book.bib

@book{field2012discovering,
  title={Discovering statistics using R},
  author={Field, Andy and Miles, Jeremy and Field, Zo{\"e}},
  year={2012},
  publisher={Sage publications}
}

@book{hastieElementsStatisticalLearning2009,
  address = {New York},
  edition = {Second},
  title = {The Elements of Statistical Learning: {{Data}} Mining, Inference, and Prediction},
  publisher = {{Springer}},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  year = {2009},
  file = {C:\\Users\\lissa102\\Zotero\\storage\\9SNFR93Q\\Elements of statistical learning.pdf}
}

@article{fukkinkDoesTrainingMatter2007,
  title = {Does Training Matter? {{A}} Meta-Analysis and Review of Caregiver Training Studies},
  volume = {22},
  issn = {08852006},
  shorttitle = {Does Training Matter?},
  abstract = {A review of studies published between 1980 and 2005 shows a significant positive effect of specialized training on the competency of caregivers in childcare (d = 0.45, S.E. = 0.10). Experimental results from the meta-analysis were significantly smaller for settings with no fixed curriculum content, delivery of the training at multiple sites and large-scale programs. Results were also smaller when tests were used that did not align closely with the content of the training. Furthermore, experimental results were smaller for the skills domain, compared to the knowledge and attitude domain. A subset of experiments with both caregiver and child data also showed a positive effect, supporting the causal link between caregiver training, caregiver competencies and child behavior in childcare, although this effect was not significant due to the small number of studies (d = 0.55, S.E. = 0.30). Based on these findings, we advocate the inclusion of instruction related to teacher\textendash{}child interaction in the curriculum of vocational training for caregivers. \textcopyright{} 2007 Elsevier Inc. All rights reserved.},
  language = {en},
  number = {3},
  journal = {Early Childhood Research Quarterly},
  doi = {10.1016/j.ecresq.2007.04.005},
  author = {Fukkink, Ruben G. and Lont, Anna},
  month = jul,
  year = {2007},
  pages = {294-311},
  file = {C:\\Users\\lissa102\\Zotero\\storage\\GNFHJ6UE\\Fukkink and Lont - 2007 - Does training matter A meta-analysis and review o.pdf}
}

@article{stroblIntroductionRecursivePartitioning2009,
  title = {An Introduction to Recursive Partitioning: {{Rationale}}, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests},
  volume = {14},
  copyright = {(c) 2016 APA, all rights reserved},
  issn = {1939-1463 1082-989X},
  shorttitle = {An Introduction to Recursive Partitioning},
  abstract = {Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.},
  language = {English},
  number = {4},
  journal = {Psychological Methods},
  doi = {10.1037/a0016973},
  author = {Strobl, Carolin and Malley, James and Tutz, Gerhard},
  year = {2009},
  keywords = {*Statistical Analysis,*Prediction,*Statistical Regression,Statistical Variables},
  pages = {323-348},
  file = {C:\\Users\\lissa102\\Zotero\\storage\\XJUZCWWV\\Strobl et al_2009_An introduction to recursive partitioning.pdf}
}

@article{vanlissaMetaForestExploringHeterogeneity2017,
  title = {{{MetaForest}}: {{Exploring}} Heterogeneity in Meta-Analysis Using Random Forests},
  shorttitle = {{{MetaForest}}},
  abstract = {Meta-analyses in psychology often lack the power to adequately account for between-studies heterogeneity. The number of studies on any topic is typically low, because research is cost- and time-intensive. At the same time, a host of potential moderators are introduced when similar research questions are examined in different labs, sampling from different populations, using idiosyncratic methods and instrumentation. Such between-studies heterogeneity presents a substantial challenge to data aggregation in classic meta-analysis. When the causes for heterogeneity are known a-priori, they can be accounted for using meta-regression. What is currently lacking, however, is an exploratory approach, to be used when heterogeneity is suspected, but it is not known which moderators most strongly influence the observed effect size. Recently, weighted regression trees have been used to explore heterogeneity in meta-analysis. Although this provides a promising first step, single trees have many limitations, which can be overcome by using random forests: A powerful learning algorithm, which is flexible, yet relatively robust to overfitting. The present paper introduces MetaForest: An adaptation of random forests for meta-analysis. We present two simulation studies, which illustrate that, in datasets as small as 20 cases, MetaForest outperforms single trees, in terms of three metrics: 1) Predictive performance; 2) power, as evidenced by the proportion of datasets in which the algorithm achieved a positive R2cv; and 3) the ability to distinguish relevant moderators from irrelevant moderators, using variable importance measures. We discuss how MetaForest can enhance the exploration of between-studies heterogeneity when conducting meta-analyses in diverse bodies of literature.
    Hosted on the Open Science Framework},
  language = {en},
  journal = {Open Science Framework},
  doi = {10.17605/OSF.IO/KHJGB},
  author = {Van Lissa, Caspar J.},
  month = sep,
  year = {2017},
  file = {C:\\Users\\lissa102\\Zotero\\storage\\B3ZCS848\\khjgb.html}
}

@article{vandennoortgateMetaanalysisMultipleOutcomes2015,
  title = {Meta-Analysis of Multiple Outcomes: A Multilevel Approach},
  volume = {47},
  issn = {1554-3528},
  shorttitle = {Meta-Analysis of Multiple Outcomes},
  language = {en},
  number = {4},
  journal = {Behavior Research Methods},
  doi = {10.3758/s13428-014-0527-2},
  author = {{Van den Noortgate}, Wim and {L\'opez-L\'opez}, Jos\'e Antonio and {Mar\'in-Mart\'inez}, Fulgencio and {S\'anchez-Meca}, Julio},
  month = dec,
  year = {2015},
  pages = {1274-1294},
  file = {C:\\Users\\lissa102\\Zotero\\storage\\EQZ9ZIU9\\Van den Noortgate et al. - 2015 - Meta-analysis of multiple outcomes a multilevel a.pdf;C:\\Users\\lissa102\\Zotero\\storage\\HHH5BIIQ\\vandennoortgate2014.pdf}
}

@Book{xie2015,
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  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}

@book{schwarzer2015meta,
  title={Meta-analysis with R},
  author={Schwarzer, Guido and Carpenter, James R and R{\"u}cker, Gerta},
  year={2015},
  publisher={Springer}
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@book{grolemund2014hands,
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  author={Grolemund, Garrett},
  year={2014},
  publisher={O'Reilly}
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@book{borenstein2011,
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@article{cuijpers2016meta,
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@article{dersimonian1986meta,
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@article{viechtbauer2005bias,
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@article{inthout2014hartung,
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  journal={BMC medical research methodology},
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  pages={25},
  year={2014},
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@article{hartung1999alternative,
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@article{hartung2001refined,
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@article{hartung2001tests,
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@article{wiksten2016hartung,
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@article{rucker2008undue,
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