Chapter 1 Introduction
4.4 Predictors for Web 2.0 technology acceptance and intention to use
4.4.1 Exploratory factor analysis
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Table 4.20: Summary of multiple regression analysis (Stepwise method)
Although the standard multiple regression has shown that the technology knowledge construct was the largest predictor of teachers’ intention to use Web 2.0 tools, the Stepwise regression has shown that technological content knowledge and TPACK and technology pedagogy knowledge accounted for 2.9% and 3.5% respectively variance to teachers’
intention to use Web 2.0 tools.
4.4 Predictors for Web 2.0 technology acceptance and intention to use
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constructs. A PCA was conducted on the 42 variables with orthogonal rotation (Varimax). Test for sampling adequacy and presence of correlations To determine that assumptions regarding a sufficient sample size and the suitability of the data to factor analysis, the KMO measure of sampling adequacy and Bartlett’s Test of sphericity were computed.
Table 4.21: KMO and Bartlett’s Test
As shown in Table 4.23, the Kaiser-Meyer-Olkin (KMO) test for measuring sampling adequacy and Barlett’s test of Sphericity gave satisfactory results. . The KMO value (0.823) is greater than 0.7 which means the data is likely to factor well. The data was considered to be fit for factor analysis (Field, 2009; Sahin, 2011). Bartlett’s Test has a null hypothesis that the correlation matrix is the identity matrix, which means that the variables are unrelated and, hence, unsuitable for factor analysis. Because the p value for Bartlett’s Test on the variables was 0.000, the null hypothesis was rejected, and the data is suitable for factor analysis. Both diagnostic tests confirm that the data are suitable for factor analysis.
EFA was run several times, each time removing communalities less than 0.65.
Communalities after extraction exceeding 0.7 – being desirable – (Field, 2009), and all communalities less than .65 were removed from the data set. Below is the final list of communalities obtained after extraction.
4.4.1.2 Communalities
Average communality is .806, (which is greater than 0.7) implying that factor analysis can be performed using these data.
.823 Approx. Chi-Square 2126.956
df 153
Sig. 0.000
KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
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Table 4.22: Communalities
Initial Extraction
Web 2.0 tools help me teach my subject area 1.000 .806 Using Web 2.0 tools in teaching will enable me to
accomplish tasks
1.000 .847
Web 2.0 useful in my teaching 1.000 .767
Web 2.0 tools will reduce my workload 1.000 .836
Web 2.0 tools will enable me to teach at my pace 1.000 .710
I find Web 2.0 tools easy to use 1.000 .808
My interaction with Web 2.0 tools is clear and understandable
1.000 .867
I possess the skills necessary to use Web 2.0 tools 1.000 .827 My institution has provided me all the facilities I need
for Web 2.0 tools
1.000 .718
My institution provides incentives to teachers who use Web 2.0
1.000 .852
My institution provides incentives to students who use Web 2.0
1.000 .861
There is technical help available if required while using Web 2.0 tools
1.000 .776
I am able to teach my students to use Web 2.0 tools 1.000 .801 I am able to integrate the use of Web 2.0 tools 1.000 .770 I am able to use conferencing software for
collaboration
1.000 .698
I will encourage my students to use Web 2.0 tools to work with other students
1.000 .856
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Initial Extraction
I will encourage my students to use Web 2.0 tools to analyse information with their classmates
1.000 .905
I will encourage my students to use Web 2.0 tools to communicate with other people about their ideas
1.000 .807
4.4.1.3 Factor analysis
In Table 4.23, six factors have eigenvalues greater than 1.0, which is a common criterion for a factor to be useful (Field, 2009).
Table 4.23: Total variance explained
Compone nt
Initial Eigenvalues Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total % of Variance
Cumulativ e %
Total % of Variance
Cumulativ e %
Total % of Variance
Cumulativ e %
1 6.01 33.39 33.39 6.01 33.39 33.39 3.20
8
17.821 17.821
2 2.734 15.187 48.576 2.734 15.187 48.576 2.66 4
14.799 32.621 3 1.987 11.041 59.617 1.987 11.041 59.617 2.54
2
14.123 46.744
4 1.503 8.352 67.969 1.503 8.352 67.969 2.30 5
12.807 59.55
5 1.231 6.836 74.805 1.231 6.836 74.805 2.23 7
12.425 71.976 6 1.047 5.815 80.621 1.047 5.815 80.621 1.55
6
8.645 80.621
7 0.57 3.167 83.787
8 0.49 2.723 86.51
119 Compone
nt
Initial Eigenvalues Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
9 0.401 2.227 88.737 10 0.338 1.878 90.615 11 0.297 1.652 92.267 12 0.263 1.462 93.729 13 0.26 1.442 95.17 14 0.231 1.281 96.452 15 0.205 1.137 97.588 16 0.187 1.041 98.629 17 0.126 0.699 99.328 18 0.121 0.672 100
Table 4.24: Total Variance explained
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Rotation Sums
of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative % Total
% of Varianc
e Cumulative %
1 6.010 33.390 33.390 6.010 33.390 33.390 3.208 17.821 17.821
2 2.734 15.187 48.576 2.734 15.187 48.576 2.664 14.799 32.621
3 1.987 11.041 59.617 1.987 11.041 59.617 2.542 14.123 46.744
4 1.503 8.352 67.969 1.503 8.352 67.969 2.305 12.807 59.550
5 1.231 6.836 74.805 1.231 6.836 74.805 2.237 12.425 71.976
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6 1.047 5.815 80.621 1.047 5.815 80.621 1.556 8.645 80.621
7 .570 3.167 83.787
8 .490 2.723 86.510
9 .401 2.227 88.737
10 .338 1.878 90.615
11 .297 1.652 92.267
12 .263 1.462 93.729
13 .260 1.442 95.170
14 .231 1.281 96.452
15 .205 1.137 97.588
16 .187 1.041 98.629
17 .126 .699 99.328
18 .121 .672 100.000
The scree plot in Figure 4.2 supports a six-factor solution to the EFA as shown in Table 4.23 and Table 4.24.
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Figure 4.2: Scree Plot
4.4.1.4 Rotated component matrix
Since for each component factor loadings less than 0.4 are ignored (Field, 2009), the table below is displaying only the factor loadings > 0.4. A factor with fewer than three items is generally weak and unstable; 5 or more strongly loading items (.50 or better) are desirable and indicate a solid factor (Costello & Osborne, 2005).
Table 4.25: Rotated component matrix
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Component
1 2 3 4 5 6
Web 2.0 tools help me teach my subject area .842
Using Web 2.0 tools in teaching will enable me to accomplish tasks .873
Web 2.0 useful in my teaching .766
Web 2.0 tools will reduce my workload .881
Web 2.0 tools will enable me to teach at my pace .715
I find Web 2.0 tools easy to use .857
My interaction with Web 2.0 tools is clear and understandable .899
I possess the skills necessary to use Web 2.0 tools .866
My institution has provided me all the facilities I need for Web 2.0 tools .817 My institution provides incentives to teachers who use Web 2.0 .889 My institution provides incentives to students who use Web 2.0 .912 There is technical help available if required while using Web 2.0 tools .849
I am able to teach my students to use Web 2.0 tools .840
I am able to integrate the use of web 2.0 tools .794
I am able to use conferencing software for collaboration .789
I will encourage my students to use web 2.0 tools to work with other students
.875
I will encourage my students to use web 2.0 tools to analyse information with their classmates
.907
I will encourage my students to use web 2.0 tools to communicate with other people about their ideas
.878
Web 2.0 tools help me teach my subject area .842
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As shown in the Table 4.25, all factor loadings are greater than 0.7 and there are no crossloadings. The usual case is that a minimum of three items must load significantly on each factor (Raubenheimer, 2004). So, factors with only two loadings were not be considered for further analysis. Only the first five factors have been considered for further analysis.
Factor1: Facilitating conditions
Factor2: Technology pedagogy knowledge Factor3: Ease of use
Factor4: Perceived usefulness Factor5: Technology knowledge
After identifying the five factors through EFA, the Cronbach’s Alpha measure was computed to determine how well a set of variables measured a single factor.
Table 4.26: Factors loaded
Factor Question (Variable) Factor
loading Factor1
Facilitating Conditions) Eigenvalue: 6.010
Variance explained = 33.390%
Cronbach’s Alpha =.910
My institution has provided me all the facilities I
need for Web 2.0 tools .817
My institution provides incentives to participants
who use Web 2.0 .889
My institution provides incentives to students who
use Web 2.0 .912
There is technical help available if required while
using Web 2.0 tools .849
Using Web 2.0 tools in teaching will enable me to accomplish tasks .873
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Factor Question (Variable) Factor
loading Factor 2
Technology Pedagogy knowledge Eigenvalue: 2.734
Variance explained = 15.187%
Cronbach’s Alpha =.916
I will encourage my students to use Web 2.0 tools
to work with other students .875
I will encourage my students to use Web 2.0 tools to analyse information with their classmates .907 I will encourage my students to use Web 2.0 tools to communicate with other people about their ideas
.878 Factor3
Ease of Use) Eigenvalue 1.55
Variance explained = 11.09%, Cronbach’s Alpha =.824
I find Web 2.0 tools easy to use
.857 My interaction with Web 2.0 tools is clear and
understandable .899
I possess the skills necessary to use Web 2.0 tools .866 Factor4
Perceived usefulness
Web 2.0 tools help me teach my subject area
.842 Using Web 2.0 tools in teaching will enable me to
accomplish tasks .873
Web 2.0 useful in my teaching
.766 Factor5
Technology Knowledge
I am able to teach my students to use Web 2.0
tools .840
I am able to integrate the use of Web 2.0 tools .794
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Factor Question (Variable) Factor
loading I am able to use conferencing software for
collaboration .789