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Choosing a Statistical Test

When Linear Models Don’t Fit Your Data, Now What?

by Karen Grace-Martin 29 Comments

When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.

Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.

[Read more…] about When Linear Models Don’t Fit Your Data, Now What?

Tagged With: binary variable, categorical variable, Censored, dependent variable, Discrete Counts, Multinomial, ordinal variable, Poisson Regression, Proportion, Proportional Odds Model, regression models, Truncated, Zero Inflated

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What Is Specification Error in Statistical Models?

by Karen Grace-Martin Leave a Comment

When we think about model assumptions, we tend to focus on assumptions like independence, normality, and constant variance. The other big assumption, which is harder to see or test, is that there is no specification error. The assumption of linearity is part of this, but it’s actually a bigger assumption.

What is this assumption of no specification error? [Read more…] about What Is Specification Error in Statistical Models?

Tagged With: curvilinear effect, interaction, Model Building, predictors, specification error, statistical model, transformation

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Member Training: Classic Experimental Designs

by TAF Support 1 Comment

Stage 2Have you ever wondered why there are so many different types of experimental designs, and how a researcher would go about choosing among them to best address their research questions? [Read more…] about Member Training: Classic Experimental Designs

Tagged With: blocking, experimental design, Randomized Trials, split plots

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Member Training: Choosing the Best Statistical Analysis

by TAF Support

Before you can write a data analysis plan, you have to choose the best statistical test or model. You have to integrate a lot of information about your research question, your design, your variables, and the data itself.

[Read more…] about Member Training: Choosing the Best Statistical Analysis

Tagged With: data analysis plan, planning, Statistical analysis, statistical model

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Four Weeds of Data Analysis That are Easy to Get Lost In

by Karen Grace-Martin 1 Comment

Every time you analyze data, you start with a research question and end with communicating an answer. But in between those start and end points are twelve other steps. I call this the Data Analysis Pathway. It’s a framework I put together years ago, inspired by a client who kept getting stuck in Weed #1. But I’ve honed it over the years of assisting thousands of researchers with their analysis.

[Read more…] about Four Weeds of Data Analysis That are Easy to Get Lost In

Tagged With: Data Analysis, data analysis plan, data issues

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Five Ways to Analyze Ordinal Variables (Some Better than Others)

by Karen Grace-Martin 1 Comment

There are not a lot of statistical methods designed just for ordinal variables.

But that doesn’t mean that you’re stuck with few options.  There are more than you’d think.

Some are better than others, but it depends on the situation and research questions.

Here are five options when your dependent variable is ordinal.
[Read more…] about Five Ways to Analyze Ordinal Variables (Some Better than Others)

Tagged With: categorical variable, non-parametric, Ordinal Logistic Regression, rank-based test

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  • Opposite Results in Ordinal Logistic Regression, Part 2

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