Factor Analysis - Chapter Four: Data Analysis and Findings

4 Chapter Four: Data Analysis and Findings

4.3 Factor Analysis

Factor analysis originated in the work of Charles Spearman (1904), when he studied human intelligence. Norusis (2012) defines factor analysis as a “statistical technique used to identify a relatively small number of factors that explain observed correlation among variables” (p. 405). It includes the following: computing a correlation matrix, extracting factors, rotating factors to make variables easy to interpret, and calculating factor scores.

The procedure adopted for factor analysis was to use Principal Components Analysis and orthogonal rotation (Varimax) as well as many options and rules of thumb to create a standardized variable score for individuals that rescaled (Mean=0 & SD=1) in order to be used in further data analysis procedures. This procedure was widely used for factor analysis in similar research such as Abu-Al-Aish & Love (2013), Jairak et al (2009), Lewis et al (2013), and Van Biljon (2006). However, deducing factors that are purely measuring a construct without overlapping with other constructs can be obtained by Principal

Components Analysis and an orthogonal solution; “meaning that the resulting factors are uncorrelated with each other” (Gall et al, 2007, 270). According to Brown (2009), Kim & Mueller (1978), and Tabachnick & Fidell (2007), practically, both methods of factor rotation, orthogonal (uncorrelated factors) and oblique (correlated factors) lead to similar results, but orthogonal solutions are easier to interpret. Both rotational procedures have been tried by the researcher, to test whether the resultant factors are loading on the same component, and assess which method offers the most stringent interpretations of patterns within the data. As a consequence of this trial, orthogonal, rather than oblique, rotation has been implemented. Primarily, Pallant (2010) discussed two steps which are required to check the suitability of the data for factor analysis. The first step in running Exploratory Factor Analysis (EFA) is to compute correlation matrix for all the items which make up all the variables. Following Bryman and Cramer (2011), depending on whether there are significant correlations between items, a decision was made to run factor analysis. Examining the correlation matrix of all variables included in the analysis in both surveys suggests that factor analysis is a valid exercise. The second step is to assess whether the sample size is sufficiently large enough to enable this exercise to be carried out. It is clearly that there is no problem with the students survey sample (870 students) as it is large enough to run the analysis, but for faculty survey (64 staff), the Kaiser-Meyer- Olkin (KMO) and Bartlett’s Test of Sphericity , measures of sampling adequacy, were used to assess the sample size (Norusis, 2012; Pallant, 2010; Tabachnick & Fidell, 2007). Kaiser (1974) stated that KMO measure in the 0.90’s is excellent and in the 0.60’s is average while any KMO measure below 0.50’s is unacceptable. On the other hand, Bartlett’s Test of Sphericity should be significant (p < .05) to consider the sample size as suitable and reject the null hypothesis that all correlation coefficients are 0. The results of both measures are shown in Table 8 below. As can be seen, both the KMO and Bartlett’s Test, support the suitability of the data from both samples for factor analysis.

Table 8: KMO and Bartlett’s Test

(Student Survey)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .905

Bartlett's Test of Sphericity

Approx. Chi-Square 12460.677

df 496

Sig. .000

(Faculty Survey)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .627

Bartlett's Test of Sphericity

Approx. Chi-Square 1421.289

df 561

Sig. .000

Principal Components Analyis and orthogonal rotation (Varimax) was undertaken using a factor loading threshold of 0.30, and this analysis ended by extracting 8 independent variables. Each extracted factor was then correlated with items included in that particular factor, as well as all the other items of all independent variables, as suggested by (Bartholomew et al., 2008, 118-119). As a result, items which belong to a particular factor, that showed a lower correlation with the extracted factor, compared to the items which don’t belong to that factor, were removed. Factor analysis showed that the removed items loaded on more than one factor with factor loadings more than .2.

Moreover, Cronbach’s Alpha was computed to examine the reliability score for each factor, and whether it might change when items were removed (see Tables 9, 10 below). This process can test the survey items for their unidimensionality and consistency. The factor loadings of factors derived from both faculty and students’ surveys are shown in Appendices 6, 7.

Table 9: Reliability Statistics “Cronbach's Alpha” Students Survey

Construct/Variable α No. of Items

Performance Expectancy .890 7

Effort Expectancy .875 3

Social Influence .676 3

Facilitating Conditions .712 to .786 when item FC4 deleted 4

Hedonic Motivation n/a 1

Price of Devices .803 5

Price of Services .773 2

Habit .922 2

Behavioural Intention .887 3

Use Behaviour in EFL .872 11

Use Behaviour in General .855 9

Experience .958 21

Voluntariness of Use .283 3

Table 10: Reliability Statistics “Cronbach's Alpha” Faculty Survey

Construct/Variable α No. of Items

Performance Expectancy .900 to .904 when item PE7 deleted 8

Effort Expectancy .846 3

Social Influence .846 3

Facilitating Conditions .649 to .724 when item FC4 deleted 4

Hedonic Motivation n/a 1

Price of Devices .720 5

Price of Services .673 2

Habit .852 2

Behavioural Intention .888 3

Use Behaviour in EFL .840 11

Use Behaviour in General .703 9

Experience .935 21

Based on the results of factor analysis and reliability analysis several changes have been made. All these changes have addressed both, students and faculty surveys except the last two changes which were mentioned below. First, the survey item FC4 (the fourth Item of Facilitating Conditions “The University provides Wi-Fi connectivity on campus”) was deleted on both student and faculty surveys due to the factor loading (.326 in Students Survey & .026 in Faculty Survey). In addition, the positive change in the reliability score and variance explained by the extracted factor (Facilitating Conditions), when the FC4 was deleted, supports the decision for deleting it (see Tables 9, 10).

Factor analysis can resulted in adding or deleting an item as in the following two cases. One is that two factors for P (Price) were extracted instead of one in both students and faculty survey, Price of Devices and Price of Services, as factor analysis showed the seven items for P loaded on two components. Hence items for P-Devices have loaded separately from items for P-Services. As a consequence of factor analysis, Price construct is reconceptualised into Price of Devices and Price of Services. On the other hand, Voluntariness of Use has been discarded from the research model due to the low internal consistency of the items (α= .283 & α= .477). According to DeVellis (2011) and Norusis (2012), to be acceptable Cronbach’s Alpha needs to be in the range from 0.70 to 0.90 and the greater is the value of Cronbach’s Alpha, the more consistent is the scale. In research into technology acceptance theory and practice, a reliability score of 0.60 or greater is considered acceptable (Venkatesh et. al., 2003; Zhang, Li, & Sun, 2006). It has been proved that looking for Voluntariness of Use in indirect way is not fruitful, and deleting items, has not improved the reliability score for the data from both surveys. Moreover, this study has considered students and faculty as consumers of mobile technologies and, in such a context, Voluntariness of Use is not an issue, as all consumers are voluntarily use these technologies. However, this variable was brought back to the model, as an auxiliary measure that would contribute to the implications of the study. If the results would show positive perception and attitude towards mobile technologies, then the organization might call for “Bring Your Own Personal Handheld Devices (PYOPHD)” for teaching and learning. At that point, if the acceptance of mobile technologies is going to be measured, the Voluntariness of Use dimension would be needed.

Since the moderator “Experience” has been measured by asking four different questions (Q.6, Q.7, Q.8, Q.9 in the Student Survey, Q.7, Q.8, Q.9, Q.10 in the Faculty Survey, see

Appendices 1, 2, 3) and each question asked about five different common mobile devices, and running factor analysis in the same way resulted in extracting five different constructs as the items related to each device loaded on the same component. Hence, the sum score for each question was calculated and then used to run factor analysis, correlation, and reliability tests because experience in general, and not with the experience of a specific device, is required.

Additionally, in both surveys there is one item for measuring Hedonic Motivation. At the early stages of developing the surveys there were two items (“Using mobile technologies in EFL learning/teaching is fun” & “Using mobile technologies in EFL learning/teaching is enjoyable” which were adopted from work by Venkatesh et. al. (2012), but, based on the face validity procedure, and as a result of piloting the surveys, it was revealed that one of those items should be removed, because the two statements are in essence identical, and therefore measure the same thing. At that stage, the researcher did not consider the data analysis procedures that require both items to be included. Nevertheless, this item was excluded from the factor analysis and reliability analysis, as it is not applicable to conduct these techniques in this situation.

Only in faculty survey, factor analysis revealed that PE7 (the seventh item of Performance Expectancy “Using mobile technologies is not about teaching, as I am learning too”) is significantly correlated with PE, EE, and SI (.589**, .382**, .353**) respectively. Therefore, this item was discarded. Furthermore, it had been mentioned before that age as a moderator was excluded from the research model for the students sample, due to the fact that the large majority (80.9%) of participants (students) were within the age category of 19-20. However, based on the preliminary data analysis an initial research model (Figure 10) was implemented for the current study.

Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions Hedonic Motivation Price of Devices Habit Behavioural Intention to Use Mobile Technologies in

Teaching & Learning EFL Use Behaviour MODERATORS: Age Gender Experience Price of Services

Figure 10: Initial Research Model for Higher Education Acceptance of Mobile Technologies in Teaching & Learning EFL

Table 11: Research Hypotheses Based on the Initial Research Model in Figure 10

Students Faculty

1.S. Performance Expectancy will significantly predict behavioural intentions to use mobile technologies in learning EFL and use behaviour.

1.F. Performance Expectancy will significantly predict behavioural intentions to use mobile technologies in teaching EFL and use behaviour. 2.S. Effort Expectancy will significantly predict