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Jeff Meyer

How the Population Distribution Influences the Confidence Interval

by Jeff Meyer  Leave a Comment

Spoiler alert, real data are seldom normally distributed. How does the population distribution influence the estimate of the population mean and its confidence interval?

To figure this out, we randomly draw 100 observations 100 times from three distinct populations and plot the mean and corresponding 95% confidence interval of each sample.
[Read more…] about How the Population Distribution Influences the Confidence Interval

Tagged With: confidence interval, Estimated marginal Means, normal distribution, population, right skewed, sample, sample size, shape of distribution, standard deviation, Uniform distribution

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  • Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?
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Correlated Errors in Confirmatory Factor Analysis

by Jeff Meyer  3 Comments

Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly.

But we can represent the latent construct by combining a set of questions on a scale, called indicators. We do this via factor analysis.

Often prior research has determined which indicators represent the latent construct. Prudent researchers will run a confirmatory factor analysis (CFA) to ensure the same indicators work in their sample.

[Read more…] about Correlated Errors in Confirmatory Factor Analysis

Tagged With: Confirmatory Factor Analysis, error term, Factor Analysis, latent variable, Model Fit

Related Posts

  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
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Member Training: Difference in Differences

by Jeff Meyer 

The great majority of all regression modeling explores and tests the association between independent and dependent variables. We are not able to claim the independent variable(s) has a causal relationship with the dependent variable. There are five specific model types that allow us to test for causality. Difference in differences models are one of the five.

[Read more…] about Member Training: Difference in Differences

Tagged With: causal models, causality, difference in differences, regression models

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What are Sums of Squares?

by Jeff Meyer  3 Comments

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 ANOVAStage 2 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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The Importance of Including an Exposure Variable in Count Models

by Jeff Meyer  11 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

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Count Models: Understanding the Log Link Function

by Jeff Meyer  2 Comments

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

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  • 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

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