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      When the present lies in the past: [Present under Past] in subjunctive clauses in Uruguayan Spanish

      1 , 2 , 2 , 3
      Glossa: a journal of general linguistics
      Open Library of the Humanities

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          Abstract

          On the basis of corpus and experimental evidence, this paper claims that the ongoing process of change affecting the use and interpretation of the [Present under Past] pattern in subjunctive argument clauses in some Spanish varieties is sensitive to the syntactic/semantic type of the clause. The pattern deviates from Sequence-of-Tense grammar in not giving rise to double access effects. In the variety explored in this paper, this only happens in the argument clauses of causative, directive, and volitional predicates, i.e. in a type of clause which is held to be lower in a scale of clausehood than the argument clauses of predicates of belief and assertion.@font-face{font-family:Calibri;panose-1:2 15 5 2 2 2 4 3 2 4;mso-font-charset:0;mso-generic-font-family:auto;mso-font-pitch:variable;mso-font-signature:3 0 0 0 1 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal{mso-style-parent:"";margin:0cm;margin-bottom:.0001pt;mso-pagination:widow-orphan;font-size:12.0pt;font-family:Calibri;mso-fareast-font-family:Calibri;mso-bidi-font-family:Calibri;}div.Section1{page:Section1;}

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          Fitting Linear Mixed-Effects Models Usinglme4

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            lmerTest Package: Tests in Linear Mixed Effects Models

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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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                Author and article information

                Contributors
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                Journal
                Glossa: a journal of general linguistics
                Open Library of the Humanities
                2397-1835
                January 14 2022
                June 17 2022
                : 7
                : 1
                Affiliations
                [1 ]Université Paris 8, UMR 7023 SFL
                [2 ]Universidad de la República, Uruguay
                [3 ]Cerence B.V.
                Article
                10.16995/glossa.7904
                159cd037-ea29-4ae4-91c4-17b5ef8404cc
                © 2022

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

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