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Esgi, Necmi – Journal of Education and Training Studies, 2016
The aim of this study was to develop a test for assessing individuals' social media addiction; and conducting a reliability and validity study of this scale. Sample for this study was composed of 285 college students between the ages of 18 and 25. Reliability coefficients Cronbach's alpha value was 0.94 and Spearman Brown value was 0.91 for our…
Descriptors: Social Media, Test Reliability, Test Validity, Addictive Behavior
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de Winter, J. C. F.; Dodou, D.; Wieringa, P. A. – Multivariate Behavioral Research, 2009
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…
Descriptors: Sample Size, Factor Analysis, Enrollment, Evaluation Methods
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Palmieri, Patrick A.; Smith, Gregory C. – Psychological Assessment, 2007
The authors examined the structural validity of the parent informant version of the Strengths and Difficulties Questionnaire (SDQ) with a sample of 733 custodial grandparents. Three models of the SDQ's factor structure were evaluated with confirmatory factor analysis based on the item covariance matrix. Although indices of fit were good across all…
Descriptors: Measures (Individuals), Prosocial Behavior, Grandparents, Factor Analysis
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Comrey, Andrew L. – Journal of Consulting and Clinical Psychology, 1978
Investigators are urged to plan factor analytic studies prior to collecting the data, to formulate a hypothesized factor structure, to develop several relatively pure measures of each factor expected, and to select an appropriate sample of at least 200 cases. Continuous variables should be used rather than dichotomous variables. (Author)
Descriptors: Behavioral Science Research, Data Collection, Factor Analysis, Factor Structure
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Lambert, Zarrel V.; And Others – Multivariate Behavioral Research, 1991
A method is presented that eliminates some interpretational limitations arising from assumptions implicit in the use of arbitrary rules of thumb to interpret exploratory factor analytic results. The bootstrap method is presented as a way of approximating sampling distributions of estimated factor loadings. Simulated datasets illustrate the…
Descriptors: Behavioral Science Research, Computer Simulation, Estimation (Mathematics), Factor Structure