• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

Correlation

Six terms that mean something different statistically and colloquially

by guest contributer Leave a Comment

by Kim Love and Karen Grace-Martin

Statistics terminology is confusing.

Sometimes different terms are used to mean the same thing, often in different fields of application. Sometimes the same term is used to mean different things. And sometimes very similar terms are used to describe related but distinct statistical concepts.

[Read more…] about Six terms that mean something different statistically and colloquially

Tagged With: bias, confusing statistical terms, Correlation, Error, odds, random, significance, terminology

Related Posts

  • Member Training: Confusing Statistical Terms
  • Why Statistics Terminology is Especially Confusing
  • Confusing Statistical Term #8: Odds
  • Best Practices for Organizing your Data Analysis

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

Related Posts

  • 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

The Difference Between Association and Correlation

by Karen Grace-Martin 1 Comment

What does it mean for two variables to be correlated?

Is that the same or different than if they’re associated or related?

This is the kind of question that can feel silly, but shouldn’t. It’s just a reflection of the confusing terminology used in statistics. In this case, the technical statistical term looks like, but is not exactly the same as, the way we mean it in everyday English. [Read more…] about The Difference Between Association and Correlation

Tagged With: association, Bivariate Statistics, Correlation, Cramer's V, Kendall's tau-b, point-biserial, Polychoric correlations, rank-biserial, Somer's D, Spearman correlation, Stuart's tau-c, tetrachoric

Related Posts

  • Member Training: Confusing Statistical Terms
  • How to Interpret the Width of a Confidence Interval
  • Six terms that mean something different statistically and colloquially
  • Effect Size Statistics: How to Calculate the Odds Ratio from a Chi-Square Cross-tabulation Table

Member Training: Interpretation of Effect Size Statistics

by guest contributer

Effect size statistics are required by most journals and committees these days ⁠— for good reason. 

They communicate just how big the effects are in your statistical results ⁠— something p-values can’t do.

But they’re only useful if you can choose the most appropriate one and if you can interpret it.

This can be hard in even simple statistical tests. But once you get into  complicated models, it’s a whole new story. [Read more…] about Member Training: Interpretation of Effect Size Statistics

Tagged With: Cohen's d, Correlation, correlation indexes, effect size, effect size statistics, empirically derived, Glass, Hedges, interpreting, null hypothesis, probability of superiority, Proportion, strength association, superiority, variance

Related Posts

  • Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?
  • Member Training: An Overview of Effect Size Statistics and Why They are So Important
  • Member Training: Inference and p-values and Statistical Significance, Oh My!
  • Member Training: Confusing Statistical Terms

Member Training: Those Darn Ratios!

by TAF Support

Ratios are everywhere in statistics—coefficient of variation, hazard ratio, odds ratio, the list goes on. You see them reported in the literature and in your output.

You comment on them in your reports. You even (kinda) understand them. Or, maybe, not quite?

Please join Elaine Eisenbeisz as she presents an overview of the how and why of various ratios we use often in statistical practice.

[Read more…] about Member Training: Those Darn Ratios!

Tagged With: Coefficient of determination, Correlation, Hazard ratio, Likelihood ratio, odds ratio, ratio, relative risk, Variance inflation factor, variation

Related Posts

  • Member Training: Confusing Statistical Terms
  • Member Training: Interpretation of Effect Size Statistics
  • Member Training: Determining Levels of Measurement: What Lies Beneath the Surface
  • Member Training: Introduction to SPSS Software Tutorial

The Difference Between Interaction and Association

by Karen Grace-Martin 18 Comments

It’s really easy to mix up the concepts of association (as measured by correlation) and interaction.  Or to assume if two variables interact, they must be associated.  But it’s not actually true.

In statistics, they have different implications for the relationships among your variables. This is especially true when the variables you’re talking about are predictors in a regression or ANOVA model.

Association

Association between two variables means the values of one variable relate in some way to the values of the other.  It is usually measured by correlation for two continuous variables and by cross tabulation and a Chi-square test for two categorical variables.

Unfortunately, there is no nice, descriptive measure for association between one [Read more…] about The Difference Between Interaction and Association

Tagged With: Correlation, easy to confuse statistical concepts, interaction

Related Posts

  • Using Pairwise Comparisons to Help you Interpret Interactions in Linear Regression
  • Interpreting Interactions Between Two Effect-Coded Categorical Predictors
  • Centering a Covariate to Improve Interpretability
  • Confusing Statistical Terms #11: Confounder

  • Go to page 1
  • Go to page 2
  • Go to Next Page »

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Introduction to SPSS Software Tutorial

Upcoming Free Webinars

Poisson and Negative Binomial Regression Models for Count Data

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT