Data Analysis

Data analysis was conducted in four stages. Each data collection stage aligned to a stage of analysis. The first stage analysed results from the first questionnaire, while the second stage included the first round of interviews. The third and fourth stages comprised analysis of the second sets of questionnaire and interview data, respectively. Analysis of questionnaire data included descriptive analysis, comparison of means and inferential statistics. The findings were used to inform interview data, which was thematically and theoretically coded for analysis. Data were analysed using SPSS 16.0 and NVivo 7.0.

The first stage of data collection began with the generation of descriptive statistics for all questionnaire items. All negative items were reversed. To improve comparability, items addressing students’ frequency of use of the internet for personal and educational purposes, as well as the frequency of sending and receiving SMS, were recoded (see Table 4.4).

Table 4.4

New Codes for Answers to Some Questions

Frequency distributions were then calculated for participants’ demographic data, their use of ICT, use of mobile phones, and use of SMS technology. Descriptive statistics (e.g. frequencies, means and standard deviations) were used to answer Research Questions

Options New code

Many times a day,

Frequently 2-3 times a day,

Once a day,

Often 2-6 times a week,

Once a week,

Rarely 2-3 times a month

Once a month

Never Never

1 and 2. Students’ perceptions and attitudes toward SMS were examined for positive and negative trends and differences among different groups of students, (e.g. gender, age, ICT use). Independent sample t-tests were conducted to examine the differences in students’

perceptions and attitudes toward the use of SMS based on their major (engineering or non-engineering), gender, use of smartphones for personal purposes, use of smartphones for educational purposes, use of laptop for personal purposes, and use of laptop for educational purposes. Analysis of variance )ANOVA( was conducted to examine variations in students’

perceptions and attitudes toward the use of SMS based on: frequency of receiving SMS, frequency of sending SMS, academic year, frequency of using the internet for personal purposes, and frequency of using the internet for educational purposes. Fisher's Least Significant Difference (LSD) test was used as a post hoc pairwise comparison test to check for significant differences between the means (Williams & Abdi, 2010). The examination of such differences helped to build an understanding of students’ perceptions and attitudes toward SMS. These results provided information on perceptions and attitudes toward SMS among different groups of students, which addresses Research Question 1 by addressing students’ perceptions of the ease of use and usefulness of SMS. Further, Research Question 2 is also addressed by exploring students’ attitudes towards the use of SMS to support learning and teaching

Correlation and regression analysis were used to answer Research Question 3.

Based on Tabachnicu and Fidell’s )2006( suggestions, simple correlations were carried out (using Pearson product- moment correlation coefficient) to identify the size of relationships between the independent variables (ease of use and usefulness of SMS) and the dependent variable )attitudes toward SMS(. According to Ary et al. )2010( correlation “seeus to

determine if the variables are related (correlated). Correlation means the extent to which the two variables vary directly )positive correlation( or inversely )negative correlation(” )p.27(.

Regression analysis was used to assess the extent of the relationship between the dependent variable (attitudes toward SMS) and the independent variables (ease of use and usefulness of SMS), the percentage of discrepancy in the dependent variable predicted by regression, and the relative importance of the two independent variables in predicting the dependent variable. Before performing multiple regressions, assumptions of multicollinearity, normality, linearity and homoscedasticity of residuals were examined (Tabachnick &

Fidell, 2006). Multicollinearity refers to a situation where two or more independent variables are very highly correlated. The lack of multicollinearity was verified through the correlation factor of independent variables (ease of use and usefulness of SMS) and the variance inflation factors )VIF( for the independent variables, where “the rule of thumb states that multicollinearity exists if the VIF for any independent variable is greater than 10” )Webster, 2013, p. 134). The assumptions of normality, linearity and homoscedasticity between predicted dependent variable scores and errors of prediction were checked through the examination of the shape of the residual’s scatter plot. If these assumptions were

supported, this would justify the use of regression models for the purposes of prediction. In the results, the correlation tests and justified regressions analysis further addressed

Research Questions 1 and 2 by exploring the relationship between students’ attitudes toward using SMS and their perceptions of the usefulness and ease of use of SMS technology.

After the second round of data collection, students’ responses to the second

questionnaire were compared with students’ responses to the first questionnaire. The paired t-tests for dependent samples were conducted to compare pre- and post-test scores of the same individuals (Ary et al., 2010). The comparison was used to determine whether students’ perceptions and attitudes toward SMS had changed over the semester. However, studies of beliefs and perceptions suggest one semester might not be long enough to result in a significant change (Richardson, 2003).

Students’ responses to the interviews in the first and second rounds of data collection were analysed using qualitative data analysis methods. The results of the

qualitative data analysis contributed to answering the three research questions, as students’

responses in the interviews helped to provide an understanding of, and explanations for, students’ perceptions and attitudes toward SMS and how these perceptions and attitudes were related (Research Question #3).

The qualitative data were analysed through organising and familiarising, coding and reducing, and interpreting and representing (Ary et al., 2010). In the organising and

familiarising stage, interviews were transcribed and translated into English. In the coding and reducing stage, students’ responses were systematically grouped according to the research questions. After this essential grouping, following Ary et al.’s )2010(

recommendations, units of meaning that included words, phrases, sentences and subjects’

ways of thinking inside each group were recognised and then tagged into initial codes.

Then, related codes were combined and reduced into broader categories. After that, the relationships amongst categories were examined and then grouped into themes. To ensure validity, the analysis of the interview transcripts and the initial coding structures were checked by another graduate student in the field of education.

The TAM framework set the main factors which used to identify the codes and themes from the qualitative data. Examples of the initial codes in relation to SMS ease of use included: "clear", "easy”, “required just a few clicks", "in smartphones that have large keyboards", "old phones", "difficult to type", "size of the keyboard", "communicate short message", "express feelings using SMS", "SMS cannot express feelings" and "Arabizi".

These codes were selected because participants frequently reported them. They were grouped into two themes in relation to SMS ease of use: general positive perceptions of SMS ease of use and issues related to the use of SMS. In the interpreting and representing stage, data were reported using narratives; the narratives involved providing explanations of the findings and reflections on participants’ responses in the first and second rounds of data collection. In addition, participants' quotes were used to demonstrate, illustrate and deepen the understanding of the qualitative findings (Corden & Sainsbury, 2006). The qualitative data were used to support and clarify the findings from the questionnaires.