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E-book
Author Heck, Ronald H.

Title Multilevel modeling of categorical outcomes using IBM SPSS / Ronald H. Heck, Scott L. Thomas, Lynn N. Tabata
Published New York : Routledge, 2012
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Description 1 online resource (xvi, 439 pages) : illustrations
Series Quantitative methodology series
Quantitative methodology series.
Contents Ch. 1 Introduction to Multilevel Models With Categorical Outcomes -- Introduction -- Our Intent -- Analysis of Multilevel Data Structures -- Scales of Measurement -- Methods of Categorical Data Analysis -- Sampling Distributions -- Link Functions -- Developing a General Multilevel Modeling Strategy -- Determining the Probability Distribution and Link Function -- Developing a Null (or No Predictors) Model -- Selecting the Covariance Structure -- Analyzing a Level-1 Model With Fixed Predictors -- Adding the Level-2 Explanatory Variables -- Examining Whether a Particular Slope Coefficient Varies Between Groups -- Covariance Structures -- Adding Cross-Level Interactions to Explain Variation in the Slope -- Selecting Level-1 and Level-2 Covariance Structures -- Model Estimation and Other Typical Multilevel Modeling Issues -- Determining How Well the Model Fits -- Syntax Versus IBM SPSS Menu Command Formulation -- Sample Size -- Power -- Missing Data
Adding an Interaction to Model 1.5 -- Interpreting the Output of Model 1.5 -- Categorical Longitudinal Models Using GENLIN MIXED -- Specifying a GEE Model Within GENLIN MIXED -- Defining Model 2.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.1 -- Examining a Random Intercept at the Between-Student Level -- Defining Model 2.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.2 -- What Variables Affect Differences in Proficiency Across Individuals? -- Defining Model 2.3 With IBM SPSS Menu Commands -- Adding Two Interactions to Model 2.3 -- Interpreting the Output of Model 2.3 -- Building a Three-Level Model in GENLIN MIXED -- The Beginning Model -- Defining Model 3.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 3.1 -- Adding Student and School Predictors -- Defining Model 3.2 With IBM SPSS Menu Commands -- Adding Two Interactions to Model 3.2 -- Adding Two More Interactions to Model 3.2
Checking the Data -- A Note About Model Building -- Summary -- ch. 3 Specification of Generalized Linear Models -- Introduction -- Describing Outcomes -- Some Differences in Describing a Continuous or Categorical Outcome -- Measurement Properties of Outcome Variables -- Explanatory Models for Categorical Outcomes -- Components for Generalized Linear Model -- Outcome Probability Distributions and Link Functions -- Continuous Scale Outcome -- Positive Scale Outcome -- Dichotomous Outcome or Proportion -- Nominal Outcome -- Ordinal Outcome -- Count Outcome -- Negative Binomial Distribution for Count Data -- Events-in-Trial Outcome -- Other Types of Outcomes -- Estimating Categorical Models With GENLIN -- GENLIN Model-Building Features -- Type of Model Command Tab -- Distribution and Log Link Function -- Custom Distribution and Link Function -- The Response Command Tab -- Dependent Variable -- Reference Category
Components for Generalized Linear Mixed Models -- Specifying a Two-Level Model -- Specifying a Three-Level Model -- Model Estimation -- Building Multilevel Models With GENLIN MIXED -- Data Structure Command Tab -- Fields and Effects Command Tab -- Target Main Screen -- Fixed Effects Main Screen -- Random Effects Main Screen -- Weight and Offset Main Screen -- Build Options Command Tab -- Selecting the Sort Order -- Stopping Rules -- Confidence Intervals -- Degrees of Freedom -- Tests of Fixed Effects -- Tests of Variance Components -- Model Options Command Tab -- Estimating Means and Contrasts -- Save Fields -- Examining Variables That Explain Student Proficiency in Reading -- Research Questions -- The Data -- The Unconditional (Null) Model -- Defining Model 1.1 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.2 -- Defining the Within-School Variables -- Defining Model 1.2 With IBM SPSS Menu Commands
Defining Model 1.5 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.5 -- Comparing the Fit -- Estimating Two-Level Count Data With GENLIN MIXED -- Defining Model 2.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.1 -- Building a Two-Level Model -- Defining Model 2.2 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.2 -- Within-Schools Model -- Defining Model 2.3 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.3 -- Examining Whether the Negative Binomial Distribution Is a Better Choice -- Defining Model 2.4 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.4 -- Does the SES-Failure Slope Vary Across Schools? -- Defining Model 2.5 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.5 -- Modeling Variability at Level 2 -- Defining Model 2.6 With IBM SPSS Menu Commands
Defining Model 2.4 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.4 Model Results -- Developing a Model With an Ordinal Outcome -- The Data -- Developing a Single-Level Model -- Preliminary Analyses -- Defining Model 3.1 with IBM SPSS Menu Commands -- Interpreting the Output of Model 3.1 -- Adding Student Background Predictors -- Defining Model 3.2 with IBM SPSS Menu Commands -- Interpreting the Output of Model 3.2 -- Testing an Interaction -- Defining Model 3.3 With IBM SPSS Menu Commands -- Adding Interactions to Model 3.3 -- Interpreting the Output of Model 3.3 -- Following Up With a Smaller Random Sample -- Developing a Multilevel Ordinal Model -- Level-1 Model -- Unconditional Model -- Defining Model 4.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.1 -- Within-School Predictor -- Defining Model 4.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.2 -- Adding the School-Level Predictors
Defining Model 4.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.3 -- Using Complementary Log-Log Link -- Interpreting a Categorical Predictor -- Other Possible Analyses -- Examining a Mediating Effect at Level 1 -- Defining Model 4.4 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.4 -- Estimating the Mediated Effect -- Summary -- Note -- ch. 7 Two-Level Models With Count Data -- Introduction -- A Poisson Regression Model With Constant Exposure -- The Data -- Preliminary Single-Level Models -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.1 -- Defining Model 1.2 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.2 -- Considering Possible Overdispersion -- Defining Model 1.3 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.3 -- Defining Model 1.4 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.4
Defining the Three-Level Model -- Defining Model 2.2 with IBM SPSS Menu Commands -- Interpreting the Output of Model 2.2 -- Summary -- ch. 5 Multilevel Models With a Categorical Repeated Measures Outcome -- Introduction -- Generalized Estimating Equations -- GEE Model Estimation -- An Example Study -- Research Questions -- The Data -- Defining the Model -- Model Specifying the Intercept and Time -- Correlation and Covariance Matrices -- Standard Errors -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.1 -- Alternative Coding of the Time Variable -- Defining Model 1.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.2 -- Defining Model 1.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.3 -- Adding a Predictor -- Defining Model 1.4 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.4 -- Adding an Interaction Between Female and the Time Parameter
Design Effects, Sample Weights, and the Complex Samples Routine in IBM SPSS -- An Example -- Differences Between Multilevel Software Programs -- Summary -- ch. 2 Preparing and Examining the Data for Multilevel Analyses -- Introduction -- Data Requirements -- File Layout -- Getting Familiar With Basic IBM SPSS Data Commands -- RECODE: Creating a New Variable Through Recoding -- 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 "Rank" to Recode the Level-1 or Level-2 Data for Nested Models -- Creating an Identifier Variable -- Creating an Individual-Level Identifier Using COMPUTE -- Creating a Group-Level Identifier Using Rank Cases -- Creating a Within-Group-Level Identifier Using Rank Cases -- Centering -- Grand-Mean Centering -- Group-Mean Centering
Interpreting the Output Results of Model 2.6 -- Adding the Cross-Level Interactions -- Defining Model 2.7 With IBM SPSS Menu Commands -- Adding Two Interactions to Model 2.7 -- Interpreting the Output Results of Model 2.7 -- Developing a Two-Level Count Model With an Offset Variable -- The Data -- Research Questions -- Offset Variable -- Specifying a Single-Level Model -- Defining Model 3.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.1 -- Adding the Offset -- Defining Model 3.2 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.2 -- Defining Model 3.3 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.3 -- Defining Model 3.4 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.4 -- Estimating the Model With GENLIN MIXED -- Defining Model 4.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 4.1
Interpreting the Output of Model 1.2 -- Examining Whether a Level-1 Slope Varies Between Schools -- Defining Model 1.3 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.3 -- Adding Level-2 Predictors to Explain Variability in Intercepts -- Defining Model 1.4 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.4 -- Adding Level-2 Variables to Explain Variation in Level-1 Slopes (Cross-Level Interaction) -- Defining Model 1.5 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.5 -- Estimating Means -- Saving Output -- Probit Link Function -- Defining Model 1.6 with IBM SPSS Menu Commands -- Interpreting Probit Coefficients -- Interpreting the Output of Model 1.6 -- Examining the Effects of Predictors on Probability of Being Proficient -- Extending the Two-Level Model to Three Levels -- The Unconditional Model -- Defining Model 2.1 with IBM SPSS Menu Commands -- Interpreting the Output of Model 2.1
Interpreting the Output of Model 3.2 -- An Example Experimental Design -- Defining Model 4.1 With IBM SPSS Menu Commands -- Summary -- ch. 6 Two-Level Models With Multinomial and Ordinal Outcomes -- Introduction -- Building a Model to Examine a Multinomial Outcome -- Research Questions -- The Data -- Defining the Multinomial Model -- Defining a Preliminary Single-Level Model -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.1 -- Developing a Multilevel Multinomial Model -- Unconditional Two-Level Model -- Defining Model 2.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.1 -- Computing Predicted Probabilities -- Level-1 Model -- Defining Model 2.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.2 -- Adding School-Level Predictors -- Defining Model 2.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.3 -- Investigating a Random Slope
Number of Events Occurring in a Set of Trials -- The Predictors Command Tab -- Predictors -- Offset -- The Model Command Tab -- Main Effects -- Interactions -- The Estimation Command Tab -- Parameter Estimation -- The Statistics Command Tab -- Model Effects -- Additional GENLIN Command Tabs -- Estimated Marginal (EM) Means -- Save -- Export -- Building a Single-Level Model -- Research Questions -- The Data -- Specifying the Model -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.1 -- Adding Gender to the Model -- Defining Model 1.2 With IBM SPSS Menu Commands -- Obtaining Predicted Probabilities for Males and Females -- Adding Additional Background Predictors -- Defining Model 1.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.3 -- Testing an Interaction -- Limitations of Single-Level Analysis -- Summary -- Note -- ch. 4 Multilevel Models With Dichotomous Outcomes -- Introduction
Bibliography Includes bibliographical references and index
Notes Print version record
Subject SPSS (Computer file)
SPSS for Windows.
Psychometrics -- Computer programs.
Psychometrics.
Social sciences -- Computer programs.
Models, Statistical.
Psychometrics -- methods.
Social Sciences.
Software.
Statistics as Topic.
Form Electronic book
Author Tabata, Lynn Naomi.
Thomas, Scott Loring.
ISBN 1136672354 (e-bk.)
1283862522 (MyiLibrary)
1848729561
9781136672354 (e-bk.)
9781283862523 (MyiLibrary)
9781848729568