As a data analyst, you will occasionally need to know how your software package is calculating the statistics.
SPSS makes the algorithms for many of its tests available at:
IBM SPSS Documentation
> Here it is for SPSS 28
Don’t expect them to be user-friendly if you’re not a statistician–these are the actual equations SPSS is using. But some have more detailed explanations than others, and sometimes you just need to make sure that the equation that SPSS is using is indeed the same one that your nicely detailed text is so nicely describing. This can be really useful when there are different versions of a test.
If you are a SPSS, SAS, or Stata user who finds yourself needing to use R (I mean, it’s free), I just found this great website: http://statmethods.net/index.html.
A new version of Amelia II, a free package for multiple imputation, has just been released today. Amelia II is available in two versions. One is part of R, and the other, AmeliaView, is a GUI package that does not require any knowledge of the R programming language. They both use the same underlying algorithms and both require having R installed.
At the Amelia II website, you can download Amelia II (did I mention it’s free?!), download R, get the very useful User’s Guide, join the Amelia listserve, and get information about multiple imputation.
If you want to learn more about multiple imputation:
I was recently asked about whether it’s okay to treat a likert scale as continuous as a predictor in a regression model. Here’s my reply. In the question, the researcher asked about logistic regression, but the same answer applies to all regression models.
1. There is a difference between a likert scale item (a single 1-7 scale, eg.) and a full likert scale , which is composed of multiple items. If it is a full likert scale, with a combination of multiple items, go ahead and treat it as numerical. (more…)
Regression models are just a subset of the General Linear Model, so you can use GLM procedures to run regressions. It is what I usually use.
But in SPSS there are options available in the GLM and Regression procedures that aren’t available in the other. How do you decide when to use GLM and when to use Regression?
GLM has these options that Regression doesn’t: (more…)
I’m sure I don’t need to explain to you all the problems that occur as a result of missing data. Anyone who has dealt with missing data—that means everyone who has ever worked with real data—knows about the loss of power and sample size, and the potential bias in your data that comes with listwise deletion.
Listwise deletion is the default method for dealing with missing data in most statistical software packages. It simply means excluding from the analysis any cases with data missing on any variables involved in the analysis.
A very simple, and in many ways appealing, method devised to (more…)
