| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000cam a2200000Mi 4500 |
| 001 |
in00005495821 |
| 006 |
m o d |
| 007 |
cr ||||||||||| |
| 008 |
201001s2020 enka fob 001 0 eng d |
| 005 |
20240923170446.7 |
| 035 |
|
|
|a (OCoLC)on1201327711
|
| 040 |
|
|
|a SGPBL
|b eng
|e rda
|e pn
|c SGPBL
|d OCLCF
|d OCLCO
|d OCLCL
|
| 020 |
|
|
|a 9781529749410
|
| 020 |
|
|
|a 1529749417
|
| 020 |
|
|
|a 9781526421036
|
| 020 |
|
|
|a 1526421038
|
| 035 |
|
|
|a (OCoLC)1201327711
|
| 050 |
|
4 |
|a GN11
|
| 082 |
0 |
4 |
|a 301.0322
|
| 049 |
|
|
|a TXAM
|
| 100 |
1 |
|
|a Leckie, George,
|d 1981-
|e author.
|
| 245 |
1 |
0 |
|a Multilevel models for continuous responses /
|c by George Leckie ; edited by Paul Atkinson, Sara Delamont, Alexandru Cernat, Joseph W. Sakshaug & Richard A. Williams.
|
| 264 |
|
1 |
|a London :
|b SAGE Publications Ltd.,
|c 2020.
|
| 300 |
|
|
|a 1 online resource :
|b illustrations
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 520 |
8 |
|
|a Multilevel models (mixed-effect models or hierarchical linear models) are now a standard approach to analysing clustered and longitudinal data in the social, behavioural, and medical sciences. This entry focuses on multilevel linear regression models for continuous responses (outcomes or dependent variables). These models can be viewed as an extension of conventional linear regression models to account for and learn from the clustering in the data. Common clustered applications include studies of school effects on student achievement, hospital effects on patient health, and neighbourhood effects on respondent attitudes. In all these examples, multilevel models allow one to study how the regression relationships vary across clusters, to identify those cluster characteristics which predict such variation, to disentangle social processes operating at different levels of analysis, and to make cluster-specific predictions. Common longitudinal applications include studies of growth curves of individual height and weight and developmental trajectories of individual behaviours. In these examples, multilevel models allow one to describe and explain variation in growth rates and to simultaneously explore predictors of both intra- and interindividual variation. This entry introduces and illustrates this powerful class of model. It starts by focusing on the most commonly applied two-level random-intercept and -slope models. These are illustrated through two detailed examples of how these models can be applied to both clustered and longitudinal data and in both observational and experimental settings. This entry then reviews more flexible three-level, cross-classified, multiple membership, and multivariate response models.
|
| 588 |
|
|
|a Description based on XML content.
|
| 650 |
|
0 |
|a Anthropology.
|
| 650 |
|
0 |
|a Business and Management.
|
| 650 |
|
0 |
|a Communication and Media Studies.
|
| 650 |
|
6 |
|a Anthropologie.
|
| 650 |
|
7 |
|a anthropology.
|2 aat
|
| 650 |
|
7 |
|a Anthropology
|2 fast
|
| 700 |
1 |
|
|a Atkinson, Paul,
|d 1947-
|e editor.
|
| 700 |
1 |
|
|a Delamont, Sara,
|d 1947-
|e editor.
|
| 700 |
1 |
|
|a Cernat, Alexandru,
|e editor.
|
| 700 |
1 |
|
|a Sakshaug, Joseph W.,
|e editor.
|
| 700 |
1 |
|
|a Williams, Richard A.,
|d active 2020,
|e editor.
|
| 758 |
|
|
|i has work:
|a Multilevel models for continuous responses (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFQdGd4VHxvwC3JWTV8Ryq
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://methods.sagepub.com/foundations/multilevel-models-for-continuous-responses
|z Connect to the full text of this electronic book
|t 0
|
| 955 |
|
|
|a SAGE eBooks
|
| 955 |
|
|
|a SAGE Research Methods Foundations
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
f |
f |
|s 2d843493-8fce-4c51-b10d-ce5fe3de23e3
|i b98f855f-e465-4df3-98e2-d6e0335ab848
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e GN11
|h Library of Congress classification
|
| 998 |
f |
f |
|a GN11
|t 0
|l Available Online
|