more information. For example,

If you will be working as a data analyst who needs to do a wide range of analyses on a regular basis, your needs may be different than if you are doing a one-time fairly limited set of analyses. So thanks for clarifying your situation. HTH.

Hi, Muhammad Syafiq Mohd Nasarudin

I would like to recommend StatSoft STATISTICA 6.0 - StatSoft STATISTICA 12.0 program.

And there are some older versions. I remember Statistica 4.3 )))).

Now it is TIBCO Statistica

And I don't know how acceptable to find old programs by using sites alike https://rutracker.org

Hi

In addition to the already mentioned suggestions, you can also try PSPP as well as Jamovi. Both are free as far I know. But this might not be suitable for advanced statistical analysis.

PSPP has the same look and feel as SPSS but is less good for printed output. But it's free, and there are workarounds to offset its limitations.

A very acceptable alternative is Anaconda cloud, which is a cloud-based data science platform! Technically you can do stats on your mobile device (tablet or phone). The basic package is free. The underlying language is Python. You can import hundreds of modules like numpy (a numerical library for matrix algebra optimized in C), pandas (which helps folk familiar with R transition to Python), scipy (scientific library for data science work in Python), and matplotlib or seaborn (a visualization library for graphics). I use it for both undergraduates (stats) and graduates (psychometrics in which I teach elements of machine learning and AI in psychological testing) with no programming experience at our HBCU! Other programs/packages can‘t compete besides Google‘s cloud based data science platform, which is also Python-based. The website is www.anaconda.cloud; Python is very easy to learn. If you have trouble, type anaconda python and whatever statistical term in which you are interested in Google and you’ll get a satisfactory result with examples. I was able to learn the language to complete my dissertation in three months. I’m a psychologist with no formal training in computer science. I did a computer simulation and used Python to do it. There is no reason why everyone isn’t using it; it’s a gem. Multithreading modules are available if you are doing parallel processing or need heavy computational power. The interface is your browser; this is 21st century tech. Hands down it is anaconda/python. Get your free account, play with it on your desktop, tablet, or phone, and tell me what you think. I am confident that you won’t be disappointed.

Excel is also a useful tool for data analysis. Excel includes a variety of statistical functions that can help you make sense of your data. Some of the statistical utilities available in Excel include:

- Descriptive statistics: Excel can calculate basic statistics such as mean, median, mode, standard deviation, and variance for a set of data

- Regression analysis: You can use Excel to create linear, polynomial, and exponential regression models to analyze how two or more variables are related.

- Hypothesis testing: Excel can help you determine the probability of certain outcomes using statistical tests such as t-tests, ANOVA, and chi-squared tests.

- Sampling: You can use Excel to randomly sample data from a larger dataset to analyze a representative subset of the data.

María-Eva Vallejo-Pascual, Excel is a good spreadsheet, but it is not a stats package, and IMO, it should not be used for any kind of serious data analysis. Many blogs & articles have issued warnings about using it for that purpose. Here is one, for example:

• https://people.umass.edu/evagold/excel.html

HTH.

You may use XLSTAT add-on in Excel.

https://www.xlstat.com/en/

Good luck

I agree Bruce Weaver but the Excel option can be considered by Muhammad Syafiq Mohd Nasarudin in order to find a rapid solution in case of having an old computer.