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Stage 3

Member Training: Translating Between Multilevel and Mixed Models

by TAF Support  1 Comment

Multilevel and Mixed models are essentially the same analysis. But they use different vocabulary, different notation, and approach the analysis considerations in different ways.
[Read more…] about Member Training: Translating Between Multilevel and Mixed Models

Tagged With: mixed models, multilevel models

Related Posts

  • Member Training: Matrix Algebra for Data Analysts: A Primer
  • Member Training: A Gentle Introduction To Random Slopes In Multilevel Models
  • Member Training: A Gentle Introduction to Multilevel Models
  • Member Training: Latent Growth Curve Models

Member Training: Analyzing Likert Scale Data

by TAF Support  1 Comment

Is it really ok to treat Likert items as continuous? And can you just decide to combine Likert items to make a scale? Likert-type data is extremely common—and so are questions like these about how to analyze it appropriately. [Read more…] about Member Training: Analyzing Likert Scale Data

Tagged With: Correlation, data transformations, Kendall's tau-b, kruskal-wallis, Likert Scale, mann-whitney u test, Ordinal Logistic Regression, predictive models, Somer's D, Spearman correlation

Related Posts

  • When Linear Models Don’t Fit Your Data, Now What?
  • The Difference Between Association and Correlation
  • Member Training: Non-Parametric Analyses
  • Member Training: Types of Regression Models and When to Use Them

Member Training: A Gentle Introduction to Bootstrapping

by TAF Support 

Bootstrapping is a methodology derived by Bradley Efron in the 1980s that provides a reasonable approximation to the sampling distribution of various “difficult” statistics. Difficult statistics are those where there is no mathematical theory to establish a distribution.

[Read more…] about Member Training: A Gentle Introduction to Bootstrapping

Tagged With: bootstrapping, distributions, sample size, sampling

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

by Karen Grace-Martin  32 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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  • 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption
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  • Proportions as Dependent Variable in Regression–Which Type of Model?

The Difference Between an Odds Ratio and a Predicted Odds

by Karen Grace-Martin  Leave a Comment

When interpreting the results of a regression model, the first step is to look at the regression coefficients. Each term in the model has one. And each one describes the average difference in the value of Y for a one-unit difference in the value of the predictor variable, X, that makes up that term. It’s the effect size statistic for that term in the model. [Read more…] about The Difference Between an Odds Ratio and a Predicted Odds

Tagged With: marginal means, odds ratio, predicted odds, regression coefficients

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  • Logistic Regression Analysis: Understanding Odds and Probability
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Member Training: Heterogeneity in Meta-analysis

by TAF Support 

Meta-analysis allows us to synthesize the results of separate studies. The goal is to assess the mean effect size and also heterogeneity – how much the effect size varies across studies.  [Read more…] about Member Training: Heterogeneity in Meta-analysis

Tagged With: effect size, heterogeneity, meta-analysis, prediction interval

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  • Member Training: Meta-analysis
  • Member Training: A Gentle Introduction to Bootstrapping
  • Member Training: Analyzing Pre-Post Data
  • Member Training: Power Analysis and Sample Size Determination Using Simulation

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