@article{LohseHildebrandtHildebrandt2021,
  author    = {Lohse, Karoline and Hildebrandt, Andrea and Hildebrandt, Frauke},
  title     = {Hypotheses in adult-child interactions stimulate children's reasoning and verbalizations},
  series = {Early childhood research quarterly},
  volume    = {58},
  journal   = {Early childhood research quarterly},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {0885-2006},
  doi       = {10.1016/j.ecresq.2021.09.014},
  pages     = {254 -- 263},
  year      = {2021},
  abstract  = {Adult-child interactions can support children's development and are established as predictors of program quality in early childhood settings. However, the linguistic components that constitute positive interactions have not yet been studied in detail. This study investigates the effects of hypotheses proposed by adults on children's responses in a dyadic picture-book viewing situation. In 2 experiments, adults' use of hypotheses (e.g., "Maybe this is a dwarf's door") was tested against the use of instructive statements ("This is a dwarf's door") and in combination with open questions ("What do you think, why is the door so small?"). In Experiment 1, hypotheses differed from instructions only by the modal marker "maybe". Children's responses to hypotheses were longer and contained more self-generated explanations as compared to responses to instructions. The use of hypotheses also seemed to encourage children to attach more importance to their own explanations. In Experiment 2, combining hypotheses with open-ended why questions elicited longer responses but no more self-generated explanations in children than openended questions alone. Results indicate that subtle differences in adults' utterances can directly influence children's reasoning and children's contributions to dialogues.},
  language  = {en}
}
@article{SchadVasishthHohensteinetal.2020,
  author    = {Schad, Daniel and Vasishth, Shravan and Hohenstein, Sven and Kliegl, Reinhold},
  title     = {How to capitalize on a priori contrasts in linear (mixed) models},
  series = {Journal of memory and language},
  volume    = {110},
  journal   = {Journal of memory and language},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {0749-596X},
  doi       = {10.1016/j.jml.2019.104038},
  pages     = {40},
  year      = {2020},
  abstract  = {Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each other, a priori contrasts (i.e., comparisons planned before the sample means are known) between specific conditions or combinations of conditions are the appropriate way to represent such hypotheses in the statistical model. Many researchers have pointed out that contrasts should be "tested instead of, rather than as a supplement to, the ordinary 'omnibus' F test" (Hays, 1973, p. 601). In this tutorial, we explain the mathematics underlying different kinds of contrasts (i.e., treatment, sum, repeated, polynomial, custom, nested, interaction contrasts), discuss their properties, and demonstrate how they are applied in the R System for Statistical Computing (R Core Team, 2018). In this context, we explain the generalized inverse which is needed to compute the coefficients for contrasts that test hypotheses that are not covered by the default set of contrasts. A detailed understanding of contrast coding is crucial for successful and correct specification in linear models (including linear mixed models). Contrasts defined a priori yield far more useful confirmatory tests of experimental hypotheses than standard omnibus F-tests. Reproducible code is available from https://osf.io/7ukf6/.},
  language  = {en}
}