Hello,

I use SPSS for the quantitative research. It will also depend on the nature and scope of your research objective. However, these two tools complement each other.

I like Yeshi's response. However, I like STATA for quant work.

As i am studying R software, i prefer R

To give a scientific answer that is related to data analysis and assist in calculation of heteroskedasticity test such as the Durbin Watson test aka autocorrelation test and other tests such as the F test and Breusch Pagan test that are used to verify the reliability and validity of data that are used for generalization of findings.

I use both software because the choice completely depends on your needs. SPSS is ideal for exploratory and descriptive analysis, examining simple relationships (using tests like t-tests and ANOVA), and data management.

On the other hand, SmartPLS is the preferred choice for working with complex theoretical models, conducting predictive research, and analysing non-normal data or data from small sample sizes.

If you are a beginner, it is best to start with SPSS to learn the fundamentals of data analysis before moving on to more advanced modelling with SmartPLS.

Radha Thangarajan It depends on your specific analysis needs in quantitative social science research, but if I had to choose one, I’d go with SPSS for its broad versatility in handling descriptive statistics, regressions, ANOVA, and factor analysis common in social sciences with an intuitive interface for beginners. SmartPLS excels specifically for PLS-SEM (e.g., with latent variables, small samples, or non-normal data), but for general-purpose work without complex modeling, SPSS is more efficient and widely supported.

Hi Radha Thangarajan As in previous answers, it depends on your needs because the two software offer different statistical methods. SmartPLS is a good option if you need multivariate analysis methods, such as PLS-SEM, CB-SEM, CFA, GSCA, regression, logistic regression and necessary condition analysis. SPPS instead offers a wider range of methods for descriptive and inferential statistics.