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STUDY Two reduction; however, the positive aspect of it is that it creates a manageable number of factors. First the activities were divided into two sections: those involving reading and the rest of the activities made up the “miscellaneous” category. Once activities were categorized, factor analysis (maximum likelihood with varimax rotation) was used to identify the different dimensions within the categories. Furthermore, varimax rotation was used in the analysis, and the rotated factor matrix was used in the analysis of the results without the small values (<.3) omitted from Tables 13 and 14, in which the factor structures are visually represented. In the second step of the analysis, scales were created from the factors in order to perform further analyses of the database and to explore possible relationships between the factors by correlation and regression analysis. Half of the EE activities in the questionnaire (nine activities) were found to be related to reading, so as a first step, these nine statements were investigated (see Table 17); factor analysis revealed that these EE activities load onto two dimensions. Consequently, Factor 1 was named “online reading” as all of the activities loading onto this dimension may be pursued online, and Factor 2 was named “paper-based reading” as activities 15, 17, and 11 are traditionally accessible in a printed format. Table 17. Results of factor analysis examining EE activities related to reading EE activities Factors 1 2 6. EE reading websites 741 18. EE reading news .677 8. EE reading blogs .659 5. EE reading Twitter posts .629 10. EE read posts on Instagram .612 13. EE reading posts on Facebook .542 15. EE reading magazines 846 17. EE reading newspapers 841 11. EE reading books .650 The rest of the EE activities (9 activities) were also subjected to factor analysis (maximum likelihood with varimax rotation), which revealed that these activities loaded onto a total of six different dimensions (see Table 18). - 101 +
