The Resource Multilevel and longitudinal modeling with IBM SPSS, Ronald H. Heck, the University of Hawai'i at Manoa, Scott L. Thomas, Claremont Graduate University, Lynn N. Tabata, the University of Hawai'i at Manoa

Multilevel and longitudinal modeling with IBM SPSS, Ronald H. Heck, the University of Hawai'i at Manoa, Scott L. Thomas, Claremont Graduate University, Lynn N. Tabata, the University of Hawai'i at Manoa

Label
Multilevel and longitudinal modeling with IBM SPSS
Title
Multilevel and longitudinal modeling with IBM SPSS
Statement of responsibility
Ronald H. Heck, the University of Hawai'i at Manoa, Scott L. Thomas, Claremont Graduate University, Lynn N. Tabata, the University of Hawai'i at Manoa
Creator
Contributor
Subject
Language
eng
Cataloging source
NhCcYBP
http://library.link/vocab/creatorName
Heck, Ronald H
Dewey number
005.5/5
Illustrations
illustrations
Index
index present
LC call number
HA32
LC item number
.H39 2014
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Tabata, Lynn Naomi
  • Thomas, Scott L
  • Ebooks Corporation
Series statement
Quantitave methodology series
http://library.link/vocab/subjectName
  • Social sciences
  • Social sciences
Label
Multilevel and longitudinal modeling with IBM SPSS, Ronald H. Heck, the University of Hawai'i at Manoa, Scott L. Thomas, Claremont Graduate University, Lynn N. Tabata, the University of Hawai'i at Manoa
Link
http://www.umanitoba.eblib.com/EBLWeb/patron/?target=patron&extendedid=P_1357604_0
Instantiates
Publication
Note
Description based on print version record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Interpreting the Output From Model 2.1
  • 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
  • Adding Explanatory Variables at Level 2
  • ch. 8
  • Cross-Classified Multilevel Models
  • Students Cross-Classified in High Schools and Postsecondary Institutions
  • Research Questions
  • 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
  • Defining Model 2.2 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
  • Add First Interaction to Model 2.2: teachqual*treatment
  • Developing a Cross-Classified Teacher Effectiveness Model
  • 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
  • Add Second Interaction to Model 2.2: classcomp*treatment
  • 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
  • Interpreting the Output From Model 2.2
  • 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
  • Investigating a Change Due to Policy Implementation
  • 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
  • Data
  • Model 3.1
  • Establishing the Prepolicy and Policy Trends
  • Contents note continued:
  • 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
  • Data and Design
  • ch. 7
  • Multivariate Multilevel Models
  • Multilevel Latent-Outcome Model
  • Data
  • Research Questions
  • Defining the Constructs
  • Organizing the Data Set
  • Specifying the Model
  • Model 1.1
  • Null or "No-Predictors" Model
  • Assumptions of the Design
  • 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
  • Steps in the Regression Discontinuity Analysis
  • 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
  • Predictors in the Models
  • 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
  • Data
  • Specifying the Model
  • 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
  • Regression Discontinuity Models to Explain Learning Differences
  • 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
  • Data
  • Research Questions
  • Defining Model 2.1 with IBM SPSS Menu Commands
  • 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
  • Illustrating the Steps in Investigating a Proposed Model
  • Data
  • Defining the Three-Level Multilevel Model
  • 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
  • 1.
  • 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
  • One-Way ANOVA (No Predictors) Model
  • 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
  • 2.
  • ch. 5
  • Examining Individual Change with Repeated Measures Data
  • Ways to Examine Repeated Observations on Individuals
  • Considerations in Specifying a Linear Mixed Model
  • Example Study
  • Research Questions
  • Data
  • Examining the Shape of Students' Growth Trajectories
  • Graphing the Linear and Nonlinear Growth Trajectories with IBM SPSS Menu Commands
  • Select Subset of Individuals
  • Analyze a Level 1 Model with Fixed Predictors
  • 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
  • 3.
  • 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
  • Add the Level 2 Explanatory Variables
  • 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
  • 4.
  • 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)
  • Examine Whether a Particular Slope Coefficient Varies Between Groups
  • 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
  • 5.
  • 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
  • Example Experimental Design
  • Machine generated contents note:
  • Adding Cross-Level Interactions to Explain Variation in the Slope
  • Summary
  • ch. 6
  • Applications of Mixed Models for Longitudinal Data
  • Examining Growth in Undergraduate Graduation Rates
  • Research Questions
  • Data
  • Defining the Model
  • Level 1 Model
  • Level 2 Model
  • Level 3 Model
  • Syntax Versus IBM SPSS Menu Command Formulation
  • 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 Estimation and Other Typical Multilevel-Modeling Issues
  • 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
  • Sample Size
  • 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
  • Regression Discontinuity Analysis of a Math Treatment --
  • 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
  • ch. 1
  • Design Effects, Sample Weights, and the Complex Samples Routine in IBM SPSS
  • 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
  • Introduction to Multilevel Modeling with IBM SPSS
  • 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"
  • Our Intent
  • Centering
  • Grand-Mean Centering
  • Group-Mean Centering
  • Checking the Data
  • 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
  • Overview of Topics
  • Research Questions
  • 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)
  • Analysis of Multilevel Data Structures
  • 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
  • Partitioning Variation in an Outcome
  • 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
  • Developing a General Multilevel-Modeling Strategy
  • 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
Dimensions
unknown
Edition
Second edition.
Extent
1 online resource.
Form of item
online
Isbn
9781135074173
Isbn Type
(electronic bk.)
Media category
computer
Media MARC source
rdamedia
Media type code
c
Reproduction note
Electronic reproduction.
Specific material designation
remote
System control number
  • EBL1357604
  • (SIRSI)u2969884
Label
Multilevel and longitudinal modeling with IBM SPSS, Ronald H. Heck, the University of Hawai'i at Manoa, Scott L. Thomas, Claremont Graduate University, Lynn N. Tabata, the University of Hawai'i at Manoa
Link
http://www.umanitoba.eblib.com/EBLWeb/patron/?target=patron&extendedid=P_1357604_0
Publication
Note
Description based on print version record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Interpreting the Output From Model 2.1
  • 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
  • Adding Explanatory Variables at Level 2
  • ch. 8
  • Cross-Classified Multilevel Models
  • Students Cross-Classified in High Schools and Postsecondary Institutions
  • Research Questions
  • 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
  • Defining Model 2.2 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
  • Add First Interaction to Model 2.2: teachqual*treatment
  • Developing a Cross-Classified Teacher Effectiveness Model
  • 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
  • Add Second Interaction to Model 2.2: classcomp*treatment
  • 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
  • Interpreting the Output From Model 2.2
  • 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
  • Investigating a Change Due to Policy Implementation
  • 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
  • Data
  • Model 3.1
  • Establishing the Prepolicy and Policy Trends
  • Contents note continued:
  • 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
  • Data and Design
  • ch. 7
  • Multivariate Multilevel Models
  • Multilevel Latent-Outcome Model
  • Data
  • Research Questions
  • Defining the Constructs
  • Organizing the Data Set
  • Specifying the Model
  • Model 1.1
  • Null or "No-Predictors" Model
  • Assumptions of the Design
  • 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
  • Steps in the Regression Discontinuity Analysis
  • 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
  • Predictors in the Models
  • 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
  • Data
  • Specifying the Model
  • 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
  • Regression Discontinuity Models to Explain Learning Differences
  • 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
  • Data
  • Research Questions
  • Defining Model 2.1 with IBM SPSS Menu Commands
  • 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
  • Illustrating the Steps in Investigating a Proposed Model
  • Data
  • Defining the Three-Level Multilevel Model
  • 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
  • 1.
  • 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
  • One-Way ANOVA (No Predictors) Model
  • 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
  • 2.
  • ch. 5
  • Examining Individual Change with Repeated Measures Data
  • Ways to Examine Repeated Observations on Individuals
  • Considerations in Specifying a Linear Mixed Model
  • Example Study
  • Research Questions
  • Data
  • Examining the Shape of Students' Growth Trajectories
  • Graphing the Linear and Nonlinear Growth Trajectories with IBM SPSS Menu Commands
  • Select Subset of Individuals
  • Analyze a Level 1 Model with Fixed Predictors
  • 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
  • 3.
  • 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
  • Add the Level 2 Explanatory Variables
  • 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
  • 4.
  • 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)
  • Examine Whether a Particular Slope Coefficient Varies Between Groups
  • 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
  • 5.
  • 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
  • Example Experimental Design
  • Machine generated contents note:
  • Adding Cross-Level Interactions to Explain Variation in the Slope
  • Summary
  • ch. 6
  • Applications of Mixed Models for Longitudinal Data
  • Examining Growth in Undergraduate Graduation Rates
  • Research Questions
  • Data
  • Defining the Model
  • Level 1 Model
  • Level 2 Model
  • Level 3 Model
  • Syntax Versus IBM SPSS Menu Command Formulation
  • 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 Estimation and Other Typical Multilevel-Modeling Issues
  • 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
  • Sample Size
  • 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
  • Regression Discontinuity Analysis of a Math Treatment --
  • 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
  • ch. 1
  • Design Effects, Sample Weights, and the Complex Samples Routine in IBM SPSS
  • 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
  • Introduction to Multilevel Modeling with IBM SPSS
  • 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"
  • Our Intent
  • Centering
  • Grand-Mean Centering
  • Group-Mean Centering
  • Checking the Data
  • 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
  • Overview of Topics
  • Research Questions
  • 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)
  • Analysis of Multilevel Data Structures
  • 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
  • Partitioning Variation in an Outcome
  • 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
  • Developing a General Multilevel-Modeling Strategy
  • 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
Dimensions
unknown
Edition
Second edition.
Extent
1 online resource.
Form of item
online
Isbn
9781135074173
Isbn Type
(electronic bk.)
Media category
computer
Media MARC source
rdamedia
Media type code
c
Reproduction note
Electronic reproduction.
Specific material designation
remote
System control number
  • EBL1357604
  • (SIRSI)u2969884

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