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Journal of Behavioral Addictions
Authors: Álvaro Sicilia, Manuel Alcaraz-Ibáñez, María-Jesús Lirola, Rafael Burgueño, and Anthony Maher

past studies between self-determined forms of motivation and EA. Indeed, the results of the correlation analysis in this study show that although self-determined forms of motivation (i.e., intrinsic motivation and integrated and identified regulations

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.02. Concurrent validity was tested by correlational analysis, whereas Cronbach’s αs were computed to ensure the internal consistency of the (sub)scale. Since different types of gamers are expected to exhibit different configurations of IGD symptoms

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). Standardized root mean squared residual (SRMR), was also computed, with its value <0.08 representing an acceptable fit. Descriptive statistics, internal consistency (≥0.70 representing a satisfactory reliability; Cortina, 1993 ), and correlation analysis were

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. G. , & Aiken , L. S. ( 2003 ). Applied multiple regression/correlation analysis for the behavioral sciences . Mahwah, NJ : Lawrence Erlbaum Associates . Cohen , S

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increased FPN/CON connections in males with versus without IGD reflects a male-IGD-specific “fuzzy boundary” between these two brain networks that may relate to relevant features of IGD in males. Correlation analysis suggested a positive association between

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Journal of Behavioral Addictions
Authors: Giacomo Grassi, Stefano Pallanti, Lorenzo Righi, Martijn Figee, Mariska Mantione, Damiaan Denys, Daniele Piccagliani, Alessandro Rossi, and Paolo Stratta

control group Correlation analysis Pearson product-moment correlation coefficients between clinical variables (symptom severity, illness duration, presence or history of tics, presence

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Journal of Behavioral Addictions
Authors: Ana Estévez, Paula Jáuregui, Inmaculada Sánchez-Marcos, Hibai López-González, and Mark D. Griffiths

), a correlational analysis using Pearson’s r was conducted to understand the relationship between attachment, gambling disorder, problematic Internet use, video game addiction, and alcohol and drug abuse (Table  2 ). Results showed that the addictive

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, descriptive statistics regarding general smartphone use were calculated. Then, correlational analysis was conducted. Finally, to delineate the factors underlying problematic smartphone use, multiple regression analysis was performed using problematic

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using SPSS 25.0 (IBM Corp., Armonk, NY, USA). First, frequency statistics and descriptive statistics were calculated to analyze the general characteristics of the sample. Second, Pearson’s correlation analysis was used to examine the relationship and

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's scores on an established social media engagement questionnaire (SMES), and two ‘addictive’ severity scales (BSNAS, SMAQ), mean scores and variability are presented in Table 1 . A Pearson's correlation analysis showed that there were moderate

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