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Jeff Meyer

What are Sums of Squares?

by Jeff Meyer Leave a Comment

A key part of the output in any linear model is the ANOVA table. It has many names in different software procedures, but every regression or ANOVA model has a table with Sums of Squares, degrees of freedom, mean squares, and F tests. Many of us were trained to skip over this table, but

[Read more…] about What are Sums of Squares?

Tagged With: ANOVA, linear regression, sum of squares

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  • Same Statistical Models, Different (and Confusing) Output Terms
  • Why ANOVA is Really a Linear Regression, Despite the Difference in Notation
  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • 7 Practical Guidelines for Accurate Statistical Model Building

The Importance of Including an Exposure Variable in Count Models

by Jeff Meyer 5 Comments

When our outcome variable is the frequency of occurrence of an event, we will typically use a count model to analyze the results. There are numerous count models. A few examples are: Poisson, negative binomial, zero-inflated Poisson and truncated negative binomial.

There are specific requirements for which count model to use. The models are not interchangeable. But regardless of the model we use, there is a very important prerequisite that they all share.

[Read more…] about The Importance of Including an Exposure Variable in Count Models

Tagged With: Count data, count model, exposure variable, incidence rate ratio, linear regression, negative binomial, offset variable, Poisson Regression

Related Posts

  • The Problem with Linear Regression for Count Data
  • The Exposure Variable in Poisson Regression Models
  • Count Models: Understanding the Log Link Function
  • Getting Accurate Predicted Counts When There Are No Zeros in the Data

Count Models: Understanding the Log Link Function

by Jeff Meyer Leave a Comment

When we run a statistical model, we are in a sense creating a mathematical equation. The simplest regression model looks like this:

Yi = β0 + β1X+ εi

The left side of the equation is the sum of two parts on the right: the fixed component, β0 + β1X, and the random component, εi.

You’ll also sometimes see the equation written [Read more…] about Count Models: Understanding the Log Link Function

Tagged With: count model, generalized linear models, linear regression, link function, log link, log transformation, Negative Binomial Regression, Poisson Regression

Related Posts

  • The Importance of Including an Exposure Variable in Count Models
  • The Difference Between Link Functions and Data Transformations
  • Getting Accurate Predicted Counts When There Are No Zeros in the Data
  • The Problem with Linear Regression for Count Data

Measurement Invariance and Multiple Group Analysis

by Jeff Meyer Leave a Comment

Creating a quality scale for a latent construct (a variable that cannot be directly measured with one variable) takes many steps. Structural Equation Modeling is set up well for this task.

One important step in creating scales is making sure the scale measures the latent construct equally well and the same way for different groups of individuals.

[Read more…] about Measurement Invariance and Multiple Group Analysis

Tagged With: Confirmatory Factor Analysis, Exploratory Factor Analysis, Factor Analysis, Structural Equation Modeling

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Why Adding Values on a Scale Can Lead to Measurement Error

by Jeff Meyer 1 Comment

Whenever you use a multi-item scale to measure a construct, a key step is to create a score for each subject in the data set.

This score is an estimate of the value of the latent construct (factor) the scale is measuring for each subject.  In fact, calculating this score is the final step of running a Confirmatory Factor Analysis.

[Read more…] about Why Adding Values on a Scale Can Lead to Measurement Error

Tagged With: Confirmatory Factor Analysis, Exploratory Factor Analysis, Factor Analysis, Structural Equation Modeling

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
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Member Training: A Guide to Latent Variable Models

by Jeff Meyer

An extremely useful area of statistics is a set of models that use latent variables: variables whole values we can’t measure directly, but instead have to infer from others. These latent variables can be unknown groups, unknown numerical values, or unknown patterns in trajectories.

[Read more…] about Member Training: A Guide to Latent Variable Models

Tagged With: Confirmatory Factor Analysis, Growth Mixture Model, latent class analysis, Latent Growth Curve Model, Latent Profile Analysis, Latent Transition Analysis, latent variable, Structural Equation Modeling

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Introduction to Structural Equation Modeling
  • Member Training: Reporting Structural Equation Modeling Results

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