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Member Training: Confusing Statistical Terms

by guest contributer 

Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing.

Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics.  [Read more…] about Member Training: Confusing Statistical Terms

Tagged With: ancova, association, confounding variable, confusing statistical terms, Correlation, Covariate, dependent variable, Error, factor, General Linear Model, generalized linear models, independent variable, learning statistics, levels, listwise deletion, multivariate, odds, pairwise deletion, random error, selection bias, significant

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  • Series on Confusing Statistical Terms
  • Six terms that mean something different statistically and colloquially
  • Confusing Statistical Term #8: Odds
  • The Difference Between Association and Correlation

In Factor Analysis, How Do We Decide Whether to Have Rotated or Unrotated Factors?

by Karen Grace-Martin  1 Comment

I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn’t get through all the questions. This is part of a series of answers to those questions.

If you missed it, you can get the webinar recording here.

Question: How do we decide whether to have rotated or unrotated factors?

Answer:

Great question. Of course, the answer depends on your situation.

When you retain only one factor in a solution, then rotation is irrelevant. In fact, most software won’t even print out rotated coefficients and they’re pretty meaningless in that situation.

But if you retain two or more factors, you need to rotate.

Unrotated factors are pretty difficult to interpret in that situation. [Read more…] about In Factor Analysis, How Do We Decide Whether to Have Rotated or Unrotated Factors?

Tagged With: coefficients, factor, PCA, principal component analysis, rotated, rotation, unrotated

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  • Four Common Misconceptions in Exploratory Factor Analysis
  • In Principal Component Analysis, Can Loadings Be Negative?
  • How To Calculate an Index Score from a Factor Analysis
  • Can You Use Principal Component Analysis with a Training Set Test Set Model?

Series on Confusing Statistical Terms

by Karen Grace-Martin  6 Comments

One of the biggest challenges in learning statistics and data analysis is learning the lingo.  It doesn’t help that half of the notation is in Greek (literally).

The terminology in statistics is particularly confusing because often the same word or symbol is used to mean completely different concepts.

I know it feels that way, but it really isn’t a master plot by statisticians to keep researchers feeling ignorant.

Really.

It’s just that a lot of the methods in statistics were created by statisticians working in different fields–economics, psychology, medicine, and yes, straight statistics.  Certain fields often have specific types of data that come up a lot and that require specific statistical methodologies to analyze.

Economics needs time series, psychology needs factor analysis.  Et cetera, et cetera.

But separate fields developing statistics in isolation has some ugly effects.

Sometimes different fields develop the same technique, but use different names or notation.

Other times different fields use the same name or notation on different techniques they developed.

And of course, there are those terms with slightly different names, often used in similar contexts, but with different meanings. These are never used interchangeably, but they’re easy to confuse if you don’t use this stuff every day.

And sometimes, there are different terms for subtly different concepts, but people use them interchangeably.  (I am guilty of this myself).  It’s not a big deal if you understand those subtle differences.  But if you don’t, it’s a mess.

And it’s not just fields–it’s software, too.

SPSS uses different names for the exact same thing in different procedures.  In GLM, a continuous independent variable is called a Covariate.  In Regression, it’s called an Independent Variable.

Likewise, SAS has a Repeated statement in its GLM, Genmod, and Mixed procedures.  They all get at the same concept there (repeated measures), but they deal with it in drastically different ways.

So once the fields come together and realize they’re all doing the same thing, people in different fields or using different software procedures, are already used to using their terminology.  So we’re stuck with different versions of the same word or method.

So anyway, I am beginning a series of blog posts to help clear this up.  Hopefully it will be a good reference you can come back to when you get stuck.

We’ve expanded on this list with a member training, if you’re interested.

If you have good examples, please post them in the comments.  I’ll do my best to clear things up.

Why Statistics Terminology is Especially Confusing

Confusing Statistical Term #1: Independent Variable

Confusing Statistical Terms #2: Alpha and Beta

Confusing Statistical Term #3: Levels

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

Confusing Statistical Term #5: Covariate

Confusing Statistical Term #6: Factor

Same Statistical Models, Different (and Confusing) Output Terms

Confusing Statistical Term #7: GLM

Confusing Statistical Term #8: Odds

Confusing Statistical Term #9: Multiple Regression Model and Multivariate Regression Model

Confusing Statistical Term #10: Mixed and Multilevel Models

Confusing Statistical Terms #11: Confounder

Six terms that mean something different statistically and colloquially

Confusing Statistical Term #13: MAR and MCAR Missing Data

Tagged With: alpha, beta, Covariate, factor, GLM, independent variable, levels, Multiple Regression, multivariate, odds, Regression, SPSS GLM, Statistical Terms

Related Posts

  • Member Training: Confusing Statistical Terms
  • Confusing Statistical Terms #2: Alpha and Beta
  • Confusing Statistical Terms #1: The Many Names of Independent Variables
  • Dummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups

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