| Description |
1 online resource. |
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text rdacontent |
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computer rdamedia |
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online resource rdacarrier |
| Series |
Quantitative methodology series.
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| 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 |
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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" |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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)
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SPSS (Computer file)
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Social sciences -- Longitudinal studies.
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Social sciences -- Statistical methods.
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| Added Author |
Thomas, Scott L., author.
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Tabata, Lynn Naomi, author.
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Ebooks Corporation
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| 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) |
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0203701240 (ebk) |
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9780415817103 (hbk) |
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0415817102 (hbk) |
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9780415817110 (pbk) |
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0415817110 (pbk) |
| OCLC # |
EBC1357604 |
