Jeff Meyer

How the Population Distribution Influences the Confidence Interval

September 6th, 2022 by

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.

Correlated Errors in Confirmatory Factor Analysis

July 13th, 2022 by

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.


Member Training: Difference in Differences

November 30th, 2021 by

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.


What are Sums of Squares?

January 9th, 2021 by

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


The Importance of Including an Exposure Variable in Count Models

November 19th, 2020 by

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.


Count Models: Understanding the Log Link Function

November 12th, 2020 by

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 (more…)