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THE PSYCHOLOGY OF INTERNET ACTIVITY                     Patrycja Rudnicka, University of Silesia, Poland  Introduction    ...
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Rudnicka_the psychology_of_internet_activity

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Rudnicka, P. (2008). The Psychology of Internet Activity (poster). XXIX International Congress of Psychology, 20-25.07.2008. Berlin, Germany.

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Rudnicka_the psychology_of_internet_activity

  1. 1. THE PSYCHOLOGY OF INTERNET ACTIVITY Patrycja Rudnicka, University of Silesia, Poland Introduction Method As the Internet becomes more popular and accessible new Procedure issues of Internet research appear in field of psychology. The Set of paper-pencil questionnaires was distributed to participants in class. Participation was voluntary, only a few internet activity and factors influencing it can be explored and resignations/refusals has been observed during research. Both verbal and written instruction were provided. explained from two different perspectives. The first one is macrolevel scale with studies explaining the role of Variables and their measures Sampl demographics, economical and cultural conditionings as well e Internet Self-efficacy - a set of persons beliefs about as global usage trends in predicting the Interent use. On Studen his or her capability to perform an Internet-related task in ts (N = 12 of over 20 d individual, microlevel scale, other factors, such as self- 88) ifferent a various situations (see also Durndell &Haag, 2002; special isations efficacy, attitudes, or anxiety towards the Internet come to the Averag Eastin & LaRose, 2000; Sam, Othman &Nordin, 2005; e ( M = 2 1 a g e : 2 1 y ea r .49; SD so fore. Their influence on perceiving and using the Internet are = 2.15) ld Tsai & Tsai, 2003, Wu & Tsai, 2006). Assessed using 852 (66 widely discussed, but there are still many questions %) wom Internet Self-Efficacy Measure (ISEM), 10-item en and Majorit 436 (34 unanswered. y of us %) men instrument based on Computer Self-Efficacy Scale longer ers with than 2 e This poster presents results of research conducted in winter years ( xperience (CSEM) designed by Compeau and Higgins (1995). n = 909 2006 among polish students. Based on data gathered from Majorit , 71%) y of us at hom er Attitudes toward the Internet - positive / negative e (n = 1 s with the Inte over 1000 students, cluster analysis and structural equation 023, 79 rnet ac .4%). cess beliefs toward the Internet. Measured using Internet modeling (SEM) were used to identify and estimate causal Attitude Scale (IAS), a modified Computer Anxiety Scale relationships between psychological factors and patterns of (CAS), 20-item instrument designed by Nickell and the Internet use. Pinto (1986). The goals of research were: Computer Anxiety - a fear, discomfort or negative emotional response when using the computer - description and analysis of internet use patterns or anticipating such situation (Bozionelos, 2001; Heinssen, Glass, & Knight, 1987; Rosen & Weil, - identification and exploration of group differences 1995). Measured using Computer Anxiety Rating Scale (CARS), 19-item scale designed by - verification of theoretical model of attitudes, anxiety and self- Heinssen, Glass and Knight (1987). efficacy influence on internet activity. Internet Anxiety - a fear, discomfort or negative emotional response toward the Internet consequences are avoidance or limitation time spent on-line (see Barbeite & Weiss, 2004; Joiner, Results et all., 2005). Internet Anxiety Scale (Internetowa Skala Lêku, ISL), 18-item instrument, has been Internet Usage Patterns Analysis designed to measure anxiety toward the Internet (Rudnicka, 2007).O1 Two-step cluster analysis has been used to identified different patterns of Internet use. The centroids for each cluster shows Table 1. Internet Activity - assessment was based on the questions on internet practice, frequency of use Table 1 Clusters’ centroids (average time on-line on daily and weekly basis), number and frequency of services (WWW, IRC, chat, IM, P2P, FTP, Usenet News, on-line games) and activities used in a period of time (web browsing, information searching, on-line shopping etc.) O Exploration of demographic differences between clusters. 2O Exploration of psychological differences between clusters using MANOVA.3 Table 2 2 Fig. 1. Percentage distribution of females and males in clusters, ÷ (3, N = 1288) = 158.84; p < .001 Descriptive statistics for ISEM, IAS, CARS/ISL in clusters (N = 1288) CARS/ISL is a composite variable because of high correlation, r(1286) =.,65; p <.,001 A multuvariate analysis of variance was conducted to assess if there were differences between 2 Fig. 2. Percentage distribution of fileds of study in clusters, ÷ (9, N = 1288) = 211.53; p < .001 self-efficacy, attitudes and anxietes level in case of four groups of users. A significant difference was found, Wilks’ Ë = .794; F(9, 3120) = 34.56; p < .001; ç2 = .074. For all variables differences between groups were significant (F = 76.62; p < .001), IAS (F = 52.10; p < .001) oraz CARS/ISL (F = 72.23; p < .001) with coefficients inidicate high values, ç = .33 for IAS, ç = .38 for CARS/ISL and ç = .39 for ISEM. Theoretical Model Verification Structural equation modeling (SEM) has been used to analyse the model. First the Fig. 3. Percentage distribution of internet access place in clusters measurement part of the model has been tested, then fit of the model using path analysis was conducted.The fit statistics were all indicative for a reasonable model fit however Discussion 2 2 the ÷ statistic was significant (i.e. ÷ (308, N = 1188) = 856, p < .001, RMSEA = .039, AGFI Different mechanisms of regulation and activity structure in four groups of users = .937, CFI = .956). presenting different patterns of usage were identified. Systematic differences between groups were based on both demographic factors (like -> gender, field of study, type of internet access) but also psychological factors (like level of self-efficacy, anxiety or attitudes). Thus, digital inequality in case of Cluster 3 was caused by technological exclusion, whereas in case of Cluster 4 a voluntary renounciation, related to their psychological characteristics, was observed. Based on SEM results the psychological factors impact on internet activity can be - > explainded as follows: - computer anxiety and low self-efficacy are most important factors influenced internet activity, - internet anxiety does not limit internet activity, - attitudes are less important. The theoretial model’s verification between four different groups of users was also Fig. 4. Path coefficients for model conducted. For all groups the model adjustment was reasonable (RMSEA= .030-.048, *p < .05, **p < .01, ***p < .001 AGFI = .865-.963, CFI = .921-.963, ÷2/df = 1.2-1.8). Then factors loadings equivalence Most relations were in the hypothesized direction, but Internet anxiety was positively has been testing to check whether the same latent variables are measured. However reacted to Internet activity and that direction was opposite to the hypothesized one. results failed, the structure of latent factors was not identical, ÷2(22) = 109.3, p < .001. Regards to hypothesized correlations between individual factors only one failed to meet Tests of further hypothesis were no longer possible, and only the analysis of significance criteria however all correlation signs have been confirmed. unstandardised solution has been conducted. The main differences were observed in References latent construct of Internet Activity in case of memebers of Cluster 2. Barbeite, F. G. & Weiss, E. M. (2004). Computer self-efficacy and anxiety scales for an Internet sample: Testing measurement equivalence of existing measures and development of new scales. Computers in Human Behavior, 20(1), 1-15. Bozionelos, N. (2001). The relationship of instrumental and expressive traits with computer anxiety. Personality and Individual Differences, 31(6), 955-974. Compeau, D. R. & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. patry Durndell, A. & Haag, Z. (2002). Computer self efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample. Computers in Human Behavior, 18(5), 521-535. cja.r Eastin, M. S. & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6(1). Retrieved December 27, 2007, from http://jcmc.indiana.edu/vol6/issue1/eastin.html udni Heinssen, R., Glass, C., & Knight, L. (1987). Assessing computer anxiety: Development and validation of the Computer Anxiety Rating Scale. Computers in Human Behavior, 3, 49-59. cka@ Joiner, R., Gavin, J., Duffield, J., Brosnan, M., Crook, C., Durndell, A., Maras, P., Miller, J., Scott, A. J., & Lovatt, P. (2005). Gender, Internet identification, and Internet anxiety: Correlates of Internet use. CyberPsychology & Behavior, 8(4), 371-378. Que us.ed st u .pl Comm ions? Nickell, G. S. & Pinto, J. N. (1986). The computer attitude scale. Computers in Human Behavior, 2, 301-306. Rosen, L. D & Weil, M. M. (1995). Computer anxiety: A cross-cultural comparison of university students in ten countries. Computers in Human Behavior, 11(1), 45-64. Rudnicka, P. (2007). Psychologiczne mechanizmy podejmowania aktywnoœci w Internecie / Psychological mechanisms of the Internet Activity. Unpublished doctoral thesis, University of Silesia, Katowice, Poland. Emai ents? Sam, H., Othman, A. E. A., & Nordin, Z. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Educational Technology & Society, 8(4), 205-219. l me! Tsai, M. J. & Tsai, C. C. (2003). Information Searching Strategies in Web Based Science Learning: The Role of Self-Efficacy. IETI, 40(1), 43-50. Wu, Y-T & Tsai, C. C. (2006). University Students Internet Attitudes and Internet Self-Efficacy: A Study at Three Universities in Taiwan. CyberPsychology & Behavior, 9(4), 441-450. XXIX International Congress of Psychology Berlin, 20-25 July 2008

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