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      Assessing Online Flow Across Cultures: A Two-Fold Measurement Invariance Study

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          Abstract

          The association between online Flow and Internet Gaming Disorder (IGD) has attracted significant attention. Despite the consensus that online Flow plays a pivotal role in the development of IGD and other Internet addictive behaviors, there has been a lack of consistency in measurement scales used to assess online Flow. Even widely used measures of online Flow have not been psychometrically assessed across culturally diverse populations of gamers. Such an assessment would enhance the accuracy of cross-cultural comparisons. Attending to this need, the present study assessed the psychometric properties of the binary coded (i.e., Yes, No) Online Flow Questionnaire (OFQ), while concurrently taking into consideration country, age, language, and mode of data collection (online or face-to-face) differences. Two sequences of successive multiple group confirmatory factor analyses were used to assess the psychometric properties of the OFQ, between: (a) emergent adults from the United States of America ( N = 482, M age = 25.23, SD = 2.746) and Australia ( N = 168, M age = 23.55, SD = 3.37) and (b) adolescents from Greece ( N = 1579, M age = 16.12, SD = 0.849) and Cyprus ( N = 1372, M age = 15.54, SD = 0.656). Configural and partial metric invariance were confirmed between the United States and Australian samples. For the Greek and Cypriot samples, results indicated full configural and metric invariance. These results provide initial information to researchers and clinicians of the extent to which the OFQ maintains its consistency when used across cultures and invite for further cross-cultural studies in the field. Implications, as well as limitations, are discussed.

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          A ‘components’ model of addiction within a biopsychosocial framework

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            Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing

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              Conducting Clinical Research Using Crowdsourced Convenience Samples.

              Crowdsourcing has had a dramatic impact on the speed and scale at which scientific research can be conducted. Clinical scientists have particularly benefited from readily available research study participants and streamlined recruiting and payment systems afforded by Amazon Mechanical Turk (MTurk), a popular labor market for crowdsourcing workers. MTurk has been used in this capacity for more than five years. The popularity and novelty of the platform have spurred numerous methodological investigations, making it the most studied nonprobability sample available to researchers. This article summarizes what is known about MTurk sample composition and data quality with an emphasis on findings relevant to clinical psychological research. It then addresses methodological issues with using MTurk--many of which are common to other nonprobability samples but unfamiliar to clinical science researchers--and suggests concrete steps to avoid these issues or minimize their impact.
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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
                15 March 2019
                2019
                : 10
                Affiliations
                1School of Psychology, Counselling and Psychotherapy, Cairnmillar Institute , Hawthorn East, VIC, Australia
                2Department of Psychology, Palo Alto University , Palo Alto, CA, United States
                3Cyprus Youth Organization , Nicosia, Cyprus
                4School of Health and Life Sciences, Federation University , Mount Helen, VIC, Australia
                Author notes

                Edited by: Elisa Pedroli, Istituto Auxologico Italiano (IRCCS), Italy

                Reviewed by: Alice Chirico, Catholic University of Sacred Heart, Italy; Roger Ho, National University of Singapore, Singapore

                *Correspondence: Elwin Hu, elwin.hu@ 123456cairnmillar.edu.au

                This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2019.00407
                6428900
                Copyright © 2019 Hu, Stavropoulos, Anderson, Clarke, Beard, Papapetrou and Gomez.

                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.

                Page count
                Figures: 4, Tables: 4, Equations: 0, References: 115, Pages: 16, Words: 0
                Categories
                Psychology
                Original Research

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