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E-raamat: Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches 4th edition [Taylor & Francis e-raamat]

(University of Vermont, USA), (University of Hawaii, Manoa, USA)
  • Formaat: 388 pages, 95 Tables, black and white; 3 Line drawings, black and white; 56 Halftones, black and white; 59 Illustrations, black and white
  • Sari: Quantitative Methodology Series
  • Ilmumisaeg: 07-Apr-2020
  • Kirjastus: Routledge
  • ISBN-13: 9780429060274
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  • Taylor & Francis e-raamat
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Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches 4th editionSuurem pilt
  • Formaat: 388 pages, 95 Tables, black and white; 3 Line drawings, black and white; 56 Halftones, black and white; 59 Illustrations, black and white
  • Sari: Quantitative Methodology Series
  • Ilmumisaeg: 07-Apr-2020
  • Kirjastus: Routledge
  • ISBN-13: 9780429060274
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429060274
Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives.

New to this edition:











An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals;





Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches;





Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements;





An expanded set of applied examples used throughout the text;





Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online.

This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.
Acknowledgments x
Preface xi
1 Introduction
1(25)
Providing a Conceptual Overview
3(4)
Contrasting Linear Models
7(4)
Univariate Analysis
11(2)
Multiple Regression
12(1)
Analysis of Variance
12(1)
Multivariate Analysis
13(3)
Multivariate Analysis of Variance
13(2)
Structural Equation Modeling
15(1)
Multilevel Data Structures
16(5)
Multilevel Multivariate Model
18(1)
Multilevel Structural Model
19(2)
Summary
21(2)
References
23(3)
2 Getting Started with Multilevel Analysis
26(40)
Introduction
26(1)
The Big Picture
27(1)
From Single-level to Multilevel Analysis
28(7)
Summarizing Some Differences between Single-level and Multilevel Analyses
32(3)
Developing a General Multilevel Modeling Strategy
35(11)
Step 1 Partitioning the Variance in an Outcome
36(4)
Step 2 Adding Level-1 Predictors to Explain Variability in Intercepts
40(1)
Step 3 Adding Level-2 Predictors to Explain Variability in Intercepts
41(1)
Step 4 Examining Possible Variation in Level-1 Slopes
42(2)
Step 5 Adding Predictors to Explain Variation in Slopes
44(2)
Specifying the Basic Two-level Model with Path Diagrams
46(2)
Model Estimation
48(11)
Maximum Likelihood Estimation
49(2)
Model Convergence and Fit
51(1)
Considerations for ML Estimation
52(2)
An ML Illustration
54(1)
Bayesian Estimation
54(2)
Bayesian and ML Estimation with a Limited Number of Groups
56(3)
Summary
59(2)
References
61(5)
3 Multilevel Regression Models
66(35)
Introduction
66(1)
Building a Model to Explain Employee Morale
67(20)
Model 1 One-way ANOVA or Null Model
70(4)
Model 2 Random-Intercept with Level-1 Predictors
74(7)
Model 3 Specifying a Level-1 Random Slope
81(2)
Model 4 Explaining Variation in the Level-2 Intercept and Slope
83(1)
Model 4 Output
84(3)
Examining Residuals
87(5)
Centering Predictors
92(6)
Centering Predictors in Models with Random Slopes
96(2)
Summary
98(2)
References
100(1)
4 Extending the Two-level Regression Model
101(30)
Introduction
101(1)
Three-level Univariate Model
101(13)
Developing a Three-level Univariate Model
103(1)
Research Questions
103(1)
Data
104(1)
Model 1 Null (No Predictors) Model
105(1)
Model 2 Defining Predictors at Level 1
106(1)
Model 3 Defining Predictors at Level 2
107(3)
Model 4 Examining an Interaction at Level 2
110(1)
Model 5 Examining a Randomly Varying Slope at Level 3
111(1)
Model 6 Adding Level-3 Predictors
112(2)
Accounting for Variance
114(1)
Cross-classified Data Structures
114(12)
Students Cross-classified in High Schools and Postsecondary Institutions
117(1)
Research Questions
118(1)
Model 1 Developing a Null Model
118(3)
Model 2 Adding Within-cell (Level-1) Predictors
121(1)
Model 3 Adding Between-cell Predictors
122(1)
Model 4 Adding a Randomly Varying Level-1 Slope
123(1)
Model 5 Explaining Variability in Random Slopes
124(2)
Summary
126(4)
References
130(1)
5 Methods for Examining Individual and Organizational Change
131(49)
Introduction
132(1)
Analyzing Longitudinal Data
132(2)
Repeated Measures ANOVA
133(1)
Growth Modeling and Other Approaches
133(1)
Random-coefficients Growth Modeling
134(5)
Defining the Level-1 Model
135(3)
Defining the Level-2 Model
138(1)
Extending the Model to Examine Changes Between Groups
139(1)
Examining Changes in Students' Math Scores
139(13)
Model 1 Unconditional Means Model
140(1)
Model 2 Unconditional Growth Model
141(3)
Model 3 Unconditional Growth Model with Random Time Parameter
144(1)
Investigating Subsets of Individuals' Trajectories
144(1)
Model 4 Adding a Quadratic Polynomial Term
144(4)
Model 5 Adding a Between-subjects Predictor
148(1)
Model 6 Deciding on the Level-1 Covariance Structure
149(3)
Building a Two-level Growth Model Using Age
152(3)
Model 1 Unconditional Growth Model with Random Age Variable
153(1)
Model 2 Final Growth Model with Between-subjects Predictor
154(1)
Examining Changes in Institutions' Graduation Rates
155(8)
Model 1 Unconditional Means Model
157(1)
Model 2 Unconditional Growth Model with Random Time Slope
157(2)
Model 3 Adding a Time-Varying Covariate
159(1)
Model 4 Examining Whether Instructional Support Influences Growth in Graduation
160(1)
Model 5 Testing a Random Effect for Instructional Support
161(1)
Model 6 Adding Between-Institution Covariates
161(2)
Developing Piecewise Growth Models
163(4)
Examining Student Growth in Literacy
163(2)
Changes Due to a Policy
165(2)
Fixed-effects Regression Models
167(6)
Graduation Growth in One Higher Education System
170(3)
Summary
173(5)
References
178(2)
6 Multilevel Models with Categorical Variables
180(48)
Introduction
180(2)
Estimating the Models
182(3)
Specifying Models for Binary, Ordinal, and Nominal Outcomes
185(17)
Binary Outcome
185(1)
Logit Link Function
186(3)
Probit Link Function
189(4)
Estimating the Intraclass Correlation
193(1)
Ordinal Logit Outcome
193(5)
Ordinal Probit Outcome
198(2)
MPLUS Latent Response Formulation
200(1)
Unordered Categorical (Nominal) Outcome
201(1)
Explaining Student Persistence
202(21)
Binary Outcome
202(1)
Null Model
203(2)
Ordinal Outcome
205(1)
Estimating Probabilities from Logit and Probit Coefficients
206(1)
Dichotomous Outcome: Adding Level-1 and Level-2 Predictors
207(2)
Ordinal Outcomes: Adding Level-1 and Level-2 Predictors
209(2)
Examining a Cross-level Interaction with Continuous by Categorical Predictors
211(2)
Examining a Cross-level Interaction with Two Continuous Variables
213(4)
Count Data
217(3)
Building a Level-1 and Level-2 Model
220(3)
Summary
223(3)
References
226(2)
7 Multilevel Structural Equation Models
228(57)
Multilevel Models with Latent Variables
228(3)
Multilevel Measurement Models
231(5)
Multilevel Factor Variance Components
234(2)
Types of Multilevel Factors
236(3)
Model 1 Individual-level Factor
236(1)
Model 2 Within-cluster Factor
236(1)
Model 3 Shared Cluster-level Factor
237(1)
Model 4 Configural Factor Structure
238(1)
Model 5 Shared and Configural Factors
239(1)
Estimating MCFA Models
239(3)
Developing a Two-level Model
242(7)
Model 1 and Model 2 Results
243(3)
Model 3 and Model 4 Results
246(3)
Extending the CFA Model to Three Levels
249(6)
Model 5 Defining a Configural Factor Model at Levels 1, 2, and 3
251(1)
Model 6 Restricting Errors to Zero at Level 2
252(3)
Multilevel CFA with Ordinal Observed Indicators
255(6)
Developing a CFA Model
256(5)
Multilevel Models with Latent Variables and Covariates
261(16)
Model 1 Specifying a Two-level Latent Factor Model with Covariates
263(5)
Model 2 Specifying a Random Level-1 Slope
268(2)
Model 3 Adding a Latent Factor Between Groups
270(5)
Model 4 Testing an Indirect (or Mediating) Effect
275(2)
Summary
277(4)
References
281(4)
8 Multilevel Latent Growth and Mixture Models
285(58)
Introduction
286(1)
Latent Growth Models
286(2)
Intercept and Slope (IS) and Level and Shape (LS) Models
287(1)
Defining the Latent Growth Model
288(12)
Measurement Model
289(2)
Structural Model
291(2)
Multilevel Analysis of Growth
293(1)
Examining Variables that Influence Student Growth in Science
294(4)
Piecewise Latent Growth Model
298(2)
Latent Variable Mixture Modeling
300(35)
Defining Latent Profiles and Classes
302(4)
An Example Latent Profile Analysis
306(7)
Two-level Models
313(1)
Examining Heterogeneity in Intercepts
313(7)
Investigating Latent Classes for Random Slopes at Level 2
320(4)
Alternative Model Specification
324(1)
Growth Mixture Models
324(2)
Examining Latent Classes in Students' Growth in Science
326(4)
Two-level GMM
330(5)
Summary
335(4)
References
339(4)
9 Data Considerations in Examining Multilevel Models
343(37)
Complex Samples, Design Effects, and Sample Weights
343(7)
An Example Using Multilevel Weights
347(3)
Parameter Bias and Statistical Power
350(12)
Parameter Bias
350(1)
Power
351(1)
An Example
352(3)
Anticipated Effect Size and Power
355(3)
Mplus Monte Carlo Study
358(4)
Design Complexity
362(1)
Missing Data
363(9)
Missing Data at Level 2
367(1)
Imputed Data Sets
368(4)
Concluding Thoughts
372(3)
References
375(5)
Index 380
Ronald H. Heck is Professor of Education at the University of Hawaii at Mnoa. His areas of interest include organizational theory, policy, and quantitative research methods.

Scott L. Thomas is Professor and Dean of the College of Education and Social Services, University of Vermont. His specialties include sociology of education, policy, and quantitative research methods
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