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library.bib
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@Article{ bates2015,
author = {Bates, Douglas and Mächler, Martin and Bolker, Ben and
Walker, Steve},
date = {2015},
doi = {10.18637/jss.v067.i01},
journaltitle = {Journal of Statistical Software},
keywords = {clean_citation,paper3_pass_t},
number = {1},
pages = {1--48},
title = {Fitting Linear Mixed-Effects Models Using Lme4},
volume = {67}
}
@Book{ bates2019,
author = {Bates, Douglas and Maechler, Martin and Bolker, Ben},
url = {https://CRAN.R-project.org/package=mlmRev},
date = {2019},
note = {R package version 1.0-7},
title = {{{mlmRev}}: Examples from Multilevel Modelling Software
Review}
}
@Book{ chang2019,
author = {Chang, Winston and Cheng, Joe and Allaire, J. J. and Xie,
Yihui and McPherson, Jonathan},
url = {https://CRAN.R-project.org/package=shiny},
date = {2019},
title = {Shiny: {{Web}} Application Framework for {{R}}}
}
@Book{ gallucci2020,
author = {Gallucci, Marcello},
url = {https://github.com/gamlj/gamlj},
date = {2020},
title = {{{GAMLj}} Suite for Jamovi}
}
@Article{ goldstein1993,
author = {Goldstein, Harvey and Rasbash, Jon and Yang, Min and
Woodhouse, Geoffrey and Pan, Huiqi and Nuttall, Desmond and
Thomas, Sally},
date = {1993-01},
doi = {10.1080/0305498930190401},
file = {/home/jt/Zotero/storage/AR945VH4/Goldstein et al. - 1993 -
A Multilevel Analysis of School Examination Result.pdf},
issn = {0305-4985, 1465-3915},
journaltitle = {Oxford Review of Education},
langid = {english},
pages = {425--433},
shortjournal = {Oxford Review of Education},
title = {A Multilevel Analysis of School Examination Results},
volume = {19}
}
@Book{ ludecke2018,
author = {Lüdecke, Daniel},
date = {2018},
doi = {10.5281/zenodo.1308157},
note = {R package version 2.6.3},
title = {{{sjPlot}}: Data Visualization for Statistics in Social
Science}
}
###Book{ ludecke2018,
author = {Lüdecke, Daniel},
date = {2018},
doi = {10.5281/zenodo.1308157},
note = {R package version 2.6.3},
title = {{{sjPlot}}: Data Visualization for Statistics in Social
Science}
}
@Article{ nuttall1989,
abstract = {Studies of school effectiveness are briefly reviewed,
pointing to the need to study effectiveness for sub-groups
within each school as well as overall. The results of a
multilevel analysis of a large dataset covering the years
1985, 1986 and 1987 and using examination performance as
the outcome measure are presented, revealing substantial
differences between ethnic groups. The findings also show
that the effectiveness of a school varies along several
dimensions, and that there is also variation over time. The
implications of these findings are discussed.},
author = {Nuttall, Desmond L. and Goldstein, Harvey and Prosser,
Robert and Rasbash, Jon},
date = {1989-01-01},
doi = {10.1016/0883-0355(89)90027-X},
file = {/home/jt/Zotero/storage/UMMGWQC4/Nuttall et al. - 1989 -
Differential school
effectiveness.pdf;/home/jt/Zotero/storage/5ZGB3VXX/088303558990027X.html},
issn = {0883-0355},
journaltitle = {International Journal of Educational Research},
langid = {english},
pages = {769--776},
shortjournal = {International Journal of Educational Research},
title = {Differential School Effectiveness},
volume = {13}
}
@Book{ thejamoviproject2019,
author = {{The jamovi project}},
url = {https://www.jamovi.org},
date = {2019},
title = {Jamovi}
}
@Article{ west2012,
abstract = {At present, there are many software procedures available
enabling statisticians to fit linear mixed models (LMMs) to
continuous dependent variables in clustered or longitudinal
data sets. LMMs are flexible tools for analyzing
relationships among variables in these types of data sets,
in that a variety of covariance structures can be used
depending on the subject matter under study. The explicit
random effects in LMMs allow analysts to make inferences
about the variability between clusters or subjects in
larger hypothetical populations, and examine cluster- or
subject-level variables that explain portions of this
variability. These models can also be used to analyze
longitudinal or clustered data sets with data that are
missing at random (MAR), and can accommodate time-varying
covariates in longitudinal data sets. While the software
procedures currently available have many features in
common, more specific analytic aspects of fitting LMMs
(e.g., crossed random effects, appropriate hypothesis
testing for variance components, diagnostics, incorporating
sampling weights) may only be available in selected
software procedures. With this article, we aim to perform a
comprehensive and up-to-date comparison of the current
capabilities of software procedures for fitting LMMs, and
provide statisticians with a guide for selecting a software
procedure appropriate for their analytic goals.},
author = {West, Brady T. and Galecki, Andrzej T.},
date = {2012},
doi = {10.1198/tas.2011.11077},
eprint = {23606752},
eprinttype = {pmid},
file = {/home/jt/Zotero/storage/4KLSDNDJ/West and Galecki - 2012 -
An Overview of Current Software Procedures for Fit.pdf},
issn = {0003-1305},
journaltitle = {The American Statistician},
pages = {274--282},
shortjournal = {Am Stat},
title = {An Overview of Current Software Procedures for Fitting
Linear Mixed Models},
volume = {65}
}