@article{MatuschekKliegl2017, author = {Matuschek, Hannes and Kliegl, Reinhold}, title = {On the ambiguity of interaction and nonlinear main effects in a regime of dependent covariates}, series = {Behavior research methods : a journal of the Psychonomic Society}, volume = {50}, journal = {Behavior research methods : a journal of the Psychonomic Society}, number = {5}, publisher = {Springer}, address = {New York}, issn = {1554-351X}, doi = {10.3758/s13428-017-0956-9}, pages = {1882 -- 1894}, year = {2017}, abstract = {The analysis of large experimental datasets frequently reveals significant interactions that are difficult to interpret within the theoretical framework guiding the research. Some of these interactions actually arise from the presence of unspecified nonlinear main effects and statistically dependent covariates in the statistical model. Importantly, such nonlinear main effects may be compatible (or, at least, not incompatible) with the current theoretical framework. In the present literature, this issue has only been studied in terms of correlated (linearly dependent) covariates. Here we generalize to nonlinear main effects (i.e., main effects of arbitrary shape) and dependent covariates. We propose a novel nonparametric method to test for ambiguous interactions where present parametric methods fail. We illustrate the method with a set of simulations and with reanalyses (a) of effects of parental education on their children's educational expectations and (b) of effects of word properties on fixation locations during reading of natural sentences, specifically of effects of length and morphological complexity of the word to be fixated next. The resolution of such ambiguities facilitates theoretical progress.}, language = {en} } @misc{HohensteinMatuschekKliegl2017, author = {Hohenstein, Sven and Matuschek, Hannes and Kliegl, Reinhold}, title = {Linked linear mixed models: A joint analysis of fixation locations and fixation durations in natural reading}, series = {Psychonomic bulletin \& review : a journal of the Psychonomic Society}, volume = {24}, journal = {Psychonomic bulletin \& review : a journal of the Psychonomic Society}, publisher = {Springer}, address = {New York}, issn = {1069-9384}, doi = {10.3758/s13423-016-1138-y}, pages = {637 -- 651}, year = {2017}, abstract = {The complexity of eye-movement control during reading allows measurement of many dependent variables, the most prominent ones being fixation durations and their locations in words. In current practice, either variable may serve as dependent variable or covariate for the other in linear mixed models (LMMs) featuring also psycholinguistic covariates of word recognition and sentence comprehension. Rather than analyzing fixation location and duration with separate LMMs, we propose linking the two according to their sequential dependency. Specifically, we include predicted fixation location (estimated in the first LMM from psycholinguistic covariates) and its associated residual fixation location as covariates in the second, fixation-duration LMM. This linked LMM affords a distinction between direct and indirect effects (mediated through fixation location) of psycholinguistic covariates on fixation durations. Results confirm the robustness of distributed processing in the perceptual span. They also offer a resolution of the paradox of the inverted optimal viewing position (IOVP) effect (i.e., longer fixation durations in the center than at the beginning and end of words) although the opposite (i.e., an OVP effect) is predicted from default assumptions of psycholinguistic processing efficiency: The IOVP effect in fixation durations is due to the residual fixation-location covariate, presumably driven primarily by saccadic error, and the OVP effect (at least the left part of it) is uncovered with the predicted fixation-location covariate, capturing the indirect effects of psycholinguistic covariates. We expect that linked LMMs will be useful for the analysis of other dynamically related multiple outcomes, a conundrum of most psychonomic research.}, language = {en} } @article{MassonRabeKliegl2017, author = {Masson, Michael E. J. and Rabe, Maximilian M. and Kliegl, Reinhold}, title = {Modulation of additive and interactive effects by trial history revisited}, series = {Memory \& cognition}, volume = {45}, journal = {Memory \& cognition}, publisher = {Springer}, address = {New York}, issn = {0090-502X}, doi = {10.3758/s13421-016-0666-z}, pages = {480 -- 492}, year = {2017}, language = {en} } @article{MatuschekKlieglVasishthetal.2017, author = {Matuschek, Hannes and Kliegl, Reinhold and Vasishth, Shravan and Baayen, Harald R. and Bates, Douglas}, title = {Balancing Type I error and power in linear mixed models}, series = {Journal of memory and language}, volume = {94}, journal = {Journal of memory and language}, publisher = {Elsevier}, address = {San Diego}, issn = {0749-596X}, doi = {10.1016/j.jml.2017.01.001}, pages = {305 -- 315}, year = {2017}, abstract = {Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance covariance structure of random effects (the maximal model; Barr, Levy, Scheepers \& Tily, 2013), presumably to keep Type I error down to the nominal a in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data. (C) 2017 The Authors. Published by Elsevier Inc.}, language = {en} } @article{BaayenVasishthKliegletal.2017, author = {Baayen, Harald R. and Vasishth, Shravan and Kliegl, Reinhold and Bates, Douglas}, title = {The cave of shadows: Addressing the human factor with generalized additive mixed models}, series = {Journal of memory and language}, volume = {94}, journal = {Journal of memory and language}, publisher = {Elsevier}, address = {San Diego}, issn = {0749-596X}, doi = {10.1016/j.jml.2016.11.006}, pages = {206 -- 234}, year = {2017}, language = {en} } @article{TetznerKlieglKraheetal.2017, author = {Tetzner, Julia and Kliegl, Reinhold and Krah{\´e}, Barbara and Busching, Robert and Esser, G{\"u}nter}, title = {Developmental problems in adolescence}, series = {Journal of Applied Developmental Psychology}, volume = {53}, journal = {Journal of Applied Developmental Psychology}, publisher = {Elsevier}, address = {New York}, issn = {0193-3973}, doi = {10.1016/j.appdev.2017.08.003}, pages = {40 -- 53}, year = {2017}, abstract = {This longitudinal study investigated patterns of developmental problems across depression, aggression, and academic achievement during adolescence, using two measurement points two years apart (N = 1665; age T1: M = 13.14; female = 49.6\%). Latent Profile Analyses and Latent Transition Analyses yielded four main findings: A three-type solution provided the best fit to the data: an asymptomatic type (i.e., low problem scores in all three domains), a depressed type (i.e., high scores in depression), an aggressive type (i.e., high scores in aggression). Profile types were invariant over the two data waves but differed between girls and boys, revealing gender specific patterns of comorbidity. Stabilities over time were high for the asymptomatic type and for types that represented problems in one domain, but moderate for comorbid types. Differences in demographic variables (i.e., age, socio-economic status) and individual characteristics (i.e., self-esteem, dysfunctional cognitions, cognitive capabilities) predicted profile type memberships and longitudinal transitions between types.}, language = {en} }