Data analysis

As mentioned earlier, this study is a mixed methods one; triangulation is a main procedure followed. Therefore, both qualitative and quantitative instruments were employed simultaneously to address the research questions. In the first three research questions (i.e., the ER impact on 1) reading speed, 2) vocabulary knowledge and 3) reading strategies), the data sources were both quantitative (i.e., reading speed tests, vocabulary tests and reading strategies checklist) and qualitative (i.e., interviews, diaries, book reports and a videotaped session). In the last two questions (i.e., the ER impact on 1) productive skills and 2) motivation for and attitudes towards ER), the data sources were qualitative (i.e., interviews, diaries, book reports and a videotaped session). To analyse such data, thematic analysis was used.

111 Starting with qualitative data, qualitative data analysis were applied on all the study questions. The approach taken for data analysis was the thematic approach of the narrative analysis, which is derived from the social constructivism philosophy (i.e., the philosophy used in this study). Thematic analysis is a common technique that is used mainly in qualitative research, and can be used to identify the important themes, derived from data, to answer the research questions (Gries, 2009).

The first step in data analysis was a detailed and in-depth scrutiny of the data from interviews, diaries, book reports and a videotaped session. This detailed analysis provided me with a closer look at the data, to identify the main themes based on the research questions and the frequency of words repeated in the data obtained. For example, among the words which the students and I used most

frequently were reading materials, anxiety and fast reading. Therefore, these words were labelled as categories.

After identifying different categories and labels from initial coding, similar categories or categories that served the same theme, were grouped together. As a result, 11 main categories were derived. These categories included: 1) reading speed, 2) vocabulary knowledge, 3) reading strategies, 4) speaking skill, 5) writing skill, 6) problems and challenges facing the students in the ER programme, 7) reading materials, 8) factors affecting motivation for and attitudes towards reading in the target language, 9) reading anxiety, 10) ER activities and 11) students’ reading experiences before the ER programme. These main categories were divided further with some labels and subcategories under them from the initial coding. For

example, in the initial coding, many aspects related to students’ motivation for and attitudes towards reading were categorised as: confidence, autonomy, extrinsic motivation and intrinsic motivation. These categories were placed as the

subcategories of factors affecting motivation for and attitudes towards reading. Having had 11 categories, I asked a colleague to review them and their

subcategories to provide me with feedback as to whether these categories serve the main goals of this study. This step was taken to obtain more credible data and to eliminate personal biases in the analysis (Creswell, 1998). Based on my colleague’s feedback and my supervisors’ recommendations, some changes to the categories were made by labelling some categories as subcategories to other main categories. The final grouping of categories included six themes, each with subcategories. These six themes, to characterise the findings, were: reading speed, vocabulary knowledge,

112 reading strategies, productive skills, motivation for and attitudes towards reading and the ER programme. These themes were mainly aimed at analysing data to answer the research questions and provide pedagogical implications.

As for quantitative data analysis, it included quantitative data sources (i.e. reading speed tests, vocabulary tests and reading strategies checklist). I used Excel Version 2011 for descriptive statistics and statistical analyses.

In order to find out the impact of ER on reading speed, vocabulary knowledge and reading strategies, students’ pre-tests results (i.e. number of words read per minute, scores of vocabulary tests and reading strategies used) were recorded and compared with post-tests results. The mean scores of all tests and students’ results, as well as the minimum and maximum scores, were provided in numbers and in percentage points.

Paired t-tests were used to determine whether the difference between pre-tests and post-tests is significant. Paired or dependent t-tests “compare two sets of data from the same group” (Rasinger, 2013, p.200). In other words, they examine whether scores from the same group under two different conditions (i.e., pre-and- post-tests) differ significantly. In this study, paired t-tests were applied to compare the students’ results in reading speed test, vocabulary tests and reading strategies checklist, at the pre and post-test stages, and examine whether the difference is significant. Thus, statistical tools were used to check the ER impact on these three language aspects. If the p value was smaller than 0.05, the difference would be statistically significant (p < 0.05).

In addition, the correlation between the number of books read by each student (i.e., amount of reading), and the students’ mean scores in reading speed tests, vocabulary tests and reading strategies checklist was checked by using Pearson’s Correlation Coefficient. Pearson’s Correlation Coefficient is a measure used to examine type and degree of correlation between two variables measured on a ratio scale (Rasinger, 2013). Using such a procedure allows me to find whether the correlation between the amount of reading and other variables (i.e., reading speed, vocabulary knowledge and reading strategies) is positive or negative and the degree of that correlation.

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