@article{StutzSchaffnerSchiefele2016,
  author    = {Stutz, Franziska and Schaffner, Ellen and Schiefele, Ulrich},
  title     = {Relations among reading motivation, reading amount, and reading comprehension in the early elementary grades},
  series = {Learning and individual differences},
  volume    = {45},
  journal   = {Learning and individual differences},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1041-6080},
  doi       = {10.1016/j.lindif.2015.11.022},
  pages     = {101 -- 113},
  year      = {2016},
  abstract  = {In this study, we examined the associations of intrinsic (i.e., involvement-oriented) and extrinsic (i.e., competition-oriented) reading motivation with reading amount and reading comprehension (at the word, sentence, and text level) in a sample of second- and third-grade elementary students (N = 1053). Cognitive ability and socioeconomic status were taken into account as control variables. Reading amount was assumed to mediate the relation between reading motivation and reading comprehension. Moreover, the potentially moderating role of gender was explored. Structural equation analyses revealed that involvement contributed significantly to reading comprehension, and this relationship was mediated through reading amount. Competition oriented reading motivation was directly and negatively related with reading comprehension. The predictive contributions of reading motivation were confirmed in an alternative model with text-level comprehension as the dependent variable and both word- and sentence-level comprehension as additional predictors. Finally, gender did not moderate the obtained relations. (C) 2015 Elsevier Inc. All rights reserved.},
  language  = {en}
}
@article{HerbstVoethDotanStoehr2017,
  author    = {Herbst - Voeth, Uta and Dotan, Hilla and St{\"o}hr, Sina},
  title     = {Negotiating with work friends},
  series = {The journal of business \& industrial marketing},
  volume    = {32},
  journal   = {The journal of business \& industrial marketing},
  publisher = {Emerald Group Publishing Limited},
  address   = {Bingley},
  issn      = {0885-8624},
  doi       = {10.1108/JBIM-12-2015-0250},
  pages     = {558 -- 566},
  year      = {2017},
  abstract  = {Purpose - This study aims to investigate whether a team of females negotiates differently than a team of males, and whether (workplace) friendship moderates the relationship between single-gender team composition and negotiation outcomes. Design/methodology/approach - The authors used two laboratory studies and paired 216 MBA students into single-gender teams of friends and non-friends, and then engaged them in several dyadic multi-issue negotiations. Findings - The results show that on average, male teams of non-friends reached significantly better outcomes than female teams of non-friends. However, and interestingly, female teams of friends perform equally to male teams of friends. Research limitations/implications - The authors contribute both to the negotiations and the workplace friendship literature because very little research has examined negotiation among friends at work and in particular team negotiations. In addition, the authors also contribute to the literature on gender differences in negotiations because existing research has rarely examined the differences between all-male and all-female teams and especially the relationship between same-sex teams and their effects on negotiation outcomes. Practical implications - This research has clear implications to managers with regard to team composition. Specifically, a winning all-female team should not be changed! Originality/value - This is the first study to examine the relationship between workplace friendship, gender and negotiation outcomes.},
  language  = {en}
}
@article{VasishthNicenboimBeckmanetal.2018,
  author    = {Vasishth, Shravan and Nicenboim, Bruno and Beckman, Mary E. and Li, Fangfang and Kong, Eun Jong},
  title     = {Bayesian data analysis in the phonetic sciences},
  series = {Journal of phonetics},
  volume    = {71},
  journal   = {Journal of phonetics},
  publisher = {Elsevier},
  address   = {London},
  issn      = {0095-4470},
  doi       = {10.1016/j.wocn.2018.07.008},
  pages     = {147 -- 161},
  year      = {2018},
  abstract  = {This tutorial analyzes voice onset time (VOT) data from Dongbei (Northeastern) Mandarin Chinese and North American English to demonstrate how Bayesian linear mixed models can be fit using the programming language Stan via the R package brms. Through this case study, we demonstrate some of the advantages of the Bayesian framework: researchers can (i) flexibly define the underlying process that they believe to have generated the data; (ii) obtain direct information regarding the uncertainty about the parameter that relates the data to the theoretical question being studied; and (iii) incorporate prior knowledge into the analysis. Getting started with Bayesian modeling can be challenging, especially when one is trying to model one's own (often unique) data. It is difficult to see how one can apply general principles described in textbooks to one's own specific research problem. We address this barrier to using Bayesian methods by providing three detailed examples, with source code to allow easy reproducibility. The examples presented are intended to give the reader a flavor of the process of model-fitting; suggestions for further study are also provided. All data and code are available from: https://osf.io/g4zpv.},
  language  = {en}
}