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Latest Blog Posts

The Four Models You Meet in Structural Equation Modeling

August 8, 2022

On a previous post (Why do I need to have knowledge of multiple regression to understand SEM?) we showed how a multiple regression model could be conceptualized using Structural Equation Model path diagrams. That's the simplest SEM you can create, but its real power lies in expanding on that regression model. Here I will discuss 4 ways to do that..

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Member Training: A Gentle Introduction to Bootstrapping

July 28, 2022

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.

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Exogenous and Endogenous Variables in Structural Equation Modeling

July 22, 2022

In most regression models, there is one response variable and one or more predictors. From the model’s point of view, it doesn’t matter if those predictors are there to predict, to moderate, to explain, or to control. All that matters is that they’re all Xs, on the right side of the equation.

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Correlated Errors in Confirmatory Factor Analysis

July 13, 2022

Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly. But we can use a set of questions on a scale, called indicators, to represent the construct together by combining them into a latent factor.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.

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Member Training: Assumptions of Linear Models

June 30, 2022

Data analysts can get away without ever understanding matrix algebra, certainly. But there are times when having even a basic understanding of how matrix algebra works and what it has to do with data can really make your analyses make a little more sense.

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

June 20, 2022

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 […]

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What Is Specification Error in Statistical Models?

June 8, 2022

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 […]

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Member Training: Analyzing Pre-Post Data

May 31, 2022

Recommendations on how to analyze pre-post data can vary. Typical recommendations include regression analysis or matched pairs analysis for within subject studies and analysis of covariance (ANCOVA) or linear mixed effects model analysis for within and between subject studies.

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The Difference Between an Odds Ratio and a Predicted Odds

May 20, 2022

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 […]

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Measures of Model Fit for Linear Regression Models

May 9, 2022

A well-fitting regression model results in predicted values close to the observed data values. The mean model, which uses the mean for every predicted value, generally would be used if there were no useful predictor variables. The fit of a proposed regression model should therefore be better than the fit of the mean model.

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