In this talk, you will see a simple example of this using fruit fly data, and learn how to interpret the Kaplan-Meier curve to estimate survival probabilities and survival percentiles..

Like logistic and Poisson regression, beta regression is a type of generalized linear model. It works nicely for proportion data because the values of a variable with a beta distribution must fall between 0 and 1. It's a bit of a funky distribution in that it's shape can change a lot depending on the values of the mean and dispersion parameters. Here are a few examples of the possible shapes of a beta distribution, with different means and variances...

By Manolo Romero Escobar If you already know the principles of general linear modeling (GLM) you are on the right path to understand Structural Equation Modeling (SEM). As you could see from my previous post, SEM offers the flexibility of adding paths between predictors in a way that would take you several GLM models and […]

If any of these fail, it’s nearly impossible to get normally distributed residuals, even with remedial transformations. Types of variables that will generally fail these criteria include: Categorical Variables, both nominal and ordinal. Count Variables, which are often distributed as Poisson or Negative Binomial.

So we infer these constructs, which are unobserved, hidden, or latent, from the data we collect on related variables we can observe and directly measure. Latent refers to the fact that even though these variables were not measured directly in the research design they are the ultimate goal of the project..

How do we determine if raters are consistent in their ratings or if a new instrument performs as well as the established ‘Gold Standard’?

One common reason for running Principal Component Analysis (PCA) or Factor Analysis (FA) is variable reduction. In other words, you may start with a 10-item scale meant to measure something like Anxiety, which is difficult to accurately measure with a single question. You could use all 10 items as individual variables in an analysis--perhaps as predictors in a regression model. But you'd end up with a mess..

A data set can contain indicator (dummy) variables, categorical variables and/or both. Initially, it all depends upon how the data is coded as to which variable type it is. For example, a categorical variable like marital status could be coded in the data set as a single variable with 5 values...

One of the most confusing things about mixed models arises from the way it’s coded in most statistical software.  Of the ones I’ve used, only HLM sets it up differently and so this doesn’t apply. But for the rest of them—SPSS, SAS, R’s lme and lmer, and Stata, the basic syntax requires the same pieces […]

The webinar presented by Yasamin Miller will cover broadly survey design and planning. It will outline the advantages and disadvantages of the various data collection modes, types of samples available to target your population, how to obtain a representative sample, and how to avoid the pitfalls of bad questionnaire design..