Random Factor

Specifying Fixed and Random Factors in Mixed Models

January 10th, 2022 by

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. (more…)


Multilevel, Hierarchical, and Mixed Models–Questions about Terminology

October 11th, 2019 by

Multilevel models and Mixed Models are generally the same thing. In our recent webinar on the basics of mixed models, Random Intercept and Random Slope Models, we had a number of questions about terminology that I’m going to answer here.

If you want to see the full recording of the webinar, get it here. It’s free.

Q: Is this different from multi-level modeling?

A: No. I don’t really know the history of why we have the different names, but the difference in multilevel modeling (more…)


The Difference Between Random Factors and Random Effects

January 9th, 2019 by

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…)


Mixed Models: Can you specify a predictor as both fixed and random?

February 16th, 2016 by

One of the most confusing things about mixed models arises from the way it’s coded in most statistical software.  Of the ones I’ve used, only HLM sets it up differently and so this doesn’t apply.

But for the rest of them—SPSS, SAS, R’s lme and lmer, and Stata, the basic syntax requires the same pieces of information.

1.       The dependent variable

2.       The predictor variables for which to calculate fixed effects and whether those (more…)


Confusing Statistical Terms #1: The Many Names of Independent Variables

November 24th, 2008 by

Statistical models, such as general linear models (linear regression, ANOVA, MANOVA), linear mixed models, and generalized linear models (logistic, Poisson, regression, etc.) all have the same general form.

On the left side of the equation is one or more response variables, Y. On the right hand side is one or more predictor variables, X, and their coefficients, B. The variables on the right hand side can have many forms and are called by many names.

There are subtle distinctions in the meanings of these names. Unfortunately, though, there are two practices that make them more confusing than they need to be.

First, they are often used interchangeably. So someone may use “predictor variable” and “independent variable” interchangably and another person may not. So the listener may be reading into the subtle distinctions that the speaker may not be implying.

Second, the same terms are used differently in different fields or research situations. So if you are an epidemiologist who does research on mostly observed variables, you probably have been trained with slightly different meanings to some of these terms than if you’re a psychologist who does experimental research.

Even worse, statistical software packages use different names for similar concepts, even among their own procedures. This quest for accuracy often renders confusion. (It’s hard enough without switching the words!).

Here are some common terms that all refer to a variable in a model that is proposed to affect or predict another variable.

I’ll give you the different definitions and implications, but it’s very likely that I’m missing some. If you see a term that means something different than you understand it, please add it to the comments. And please tell us which field you primarily work in.

Predictor Variable, Predictor

This is the most generic of the terms. There are no implications for being manipulated, observed, categorical, or numerical. It does not imply causality.

A predictor variable is simply used for explaining or predicting the value of the response variable. Used predominantly in regression.

Independent Variable

I’ve seen Independent Variable (IV) used different ways.

1. It implies causality: the independent variable affects the dependent variable. This usage is predominant in ANOVA models where the Independent Variable is manipulated by the experimenter. If it is manipulated, it’s generally categorical and subjects are randomly assigned to conditions.

2. It does not imply causality, but it is a key predictor variable for answering the research question. In other words, it is in the model because the researcher is interested in understanding its relationship with the dependent variable. In other words, it’s not a control variable.

3. It does not imply causality or the importance of the variable to the research question. But it is uncorrelated (independent) of all other predictors.

Honestly, I only recently saw someone define the term Independent Variable this way. Predictor Variables cannot be independent variables if they are at all correlated. It surprised me, but it’s good to know that some people mean this when they use the term.

Explanatory Variable

A predictor variable in a model where the main point is not to predict the response variable, but to explain a relationship between X and Y.

Control Variable

A predictor variable that could be related to or affecting the dependent variable, but not really of interest to the research question.

Covariate

Generally a continuous predictor variable. Used in both ANCOVA (analysis of covariance) and regression. Some people use this to refer to all predictor variables in regression, but it really means continuous predictors. Adding a covariate to ANOVA (analysis of variance) turns it into ANCOVA (analysis of covariance).

Sometimes covariate implies that the variable is a control variable (as opposed to an independent variable), but not always.

And sometimes people use covariate to mean control variable, either numerical or categorical.

This one is so confusing it got it’s own Confusing Statistical Terms article.

Confounding Variable, Confounder

These terms are used differently in different fields. In experimental design, it’s used to mean a variable whose effect cannot be distinguished from the effect of an independent variable.

In observational fields, it’s used to mean one of two situations. The first is a variable that is so correlated with an independent variable that it’s difficult to separate out their effects on the response variable. The second is a variable that causes the independent variable’s effect on the response.

The distinction in those interpretations are slight but important.

Exposure Variable

This is a term for independent variable in some fields, particularly epidemiology. It’s the key predictor variable.

Risk Factor

Another epidemiology term for a predictor variable. Unlike the term “Factor” listed below, it does not imply a categorical variable.

Factor

A categorical predictor variable. It may or may not indicate a cause/effect relationship with the response variable (this depends on the study design, not the analysis).

Independent variables in ANOVA are almost always called factors. In regression, they are often referred to as indicator variables, categorical predictors, or dummy variables. They are all the same thing in this context.

Also, please note that Factor has completely other meanings in statistics, so it too got its own Confusing Statistical Terms article.

Feature

Used in Machine Learning and Predictive models, this is simply a predictor variable.

Grouping Variable

Same as a factor.

Fixed factor

A categorical predictor variable in which the specific values of the categories are intentional and important, often chosen by the experimenter. Examples include experimental treatments or demographic categories, such as sex and race.

If you’re not doing a mixed model (and you should know if you are), all your factors are fixed factors. For a more thorough explanation of fixed and random factors, see Specifying Fixed and Random Factors in Mixed or Multi-Level Models

Random factor

A categorical predictor variable in which the specific values of the categories were randomly assigned. Generally used in mixed modeling. Examples include subjects or random blocks.

For a more thorough explanation of fixed and random factors, see Specifying Fixed and Random Factors in Mixed or Multi-Level Models

Blocking variable

This term is generally used in experimental design, but I’ve also seen it in randomized controlled trials.

A blocking variable is a variable that indicates an experimental block: a cluster or experimental unit that restricts complete randomization and that often results in similar response values among members of the block.

Blocking variables can be either fixed or random factors. They are never continuous.

Dummy variable

A categorical variable that has been dummy coded. Dummy coding (also called indicator coding) is usually used in regression models, but not ANOVA. A dummy variable can have only two values: 0 and 1. When a categorical variable has more than two values, it is recoded into multiple dummy variables.

Indicator variable

Same as dummy variable.

The Take Away Message

Whenever you’re using technical terms in a report, an article, or a conversation, it’s always a good idea to define your terms. This is especially important in statistics, which is used in many, many fields, each of whom adds their own subtleties to the terminology.

 

Confusing Statistical Terms Series

Confusing Statistical Terms #1: The Many Names of Independent Variables

Confusing Statistical Terms #2: Alpha and Beta

Confusing Statistical Terms #3: Levels

Confusing Statistical Term #4: Hierarchical Regression vs. Hierarchical Model

Confusing Statistical Term #5: Covariate

Confusing Statistical Term #6: Factor

Confusing Statistical Term #7: GLM