Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.

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# Mixed and Multilevel Models

## Member Training: Translating Between Multilevel and Mixed Models

Multilevel and Mixed models are essentially the same analysis. But they use different vocabulary, different notation, and approach the analysis considerations in different ways.

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## An Example of Specifying Within-Subjects Factors in Repeated Measures

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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## Measures of Model Fit for Linear Regression Models

A well-fitting regression model results in predicted values close to the observed data values.

The mean model, which uses the mean for every predicted value, generally would be used if there were no useful predictor variables. The fit of a proposed

regression model should therefore be better than the fit of the mean model. [Read more…] about Measures of Model Fit for Linear Regression Models

## Specifying Fixed and Random Factors in Mixed Models

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.

## Member Training: Matrix Algebra for Data Analysts: A Primer

If you’ve been doing data analysis for very long, you’ve certainly come across terms, concepts, and processes of matrix algebra. Not just matrices, but:

- Matrix addition and multiplication
- Traces and determinants
- Eigenvalues and Eigenvectors
- Inverting and transposing
- Positive and negative definite

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