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Bivariate Statistics

Member Training: Preparing to Use (and Interpret) a Linear Regression Model

by TAF Support

You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?

[Read more…] about Member Training: Preparing to Use (and Interpret) a Linear Regression Model

Tagged With: Bivariate Statistics, histogram, interpreting regression coefficients, linear regression, Multiple Regression, scatterplot, Univariate statistics

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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

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How to Interpret the Width of a Confidence Interval

by Christos Giannoulis 2 Comments

One issue with using tests of significance is that black and white cut-off points such as 5 percent or 1 percent may be difficult to justify.

Significance tests on their own do not provide much light about the nature or magnitude of any effect to which they apply.

One way of shedding more light on those issues is to use confidence intervals. Confidence intervals can be used in univariate, bivariate and multivariate analyses and meta-analytic studies.

[Read more…] about How to Interpret the Width of a Confidence Interval

Tagged With: Bivariate Statistics, confidence interval, multivariate analysis, sample size, standard error, Univariate statistics

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Eight Ways to Detect Multicollinearity

by Karen Grace-Martin 5 Comments

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.

[Read more…] about Eight Ways to Detect Multicollinearity

Tagged With: Bivariate Statistics, Correlated Predictors, linear regression, logistic regression, Multicollinearity, p-value, predictor variable, regression models

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Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong

by Karen Grace-Martin Leave a Comment

You’ve probably experienced this before. You’ve done a statistical analysis, you’ve figured out all the steps, you finally get results and are able to interpret them. But they just look…wrong. Backwards, or even impossible—theoretically or logically.

This happened a few times recently to a couple of my consulting clients, and once to me. So I know that feeling of panic well. There are so many possible causes of incorrect results, but there are a few steps you can take that will help you figure out which one you’ve got and how (and whether) to correct it.

Errors in Data Coding and Entry

In both of my clients’ cases, the problem was that they had coded missing data with an impossible and extreme value, like 99. But they failed to define that code as missing in SPSS. So SPSS took 99 as a real data point, which [Read more…] about Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong

Tagged With: Bivariate Statistics, interaction, interpreting regression coefficients, logistic regression, Missing Data, Multicollinearity, Univariate statistics

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