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Intraclass Correlation Coefficient

Member Training: Inter-Rater Reliability

by guest contributer  Leave a Comment

In many fields, the only way to measure a construct of interest is to have someone produce ratings:

  • radiologists’ ratings of disease presence or absence on an X-ray
  • researchers rate the amount of bullying occurring in an observed classroom
  • coders sort qualitative responses into different response categories

It’s well established in research that multiple raters need to rate the same stimuli to ensure ratings are accurate.  There are a number of ways to measure the agreement among raters using measures of reliability. These differ depending on a host of details, including: the number of raters; whether ratings are nominal, ordinal, or numerical; and whether one rating can be considered a “Gold Standard.”

In this webinar, we will discuss these and other issues in measures of inter and intra rater reliability, the many variations of the Kappa statistic, and Intraclass correlations.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Not a Member? Join!

About the Instructor

Audrey Schnell is a statistical consultant and trainer at The Analysis Factor.

Audrey first realized her love for research and, in particular, data analysis in a career move from clinical psychology to research in dementia. As the field of genetic epidemiology and statistical genetics blossomed, Audrey moved into this emerging field and analyzed data on a wide variety of common diseases believed to have a strong genetic component including hypertension, diabetes and psychiatric disorders. She helped develop software to analyze genetic data and taught classes in the US and Europe.

Audrey has worked for Case Western Reserve University, Cedars-Sinai, University of California at San Francisco and Johns Hopkins. Audrey has a Master’s Degree in Clinical Psychology and a Ph.D. in Epidemiology and Biostatistics.

Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

Tagged With: consistent, correlations, instrument, intra-rater, Intraclass Correlation Coefficient, Kappa statistic, raters, ratings, reliability

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The Intraclass Correlation Coefficient in Mixed Models

by Karen Grace-Martin  22 Comments

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 [Read more…] about The Intraclass Correlation Coefficient in Mixed Models

Tagged With: Intraclass Correlation Coefficient, mixed model

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Three Issues in Sample Size Estimates for Multilevel Models

by Karen Grace-Martin  4 Comments

If you’ve ever worked with multilevel models, you know that they are an extension of linear models. For a researcher learning them, this is both good and bad news.

The good side is that many of the concepts, calculations, and results are familiar. The down side of the extension is that everything is more complicated in multilevel models.

This includes power and sample size calculations. [Read more…] about Three Issues in Sample Size Estimates for Multilevel Models

Tagged With: Intraclass Correlation Coefficient, multilevel model, Sample Size Calculations

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