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      Performing Multilingual Analysis With Linguistic Inquiry and Word Count 2015 (LIWC2015). An Equivalence Study of Four Languages

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          Abstract

          Today, there is a range of computer-aided techniques to convert text into data. However, they convey not only strengths but also vulnerabilities compared to traditional content analysis. One of the challenges that have gained increasing attention is performing automatic language analysis to make sound inferences in a multilingual assessment setting. The current study is the first to test the equivalence of multiple versions of one of the most appealing and widely used lexicon-based tools worldwide, Linguistic Inquiry and Word Count 2015 (LIWC2015). For this purpose, we employed supervised learning in a classification problem and computed Pearson's correlations and intraclass correlation coefficients on a large corpus of parallel texts in English, Dutch, Brazilian Portuguese, and Romanian. Our findings suggested that LIWC2015 is a valuable tool for multilingual analysis, but within-language standardization is needed when the aim is to analyze texts sourced from different languages.

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          Most cited references56

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          Intraclass correlations: uses in assessing rater reliability.

          Reliability coefficients often take the form of intraclass correlation coefficients. In this article, guidelines are given for choosing among six different forms of the intraclass correlation for reliability studies in which n target are rated by k judges. Relevant to the choice of the coefficient are the appropriate statistical model for the reliability and the application to be made of the reliability results. Confidence intervals for each of the forms are reviewed.
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            Forming inferences about some intraclass correlation coefficients.

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              Centering predictor variables in cross-sectional multilevel models: a new look at an old issue.

              Appropriately centering Level 1 predictors is vital to the interpretation of intercept and slope parameters in multilevel models (MLMs). The issue of centering has been discussed in the literature, but it is still widely misunderstood. The purpose of this article is to provide a detailed overview of grand mean centering and group mean centering in the context of 2-level MLMs. The authors begin with a basic overview of centering and explore the differences between grand and group mean centering in the context of some prototypical research questions. Empirical analyses of artificial data sets are used to illustrate key points throughout. The article provides a number of practical recommendations designed to facilitate centering decisions in MLM applications. Copyright 2007 APA, all rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                12 July 2021
                2021
                : 12
                : 570568
                Affiliations
                Department of Psychology, West University of Timisoara , Timisoara, Romania
                Author notes

                Edited by: Sidarta Ribeiro, Federal University of Rio Grande do Norte, Brazil

                Reviewed by: Jing Yang, Zhejiang University, China; Jovana Bjekic, University of Belgrade, Serbia; Dalibor Kučera, University of South Bohemia in České Budějovice, Czechia

                *Correspondence: Florin Alin Sava florin.sava@ 123456e-uvt.ro

                This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2021.570568
                8311520
                34322047
                f591081c-28ad-4b90-9bec-a710397018bd
                Copyright © 2021 Dudău and Sava.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 June 2020
                : 18 June 2021
                Page count
                Figures: 1, Tables: 5, Equations: 0, References: 58, Pages: 18, Words: 13828
                Funding
                Funded by: Ministerul Educaței și Cercetării Științifice 10.13039/501100006730
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                multilingual analysis,content analyses,automatic text analysis,linguistic inquiry and word count,liwc,liwc2015

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