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Author Heck, Ronald H., author.
Title Multilevel and longitudinal modeling with IBM SPSS Ronald H. Heck, Scott L. Thomas, Lynn N. Tabata.
Publication Info. New York : Routledge, Taylor & Francis Group, 2014.
Edition Second edition.
Location Call No. Status Notes
 Libraries Electronic Books  ELECTRONIC BOOKS-EBL    AVAIL. ONLINE
Description 1 online resource.
Series Quantitative methodology series.
Bibliography Includes bibliographical references and indexes.
Reproduction Electronic reproduction. Perth, W.A. Available via World Wide Web.
Note Description based on print version record.
Contents Ch. 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 Multilevel-Modeling Strategy -- Illustrating the Steps in Investigating a Proposed Model -- 1.One-Way 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 Cross-Level Interactions to Explain Variation in the Slope -- Syntax Versus IBM SPSS Menu Command Formulation -- Model Estimation and Other Typical Multilevel-Modeling 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 -- ch. 2 Preparing and Examining the Data for Multilevel Analyses -- Data Requirements -- File Layout -- Getting 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 -- 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 -- Checking the Data -- A Note About Model Building -- Summary -- ch. 3 Defining a Basic Two-Level Multilevel Regression Model -- From Single-Level to Multilevel Analysis -- Building a Two-Level Model -- Research Questions -- The Data -- Specifying the Model -- Graphing the Relationship Between SES and Math Test Scores with IBM SPSS Menu Commands -- Graphing the Subgroup Relationships Between SES and Math Test Scores with IBM SPSS Menu Commands -- Building 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 Individual-Level (or Level 1) Random Intercept Model -- Defining Model 2 with IBM SPSS Menu Commands -- Interpreting the Output From Model 2
Step 3 Building the Group-Level (or Level 2) Random Intercept Model -- Defining Model 3 with IBM SPSS Menu Commands -- Interpreting the Output From Model 3 -- Defining Model 3A (Public as Covariate) with IBM SPSS -- Menu Commands -- Step 4 Adding a Randomly Varying Slope (the Random Slope and Intercept Model) -- Defining Model 4 with IBM SPSS Menu Commands -- Interpreting the Output From Model 4 -- Step 5 Explaining Variability in the Random Slope (More Complex Random Slopes and Intercept Models) -- Defining Model 5 with IBM SPSS Menu Commands -- Add First Interaction to Model 5: ses_mean*ses -- Add Second Interaction to Model 5: pro4yrc*ses -- Add Third Interaction to Model 5: public*ses -- Interpreting the Output From Model 5 -- Defining Model 5A with IBM SPSS Menu Commands -- Graphing a Cross-Level Interaction (SES-Achievement Relationships in High- and Low-Achieving Schools) with IBM SPSS Menu Commands -- Centering Predictors
Centering Predictors in Models with Random Slopes -- Summary -- ch. 4 Three-Level Univariate Regression Models -- Three-Level Univariate Model -- Research Questions -- The Data -- Defining the Three-Level Multilevel Model -- The Null Model (No Predictors) -- Defining Model 1 (Null) with IBM SPSS Menu Commands -- Interpreting the Output From Model 1 (Null) -- Model 2 Defining Predictors at Each Level -- Defining Model 2 with IBM SPSS Menu Commands -- Interpreting the Output From Model 2 -- Model 3 Group-Mean Centering -- Defining Model 3 with IBM SPSS Menu Commands -- Interpreting the Output From Model 3 -- Covariance Estimates -- Model 4 Does the Slope Vary Randomly Across Schools? -- Defining Model 4 with IBM SPSS Menu Commands -- Interpreting the Output From Model 4 -- Developing an Interaction Term -- Preliminary Investigation of the Interaction -- Defining Models A and B (Preliminary Testing of Interactions) with IBM SPSS Menu Commands
Model A Test Interaction: teacheffect*classlowses_mean -- Model B Test Interaction: gmteacheffect*gmclasslowses_mean -- Model 5 Examining a Level 2 Interaction -- Defining Model 5 with IBM SPSS Menu Commands -- Add Interaction to Model 5: gmclasslowses_mean*gmteacheffect -- Interpreting the Output From Model 5 -- Comparing the Fit of Successive Models -- Summary -- ch. 5 Examining Individual Change with Repeated Measures Data -- Ways to Examine Repeated Observations on Individuals -- Considerations in Specifying a Linear Mixed Model -- An Example Study -- Research Questions -- The Data -- Examining the Shape of Students' Growth Trajectories -- Graphing the Linear and Nonlinear Growth Trajectories with IBM SPSS Menu Commands -- Select Subset of Individuals -- Generate Figure 5.3 (Linear Trajectory) -- Generate Figure 5.4 (Nonlinear Quadratic Trajectory) -- Coding the Time-Related Variables
Coding Time Interval Variables (time to quadtime) with IBM SPSS Menu Commands -- Coding Time Interval Variables (time to orthtime, orthquad) with IBM SPSS Menu Commands -- Specifying the Two-Level Model of Individual Change -- Level 1 Covariance Structure -- Repeated Covariance Dialog Box -- Model 1.1 Model with No Predictors -- Defining Model 1.1 (Null) with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.1 (Null) -- Model 1.1A What Is the Shape of the Trajectory? -- Defining Model 1.1A with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.1A -- Does the Time-Related Slope Vary Across Groups? -- Level 2 Covariance Structure -- Defining Model 1.1B with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.1B -- Examining Orthogonal Components -- Defining Model 1.2 with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.2 -- Specifying the Level 1 Covariance Structure
Investigating Other Level 1 Covariance Structures -- Defining Other Level 1 Covariance Structures Using IBM SPSS Menu Commands -- Model 1 ID (Level 1), UN (Level 2) -- Scaled Identity Covariance Matrix at Level 1 -- Unstructured Covariance Matrix at Level 2 -- Model 2 DIAG (Level 1), DIAG (Level 2) -- Diagonal Covariance Matrix at Level 1 -- Diagonal Covariance Matrix at Level 2 -- Model 3 DIAG (Level 1), UN (Level 2) -- Diagonal Covariance Matrix at Level 1 -- Unstructured Covariance Matrix at Level 2 -- Model 4 AR1 (Level 1), DIAG (Level 2) -- Autoregressive Errors (AR1) Covariance Matrix at Level 1 -- Diagonal Covariance Matrix at Level 2 -- Model 1.3 Adding the Between-Subjects Predictors -- Defining Model 1.3 with IBM SPSS Menu Commands -- Add First Cross-Level Interaction to Model 1.3: ses*orthtime -- Add Second Cross-Level Interaction to Model 1.3: effective*orthtime -- Interpreting the Output From Model 1.3 -- Graphing the Results
Graphing the Growth Rate Trajectories with SPSS Menu Commands -- Examining Growth Using an Alternative Specification of the Time-Related Variable -- Coding Time Interval Variables (time to timenonlin Variations) with IBM SPSS Menu Commands -- Estimating the Final Time-Related Model -- Defining Model 2.1 with IBM SPSS Menu Commands -- Adding the Two Predictors -- Defining Model 2.2 with IBM SPSS Menu Commands -- Add First Interaction to Model 2.2: ses*timenonlin -- Add Second Interaction to Model 2.2: effective*timenonlin -- Interpreting the Output From Model 2.2 -- An Example Experimental Design -- Summary -- ch. 6 Applications of Mixed Models for Longitudinal Data -- Examining Growth in Undergraduate Graduation Rates -- Research Questions -- The Data -- Defining the Model -- Level 1 Model -- Level 2 Model -- Level 3 Model -- The Null Model: No Predictors -- Level 1 Error Structures -- Defining Model 1.1 (Null) with IBM SPSS Menu Commands
Interpreting the Output From Model 1.1 (Null) -- Model 1.2 Adding Growth Rates -- Level 1 Model -- Coding the Time Variable -- Defining Model 1.2 with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.2 -- Model 1.3 Adding Time-Varying Covariates -- Defining Model 1.3 with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.3 -- Model 1.4 Explaining Differences in Growth Trajectories Between Institutions -- Defining Model 1.4 with IBM SPSS Menu Commands -- Add First Interaction to Model 1.4: time1*mathselect -- Add Second Interaction to Model 1.4: time1*percentFTfaculty -- Interpreting the Output From Model 1.4 -- Model 1.5 Adding a Model to Examine Growth Rates at Level 3 -- Defining Model 1.5 with IBM SPSS Menu Commands -- Add First Interaction to Model 1.5: time1*aveFamilyshare -- Add Second Interaction to Model 1.5: time1*aveRetention -- Add Third Interaction to Model 1.5: time1*mathselect
Add Fourth Interaction to Model 1.5: time1*percentFTfaculty -- Interpreting the Output From Model 1.5 -- A Regression Discontinuity Analysis of a Math Treatment -- The Data and Design -- Assumptions of the Design -- Steps in the Regression Discontinuity Analysis -- Predictors in the Models -- Specifying the Model -- Regression Discontinuity Models to Explain Learning Differences -- Defining Model 2.1 with IBM SPSS Menu Commands -- Interpreting the Output From Model 2.1 -- Adding Explanatory Variables at Level 2 -- Defining Model 2.2 with IBM SPSS Menu Commands -- Add First Interaction to Model 2.2: teachqual*treatment -- Add Second Interaction to Model 2.2: classcomp*treatment -- Interpreting the Output From Model 2.2 -- Investigating a Change Due to Policy Implementation -- The Data -- Model 3.1 Establishing the Prepolicy and Policy Trends -- Defining Model 3.1 with IBM SPSS Menu Commands -- Interpreting the Output From Model 3.1
Final Model with Covariates Added -- Defining Model 3.2 with IBM SPSS Menu Commands -- Add First Interaction to Model 3.2: implement0*private -- Add Second Interaction to Model 3.2: implement0*prestige -- Add Third Interaction to Model 3.2: implement1*private -- Add Fourth Interaction to Model 3.2: implement1*prestige -- Interpreting the Output From Model 3.2 -- Summary -- ch. 7 Multivariate Multilevel Models -- Multilevel Latent-Outcome Model -- The Data -- Research Questions -- Defining the Constructs -- Organizing the Data Set -- Specifying the Model -- Model 1.1 The Null or "No-Predictors" Model -- Defining the Model 1.1 (Null) with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.1 (Null) -- Conducting a Likelihood Ratio Test -- Defining Model 1.2 (Final Null Model) with IBM SPSS Menu Commands -- Model 1.3 Adding Level 2 Predictors -- Defining Model 1.3 with IBM SPSS Menu Commands
Add First Interaction to Model 1.3: stability*assessjob -- Add Second Interaction to Model 1.3: female*assessjob -- Interpreting the Output From Model 1.3 -- Model 1.4 Adding the Organizational Predictors -- Defining Model 1.4 with IBM SPSS Menu Commands -- Add First Interaction to Model 1.4: gmorgprod*assessjob -- Add Second Interaction to Model 1.4: gmresources*assessjob -- Add Third Interaction to Model 1.4: stability*assessjob -- Add Fourth Interaction to Model 1.4: female*assessjob -- Interpreting the Output From Model 1.4 -- Examining Equality Constraints -- Defining Model 1.5 with IBM SPSS Menu Commands -- Investigating a Random Level 2 Slope -- Defining Models 1.6 and 1.7 with IBM SPSS Menu Commands -- Model 1.6 -- Model 1.7 -- Add First Interaction to Model 1.7: gmorprod*assessjob -- Add Second Interaction to Model 1.7: gmresources*assessjob -- Add Third Interaction to Model 1.7: stability*assessjob
Add Fourth Interaction to Model 1.7: female*assessjob -- Multivariate Multilevel Model for Correlated Observed Outcomes -- The Data -- Research Questions -- Formulating the Basic Model -- Model 2.1 Null Model (No Predictors) -- Defining Model 2.1 (Null) with IBM SPSS Menu Commands -- Examining the Syntax Commands -- Interpreting the Output From Model 2.1 -- Model 2.2 Building a Complete Model (Predictors and Cross-Level Interactions) -- Defining Model 2.2 with IBM SPSS Menu Commands -- Add First Interaction to Model 2.2: Index1*gmacadpress -- Add Second Interaction to Model 2.2: Index1*female -- Interpreting the Output From Model 2.2 -- Testing the Hypotheses -- Correlations Between Tests at Each Level -- Defining Model 2.3 with IBM SPSS Menu Commands -- Investigating a Random Slope -- Defining a Parallel Growth Process -- The Data -- Research Questions -- Preparing the Data -- Model 3.1 Specifying the Time Model
Defining Model 3.1 with IBM SPSS Menu Commands -- Add First Interaction to Model 3.1: math*orthtime -- Add Second Interaction to Model 3.1: math*orthquadtime -- Interpreting the Output From Model 3.1 -- Model 3.2 Adding the Predictors -- Defining Model 3.2 with IBM SPSS Menu Commands -- Add First Interaction to Model 3.2: math*schcontext -- Add Second Interaction to Model 3.2: math*female -- Add Third Interaction to Model 3.2: math*orthtime -- Add Fourth Interaction to Model 3.2: math*orthquadtime -- Add Fifth Interaction to Model 3.2: schcontext*math*orthtime -- Add Sixth Interaction to Model 3.2: female*math*orthtime -- Interpreting the Output From Model 3.2 -- Further Considerations -- Defining Model 3.3 with IBM SPSS Menu Commands -- Summary -- ch. 8 Cross-Classified Multilevel Models -- Students Cross-Classified in High Schools and Postsecondary Institutions -- Research Questions -- The Data -- Descriptive Statistics -- Defining Models in IBM SPSS
Model 1.1 Adding a Set of Level 1 and Level 2 Predictors -- Defining Model 1.1 with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.1 -- Model 1.2 Investigating a Random Slope -- Defining Model 1.2 with IBM SPSS Menu Commands -- Interpreting the Output From Model 1.2 -- Model 1.3 Explaining Variation Between Variables -- Defining Model 1.3 with IBM SPSS Menu Commands -- Add Interaction to Model 1.3: gmlowSES_mean*gmfemale -- Interpreting the Output From Model 1.3 -- Developing a Cross-Classified Teacher Effectiveness Model -- The Data Structure and Model -- Research Questions -- Model 2.1 Intercept-Only Model (Null) -- Defining Model 2.1 (Null) with IBM SPSS Menu Commands -- Interpreting Output From Model 2.1 (Null) -- Model 2.2 Defining the Cross-Classified Model with Previous Achievement -- Defining Model 2.2 with IBM SPSS Menu Commands -- Interpreting the Output From Model 2.2
Model 2.3 Adding Teacher Effectiveness and a Student Background Control -- Defining Model 2.3 with IBM SPSS Menu Commands -- Interpreting the Output From Model 2.3 -- Model 2.4 Adding a School-Level Predictor and a Random Slope -- Defining Model 2.4 with IBM SPSS Menu Commands -- Interpreting the Output From Model 2.4 -- Model 2.5 Examining Level 3 Differences Between Institutions -- Defining Model 2.5 with IBM SPSS Menu Commands -- Interpreting the Output From Model 2.5 -- Model 2.6 Adding a Level 3 Cross-Level Interaction -- Defining Model 2.6 with IBM SPSS Menu Commands -- Add Interaction to Model 2.6: effmath2*schqual -- Interpreting the Output From Model 2.6 -- Summary -- ch. 9 Concluding Thoughts -- References -- Appendices -- Appendix A Syntax Statements -- Appendix B Model Comparisons Across Software Applications -- Appendix C Syntax Routine to Estimate Rho From Model's Variance Components.
Subject PASW (Computer file)
SPSS (Computer file)
Social sciences -- Longitudinal studies.
Social sciences -- Statistical methods.
Added Author Thomas, Scott L., author.
Tabata, Lynn Naomi, author.
Ebooks Corporation
Related To Print version: Heck, Ronald H. Multilevel and longitudinal modeling with IBM SPSS. Second edition. New York ; London : Routledge, 2013 9780415817103 (DLC) 2013004161
ISBN 9780203701249 (ebk)
0203701240 (ebk)
9780415817103 (hbk)
0415817102 (hbk)
9780415817110 (pbk)
0415817110 (pbk)
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