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Karen Grace-Martin

Member Training: Elements of Experimental Design

by Karen Grace-Martin

Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.

The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements. [Read more…] about Member Training: Elements of Experimental Design

Tagged With: ANOVA, blocking, Crossed factors, Crossover Design, Latin squares, multilevel model, nested models, Regression, Repeated Measures, replication

Related Posts

  • Member Training: Hierarchical Regressions
  • December Member Training: Missing Data
  • Member Training: Crossed and Nested Factors
  • Member Training: Interactions in ANOVA and Regression Models, Part 2

When to Use Logistic Regression for Percentages and Counts

by Karen Grace-Martin 1 Comment

One important yet difficult skill in statistics is choosing a type model for different data situations. One key consideration is the dependent variable.

For linear models, the dependent variable doesn’t have to be normally distributed, but it does have to be continuous, unbounded, and measured on an interval or ratio scale.

Percentages don’t fit these criteria. Yes, they’re continuous and ratio scale. The issue is the [Read more…] about When to Use Logistic Regression for Percentages and Counts

Tagged With: binomial, Count data, count model, dependent variable, events, logistic regression, Negative Binomial Regression, percentage data, Poisson Regression, trials

Related Posts

  • Member Training: Count Models
  • When Dependent Variables Are Not Fit for Linear Models, Now What?
  • Proportions as Dependent Variable in Regression–Which Type of Model?
  • Poisson Regression Analysis for Count Data

Why ANOVA is Really a Linear Regression, Despite the Difference in Notation

by Karen Grace-Martin 2 Comments

When I was in graduate school, stat professors would say “ANOVA is just a special case of linear regression.”  But they never explained why.

And I couldn’t figure it out.

The model notation is different.

The output looks different.

The vocabulary is different.

The focus of what we’re testing is completely different. How can they be the same model?

[Read more…] about Why ANOVA is Really a Linear Regression, Despite the Difference in Notation

Tagged With: ANOVA, linear regression, notation, regression models

Related Posts

  • 7 Practical Guidelines for Accurate Statistical Model Building
  • The Steps for Running any Statistical Model
  • Beyond Median Splits: Meaningful Cut Points
  • Why ANOVA and Linear Regression are the Same Analysis

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

Related Posts

  • Member Training: Model Building Approaches
  • Differences in Model Building Between Explanatory and Predictive Models
  • Spotlight Analysis for Interpreting Interactions
  • Five Common Relationships Among Three Variables in a Statistical Model

What Is an Exact Test?

by Karen Grace-Martin 2 Comments

Most of the p-values we calculate are based on an assumption that our test statistic meets some distribution. These distributions are generally a good way to calculate p-values as long as assumptions are met.

But it’s not the only way to calculate a p-value.

Rather than come up with a theoretical probability based on a distribution, exact tests calculate a p-value empirically.

The simplest (and most common) exact test is a Fisher’s exact for a 2×2 table.

Remember calculating empirical probabilities from your intro stats course? All those red and white balls in urns? [Read more…] about What Is an Exact Test?

Tagged With: empirical probability, fisher exact test, Fisher's, p-value

Related Posts

  • Missing Data: Two Big Problems with Mean Imputation
  • September Member Training: Inference and p-values and Statistical Significance, Oh My!
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Can I Treat 5 Waves of Repeated Measurements as Categorical or Continuous?

by Karen Grace-Martin Leave a Comment

Question: Can you talk more about categorical and repeated Time? If I have 5 waves at ages 0, 1  year, 3 years, 5 years, and 9 years, would that be categorical or repeated? Does mixed account for different spacing in time?

 

Mixed models can account for different spacing in time and you’re right, it entirely depends on whether you treat Time as categorical or continuous.

First let me mention that not all designs can treat time as either categorical or continuous. The reason it could go either way in your example is because time is measured discretely, yet there are enough numerical values that you could fit a line to it. [Read more…] about Can I Treat 5 Waves of Repeated Measurements as Categorical or Continuous?

Tagged With: continuous time, linear mixed model, Repeated Measures

Related Posts

  • Six Differences Between Repeated Measures ANOVA and Linear Mixed Models
  • Linear Mixed Models for Missing Data in Pre-Post Studies
  • Mixed Models: Can you specify a predictor as both fixed and random?
  • Three Designs that Look Like Repeated Measures, But Aren’t

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This Month’s Statistically Speaking Live Training

  • January Member Training: A Gentle Introduction To Random Slopes In Multilevel Models

Upcoming Workshops

  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
  • Introduction to Generalized Linear Mixed Models (May 2021)

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Data Analysis with SPSS
(4th Edition)

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Karen Grace-Martin

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