mixed model

Member Training: Power Analysis and Sample Size Determination Using Simulation

July 30th, 2018 by
This webinar will show you strategies and steps for using simulations to estimate sample size and power. You will learn:
  • A review of basic concepts of statistical power and effect size
  • A simulation-based approach to power analysis
  • An overview of how to implement simulations in various popular software programs.

Six Differences Between Repeated Measures ANOVA and Linear Mixed Models

January 22nd, 2018 by

As mixed models are becoming more widespread, there is a lot of confusion about when to use these more flexible but complicated models and when to use the much simpler and easier-to-understand repeated measures ANOVA.

One thing that makes the decision harder is sometimes the results are exactly the same from the two models and sometimes the results are (more…)

Member Training: Crossed and Nested Factors

May 1st, 2017 by

We often talk about nested factors in mixed models — students nested in classes, observations nested within subject.

But in all but the simplest designs, it’s not that straightforward. (more…)

Examples for Writing up Results of Mixed Models

September 12th, 2014 by

One question I always get in my Repeated Measures Workshop is:

“Okay, now that I understand how to run a linear mixed model for my study, how do I write up the results?”

This is a great question.

There are many pieces of the linear mixed models output that are identical to those of any linear model–regression coefficients, F tests, means.

But there is also a lot that is new, like intraclass correlations and (more…)

The Intraclass Correlation Coefficient in Mixed Models

August 22nd, 2013 by

The ICC, or Intraclass Correlation Coefficient, can be very useful in many statistical situations, but especially so in Linear Mixed Models.

Linear Mixed Models are used when there is some sort of clustering in the data.

Two common examples of clustered data include:

  • individuals were sampled within sites (hospitals, companies, community centers, schools, etc.). The site is the cluster.
  • repeated measures or longitudinal data where multiple observations are collected from the same individual. The individual is the cluster in which multiple observations are (more…)

The Repeated and Random Statements in Mixed Models for Repeated Measures

September 30th, 2011 by

“Because mixed models are more complex and more flexible than the general linear model, the potential for confusion and errors is higher.”

– Hamer & Simpson (2005)

Linear Mixed Models, as implemented in SAS’s Proc Mixed, SPSS Mixed, R’s LMER, and Stata’s xtmixed, are an extension of the general linear model.  They use more sophisticated techniques for estimation of parameters (means, variances, regression coefficients, and standard errors), and as the quotation says, are much more flexible.

Here’s one example of the flexibility of mixed models, and its resulting potential for confusion and error. (more…)