Some repeated measures designs make it quite challenging to specify within-subjects factors. Especially difficult is when the design contains two “levels” of repeat, but your interest is in testing just one.
Let’s look at a great example of what this looks like and how to deal with it in this question from a reader :
The Design:
I want to do a GLM (repeated measures ANOVA) with the valence of some actions of my test-subjects (valence = desirability of actions) as a within-subject factor. My subjects have to rate a number of actions/behaviours in a pre-set list of 20 actions from ‘very likely to do’ to ‘will never do this’ on a scale from 1 to 7, and some of these actions are desirable (e.g. help a blind man crossing the street) and therefore have a positive valence (in psychology) and some others are non-desirable (e.g. play loud music at night) and therefore have negative valence in psychology.
My question is how I can use valence as a within-subjects factor in GLM. Is there a way to tell SPSS some actions have positive valence and others have negative valence ? I assume assigning labels to the actions will not do it, as SPSS does not make analyses based on labels …
Please help. Thank you.
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One of the difficult decisions in mixed modeling is deciding which factors are fixed and which are random. And as difficult as it is, it’s also very important. Correctly specifying the fixed and random factors of the model is vital to obtain accurate analyses.
Now, you may be thinking of the fixed and random effects in the model, rather than the factors themselves, as fixed or random. If so, remember that each term in the model (factor, covariate, interaction or other multiplicative term) has an effect. We’ll come back to how the model measures the effects for fixed and random factors.
Sadly, the definitions in many texts don’t help much with decisions to specify factors as fixed or random. Textbook examples are often artificial and hard to apply to the real, messy data you’re working with.
Here’s the real kicker. The same factor can often be fixed or random, depending on the researcher’s objective.
That’s right, there isn’t always a right or a wrong decision here. It depends on the inferences you want to make about this factor. This article outlines a different way to think about fixed and random factors.
An Example
Consider an experiment that examines beetle damage on cucumbers. The researcher replicates the experiment at five farms and on four fields at each farm.
There are two varieties of cucumbers, and the researcher measures beetle damage on each of 50 plants at the end of the season. The researcher wants to measure differences in how much damage the two varieties sustain.
The experiment then has the following factors: VARIETY, FARM, and FIELD.
Fixed factors can be thought of in terms of differences.
The effect of a categorical fixed factor is measured by differences from the overall mean.
The effect of a continuous fixed predictor (usually called a covariate) is defined by its slope–how the mean of the dependent variable differs with differing values of the predictor.
The output for fixed predictors, then, gives estimates for mean-differences or slopes.
Conclusions regarding fixed factors are particular to the values of these factors. For example, if one variety of cucumber has significantly less damage than the other, you can make conclusions about these two varieties. This says nothing about cucumber varieties that were not tested.
Random factors, on the other hand, are defined by a distribution and not by differences.
The values of a random factor are assumed to be chosen from a population with a normal distribution with a certain variance.
The output for a random factor is an estimate of this variance and not a set of differences from a mean. Effects of random factors are measured in terms of variance, not mean differences.
For example, we may find that the variance among fields makes up a certain percentage of the overall variance in beetle damage. But we’re not measuring how much higher the beetle damage is in one field compared to another. We only care that they vary, not how much they differ.
Software specification of fixed and random factors
Before we get into specifying factors as fixed and random, please note:
When you’re specifying random effects in software, like intercepts and slopes, the random factor is itself the Subject variable in your software. This differs in different software procedures. Some specifically ask for code like “Subject=FARM.” Others just put FARM after a || or some other indicator of the subject for which random intercepts and slopes are measured.
Some software, like SAS and SPSS, allow you to specify the random factors two ways. One is to list the random effects (like intercept) along with the random factor listed as a subject.
/Random Intercept | Subject(Farm)
The other is to list the random factors themselves.
/Random Farm
Both of those give you the same output.
Other software, like R and Stata, only allow the former. And neither uses the term “Subject” anywhere. But that random factor, the subject, still needs to be specified after the | or || in the random part of the model.
Specifying fixed effects is pretty straightforward.
Situations that indicate a factor should be specified as fixed:
1. The factor is the primary treatment that you want to compare.
In our example, VARIETY is definitely fixed as the researcher wants to compare the mean beetle damage on the two varieties.
2. The factor is a secondary control variable, and you want to control for differences in the specific values of this factor.
Say the researcher chose these farms specifically for some feature they had, such as specific soil types or topographies that may affect beetle damage. If the researcher wants to compare the farms as representatives of those soil types, then FARM should be fixed.
3. The factor has only two values.
Even if everything else indicates that a factor should be random, if it has only two values, the variance cannot be calculated, and it should be fixed.
Situations that indicate random factors:
1. Your interest is in quantifying how much of the overall variance to attribute to this factor.
If you want to know how much of the variation in beetle damage you can attribute to the farm at which the damage took place, FARM would be random.
2. Your interest is not in knowing which specific means differ, but you want to account for the variation in this factor.
If the farms were chosen at random, FARM should be random.
This choice of the specific farms involved in the study is key. If you can rerun the study using different specific farms–different values of the Farm factor–and still be able to draw the same conclusions, then Farm should be random. However, if you want to compare or control for these particular farms, then Farm is a fixed factor.
3. You would like to generalize the conclusions about this factor to the whole population.
There is nothing about comparing these specific fields that is of interest to the researcher. Rather, the researcher wants to generalize the results of this experiment to all fields, so FIELD is random.
4. Any interaction with a random factor is also random.
How the factors of a model are specified can have great influence on the results of the analysis and on the conclusions drawn.
Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.
When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).
So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.
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Mixed models are hard.
They’re abstract, they’re a little weird, and there is not a common vocabulary or notation for them.
But they’re also extremely important to understand because many data sets require their use.
Repeated measures ANOVA has too many limitations. It just doesn’t cut it any more.
One of the most difficult parts of fitting mixed models is figuring out which random effects to include in a model. And that’s hard to do if you don’t really understand what a random effect is or how it differs from a fixed effect. (more…)
I have recently worked with two clients who were running generalized linear mixed models in SPSS.
Both had repeated measures experiments with a binary outcome.
The details of the designs were quite different, of course. But both had pretty complicated combinations of within-subjects factors.
Fortunately, both clients are intelligent, have a good background in statistical modeling, and are willing to do the work to learn how to do this. So in both cases, we made a lot of progress in just a couple meetings.
I found it interesting, through, that both were getting stuck on the same subtle point. It’s the same point I was missing for a long time in my own learning of mixed models.
Once I finally got it, a huge light bulb turned on. (more…)
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…)
