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      Psychometric properties of the Liebowitz Social Anxiety Scale in a large cross-cultural Spanish and Portuguese speaking sample

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          Abstract

          Objective:

          To examine the psychometric properties of the Liebowitz Social Anxiety Scale-Self Report (LSAS-SR) based on a large sample recruited from 16 Latin American countries, Spain, and Portugal.

          Methods:

          Two groups of participants were included: a non-clinical sample involving 31,243 community subjects and a clinical sample comprising 529 patients with a diagnosis of social anxiety disorder (SAD). Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) were used in order to determine the psychometric properties of the LSAS-SR.

          Results:

          EFA identified five factors with eigenvalues greater than 1.00 explaining 50.78% of the cumulative variance. CFA and ESEM supported this 5-factor structure of the LSAS-SR. The factors included: 1) speaking in public; 2) eating/drinking in front of other people; 3) assertive behaviors; 4) working/writing while being observed; and 5) interactions with strangers. Other psychometric properties such as inter-factor correlations, invariance, reliability, and validity of the scale were also found.

          Conclusion:

          Psychometric data support the internal consistency and convergent validity of the LSAS-SR. It seems to be a valid and reliable measure of global social anxiety for Spanish and Portuguese-speaking countries, although when considering a multidimensional approach (factor-based assessment) it seems to be lacking some relevant social situations that are feared in those countries.

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          Most cited references38

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          Missing data: our view of the state of the art.

          Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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            Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis.

            Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM) have long histories in clinical research. Although CFA has largely superseded EFA, CFAs of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors in support of discriminant validity. Part of the problem is undue reliance on overly restrictive CFAs in which each item loads on only one factor. Exploratory SEM (ESEM), an overarching integration of the best aspects of CFA/SEM and traditional EFA, provides confirmatory tests of a priori factor structures, relations between latent factors and multigroup/multioccasion tests of full (mean structure) measurement invariance. It incorporates all combinations of CFA factors, ESEM factors, covariates, grouping/multiple-indicator multiple-cause (MIMIC) variables, latent growth, and complex structures that typically have required CFA/SEM. ESEM has broad applicability to clinical studies that are not appropriately addressed either by traditional EFA or CFA/SEM.
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              A Bifactor Exploratory Structural Equation Modeling Framework for the Identification of Distinct Sources of Construct-Relevant Psychometric Multidimensionality

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                Author and article information

                Contributors
                Role: CISO-A Research Team
                Journal
                Braz J Psychiatry
                Braz J Psychiatry
                bjp
                Revista Brasileira de Psiquiatria
                Associação Brasileira de Psiquiatria
                1516-4446
                1809-452X
                11 October 2018
                Mar-Apr 2019
                : 41
                : 2
                : 122-130
                Affiliations
                [1 ]Facultad de Psicología, Universidad de Granada, Granada, España
                [2 ]Centro de Psicología Clínica FUNVECA, Granada, España
                [3 ]Facultad de Psicología, Universidad de Salamanca, Salamanca, España
                [4 ]Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
                Author notes
                Correspondence: Vicente E. Caballo, Facultad de Psicología, Universidad de Granada, Campus de la Cartuja, s/n, 18071 Granada, Spain. E-mail: vcaballo@ 123456ugr.es
                Author information
                http://orcid.org/0000-0002-2767-8028
                Article
                10.1590/1516-4446-2018-0006
                6781681
                30328964
                64d6cb9e-e66a-4a36-8442-40cbf1c6680d

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 August 2017
                : 11 January 2018
                Categories
                Original Article

                social anxiety disorder,questionnaires,psychometrics,cross-cultural comparison

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