- Matrix addition and multiplication
- Traces and determinants
- Eigenvalues and Eigenvectors
- Inverting and transposing
- Positive and negative definite
Creating a quality scale for a latent construct (a variable that cannot be directly measured with one variable) takes many steps. Structural Equation Modeling is set up well for this task.
One important step in creating scales is making sure the scale measures the latent construct equally well and the same way for different groups of individuals.
Most of the time when we plan a sample size for a data set, it’s based on obtaining reasonable statistical power for a key analysis of that data set. These power calculations figure out how big a sample you need so that a certain width of a confidence interval or p-value will coincide with a scientifically meaningful effect size.
But that’s not the only issue in sample size, and not every statistical analysis uses p-values.
Whenever you use a multi-item scale to measure a construct, a key step is to create a score for each subject in the data set.
This score is an estimate of the value of the latent construct (factor) the scale is measuring for each subject. In fact, calculating this score is the final step of running a Confirmatory Factor Analysis.
Based on questions I’ve been asked by clients, most analysts prefer using the factor analysis procedures in their general statistical software to run a confirmatory factor analysis.
While this can work in some situations, you’re losing out on some key information you’d get from a structural equation model. This article highlights one of these.
by Christos Giannoulis
Many data sets contain well over a thousand variables. Such complexity, the speed of contemporary desktop computers, and the ease of use of statistical analysis packages can encourage ill-directed analysis.
It is easy to generate a vast array of poor ‘results’ by throwing everything into your software and waiting to see what turns up. [Read more…] about How to Reduce the Number of Variables to Analyze