Crimes of Statistics: Longitudinal Studies or Repeated Measures – What are the implications?

What is a longitudinal or repeated measures study?

Let’s take a little step back first and recall the conversation we had back in the Fall semester  – experimental unit – the unit to which the treatment is applied to.  This is a VERY crucial concept and definition when we talk about a repeated measures study.

Like the term says repeated measures, the researcher is taking the same measurements on “some unit” repeatedly.  We often think of this in terms of time.  I’m going to take weight measures or height measures every month during the summer growing period.  The question that needs to be answered is “What” unit?  Is it the same experimental unit?  If yes, then we have a classic repeated measures study.  If no, then we have reps.

Longitudinal study is a term often used in the social sciences.  We tend to think of a longitudinal study – again in terms of time – and usually in the context of a longitudinal survey.  The experimental units, in this case, are the survey respondents, and they will be answering the same survey several times in a year or across many years.

Bottonline, a longitudinal or repeated measures study is a study where the experimental unit is measured more than once.

Examples of longitudinal or repeated measures study

  • An educational survey where students answer the survey after high school, after their 1st year of University, 2nd year, 3rd year, and after graduating
  • A dairy lactation study, where the same cows in a herd are milked and measured each day during their first 3 lactations.
  • A new diet trial, where feed consumed by dogs is measured every day for a 21 day trial
  • A new herbicide trial, where plots in a field are measured every week for weed counts
  • A soil texture trial, where texture is measured at 4 depths of a soil core.

Challenges with a longitudinal or repeated measures study

The goal of many studies is to examine or determine whether differences exist between treatments of interest in the study.  We gather our data and conduct the statistical analysis to look at the variation between our treatments in that study.  When we enter our data, chances are we will enter an observation every time we take a measurement.  For example, if we have 20 dogs on our trial and we are measuring their feed intake for 21 days, we will have 420 lines of data.  OR we may have a dataset that has 20 lines, with each line containing 21 measures for each dog.  Either way, we have 21 measurements for each experimental unit.  The big challenge of a repeated measures analysis is to recognize that the variation within the experimental unit, dog in this example, needs to be accounted for, before looking at the differences between the treatments, diets in this case.

If I use my data cloud visual to try and explain.  We have 420 measures in our experiment – let’s throw this data up and think of it as a big cloud of data.  With our analysis, the goal is to partition that cloud into the treatment groups and hopefully be able to see distinct treatment groups.  However, we have 21 measurements for each dog and we want to ensure that when we start to look at treatments effects, that we keep those 21 measures for the dog together as a unit.  Remember we only have 20 experimental units and that’s where we should be concentrating when we look for treatment effects.  We do NOT have 420 experimental units!

No matter what statistical software package you use, there will be options to identify your experimental unit!  You need to find it.

Can you think of trials or studies that you have done in the past or will be doing in the future, is it a longitudinal or repeated measures study?

Questions to ask to help you determine if you have a longitudinal or repeated measures study:

  1. What is your experimental unit?
  2. Is your experimental unit being measured more than once?


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