Description 
1 online resource 
Series 
Quantitave methodology series 

Quantitave methodology series

Contents 
Cover; Multilevel and Longitudinal Modeling with IBM SPSS; Title Page; Copyright Page; Table of Contents; Preface; Chapter 1 Introduction to Multilevel Modeling with IBM SPSS; Our Intent; Overview of Topics; Analysis of Multilevel Data Structures; Partitioning Variation in an Outcome; Developing a General MultilevelModeling Strategy; Illustrating the Steps in Investigating a Proposed Model; 1. OneWay ANOVA (No Predictors) Model; 2. Analyze a Level 1 Model with Fixed Predictors; 3. Add the Level 2 Explanatory Variables; 4. Examine Whether a Particular Slope Coefficient Varies Between Groups 

5. Adding CrossLevel Interactions to Explain Variation in the SlopeSyntax Versus IBM SPSS Menu Command Formulation; Model Estimation and Other Typical MultilevelModeling Issues; Sample Size; Power; Differences Between Multilevel Software Programs; Standardized and Unstandardized Coefficients; Missing Data; Missing Data at Level 2; Missing Data in Vertical Format in IBM SPSS MIXED; Design Effects, Sample Weights, and the Complex Samples Routine in IBM SPSS; An Example Using Multilevel Weights; Summary; Chapter 2 Preparing and Examining the Data for Multilevel Analyses; Data Requirements 

Creating an IndividualLevel Identifier Using "Compute"Creating a GroupLevel Identifier Using "Rank Cases"; Creating a WithinGroupLevel Identifier Using "Rank Cases"; Centering; GrandMean Centering; GroupMean Centering; Checking the Data; A Note About Model Building; Summary; Chapter 3 Defining a Basic TwoLevel Multilevel Regression Model; From SingleLevel to Multilevel Analysis; Building a TwoLevel Model; Research Questions; The Data; Specifying the Model; Graphing the Relationship Between SES and Math Test Scores with IBM SPSS Menu Commands 

File LayoutGetting Familiar with Basic IBM SPSS Data Commands; Recode: Creating a New Variable Through Recoding; Recoding Old Values to New Values; Recoding Old Values to New Values Using "Range"; Compute: Creating a New Variable That Is a Function of Some Other Variable; Match Files: Combining Data From Separate IBM SPSS Files; Aggregate: Collapsing Data Within Level 2 Units; VARSTOCASES: Vertical Versus Horizontal Data Structures; Using "Compute" and "Rank" to Recode the Level 1 or Level 2 Data for Nested Models; Creating an Identifier Variable 

Graphing the Subgroup Relationships Between SES and Math Test Scores with IBM SPSS Menu CommandsBuilding a Multilevel Model with IBM SPSS MIXED; Step 1: Examining Variance Components Using the Null Model; Defining Model 1 (Null) with IBM SPSS Menu Commands; Interpreting the Output From Model 1 (Null); Step 2: Building the IndividualLevel (or Level 1) Random Intercept Model; Defining Model 2 with IBM SPSS Menu Commands; Interpreting the Output From Model 2; Step 3: Building the GroupLevel (or Level 2) Random Intercept Model; Defining Model 3 with IBM SPSS Menu Commands 
Summary 
This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixedeffects program (MIXED). Annotated screen shots provide readers with a stepbystep understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research que 
Bibliography 
Includes bibliographical references and index 
Notes 
Print version record 
Subject 
PASW (Computer file)


SPSS (Computer file)


Social sciences  Longitudinal studies.


Social sciences  Statistical methods.

Genre/Form 
Longitudinal studies.

Form 
Electronic book

Author 
Tabata, Lynn Naomi.


Thomas, Scott L.

ISBN 
1135074178 (electronic bk.) 

1299813968 (ebk) 

9781135074173 (electronic bk.) 

9781299813960 (ebk) 
