OptinMon

The Difference Between R-squared and Adjusted R-squared

August 22nd, 2022 by

When is it important to use adjusted R-squared instead of R-squared?

R², the Coefficient of Determination, is one of the most useful and intuitive statistics we have in linear regression.Stage 2

It tells you how well the model predicts the outcome and has some nice properties. But it also has one big drawback.

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The Four Models You Meet in Structural Equation Modeling

August 8th, 2022 by

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 four types of structural equation models.

Path Analysis

More interesting research questions could be asked and answered using Path Analysis. Path Analysis is a type of structural equation modeling without latent variables. (more…)


Exogenous and Endogenous Variables in Structural Equation Modeling

July 22nd, 2022 by

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 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.

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Member Training: Assumptions of Linear Models

June 30th, 2022 by

Stage 2What are the assumptions of linear models? If you compare two lists of assumptions, most of the time they’re not the same.
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When Linear Models Don’t Fit Your Data, Now What?

June 20th, 2022 by

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.

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