Why use factor analysis?
Factor analysis is a useful tool for investigating variable relationships for complex concepts such as socioeconomic status, dietary patterns, or 
psychological scales.
It allows researchers to investigate concepts they cannot measure directly. It does this by using a large number of variables to esimate a few interpretable underlying factors.
What is a factor?
The key concept of factor analysis is that multiple observed variables have similar patterns of responses because they are all associated with a latent variable (i.e. not directly measured). (more…)
Like some of the other terms in our list–level and beta–GLM has two different meanings.
It’s a little different than the others, though, because it’s an abbreviation for two different terms:
General Linear Model and Generalized Linear Model.
It’s extra confusing because their names are so similar on top of having the same abbreviation.
And, oh yeah, Generalized Linear Models are an extension of General Linear Models.
And neither should be confused with Generalized Linear Mixed Models, abbreviated GLMM.
Naturally. (more…)
Before you run a Cronbach’s alpha or factor analysis on scale items, it’s generally a good idea to reverse code items that are negatively worded so that a high value indicates the same type of response on every item.
So for example let’s say you have 20 items each on a 1 to 7 scale. For most items, a 7 may indicate a positive attitude toward some issue, but for a few items, a 1 indicates a positive attitude.
I want to show you a very quick and easy way to reverse code them using a single command line. This works in any software. (more…)
If you’ve ever done any sort of repeated measures analysis or mixed models, you’ve probably heard of the unstructured covariance matrix. They can be extremely useful, but they can also blow up a model if not used appropriately. In this article I will investigate some situations when they work well and some when they don’t work at all.
The Unstructured Covariance Matrix
The easiest to understand, but most complex to estimate, type of covariance matrix is called an unstructured matrix. Unstructured means you’re not imposing any constraints on the values. For example, if we had a good theoretical justification that all variances were equal, we could impose that constraint and have to only estimate one variance value for every variance in the table.
But in an unstructured covariance matrix there are no constraints. Each (more…)
by Ritu Narayan
Sampling is a critical issue in any research study design. Most of us have grappled with balancing costs, time and of course, statistical power when deciding our sampling strategies.
How do we know when to go for a simple random sample or to go for stratification or for clustering? Let’s talk about stratified sampling here and one research scenario when it is useful.
One Scenario for Stratified Sampling
Suppose you are studying minority groups and their behavior, say Yiddish speakers in the U.S. and their voting. Yiddish speakers are a small subset of the US population, just .6%. (more…)
Odds ratios are one of those concepts in statistics that are just really hard to wrap your head around. Although probability and odds both measure how likely it is that something will occur, probability is just so much easier to understand for most of us.
I’m not sure if it’s just a more intuitive concepts, or if it’s something were just taught so much earlier so that it’s more ingrained. In either case, without a lot of practice, most people won’t have an immediate understanding of how likely something is if it’s communicated through odds.
So why not always use probability? (more…)
psychological scales.