Structural Equation Modelling (SEM) increasingly is a ‘must’ for researchers in the social sciences and business analytics. However, the issue of how consistent the theoretical model is with the 
data, known as model fit, is by no means agreed upon: There is an abundance of fit indices available – and wide disparity in agreement on which indices to report and what the cut-offs for various indices actually are. (more…)
Karen Grace-Martin
Member Training: Model Fit Statistics in Structural Equation Modeling
December 1st, 2017 by Karen Grace-Martin
Impact of Covariance Terms on Random Slope Model
September 6th, 2017 by Karen Grace-Martin
In this video I will answer a question from a recent webinar, Random Intercept and Random Slope Models.
We are answering questions here because we had over 500 people live on the webinar so we didn’t have time to get through all the questions.
If you missed the webinar live, this and the other questions in this series may make more sense if you watch that first. It was part of our free webinar series, The Craft of Statistical Analysis, and you can sign up to get the free recording, handout, and data set at this link:
http://TheCraftofStatisticalAnalysis.com/random-intercept-random-slope-models
What Are Nested Models?
July 28th, 2017 by Karen Grace-Martin
Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.
This includes favorites like:
- All Generalized Linear Models, including logistic, probit, beta, Poisson, negative binomial regression
- Linear Mixed Models
- Generalized Linear Mixed Models
- Parametric Survival Analysis models, like Weibull models
- Structural Equation Models
That’s a lot of models.
If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require (more…)
Tricks for Using Word to Make Statistical Syntax Easier
March 13th, 2017 by Karen Grace-Martin
We’ve talked a lot around here about the reasons to use syntax — not only menus — in your statistical analyses.
Regardless of which software you use, the syntax file is pretty much always a text file. This is true for R, SPSS, SAS, Stata — just about all of them.
This is important because it means you can use an unlikely tool to help you code: Microsoft Word.
I know what you’re thinking. Word? Really?
Yep, it’s true. Essentially it’s because Word has much better Search-and-Replace options than your stat software’s editor.
Here are a couple features of Word’s search-and-replace that I use to help me code faster:
Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?
March 1st, 2017 by Karen Grace-Martin
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:
Differences Between the Normal and Poisson Distributions
December 23rd, 2016 by Karen Grace-Martin
The normal distribution is so ubiquitous in statistics that those of us who use a lot of statistics tend to forget it’s not always so common in actual data.
And since the normal distribution is continuous, many people describe all numerical variables as continuous. I get it: I’m guilty of using those terms interchangeably, too, but they’re not exactly the same.
Numerical variables can be either continuous or discrete.
The difference? Continuous variables can take any number within a range. Discrete variables can only take on specific values. For numeric discrete data, these are often, but don’t have to be, whole numbers*.
Count variables, as the name implies, are frequencies of some event or state. Number of arrests, fish (more…)