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      Entwicklung und Validierung der Online-Privatheitskompetenzskala (OPLIS)

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          Abstract

          Zusammenfassung. Online-Privatheitskompetenz gilt in der medienpsychologischen Forschung als wichtiger Einflussfaktor auf das Privatheitsverhalten in Online-Umgebungen. Eine Skala zur Erfassung dieser Kompetenz fehlt jedoch. Ziel dieser Arbeit war entsprechend die Entwicklung und Validierung einer umfassenden Skala zur Messung von Online-Privatheitskompetenz. In Vorarbeiten wurden anhand einer qualitativen Inhaltsanalyse die Dimensionen des Konstrukts identifiziert ( Trepte et al., 2015 ). Darauf aufbauend wurde aus 113 Wissensfragen eine aus 20 Fragen bestehende Skala entwickelt, die vier Wissensbereiche abdeckt: Wissen über 1) institutionelle Praktiken, 2) technische Aspekte des Datenschutzes, 3) Datenschutzrecht und 4) Datenschutzstrategien. Die Ergebnisse von drei konsekutiven Studien sprechen für ein Bi-Faktor-Modell, wobei der globale Faktor die Online-Privatheitskompetenz widerspiegelt. Die Konstrukt- und Kriteriumsvalidität wurde anhand einer Quotenstichprobe deutscher Internetnutzender ( N = 1 945) überprüft: Der globale Faktor korrelierte positiv mit der subjektiven Kompetenzeinschätzung der Probandinnen und Probanden und erwies sich als angemessener Prädiktor für die Umsetzung unterschiedlicher Datenschutzmaßnahmen.

          Development and Validation of the Online Privacy Literacy Scale (OPLIS)

          Abstract. Online privacy literacy has been regarded as an important antecedent of online privacy behavior. However, a scale measuring literacy is missing. Hence, the aim of this study was to develop and validate a comprehensive scale to measure online privacy literacy. Relevant dimensions of the concept were identified in a prior study using a qualitative content analysis ( Trepte et al., 2015 ). Based on these findings, an initial item pool with 113 knowledge questions was used to develop a 20-item scale, including four dimensions, that is, knowledge about (a) institutional practices, (b) technical aspects of data protection, (c) the data protection law, and (d) data protection strategies. The results from three consecutive studies suggest a bifactor structure, in which online privacy literacy is represented by the global factor. We tested the construct and criterion validity in a quota sample of German Internet users ( N = 1 945): The global factor correlated positively with subjective privacy literacy and proved to be an adequate predictor of the implementation of data protection measures.

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          Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores.

          The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures (typically caused by parcels of items that tap similar content domains). As such, structural ambiguity leads to seemingly endless "confirmatory" factor analytic studies in which the research question is whether scale scores can be interpreted as reflecting variation on a single trait. An alternative to the more commonly observed unidimensional, correlated traits, or second-order representations of a measure's latent structure is a bifactor model. Bifactor structures, however, are not well understood in the personality assessment community and thus rarely are applied. To address this, herein we (a) describe issues that arise in conceptualizing and modeling multidimensionality, (b) describe exploratory (including Schmid-Leiman [Schmid & Leiman, 1957] and target bifactor rotations) and confirmatory bifactor modeling, (c) differentiate between bifactor and second-order models, and (d) suggest contexts where bifactor analysis is particularly valuable (e.g., for evaluating the plausibility of subscales, determining the extent to which scores reflect a single variable even when the data are multidimensional, and evaluating the feasibility of applying a unidimensional item response theory (IRT) measurement model). We emphasize that the determination of dimensionality is a related but distinct question from either determining the extent to which scores reflect a single individual difference variable or determining the effect of multidimensionality on IRT item parameter estimates. Indeed, we suggest that in many contexts, multidimensional data can yield interpretable scale scores and be appropriately fitted to unidimensional IRT models.
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            The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure

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              Digital Literacy and Privacy Behavior Online

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                Author and article information

                Contributors
                Journal
                dia
                Diagnostica
                Hogrefe Verlag, Göttingen
                0012-1924
                2190-622X
                2017
                : 63
                : 4
                : 256-268
                Affiliations
                [ 1 ]Universität Hohenheim, Lehrstuhl für Medienpsychologie
                Author notes
                Philipp K. Masur, Doris Teutsch, Prof. Dr. Sabine Trepte, Universität Hohenheim, Lehrstuhl für Medienpsychologie, Wollgrasweg 23, 70599 Stuttgart, E-Mail sabine.trepte@ 123456uni-hohenheim.de
                Article
                dia_63_4_256
                10.1026/0012-1924/a000179
                07bde166-d1f0-4b3e-b6ae-a3798fe563a4
                Distributed as a Hogrefe OpenMind article under the license CC BY 4.0 (https://creativecommons.org/licenses/by/4.0)

                Distributed as a Hogrefe OpenMind article under the license CC BY 4.0 ( https://creativecommons.org/licenses/by/4.0)

                History
                Categories
                Originalarbeit

                Psychology,Clinical Psychology & Psychiatry
                Online-Privatheit,Privatheitskompetenz,Wissen,Skalenkonstruktion,Bi-Faktor-Modell,online privacy,privacy literacy,knowledge,scale construction,bifactor model

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