@book{bates2010,
  title = {Lme4: {{Mixed-effects}} Modeling with {{R}}},
  author = {Bates, Douglas M},
  year = {2010},
  publisher = {{Springer}},
  langid = {english},
  }

@article{biesanz2004,
  title = {The {{Role}} of {{Coding Time}} in {{Estimating}} and {{Interpreting Growth Curve Models}}.},
  author = {Biesanz, Jeremy C. and {Deeb-Sossa}, Natalia and Papadakis, Alison A. and Bollen, Kenneth A. and Curran, Patrick J.},
  year = {2004},
  journal = {Psychological Methods},
  volume = {9},
  number = {1},
  pages = {30--52},
  issn = {1939-1463, 1082-989X},
  doi = {10.1037/1082-989X.9.1.30},
  langid = {english},
  }

@book{finch2019,
  title = {Multilevel Modeling Using {{R}}},
  author = {Finch, W Holmes and Bolin, Jocelyn E and Kelley, Ken},
  year = {2019},
  edition = {Second Edition},
  publisher = {Routledge},
  isbn = {9781138480674},
  langid = {english},
  }

@book{fox2016,
  title = {Applied Regression Analysis and Generalized Linear Models},
  author = {Fox, John and Fox, John},
  year = {2016},
  edition = {Third Edition},
  publisher = {{SAGE}},
  address = {{Los Angeles}},
  isbn = {978-1-4522-0566-3},
  langid = {english},
  }

@book{heck2020,
  title = {An Introduction to Multilevel Modeling Techniques: {{MLM}} and {{SEM}} Approaches},
  author = {Heck, Ronald H and Thomas, Scott Loring},
  year = {2020},
  edition = {Fourth Edition},
  publisher = {Routledge},
  isbn = {9780367182441},
  langid = {english},
  }

@book{hoffman2015,
  title = {Longitudinal Analysis: {{Modeling}} within-Person Fluctuation and Change},
  author = {Hoffman, Lesa},
  year = {2015},
  publisher = {Routledge},
  isbn = {978-0415876025},
  }

@book{hox2018,
  title = {Multilevel Analysis: {{Techniques}} and Applications},
  author = {Hox, Joop J and Moerbeek, Mirjam and van de Schoot, Rens},
  year = {2018},
  publisher = {{Taylor and Francis}},
  langid = {english},
  isbn = {9781138121362}
  }


@inbook{kim2014,
  title = {Multilevel Analysis of Assessment Data},
  author = {Kim, Jee-Seon and Anderson, Carolyn J and Keller, Bryan},
  booktitle = {Handbook of International Large-Scale Assessment},
  chapter = {18},
  publisher = {Routledge},
  year = {2014},
  pages = {389-424},
  langid = {english},
  url = {http://www.columbia.edu/~bsk2131/Kim_Anderson_Keller_2014.pdf}
}

@book{kreft1998,
  title = {Introducing Multilevel Modeling},
  author = {{Kreft, Ita} and {de Leeuw, Jan}},
  year = {1998},
  publisher = {{Sage Publications}},
  isbn = {0761951405}
}

@article{lai2015,
  title = {Examining the {{Rule}} of {{Thumb}} of {{Not Using Multilevel Modeling}}: {{The}} ``{{Design Effect Smaller Than Two}}'' {{Rule}}},
  shorttitle = {Examining the {{Rule}} of {{Thumb}} of {{Not Using Multilevel Modeling}}},
  author = {Lai, Mark H. C. and Kwok, Oi-man},
  year = {2015},
  month = jul,
  journal = {The Journal of Experimental Education},
  volume = {83},
  number = {3},
  pages = {423--438},
  issn = {0022-0973, 1940-0683},
  doi = {10.1080/00220973.2014.907229},
  abstract = {Educational researchers commonly use the rule of thumb of ``design effect smaller than 2'' as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models (which differ in the location of the clustering effect). With a 3 (design effect) \texttimes{} 5 (cluster size) \texttimes{} 4 (number of clusters) Monte Carlo simulation study we found that the rule should not be applied when researchers: (a) are interested in the effects of higher-level predictors, or (b) have a cluster size less than 10. Implications of the findings and limitations of the study are discussed.},
  langid = {english},
  }


@book{leeuw2008,
  title = {Handbook of Multilevel Analysis},
  editor = {de Leeuw, Jan and Meijer, Erik},
  year = {2008},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-73183-4 978-0-387-73186-5},
  langid = {english},
  lccn = {H61.25 .H357 2008},
  keywords = {Multilevel models (Statistics),Research Mathematical models,Social sciences},
  }

@article{peugh2010,
  title = {A Practical Guide to Multilevel Modeling},
  author = {Peugh, James L.},
  year = {2010},
  month = feb,
  journal = {Journal of School Psychology},
  volume = {48},
  number = {1},
  pages = {85--112},
  issn = {00224405},
  doi = {10.1016/j.jsp.2009.09.002},
  abstract = {Collecting data from students within classrooms or schools, and collecting data from students on multiple occasions over time, are two common sampling methods used in educational research that often require multilevel modeling (MLM) data analysis techniques to avoid Type-1 errors. The purpose of this article is to clarify the seven major steps involved in a multilevel analysis: (1) clarifying the research question, (2) choosing the appropriate parameter estimator, (3) assessing the need for MLM, (4) building the level-1 model, (5) building the level-2 model, (6) multilevel effect size reporting, and (7) likelihood ratio model testing. The seven steps are illustrated with both a cross-sectional and a longitudinal MLM example from the National Educational Longitudinal Study (NELS) dataset. The goal of this article is to assist applied researchers in conducting and interpreting multilevel analyses and to offer recommendations to guide the reporting of MLM analysis results.},
  langid = {english},
  }

@book{raudenbush2002,
  title = {Hierarchical Linear Models: {{Applications}} and Data Analysis Methods},
  author = {Raudenbush, Stephen W. and Bryk, Anthony S.},
  year = {2002},
  publisher = {SAGE},
  isbn = {9780761919049},
}

@book{singer2003,
  title = {Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence},
  author = {Singer, Judith D. and Willett, John B.},
  year = {2003},
  publisher = {{Oxford University Press}},
  isbn = {978-0-19-515296-8},
  lccn = {H62 .S47755 2002},
  keywords = {Longitudinal method,Research,Social sciences},
  }

@book{snijders2012,
  title = {Multilevel Analysis},
  author = {Snijders, Tom A. B. and Bosker, Roel J.},
  year = {2012},
  publisher = {SAGE},
  isbn = {9781849202015},
}

@book{west2022,
  title = {Linear Mixed Models: {{A}} Practical Guide Using Statistical Software},
  shorttitle = {Linear Mixed Models},
  author = {West, Brady T. and Welch, Kathleen B. and Galecki, Andrzej T},
  year = {2022},
  edition = {Third Edition},
  publisher = {Routledge},
  isbn = {9781032019321},
  langid = {english},
 }

@article{Wu2012,
  title = {Comparison of Methods for Estimating the Intraclass Correlation Coefficient for Binary Responses in Cancer Prevention Cluster Randomized Trials},
  author = {Wu, Sheng and Crespi, Catherine M. and Wong, Weng Kee},
  year = {2012},
  journal = {Contemporary Clinical Trials},
  volume = {33},
  number = {5},
  pages = {869--880},
  issn = {1559-2030},
  doi = {10.1016/j.cct.2012.05.004},
  abstract = {The intraclass correlation coefficient (ICC) is a fundamental parameter of interest in cluster randomized trials as it can greatly affect statistical power. We compare common methods of estimating the ICC in cluster randomized trials with binary outcomes, with a specific focus on their application to community-based cancer prevention trials with primary outcome of self-reported cancer screening. Using three real data sets from cancer screening intervention trials with different numbers and types of clusters and cluster sizes, we obtained point estimates and 95\% confidence intervals for the ICC using five methods: the analysis of variance estimator, the Fleiss-Cuzick estimator, the Pearson estimator, an estimator based on generalized estimating equations and an estimator from a random intercept logistic regression model. We compared estimates of the ICC for the overall sample and by study condition. Our results show that ICC estimates from different methods can be quite different, although confidence intervals generally overlap. The ICC varied substantially by study condition in two studies, suggesting that the common practice of assuming a common ICC across all clusters in the trial is questionable. A simulation study confirmed pitfalls of erroneously assuming a common ICC. Investigators should consider using sample size and analysis methods that allow the ICC to vary by study condition.},
  langid = {english},
 }
}

@Manual{clubSandwich,
    title = {clubSandwich: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample
Corrections},
    author = {James Pustejovsky},
    year = {2022},
    note = {R package version 0.5.8},
    url = {https://CRAN.R-project.org/package=clubSandwich},
  }
