Computer self-efficacy and anxiety scales for an Internet sample : testing measurement equivalence of existing measures and development of new scales

指導教授：溫嘉榮 博士暑資碩三： m9253316 周至宏

Abstract Two studies investigated the validity of computer self-efficacy and computer anxiety scales when

administered to an Internet sample.

In the first study, it was found that existing measures of computer self-efficacy and anxiety, originally developed through paper-and-pencil methods with more traditional samples, were not adequately equivalent when administered to a sample that was recruited and tested via the Internet.

In the second study, the existing measures were adapted, and new items were developed to create new measures of computer self-efficacy and anxiety.

that these new measures are adequate for capturing confidence and aversion towards computers when administered to an Internet sample.

Introduce Measurement equivalence between traditional and Internet sam

ples/methods Computer self-efficacy and computer anxiety

Computer self-efficacy

a belief of one’s capability to use the computer (Compeau & Higgins, 1995)

computer anxiety

a fear of computers when using one, or fearing the possibility of using a computer (Chua, Chen, & Wong, 1999 )

Study1-Method participants A random sample of 612 members of the research panel

population (entire available population at time of study was 4100) received an e-mail that described the study and provided a link to where the survey could be completed.

Of those invited to participate, 413 respondents provided usable survey data.

Of this available data, 226 were used for the current study.

Study1-Method Measures and procedure Participants provided their panel number to enter the survey site. When the survey was completed, data was sent electronically to a

local database. Participants completed two computer self-efficacy scales and three

computer anxiety scales: CAS CARS CACAS CSE CCCAS Reliabilities, and sample data from these studies are shown in Table 1.

Study1-Method Analysis First, exploratory factor analysis was conducted to determine if the nu

mber of factors for each scale measured in the current sample was consistent with those found in previous studies.

Factor analysis was conducted using principal axis factoring and direct oblimin rotation. Number of factors was determined by parallel analysis and scree plot evidence

Second, confirmatory factor analysis (CFA) was conducted in order to test the consistency of factor loading among items for each scale.

CFA was conducted on LISREL 8.52 (Jo reskog &So rbom, 1998) u‥ ‥sing maximum likelihood. Factor loading misspecification was measured using chi-square as well as Comparative Fit Index (CFI) and RMSEA

Study1-Method Analysis Results of the EFA and CFA analysis are shown in Table 2. Therefore, it would not be meaningful to test mean differences

between the current data and previous data collected through paper-and-pencil methods.

Study1-Results

■Item pool reduction

a panel of 12 raters independently categorized and rated each item. Participants first categorized each item into one of three categories: out-of date,

common knowledge, or opinion. Items were retained if six or more panelists categorized an item as opinion. Four items were removed and 83 remained. Items with a mean rating equal to or greater than the midpoint were retained. Six items were removed. In total, 10 items were removed and 77 remained.

Study 2

■ Exploratory factor analysis of new item pool The 77 items were subjected to exploratory factor analysis. Factor a

nalytic and interpretational methods were the same as those used in Experiment 1.

The resulting four factors with a total of 16 items were labeledas followed:

computer self-efficacy for general/beginner activities (a=0.90), computer self-efficacy for advanced activities (a=0.89), anxiety of computer-related activities(a=0.89), anxiety of computer use (a=0.88).

Study 2

Study2-Method Participants Participants were members of the same online research

panel None of the participants were involved in Study 1. Eight hundred members of the research panel population

(entire available population at time of study was 4100) received an e-mail that described the study and provided

a link to where the survey could be completed. Of those invited to participate, 476 respondents provided usable survey data.

Of this available data, 227 were used for the current study.

Study2-Method

Measures and procedure As in Study 1, participants received an email that described the study

and provided a link to where the survey could be completed.

Study2-Result Coefficient a reliability for each factor as well as the factor loading, me

an, and standard deviation for each item are shown in Table 3. Table 4 shows the intercorrelation among the four instruments as well Table 5 shows results from simultaneous regression analyses where t

he self-efficacy and anxiety instruments are used to predict comfort and use.

The current study tested the measurement equivalence of two important sets of measures: computer self-efficacy and computer anxiety.

the self-efficacy scales predicted several use and comfort variables.

Anxiety of computer-related activities was the poorest predictor of all four scales.

Discussion

In the current study, anxiety of computer use was a significant predictor of self-efficacy for advanced activities and was significantly correlated with several indicators of computer and Internet use.

self-efficacy for advanced activities was a significant predictor of computer and Internet use. This is consistent with Bandura’s self-efficacy framework (1997)

the scales consist of only four items each, they can be easily incorporated into the method of any experiment. Accumulation of such information can provide a better understanding of how to better make use of the Internet as a research tool.

Discussion