# Multilevel Modeling in Plain Language

Multilevel Modeling in Plain Language

Robson, Karen; Pevalin, David

SAGE Publications Ltd

11/2015

160

Dura

Inglês

9780857029157

15 a 20 dias

440

Mixing levels of analysis

Theoretical reasons for multilevel modeling

What are the advantages of using multilevel models?

Statistical reasons for multilevel modeling

Assumptions of OLS

Software

How this book is organized

Chapter 2: Random Intercept Models: When intercepts vary

A review of single-level regression

Nesting structures in our data

Getting starting with random intercept models

What do our findings mean so far?

Changing the grouping to schools

Adding Level 1 explanatory variables

Adding Level 2 explanatory variables

Group mean centring

Interactions

Model fit

What about R-squared?

R-squared?

A further assumption and a short note on random and fixed effects

Chapter 3: Random Coefficient Models: When intercepts and coefficients vary

Getting started with random coefficient models

Trying a different random coefficient

Shrinkage

Fanning in and fanning out

Examining the variances

A dichotomous variable as a random coefficient

More than one random coefficient

A note on parsimony and fitting a model with multiple random coefficients

A model with one random and one fixed coefficient

Adding Level 2 variables

Residual diagnostics

First steps in model-building

Some tasters of further extensions to our basic models

Where to next?

Chapter 4: Communicating Results to a Wider Audience

Creating journal-formatted tables

The fixed part of the model

The importance of the null model

Centring variables

Stata commands to make table-making easier

What do you talk about?

Models with random coefficients

What about graphs?

Cross-level interactions

Parting words

**Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.**

Mixing levels of analysis

Theoretical reasons for multilevel modeling

What are the advantages of using multilevel models?

Statistical reasons for multilevel modeling

Assumptions of OLS

Software

How this book is organized

Chapter 2: Random Intercept Models: When intercepts vary

A review of single-level regression

Nesting structures in our data

Getting starting with random intercept models

What do our findings mean so far?

Changing the grouping to schools

Adding Level 1 explanatory variables

Adding Level 2 explanatory variables

Group mean centring

Interactions

Model fit

What about R-squared?

R-squared?

A further assumption and a short note on random and fixed effects

Chapter 3: Random Coefficient Models: When intercepts and coefficients vary

Getting started with random coefficient models

Trying a different random coefficient

Shrinkage

Fanning in and fanning out

Examining the variances

A dichotomous variable as a random coefficient

More than one random coefficient

A note on parsimony and fitting a model with multiple random coefficients

A model with one random and one fixed coefficient

Adding Level 2 variables

Residual diagnostics

First steps in model-building

Some tasters of further extensions to our basic models

Where to next?

Chapter 4: Communicating Results to a Wider Audience

Creating journal-formatted tables

The fixed part of the model

The importance of the null model

Centring variables

Stata commands to make table-making easier

What do you talk about?

Models with random coefficients

What about graphs?

Cross-level interactions

Parting words

**Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.**