Hierarchical linear models: applications and data analysis methods
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Thousand Oaks [u.a.]
Sage Publ.
2006
|
| Ausgabe: | 2. ed., [Nachdr.] |
| Schriftenreihe: | Advanced quantitative techniques in the social sciences
1 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Bryk's name appears first on the previous edition. - Includes bibliographical references (p. 467-476) and index |
| Beschreibung: | XXIV, 485 S. graph. Darst. |
| ISBN: | 9780761919049 076191904X |
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| 245 | 1 | 0 | |a Hierarchical linear models |b applications and data analysis methods |c Stephen W. Raudenbush ; Anthony S. Bryk |
| 250 | |a 2. ed., [Nachdr.] | ||
| 264 | 1 | |a Thousand Oaks [u.a.] |b Sage Publ. |c 2006 | |
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Datensatz im Suchindex
| _version_ | 1804136771990061056 |
|---|---|
| adam_text | Contents
Acknowledgments for the Second Edition
Series Editor s Introduction to Hierarchical Linear Models
Series Editor s Introduction to the Second Edition
1.
Hierarchical Data Structure: A Common Phenomenon
Persistent Dilemmas in the Analysis of Hierarchical Data
A Brief History of the Development of Statistical Theory for
Hierarchical Models
Early Applications of Hierarchical Linear Models
Improved Estimation of Individual Effects
Modeling Cross-Level Effects
Partitioning Variance-Covariance Components
New Developments Since the First Edition
An Expanded Range of Outcome Variables
Incorporating Cross-Classified Data Structures
Multivariate Model
Latent Variable Models
Bayesian Inference
Organization of the Book
2.
Preliminaries
A Study of the SES-Achievement Relationship in One School
A Study of the SES-Achievement Relationship in Two
Schools
A Study of the SES-Achievement Relationship in
A General Model and Simpler Submodels
One-Way ANOVA with Random Effects
Means-as-Outcomes Regression
One-Way ANCOVA with Random Effects
Random-Coefficients Regression Model
Intercepts- and Slopes-as-Outcomes
A Model with Nonrandomly Varying Slopes
Section Recap
Generalizations of the Basic Hierarchical Linear Model
Multiple Xs and Multiple
Generalization of the Error Structures at Level
Level
Extensions Beyond the Basic Two-Level Hierarchical
Linear Model
Choosing the Location of X and
Location of the Xs
Location of Ws
Summary of Terms and Notation Introduced in This Chapter
A Simple Two-Level Model
Notation and Terminology Summary
Some Definitions
Submodel Types
Centering Definitions
Implications for
3.
Hierarchical Linear Models
Estimation Theory
Estimation of Fixed Effects
Estimation of Random Level-
Estimation of Variance and Covariance Components
Hypothesis Testing
Hypothesis Tests for Fixed Effects
Hypothesis Tests for Random Level-1 Coefficients
Hypothesis Testing for Variance and Covariance
Components
Summary of Terms Introduced in This Chapter
4.
Introduction
The One-Way ANOVA
The Model
Results
Regression with Means-as-Outcomes
The Model
Results
The Random-Coefficient Model
The Model
Results
An Intercepts- and Slopes-as-Outcomes Model
The Model
Results
Estimating the Level-
Ordinary Least Squares
Unconditional Shrinkage
Conditional Shrinkage
Comparison of Interval Estimates
Cautionary Note
Summary of Terms Introduced in This Chapter
5.
Background Issues in Research on Organizational Effects
Formulating Models
Person-Level Model (Level
Organization-Level Model (Level
Case
Random-Intercept Models
A Simple Random-Intercept Model
Example: Examining School Effects on Teacher Efficacy
Comparison of Results with Conventional Teacher-Level
and School-Level Analyses
A Random-Intercept Model with Level-1 Covariates 111
Example: Evaluating Program Effects on Writing
Comparison of Results with Conventional Student- and
Classroom-Level Analyses
Case
Organizations via Intercepts- and Slopes-as-Outcomes
Models
Difficulties Encountered in Past Efforts at Modeling
Regression Slopes-as-Outcomes
Example: The Social Distribution of Achievement in Public
and Catholic High Schools
Applications with Both Random and Fixed Level-] Slopes
Special Topics
Applications with Heterogeneous Level-
Example: Modeling Sector Effects on the Level-] Residual
Variance in Mathematics Achievement
Data-Analytic Advice About the Presence of Heterogeneity
at
Centering Level-
Applications
Estimating Fixed Level-
Disentangling Person-Level and Compositional Effects
Estimating Level-2 Effects While Adjusting for Level-1
Covariates
Estimating the Variances of Level-1 Coefficients
Estimating Random Level-1 Coefficients
Use of Proportion Reduction in Variance Statistics
Estimating the Effects of Individual Organizations
Conceptualization of Organization Specific Effects
Commonly Used Estimates of School Performance
Use of Empirical
Threats to Valid Inference Regarding Performance
Indicators
Power Considerations in Designing Two-Level Organization
Effects Studies
6.
Background Issues in Research on Individual Change
Formulating Models
Repeated-Observations Model (Level
Person-Level Model (Level
A Linear Growth Model
Example: The Effect of Instruction on Cognitive Growth
A Quadratic Growth Model
Example: The Effects of Maternal Speech on Children s
Vocabulary
Some Other Growth Models
More Complex Level-
Piecewise Linear Growth Models
Time-Varying Covariates
Centering of Level-
Change
Definition of the Intercept in Linear Growth Models
Definitions of Other Growth Parameters in Higher-Order
Polynomial Models
Possible Biases in Studying
Estimation of the Variance of Growth Parameters
Comparison of Hierarchical, Multivariate Repeated-Measures,
and Structural Equation Models
Multivariate Repeated-Measures (MRM) Model
Structural Equation Models
Case
Case
Case
Effects of Missing Observations at Level
Using a Hierarchical Model to Predict Future Status
Power Considerations in Designing Studies of Growth and
Change
7.
Other Cases where Level-
Introduction
The Hierarchical Structure of Meta-Analytic Data
Extensions to Other Level-
Organization of This Chapter
Formulating Models for Meta-Analysis
Standardized Mean Differences
Level-1 (Within-Studies) Model
Level-2 (Between-Studies) Model
Combined Model
Estimation
Example: The Effect of Teacher Expectancy on Pupil IQ
Unconditional Analysis
Conditional Analysis
Bayesian Meta-AnalysL·
Other Level-
Example: Correlates of Diversity
The Multivariate V-Known Model
Level-1 Model
Level-2 Model
Meta-
Level-1 Model
Level-2 Model
illustrative Example
8.
Formulating and Testing Three-Level Models
A Fully Unconditional Model
Conditional Models
Many Alternative Modeling Possibilities
Hypothesis Testing in the Three-Level Model
Example: Research on Teaching
Studying Individual Change Within Organizations
Unconditional Model
Conditional Model
Measurement Models at Level
Example: Research on School Climate
Example: Research on School-Based Professional
Community and the Factors That Facilitate It
Estimating Random Coefficients in Three-Level Models
9.
Introduction
Thinking about Model Assumptions
Organization of the Chapter
Key Assumptions of a Two-Level Hierarchical Linear Model
Building the Level-1 Model
Empirical Methods to Guide Model Building at Level
Specification Issues at Level
Examining Assumptions about Level-1 Random Effects
Building the Level-2 Model
Empirical Methods to Guide Model Building at Level
Specification Issues at Level
Examining Assumptions about Level-2 Random Effects
Robust Standard Errors
Illustration
Validity of Inferences when Samples are Small
Inferences about the Fixed Effects
Inferences about the Variance Components
Inferences about Random Level-1 Coefficients
Appendix
Misspecification of the Level-1 Structural Model
Level-1 Predictors Measured with Error
10.
The Two-Level
Level-1 Sampling Model
Level-1 Link Function
Level-1 Structural Model
Two- and Three-Level Models for Binary Outcomes
Level-1 Sampling Model
Level-l Link Function
Level-1 Structural Model
Level-2 and
A Bernoulli Example: Grade Retention in Thailand
Population-Average Models
A Binomial Example: Course Failures During First
Semester of Ninth Grade
Hierarchical Models for Count Data
Level-1 Sampling Model
Level-1 Link Function
Level-1 Structural Model
Leveli
Example: Homicide Rates in Chicago Neighborhoods
Hierarchical Models for Ordinal Data
The Cumulative Probability Model for Single-Level Data
Extension to Two Levels
An Example: Teacher Control and Teacher Commitment
Hierarchical Models for Multinomial Data
Level-1 Sampling Model
Level-1 Link Function
Level-1 Structural Model
Level-2 Model
Illustrative Example: Postsecondary Destinations
Estimation Considerations in Hierarchical Generalized Linear
Models
Summary of Terms Introduced in This Chapter
11.
Regression
Multiple Model-Based Imputation
Applying HIM to the Missing Data Problem
Regression when Predictors are Measured with Error
incorporating Information about Measurement Error in
Hierarchical Models
Regression with Missing Data and Measurement Errors
Estimating Direct and Indirect Effects of Latent Variables
A Three-Level Illustrative Example with Measurement
Error and Missing Data
The Model
A Two-Level Latent Variable Example for Individual
Growth
Nonlinear Item Response Models
A Simple Item Response Model
An Item Response Model for Multiple Traits
Two-Parameter Models
Summary of Terms Introduced in This Chapter
Missing Data Problems
Measurement Error Problems
12.
Formulating and Testing Models for Cross-Classified Random
Effects
Unconditional Model
Conditional Models
Example
Attainment in Scotland
Unconditional Model
Conditional Model
Estimating a Random Effect of Social Deprivation
Example
Growth During the Primary Years
Summary
Summary of Terms Introduced in This Chapter
13.
An Introduction to Bayesian Inference
Classical View
Bayesian View
Example: Inferences for a Normal Mean
Ctossical Approach
Bayesian Approach
Some Generalizations and Inferential Concerns
A Bayesian Perspective on Inference in Hierarchical Linear
Models
Full Maximum Likelihood (ML) ofy,T, and
REML Estimation of
The Basics of Bayesian Inference for the Two-Level HLM
Model for the Observed Data A 2
Stage-1 Prior All
Stage-2 Prior
Posterior Distributions
Relationship Between Fully
Inference
Example:
Bayes
Parameter Estimation and Inference
A Comparison Between Fully
Inference
Gibbs Sampling and Other Computational Approaches
Application of the Gibbs Sampler to Vocabulary Growth
Data
Summary of Terms Introduced in This Chapter
14.
Models, Estimators, and Algorithms
Overview of Estimation via ML and
ML Estimation
Bayesian Inference
ML Estimation for Two-Level HLMs
ML Estimation via EM
The Model
M
E
Putting the Pieces Together
ML Estimation for HLM via Fisher Scoring
Application of Fisher-IGLS to Two-Level ML
ML Estimation for the Hierarchical Multivariate Linear
Model (HMLM)
The Model
EM Algorithm
Fisher-IGLS Algorithm
Estimation
Discussion
Estimation
Numerical Integration for Hierarchical Models
Application to Two-Level Data with Binary Outcomes
Penalized Quasi-Likelihood
Closer Approximations to ML
Representing the Integral as a Laplace Transform
Application of Laplace to Two-Level Binary Data A&l
Generalizations to other Level-1 Models
Summary and Conclusions
References
Index
About the Authors
|
| adam_txt |
Contents
Acknowledgments for the Second Edition
Series Editor's Introduction to Hierarchical Linear Models
Series Editor's Introduction to the Second Edition
1.
Hierarchical Data Structure: A Common Phenomenon
Persistent Dilemmas in the Analysis of Hierarchical Data
A Brief History of the Development of Statistical Theory for
Hierarchical Models
Early Applications of Hierarchical Linear Models
Improved Estimation of Individual Effects
Modeling Cross-Level Effects
Partitioning Variance-Covariance Components
New Developments Since the First Edition
An Expanded Range of Outcome Variables
Incorporating Cross-Classified Data Structures
Multivariate Model
Latent Variable Models
Bayesian Inference
Organization of the Book
2.
Preliminaries
A Study of the SES-Achievement Relationship in One School
A Study of the SES-Achievement Relationship in Two
Schools
A Study of the SES-Achievement Relationship in
A General Model and Simpler Submodels
One-Way ANOVA with Random Effects
Means-as-Outcomes Regression
One-Way ANCOVA with Random Effects
Random-Coefficients Regression Model
Intercepts- and Slopes-as-Outcomes
A Model with Nonrandomly Varying Slopes
Section Recap
Generalizations of the Basic Hierarchical Linear Model
Multiple Xs and Multiple
Generalization of the Error Structures at Level
Level
Extensions Beyond the Basic Two-Level Hierarchical
Linear Model
Choosing the Location of X and
Location of the Xs
Location of Ws
Summary of Terms and Notation Introduced in This Chapter
A Simple Two-Level Model
Notation and Terminology Summary
Some Definitions
Submodel Types
Centering Definitions
Implications for
3.
Hierarchical Linear Models
Estimation Theory
Estimation of Fixed Effects
Estimation of Random Level-
Estimation of Variance and Covariance Components
Hypothesis Testing
Hypothesis Tests for Fixed Effects
Hypothesis Tests for Random Level-1 Coefficients
Hypothesis Testing for Variance and Covariance
Components
Summary of Terms Introduced in This Chapter
4.
Introduction
The One-Way ANOVA
The Model
Results
Regression with Means-as-Outcomes
The Model
Results
The Random-Coefficient Model
The Model
Results
An Intercepts- and Slopes-as-Outcomes Model
The Model
Results
Estimating the Level-
Ordinary Least Squares
Unconditional Shrinkage
Conditional Shrinkage
Comparison of Interval Estimates
Cautionary Note
Summary of Terms Introduced in This Chapter
5.
Background Issues in Research on Organizational Effects
Formulating Models
Person-Level Model (Level
Organization-Level Model (Level
Case
Random-Intercept Models
A Simple Random-Intercept Model
Example: Examining School Effects on Teacher Efficacy
Comparison of Results with Conventional Teacher-Level
and School-Level Analyses
A Random-Intercept Model with Level-1 Covariates 111
Example: Evaluating Program Effects on Writing
Comparison of Results with Conventional Student- and
Classroom-Level Analyses
Case
Organizations via Intercepts- and Slopes-as-Outcomes
Models
Difficulties Encountered in Past Efforts at Modeling
Regression Slopes-as-Outcomes
Example: The Social Distribution of Achievement in Public
and Catholic High Schools
Applications with Both Random and Fixed Level-] Slopes
Special Topics
Applications with Heterogeneous Level-
Example: Modeling Sector Effects on the Level-] Residual
Variance in Mathematics Achievement
Data-Analytic Advice About the Presence of Heterogeneity
at
Centering Level-
Applications
Estimating Fixed Level-
Disentangling Person-Level and Compositional Effects
Estimating Level-2 Effects While Adjusting for Level-1
Covariates
Estimating the Variances of Level-1 Coefficients
Estimating Random Level-1 Coefficients
Use of Proportion Reduction in Variance Statistics
Estimating the Effects of Individual Organizations
Conceptualization of Organization Specific Effects
Commonly Used Estimates of School Performance
Use of Empirical
Threats to Valid Inference Regarding Performance
Indicators
Power Considerations in Designing Two-Level Organization
Effects Studies
6.
Background Issues in Research on Individual Change
Formulating Models
Repeated-Observations Model (Level
Person-Level Model (Level
A Linear Growth Model
Example: The Effect of Instruction on Cognitive Growth
A Quadratic Growth Model
Example: The Effects of Maternal Speech on Children's
Vocabulary
Some Other Growth Models
More Complex Level-
Piecewise Linear Growth Models
Time-Varying Covariates
Centering of Level-
Change
Definition of the Intercept in Linear Growth Models
Definitions of Other Growth Parameters in Higher-Order
Polynomial Models
Possible Biases in Studying
Estimation of the Variance of Growth Parameters
Comparison of Hierarchical, Multivariate Repeated-Measures,
and Structural Equation Models
Multivariate Repeated-Measures (MRM) Model
Structural Equation Models
Case
Case
Case
Effects of Missing Observations at Level
Using a Hierarchical Model to Predict Future Status
Power Considerations in Designing Studies of Growth and
Change
7.
Other Cases where Level-
Introduction
The Hierarchical Structure of Meta-Analytic Data
Extensions to Other Level-
Organization of This Chapter
Formulating Models for Meta-Analysis
Standardized Mean Differences
Level-1 (Within-Studies) Model
Level-2 (Between-Studies) Model
Combined Model
Estimation
Example: The Effect of Teacher Expectancy on Pupil IQ
Unconditional Analysis
Conditional Analysis
Bayesian Meta-AnalysL·
Other Level-
Example: Correlates of Diversity
The Multivariate V-Known Model
Level-1 Model
Level-2 Model
Meta-
Level-1 Model
Level-2 Model
illustrative Example
8.
Formulating and Testing Three-Level Models
A Fully Unconditional Model
Conditional Models
Many Alternative Modeling Possibilities
Hypothesis Testing in the Three-Level Model
Example: Research on Teaching
Studying Individual Change Within Organizations
Unconditional Model
Conditional Model
Measurement Models at Level
Example: Research on School Climate
Example: Research on School-Based Professional
Community and the Factors That Facilitate It
Estimating Random Coefficients in Three-Level Models
9.
Introduction
Thinking about Model Assumptions
Organization of the Chapter
Key Assumptions of a Two-Level Hierarchical Linear Model
Building the Level-1 Model
Empirical Methods to Guide Model Building at Level
Specification Issues at Level
Examining Assumptions about Level-1 Random Effects
Building the Level-2 Model
Empirical Methods to Guide Model Building at Level
Specification Issues at Level
Examining Assumptions about Level-2 Random Effects
Robust Standard Errors
Illustration
Validity of Inferences when Samples are Small
Inferences about the Fixed Effects
Inferences about the Variance Components
Inferences about Random Level-1 Coefficients
Appendix
Misspecification of the Level-1 Structural Model
Level-1 Predictors Measured with Error
10.
The Two-Level
Level-1 Sampling Model
Level-1 Link Function
Level-1 Structural Model
Two- and Three-Level Models for Binary Outcomes
Level-1 Sampling Model
Level-l Link Function
Level-1 Structural Model
Level-2 and
A Bernoulli Example: Grade Retention in Thailand
Population-Average Models
A Binomial Example: Course Failures During First
Semester of Ninth Grade
Hierarchical Models for Count Data
Level-1 Sampling Model
Level-1 Link Function
Level-1 Structural Model
Leveli
Example: Homicide Rates in Chicago Neighborhoods
Hierarchical Models for Ordinal Data
The Cumulative Probability Model for Single-Level Data
Extension to Two Levels
An Example: Teacher Control and Teacher Commitment
Hierarchical Models for Multinomial Data
Level-1 Sampling Model
Level-1 Link Function
Level-1 Structural Model
Level-2 Model
Illustrative Example: Postsecondary Destinations
Estimation Considerations in Hierarchical Generalized Linear
Models
Summary of Terms Introduced in This Chapter
11.
Regression
Multiple Model-Based Imputation
Applying HIM to the Missing Data Problem
Regression when Predictors are Measured with Error
incorporating Information about Measurement Error in
Hierarchical Models
Regression with Missing Data and Measurement Errors
Estimating Direct and Indirect Effects of Latent Variables
A Three-Level Illustrative Example with Measurement
Error and Missing Data
The Model
A Two-Level Latent Variable Example for Individual
Growth
Nonlinear Item Response Models
A Simple Item Response Model
An Item Response Model for Multiple Traits
Two-Parameter Models
Summary of Terms Introduced in This Chapter
Missing Data Problems
Measurement Error Problems
12.
Formulating and Testing Models for Cross-Classified Random
Effects
Unconditional Model
Conditional Models
Example
Attainment in Scotland
Unconditional Model
Conditional Model
Estimating a Random Effect of Social Deprivation
Example
Growth During the Primary Years
Summary
Summary of Terms Introduced in This Chapter
13.
An Introduction to Bayesian Inference
Classical View
Bayesian View
Example: Inferences for a Normal Mean
Ctossical Approach
Bayesian Approach
Some Generalizations and Inferential Concerns
A Bayesian Perspective on Inference in Hierarchical Linear
Models
Full Maximum Likelihood (ML) ofy,T, and
REML Estimation of
The Basics of Bayesian Inference for the Two-Level HLM
Model for the Observed Data A\2
Stage-1 Prior All
Stage-2 Prior
Posterior Distributions
Relationship Between Fully
Inference
Example:
Bayes
Parameter Estimation and Inference
A Comparison Between Fully
Inference
Gibbs Sampling and Other Computational Approaches
Application of the Gibbs Sampler to Vocabulary Growth
Data
Summary of Terms Introduced in This Chapter
14.
Models, Estimators, and Algorithms
Overview of Estimation via ML and
ML Estimation
Bayesian Inference
ML Estimation for Two-Level HLMs
ML Estimation via EM
The Model
M
E
Putting the Pieces Together
ML Estimation for HLM via Fisher Scoring
Application of Fisher-IGLS to Two-Level ML
ML Estimation for the Hierarchical Multivariate Linear
Model (HMLM)
The Model
EM Algorithm
Fisher-IGLS Algorithm
Estimation
Discussion
Estimation
Numerical Integration for Hierarchical Models
Application to Two-Level Data with Binary Outcomes
Penalized Quasi-Likelihood
Closer Approximations to ML
Representing the Integral as a Laplace Transform
Application of Laplace to Two-Level Binary Data A&l
Generalizations to other Level-1 Models
Summary and Conclusions
References
Index
About the Authors |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Raudenbush, Stephen W. |
| author_facet | Raudenbush, Stephen W. |
| author_role | aut |
| author_sort | Raudenbush, Stephen W. |
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| dewey-ones | 300 - Social sciences |
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| dewey-sort | 3300.72 |
| dewey-tens | 300 - Social sciences |
| discipline | Soziologie Psychologie |
| discipline_str_mv | Soziologie Psychologie |
| edition | 2. ed., [Nachdr.] |
| format | Book |
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| id | DE-604.BV022614718 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:17:47Z |
| indexdate | 2024-07-09T21:01:42Z |
| institution | BVB |
| isbn | 9780761919049 076191904X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015820874 |
| oclc_num | 254520804 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-945 |
| owner_facet | DE-473 DE-BY-UBG DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-945 |
| physical | XXIV, 485 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Sage Publ. |
| record_format | marc |
| series | Advanced quantitative techniques in the social sciences |
| series2 | Advanced quantitative techniques in the social sciences |
| spelling | Raudenbush, Stephen W. Verfasser aut Hierarchical linear models applications and data analysis methods Stephen W. Raudenbush ; Anthony S. Bryk 2. ed., [Nachdr.] Thousand Oaks [u.a.] Sage Publ. 2006 XXIV, 485 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced quantitative techniques in the social sciences 1 Bryk's name appears first on the previous edition. - Includes bibliographical references (p. 467-476) and index Sozialwissenschaften Linear models Statistics Social sciences Statistical methods Datenanalyse (DE-588)4123037-1 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Hierarchisches System (DE-588)4159833-7 gnd rswk-swf Empirische Sozialforschung (DE-588)4014606-6 gnd rswk-swf Lineares Modell (DE-588)4134827-8 gnd rswk-swf Lineares Modell (DE-588)4134827-8 s Empirische Sozialforschung (DE-588)4014606-6 s DE-604 Hierarchisches System (DE-588)4159833-7 s 1\p DE-604 Datenanalyse (DE-588)4123037-1 s 2\p DE-604 Sozialwissenschaften (DE-588)4055916-6 s 3\p DE-604 Bryk, Anthony S. Sonstige oth Advanced quantitative techniques in the social sciences 1 (DE-604)BV023546702 1 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015820874&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Raudenbush, Stephen W. Hierarchical linear models applications and data analysis methods Advanced quantitative techniques in the social sciences Sozialwissenschaften Linear models Statistics Social sciences Statistical methods Datenanalyse (DE-588)4123037-1 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Hierarchisches System (DE-588)4159833-7 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Lineares Modell (DE-588)4134827-8 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4055916-6 (DE-588)4159833-7 (DE-588)4014606-6 (DE-588)4134827-8 |
| title | Hierarchical linear models applications and data analysis methods |
| title_auth | Hierarchical linear models applications and data analysis methods |
| title_exact_search | Hierarchical linear models applications and data analysis methods |
| title_exact_search_txtP | Hierarchical linear models applications and data analysis methods |
| title_full | Hierarchical linear models applications and data analysis methods Stephen W. Raudenbush ; Anthony S. Bryk |
| title_fullStr | Hierarchical linear models applications and data analysis methods Stephen W. Raudenbush ; Anthony S. Bryk |
| title_full_unstemmed | Hierarchical linear models applications and data analysis methods Stephen W. Raudenbush ; Anthony S. Bryk |
| title_short | Hierarchical linear models |
| title_sort | hierarchical linear models applications and data analysis methods |
| title_sub | applications and data analysis methods |
| topic | Sozialwissenschaften Linear models Statistics Social sciences Statistical methods Datenanalyse (DE-588)4123037-1 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Hierarchisches System (DE-588)4159833-7 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Lineares Modell (DE-588)4134827-8 gnd |
| topic_facet | Sozialwissenschaften Linear models Statistics Social sciences Statistical methods Datenanalyse Hierarchisches System Empirische Sozialforschung Lineares Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015820874&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023546702 |
| work_keys_str_mv | AT raudenbushstephenw hierarchicallinearmodelsapplicationsanddataanalysismethods AT brykanthonys hierarchicallinearmodelsapplicationsanddataanalysismethods |