I was trying to run a set of statistical analyses on climatic variables that include temperature and precipitation on a monthly time scale

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

My overall experience with SPSS has been great and I strongly recommend it. It is very useful in market research and business analytics industry as most research projects deal with descriptive and multivariate statistical approach. I have been using SPSS for last ten years and it has provided very good functionality with each update and version. Overall I highly recommend this software package.

Very easy to use. High level functionality and easy to perform mechanisms. Fine way of operations. Can run anything from basic descriptive statistics to high level simulations. Easy to import data in different formats (csv, xls, sav, etc.). Data cleaning process are easily executed which includes missing value imputation, data recoding, and data restructuring. Very functional in running multivariate methods like exploratory factor analysis, ANOVA, linear regression, logistic regression, discriminant analysis, and MANCOVA. Most operations are done through interactive dashboard, which is hassle free. Overall it is a great statistical package to run different analysis at a very affordable price.

It is tedious to do multiple iterations using SPSS. Syntax functionality is low and lines of codes become exhaustive when you run monte carlo simulations. The most challenging aspect is to run simulations with large data sets. The support provided by the support team doesn't extend beyond technical support. SPSS requires add ons for running the simulations which cost extra to the users. Overall it is deficient in running certain simulations.

It has been good so far

What I like most is; it comes with Python IDE

I can't perform most operational research analysis with it

It provides fantastic choices for the graphical display. I am happy to use this software for our social research purpose.

IBM SPSS offers a wide variety of extremely flexible factual models, with the general direct model and a number of calculated relapse models standing out. They enable me to have a single piece of software that can essentially handle all of our information-research requirements. A substantial chunk of the competing factual programming products, including as R, SAS, and Stata, rely heavily on programming languages as their primary feature. Even while learning a programming language takes time, there are some significant benefits. Additionally, the complexity frequently deters me from trying with fresh and out-of-the-box strategies. Before starting our information inquiry, I need to see how our information behaves. The use of graphics to do this is preferred. Using the scatterplots, boxplots, and histograms in IBM SPSS, I can see examples in our data.

The cost of obtaining SPSS may be prohibitive for some users. In order to properly exploit the abundance of available elements, further planning is typically required. The highlights of the chart are not quite as simple as those of Microsoft Excel. Information on impact size and certainty spans is lacking for some approaches.