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.
SPSS
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).
PSPP
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.
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: https://www.gnu.org/software/pspp/
JASP
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 (https://jasp-stats.org/2017/11/01/jasp-vs-spss/).
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
R
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.
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