For a fuller treatment on what one-way ANOVA is, see our One-Way ANOVA tutorial. This tutorial will go over how to conduct one-way ANOVA using SAS.
The data we are using can be downloaded as a file from our GitHub repository.
We want to study the effectiveness of different treatments on anxiety. We collect a sample of 75 subjects in the following categories:
No treatment (\(n_1\) = 27). Biofeedback (\(n_2\) = 24).

This tutorial is going to take the theory learned in our Two-Way ANOVA tutorial and walk through how to apply it using SAS. We will be using the Moore dataset, which can be downloaded from our GitHub repository.
This data frame consists of subjects in a “social-psychological experiment who were faced with manipulated disagreement from a partner of either of low or high status. The subjects could either conform to the partner’s judgment or stick with their own judgment.

All three types of \(t\)-tests can be performed in SAS. This tutorial will demonstrate the steps and syntax needed to conduct one sample, two independent samples, and paired samples t-tests.
There are two datafiles used in this tutorial. The iq_wide can be downloaded from our GitHub repository here, and the iq_long data can be downloaded from our GitHub repository here. The one-sample and independent samples examples will use the iq_long data, and the paired samples example will use iq_wide.

This tutorial is going to take the theory learned in our Two-Way ANOVA tutorial and walk through how to apply it using SPSS. We will use the Moore dataset which can be downloaded using this from the Scilab website.
The data is from an experimental study which consists of subjects in a “social-psychological experiment who were faced with manipulated disagreement from a partner of either of low or high status. The subjects could either conform to the partner’s judgment or stick with their own judgment.

This tutorial is going to take the theory learned in our Two-Way ANOVA tutorial and walk through how to apply it using R. We will be using the Moore dataset from the carData package. This data frame consists of subjects in a “social-psychological experiment who were faced with manipulated disagreement from a partner of either of low or high status. The subjects could either conform to the partner’s judgment or stick with their own judgment.

We discussed how to conduct a 2-way factorial ANOVA in this tutorial, and we talked about the three different types of Sum of Squares here. We will build on these and discuss how to run post hoc analyses when you have a significant interaction. We will use the Moore dataset from the carData package in R. This data frame consists of subjects in a “social-psychological experiment who were faced with manipulated disagreement from a partner of either of low or high status.

You may have noticed that your software output specified a *Type __* Sum of Squares. This tutorial will explain what that means, and the differences between Type I, Type II, and Type III sum of squares using two-way factorial ANOVA as an example.
It can be shown that the \(F\) test for a one-way ANOVA is equivalent to comparing the full model to a reduced model. This is equivalent to asking if we are able to improve our prediction of the outcome by including the variable in the model, or if our guesses are just as noisy with the variable as without.

This tutorial is going to take what we learned in one-way ANOVA and extend it to two-way ANOVA. In a one-way ANOVA, we have
A single dependent variable measured on an interval scale A single independent variable measured on a nominal scale The example used in our one-way ANOVA tutorial was the cook times of four different brands of pasta. The brand of pasta was the independent variable, and the cook time (in minutes) was the dependent variable.

This tutorial shows how to estimate a full structural equation model (SEM) with latent variables using the lavaan package in R. The model consists of three latent variables and eleven manifest variables, as described in our previous post setting up a running CFA and SEM example. To review, the model to be fit is the following:
The data can be accessed from the built-in Bollen dataset in the sem package.

This tutorial shows how to estimate a confirmatory factor analysis (CFA) model using the R lavaan package. The model, which consists of two latent variables and eight manifest variables, is described in our previous post which sets up a running CFA and SEM example. To review, the model to be fit is the following:
The data can be accessed from the built-in Bollen dataset in the sem package.
library(sem) ## Warning: package 'sem' was built under R version 3.