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Discussion
XXIX International Congress of Psychology
Berlin, 20-25 July 2008
SampleStudents of over 20 different specialisations
(N = 1288)
Average age: 21 years old(M = 21.49; SD = 2.15)852 (66%) women and 436 (34%) menMajority of users with experience
longer than 2 years (n = 909, 71%)Majority of users with the Internet access
at home (n = 1023, 79.4%).
THE PSYCHOLOGY OF INTERNET ACTIVITYPatrycja Rudnicka, University of Silesia, Poland
Results
Set of paper-pencil questionnaires was distributed to participants in class. Participation was voluntary, only a few
resignations/refusals has been observed during research. Both verbal and written instruction were provided.
Internet Activity - assessment was based on the questions on internet practice, frequency of use
(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.)
Method
Internet Self-efficacy - a set of person's beliefs about
his or her capability to perform an Internet-related task in
a various situations (see also Durndell &Haag, 2002;
Eastin & LaRose, 2000; Sam, Othman &Nordin, 2005;
Tsai & Tsai, 2003, Wu & Tsai, 2006). Assessed using
Internet Self-Efficacy Measure (ISEM), 10-item
instrument based on Computer Self-Efficacy Scale
(CSEM) designed by Compeau and Higgins (1995).
Attitudes toward the Internet - positive / negative
beliefs toward the Internet. Measured using Internet
Attitude Scale (IAS), a modified Computer Anxiety Scale
(CAS), 20-item instrument designed by Nickell and
Pinto (1986).
Computer Anxiety - a fear, discomfort or negative emotional response when using the computer
or anticipating such situation (Bozionelos, 2001; Heinssen, Glass, & Knight, 1987; Rosen & Weil,
1995). Measured using Computer Anxiety Rating Scale (CARS), 19-item scale designed by
Heinssen, Glass and Knight (1987).
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,
et all., 2005). Internet Anxiety Scale (Internetowa Skala Lêku, ISL), 18-item instrument, has been
designed to measure anxiety toward the Internet (Rudnicka, 2007).
Variables and their measures
As the Internet becomes more popular and accessible new
issues of Internet research appear in field of psychology. The
internet activity and factors influencing it can be explored and
explained from two different perspectives. The first one is
macrolevel scale with studies explaining the role of
demographics, economical and cultural conditionings as well
as global usage trends in predicting the Interent use. On
individual, microlevel scale, other factors, such as self-
efficacy, attitudes, or anxiety towards the Internet come to the
fore. Their influence on perceiving and using the Internet are
widely discussed, but there are still many questions
unanswered.
This poster presents results of research conducted in winter
2006 among polish students. Based on data gathered from
over 1000 students, cluster analysis and structural equation
modeling (SEM) were used to identify and estimate causal
relationships between psychological factors and patterns of
the Internet use.
The goals of research were:
- description and analysis of internet use patterns
- identification and exploration of group differences
- verification of theoretical model of attitudes, anxiety and self-
efficacy influence on internet activity.
Introduction
Two-step cluster analysis has been used to identified different . The centroids for each cluster shows Table 1.
patterns of Internet use
Procedure
Internet Usage Patterns Analysis
Table 1Clusters’ centroids
Different mechanisms of regulation and activity structure in four groups of users
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
conducted.
were observed in
latent construct of Internet Activity in case of memebers of Cluster 2.
For all groups the model adjustment was reasonable (RMSEA= .030-.048,
2AGFI = .865-.963, CFI = .921-.963, ÷ /df = 1.2-1.8). Then factors loadings equivalence
has been testing to check whether the same latent variables are measured. However
2results failed, the structure of latent factors was not identical, ÷ (22) = 109.3, p < .001.
Tests of further hypothesis were no longer possible, and only the analysis of
unstandardised solution has been conducted. The main differences
Theoretical Model Verification
1O
Exploration of psychological using MANOVA.differences between clusters 3O
Exploration of .demographic differences between clustersO2
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.
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.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 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.
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. 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.
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.
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.
http://jcmc.indiana.edu/vol6/issue1/eastin.html
Structural equation modeling (SEM) has been used to analyse the model. First the
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
2 2the ÷ statistic was significant (i.e. ÷ (308, N = 1188) = 856, p < .001, RMSEA = .039, AGFI
= .937, CFI = .956).
Fig. 2. Percentage distribution of fileds of study in clusters, 2
÷ (9, N = 1288) = 211.53; p < .001
Fig. 1. Percentage distribution of females and males in clusters, 2
÷ (3, N = 1288) = 158.84; p < .001
Fig. 3. Percentage distribution of internet access place in clusters
Most relations were in the hypothesized direction, but Internet anxiety was positively
reacted to Internet activity and that direction was opposite to the hypothesized one.
Regards to hypothesized correlations between individual factors only one failed to meet
significance criteria however all correlation signs have been confirmed.
Fig. 4. Path coefficients for model*p < .05, **p < .01, ***p < .001
Table 2Descriptive 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
self-efficacy, attitudes and anxietes level in case of four groups of users. A significant difference
was found, 2
Wilks’ Ë = .794; F(9, 3120) = 34.56; p < .001; ç = .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.
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