# 2 Preparation

## 2.1 The Model Comparisson Approach

This step by step resource follows closely the Model Comparison Approach (MCA) proposed by Charles Judd and colleagues^{1}. The MCA is framework for understanding and reporting data analysis. It is grounded in just a few core concepts from statistical modeling and statistical inference and allows a common language to understand the most popular statistical tests in social sciences. If this is your first contact with the MCA you might miss the cookbook appeal of matching a specific research question with a specific statistical test. The main benefit form the MCA is that you will no longer be stuck to a specific data analysis recipe: i) it becomes clear that the different linear models all resort to the same model building principles (from linear regressions to ANOVAs/ANCOVAs and to mixed measure models) and that; ii) it also becomes clear what statistical inference is saying about your models and when it makes sense to actually listen to it.

## 2.2 Load the Dabase

This step by step uses a mock database for a *Happiness Study* with undergraduate and graduate students. The fist step before diving into each of the other chapters is to **Download the data base load it in the R environment.** The data base of the Happiness Study is *available here*. A the relevant code is here:

`<- read.csv("Happy.csv") Happy `

Judd, C. M., McClelland, J. C., & Ryan, C. S. (2008). Data Analysis: A Model Comparison Approach. Routledge.↩︎