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      (a-)Topics and animacy

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      Glossa: a journal of general linguistics
      Open Library of the Humanities

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          Abstract

          The aim of this paper is twofold: first, we intend to contribute to the debate on the identification of the features to which syntactic locality expressed in terms of the featural Relativized Minimality/ fRM principle appears to be sensitive (Rizzi 2004; Friedmann, Belletti & Rizzi 2009); second, we aim at providing a better characterization of the distributional and interpretive properties of the process of a-marking in the Topic position of the Italian left periphery identified by syntactic cartography, in relation to (in)animacy (Belletti & Manetti 2019).To these ends, we examined the role of animacy in a production experiment eliciting left dislocated topics with 5-year-old Italian-speaking children. To the extent that a-marking is related to a kind of affectedness of object topics (Belletti 2018a), we examined whether an inanimate left dislocated object could constitute a felicitous a-Topic. Furthermore, the question is directly addressed whether complexity effects in fRM configurations can be modulated in the animacy mismatch condition, with an inanimate left dislocated object and an intervening (animate) lexical subject in ClLDs. Our results show that, in the tested animacy mismatch condition, children seldom a-marked the pre-posed object. Instead, they appeared to creatively explore other solutions to overcome the production of the hard intervention structure, mainly using null subjects. As children are not ready to compute the intervention configuration with a lexical preverbal subject, but could not naturally adjust it through a-marking of the inanimate topic, they ended up opting for different types of productions in which intervention was eliminated. If the animacy feature seems to be implicated in the process of a-marking to some extent, it is not a feature to which the fRM principle is sensitive in building the object A’-dependency in ClLD: we conclude, in line with previous work, that animacy is not among the features implicated in triggering syntactic movement (in Italian).

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Random effects structure for confirmatory hypothesis testing: Keep it maximal.

            Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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              Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models.

              This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.
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                Author and article information

                Journal
                Glossa: a journal of general linguistics
                Open Library of the Humanities
                2397-1835
                March 4 2020
                September 30 2021
                : 39
                : 1
                Affiliations
                [1 ]University of Siena
                Article
                10.16995/glossa.5711
                80f64b38-f4aa-4082-9111-bf3bbca3e2a7
                © 2021

                https://creativecommons.org/licenses/by/4.0

                Product
                Self URI (article page): https://www.glossa-journal.org/article/id/5711/

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