Confusing Statistical Terms #3: Levels of a Factor in Multilevel Models Measured at a Nominal Level

by Karen Grace-Martin


It struck me today in answering a question that statisticians have not been very helpful to those trying to learn statistics in the way they name statistical terms.

I can think of other examples (how many totally different concepts does alpha refer to in statistics?), but the term I was using today was levels.

Specifically, there are Multilevel models with two or more sources of random variation.  A two level model has two sources of random variation, and can have predictors at each level.  A common example is where students are sampled within schools.  Predictors can be measured at the student level (eg. gender, SES, age) or the school level (enrollment, % who go on to college).  The dependent variable has variation from student to student (level 1) and from school to school (level 2).

If a predictor is a fixed factor (meaning it is a categorical predictor), it can have two or more levels, meaning categories.  In ANOVA, factors (categorical independent variables) have 2 or more levels (2 or more categories).

Then we get to levels of measurement: nominal, ordinal, interval, ratio.  These levels refer to how much information a variable contains.  Does it indicate a category, indicate a quantity, etc?

So, a factor with 3 levels that is measured at level 2 of a model has a nominal level of measurement.

Confusing Statistical Terms #1: Independent Variable

Confusing Statistical Terms #2: Alpha and Beta

{ 2 comments… read them below or add one }

abu asare September 3, 2014 at 11:59 am

i am beginning to appreciate the note


abu asare September 3, 2014 at 11:53 am

i don’t understand


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