How do you know your variables are measuring what you think they are? And how do you know they’re doing it well?
The last, and sometimes hardest, step for running any statistical model is writing up results.
As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.
Any good statistical report includes enough information that someone else could replicate your results with your data.
After you are done with the odyssey of exploratory factor analysis (aka a reliable and valid instrument)…you may find yourself at the beginning of a journey rather than the ending.
The process of performing exploratory factor analysis usually seeks to answer whether a given set of items form a coherent factor (or often several factors). If you decide on the number and type of factors, the next step is to evaluate how well those factors are measured.
Some variables are straightforward to measure without error – blood pressure, number of arrests, whether someone knew a word in a second language.
But many – perhaps most – are not. Whenever a measurement has a potential for error, a key criterion for the soundness of that measurement is reliability.
Think of reliability as consistency or repeatability in measurements. (more…)
There are many rules of thumb in statistical analysis that make decision making and understanding results much easier.
Have you ever stopped to wonder where these rules came from, let alone if there is any scientific basis for them? Is there logic behind these rules, or is it propagation of urban legends?
In this webinar, we’ll explore and question the origins, justifications, and some of the most common rules of thumb in statistical analysis, like:
There are two main types of factor analysis: exploratory and confirmatory. Exploratory factor analysis (EFA) is data driven, such that the collected data determines the resulting factors. 
Confirmatory factor analysis (CFA) is used to test factors that have been developed a priori.
Think of CFA as a process for testing what you already think you know.
CFA is an integral part of structural equation modeling (SEM) and path analysis. The hypothesized factors should always be validated with CFA in a measurement model prior to incorporating them into a path or structural model. Because… garbage in, garbage out.
CFA is also a useful tool in checking the reliability of a measurement tool with a new population of subjects, or to further refine an instrument which is already in use.
Elaine will provide an overview of CFA. She will also (more…)