Cai Yun: Software in Psychology (For Statistical Analysis)

Throughout our study in psychology, many of us undergraduates may find ourselves exposed to very few softwares for statistical analysis. This is in part because most courses teach the orthodox software that is used by many in the field (yes, that software from IBM).

However, there are several alternatives that we can utilize--each with its pros and cons--and you can consider learning a statistical package that is more suited to your needs and the types of statistical analyses that you use more frequently (and also your budget). This post will give a brief introduction to the different types of software for statistical analysis that may be applicable to individuals in the psychology field.


In most undergraduate psychology courses, SPSS is the mainstream software for statistical analyses. One of the major benefits of SPSS is that it has a simple and intuitive user interface which allows the user to easily select the desired analysis and ‘drag-and-drop’ the desired variables into the relevant boxes: 

Compared to software such as R, SPSS does not require any prior knowledge on coding (although syntax is also available in SPSS), making it much easier to learn. Unfortunately, SPSS is a paid software. Furthermore, conducting analyses such as structural equation modeling requires you to purchase an extension package (i.e., you need to pay more).


Apart from the uncanny resemblance in their names, SPSS and PSPP have a very similar user interface, namely, it is easy to navigate and the analyses can be done with a couple of clicks. The main difference--as the creators of PSPP put it--is that there are no “time bombs” and the software will not expire; it is free.
 (This looks oddly familiar…)

However, PSPP has fewer statistical capabilities than SPSS. For those who do not require the more advanced statistical analyses, PSPP may be a more economical alternative. Visit their website to find out more about PSPP:


JASP is yet another free software for statistical analysis. There are a couple of benefits (and disadvantages) for using JASP as compared to SPSS, as emphasized on their website (
Some of the notable benefits include a much more user-friendly interface where the data and variable view are combined; APA-formatted tables and figures; and the ability to conduct Bayesian analyses as well as meta-analysis.

Forest plot for meta-analysis in JASP


As mentioned in our other post (What is R?), R is a language and environment for statistical computing and graphics. Apart from being free, R is very extensive--covering a wide range of statistical analyses that simply requires you to install the necessary packages to run (like structural equation modeling… but for free).

Although R may be slightly difficult to learn for those without programming background, it is a useful software to pick up due to its utility and widespread use. There are also many other uses for R, like creating web apps with the Shiny package. In sum, R is a more holistic software that serves multiple purposes, but if you find that you only require the basic analyses or functions that are provided by other softwares, then perhaps you may not want to expend your time in learning R.

So which software should I use?!

While SPSS is still the main software taught in many undergraduate courses, it may not be readily available after you graduate and enter the workforce (depending on whether your company is willing to pay for it). If you are looking to run basic analyses, picking up PSPP or JASP may be a good alternative. On the other hand, R is difficult to pick up but is more versatile and is useful even outside of the psychology field.

Do note that this list is not exhaustive and that there are other software you could learn to analyse your data (e.g., SAS, Stata); these are simply the more economically-friendly ones that are more applicable to psychology undergraduates.