Chapter 1 Introduction
4.3 Influence of teachers’ expertise on intention to use Web 2.0 tools
4.3.2 Exploratory factor analysis
Exploratory factor analysis (EFA) is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlying theoretical structure of the phenomena. Principal Component Analysis (PCA)is a dimension-reduction tool that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set. EFA with PCA as the extraction and rotated with Varimax rotation was carried out with all items constituting the different TPACK constructs. Factor analysis is a technique used to verify whether the items of a construct are really measuring that construct and thus helps to produce a rigorous instrument. PCA deals with determining which linear components exist within a data set and how variables might impact on that component or construct (Field, 2009).
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A review of TPACK literature revealed two main approaches to EFA. One approach is to take all the items of the different constructs together and then perform factor analysis to find out the number of factors that will be derived and which items will load under the extracted factors (Koh et al., 2010; Lux, Bangert, & Whittier, 2011). Another approach is to run separate factor analysis for each of the constructs of the TPACK framework (Sahin, 2011; Schmidt et al., 2009). Koh et al. (2010) and Lux et al. (2011) sought to find out whether TPACK really comprised all the seven constructs while Sahin (2011) and Schmidt et al. (2009) concluded that TPACK had all the seven constructs from literature and thus were interested in determining items that will assist to assess the different constructs. Since this aim of this research was not to examine each of the different subscales of the TPACK framework as shown in Sahin (2011) and Schmidt et al. (2009) but rather to find out which type of knowledge will reveal teachers’ intention to use Web 2.0 tools, the EFA was run for all items constituting the different TPACK constructs.
A PCA was conducted on the 18 variables with orthogonal rotation (Varimax).
4.3.2.1 Test for sampling adequacy and presence of correlations
Table 4.10: KMO and Bartlett's Test of Sphericity
KMO Measure of Sampling
Adequacy. .814
Bartlett's Test of Sphericity
Approx. Chi-
Square 1719.252
df 153
Sig. .000
The Kaiser-Meyer-Olkin (KMO) test is used for measuring sampling adequacy. Bartlett's test of sphericity tests the hypothesis that one’s correlation matrix is an identity matrix, which would indicate that one’s variables are unrelated and therefore unsuitable for structure detection. As indicated in Table 4.10, the KMO value (0.814) is greater than 0.7 which means the data is likely to factor well. The data was considered to be fit for factor
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analysis (Field, 2009; Sahin, 2011). The Bartlett’s Test of Sphericity is a statistical test for the presence of correlations among the variables that provides the statistical probability that the correlation matrix has significant correlations among several of the variables (Field, 2009). 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. For this study the Bartlett’s Test of Sphericity demonstrated that this statistical probability of significant correlations existed with these data (X2 = 1719.252, df = 153; p < .001).
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.
4.3.2.2 Communalities
Communalities indicate the amount of variance in each variable that is accounted for.
Table 4.11: Communalities
Initial Extraction
Item 1 I am able to use Web 2.0 for personal purpose 1.000 .402 Item 2 I am able to teach my students to use Web 2.0 tools 1.000 .769 Item 3 I am able to integrate the use of Web 2.0 tools 1.000 .741 Item 4 I am able to use conferencing software for collaboration 1.000 .674 Item 5 I teach my students to adopt appropriate learning
strategies 1.000 .698
Item 6 I know how to guide my students to discuss effectively
during group work 1.000 .705
Item 7 I know how to guide my student to learn independently 1.000 .624 Item 8 I have sufficient knowledge about my subject area 1.000 .822
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Initial Extraction
Item 9 I have various ways and strategies of developing my
understanding of my subject area 1.000 .727
Item 10 I will encourage my students to use Web 2.0 tools to work
with other students 1.000 .816
Item 11 I will encourage my students to use Web 2.0 tools to
analyse information with their classmates 1.000 .875 Item 12 I will encourage my students to use Web 2.0 tools to
communicate with other people about their ideas 1.000 .778 Item 13 I can help my students to understand the content
knowledge of my subject area in various ways 1.000 .605 Item 14 I know how to select effective teaching approaches to
guide student thinking and learning in my subject area 1.000 .497 Item 15 I know about technologies that I can use for
understanding and doing my subject area 1.000 .685 Item 16 I can use appropriate technologies to represent the
content of my subject area 1.000 .729
Item 17 I can teach lessons that appropriately combine my subject
area, technologies and teaching approaches 1.000 .763 Item 18
I can select technologies to use in my classroom that enhance what I teach, how I teach and what students learn
1.000 .709
Extraction Method: Principal Component Analysis
4.3.2.3 As shown in Table 4.11, items 1, 7, 13 and 14 had communalities less than 0.7 and were deleted from the data set. According to Field (2009), when there
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are less than 30 variables, communalities after extraction exceeding 0.7 are desirable. Rotated Component matrix
Table 4.12: Rotated Component matrix
Rotated Component Matrixa
Component
1 2 3 4 5
Item 1 I am able to use Web 2.0 for personal
purposes .462
Item 2 I am able to teach my students to use
Web 2.0 tools .839
Item 3 I am able to integrate the use of Web
2.0 tools .806
Item 4 I am able to use conferencing software
for collaboration .814
Item 5 I teach my students to adopt
appropriate learning strategies .761 Item 6 I know how to guide my students to
discuss effectively during group work .800 Item 7 I know how to guide my student to
learn independently .739
Item 8 I have sufficient knowledge about my
subject area .886
Item 9
I have various ways and strategies of developing my understanding of my subject area
.772
Item 10
I will encourage my students to use Web 2.0 tools to work with other students
.868
100 Rotated Component Matrixa
Item 11
I will encourage my students to use Web 2.0 tools to analyse information with their classmates
.901
Item 12
I will encourage my students to use Web 2.0 tools to communicate with other people about their ideas
.850
Item 13
I can help my students to understand the content knowledge of my subject area in various ways
.467 .449 .424
Item 14
I know how to select effective teaching approaches to guide student thinking and learning in my subject area
.497
Item 15
I know about technologies that I can use for understanding and doing my subject area
.735
Item 16
I can use appropriate technologies to represent the content of my subject area
.813
Item 17
I can teach lessons that appropriately combine my subject area, technologies and teaching approaches
.844
Item 18
I can select technologies to use in my classroom that enhance what I teach, how I teach and what students learn
.764
As shown in Table 4.12, only factor loadings greater than 0.4 are displayed. Generally, factor loadings less than 0.4 are ignored (Field, 2009), the above table is displaying only the factor loadings greater than .04. A factor with fewer than three loading 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). The fourth and fifth factors had less than three loadings. These two factors were not considered for further analysis. Therefore item
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8 and item 9 were removed from the data set. A “crossloading” item is an item that loads at 0.32 or higher on two or more factors. According to Costello and Osborne (2005) a crossloading item should be dropped from the analysis if there are several crossloaders adequate to strong loaders (.50 or better) on each factor. If there are several crossloaders, the item may be poorly written or the a priori factor structure could be flawed (Costello &
Osborne, 2005). Item 13 was removed from the data set since it was crossloaded on three factors.
The EFA was rerun with the following results:
Table 4.13: Communalities recalculated
Initial Extraction
I am able to teach my students to use Web 2.0 tools
1.000 .784
I am able to integrate the use of Web 2.0 tools 1.000 .754
I am able to use conferencing software for collaboration
1.000 .707
I teach my students to adopt appropriate learning strategies
1.000 .793
I know how to guide my students to discuss effectively during group work
1.000 .783
I have sufficient knowledge about my subject area
1.000 .834
I have various ways and strategies of developing my understanding of my subject area
1.000 .751
I will encourage my students to use Web 2.0 tools to work with other students
1.000 .853
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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 .810
I know about technologies that I can use for understanding and doing my subject area
1.000 .655
I can use appropriate technologies to represent the content of my subject area
1.000 .752
I can teach lessons that appropriately combine my subject area, technologies and teahcing approaches
1.000 .774
I can select technologies to use in my classroom that enhance what I teach, how I teach and what students learn
1.000 .737
Average communality .778
As indicated in Table 4.13 the average communality is greater than 0.7. This implies that factor analysis can be performed using these data.
103 4.3.2.4 Factor analysis
Table 4.14: Percentage variance: 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
Variance
Cumulative
%
1
4.935 35.251 35.251 4.935 35.251 35.251 2.797 19.981 19.981
2
1.878 13.411 48.662 1.878 13.411 48.662 2.634 18.811 38.792
3
1.553 11.095 59.757 1.553 11.095 59.757 2.280 16.285 55.076
104 Component
Initial
Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
4
1.487 10.619 70.376 1.487 10.619 70.376 1.617 11.551 66.627
5
1.041 7.438 77.814 1.041 7.438 77.814 1.566 11.188 77.814
6
.603 4.304 82.119
7
.539 3.851 85.970
8
.481 3.433 89.402
105 Component
Initial
Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
9
.335 2.391 91.793
10
.303 2.167 93.960
11
.271 1.933 95.893
12
.233 1.664 97.558
13
.223 1.595 99.153
106 Component
Initial
Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
14
.119 .847 100.000
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According to the eigenvalues in the above table, five factors have eigenvalues greater than 1.0, which is a common criterion for a factor to be useful (Field, 2009).
The scree plot below supports a five-factor solution.
Figure 4.1: Scree plot
108 4.3.2.5 Recalculation of rotated components
Table 4.15: Rotated components recalculated
Component
1 2 3 4 5
I am able to teach my students to use Web 2.0 tools
.854
I am able to integrate the use of Web 2.0 tools
.820 I am able to use conferencing software for
collaboration
.832
I teach my students to adopt appropriate learning strategies
.832 I know how to guide my students to discuss
effectively during group work
.829
I have sufficient knowledge about my subject area
.900
I have various ways and strategies of
developing my understanding of my subject area
.803
I will encourage my students to use Web 2.0 tools to work with other students
.884 I will encourage my students to use Web 2.0
tools to analyse information with their classmates
.910
I will encourage my students to use Web 2.0 tools to communicate with other people about their ideas
.859
I know about technologies that I can use for understanding and doing my subject area
.733
109 Component
1 2 3 4 5
I can use appropriate technologies to represent the content of my subject area
.828
I can teach lessons that appropriately combine my subject area, technologies and teahcing approaches
.862
I can select technologies to use in my classroom that enhance what I teach, how I teach and what students learn
.777
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalisationa a. Rotation converged in 5 iterations.
As shown in the Table 4.15, 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.
The three factors that were used for further analysis are:
Factor1: Technological content knowledge and TPACK Factor2: Technology knowledge
Factor3: Technology pedagogy knowledge
After identifying the three factors through EFA, the Cronbach’s Alpha measure was computed to determine how well a set of variables measured a single factor. An Alpha value of .6 to .7 is a lenient but acceptable measure of reliability, .7 to .8 is good, and higher than .8 is very good (Field, 2009). All the alpha values were higher than .8. These values also are listed in Table 4.16.
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Table 4.16: Summary of factors loaded
Factor Question (Variable) Factor
loading Factor 1
TCK and TPACK (Technology Content Knowledge together with pedagogy knowledge)
Eigenvalue 4.85
Variance explained = .35.251 Cronbach’s Alpha =.855
I know about technologies that I can use for
understanding and doing my subject area .733 I can use appropriate technologies to
represent the content of my subject area .828 I can teach lessons that appropriately combine my subject area, technologies and teaching approaches
.862
I can select technologies to use in my classroom that enhance what I teach, how I teach and what students learn
.777
Factor 2
Technology Pedagogy knowledge Eigenvalue 1.81
Variance explained = 13.411%
Cronbach’s Alpha =.916
I will encourage my students to use Web 2.0
tools to work with other students .884 I will encourage my students to use Web 2.0
tools to analyse information with their classmates
.910
I will encourage my students to use Web 2.0 tools to communicate with other people about their ideas
.859
Factor 3
Technology Knowledge Eigenvalue 1.55
Variance explained = 11.09%, Cronbach’s Alpha =.824
I am able to teach my students to use Web 2.0
tools .854
I am able to integrate the use of Web 2.0 tools .820
I am able to use conferencing software for
collaboration .832
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As shown in Table 4.16, Cronbach’s Alpha for the four factors range between .75 and .92 indicating a “good” reliability (Field, 2009).