Data analysis

Bernard (2013:394) states that “analysis is the search for patterns in data

and for ideas that help explain why those patterns are there in the first place”. According to Bryman and Bell (2007), to understand the data

collected, they need to be processed, analysed and interpreted. As illustrated by Neuman (2011), some of the similarities between quantitative and qualitative data analysis are that both of them infer from empirical data to abstract ideas; both use a public process; both make comparisons; and both avoid errors and false conclusions.

Table 4.11: Differences between quantitative and qualitative data analysis

Quantitative data analysis Qualitative data analysis

Quantitative uses a few shared and standardised techniques.

Qualitative uses many diverse and non-standardised techniques. Quantitative analysis begins after all

data have been collected.

Qualitative begins data analysis while still collecting data.

Quantitative uses precise and compact abstract data.

Qualitative uses imprecise, diffuse and relatively concrete data.

Source: Neuman (2011)

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screening the data and selecting the appropriate data analysis strategy.

4.14.1 Procedures of data analysis of questionnaires

The questionnaire was used to investigate students’ attitudes and perceptions concerning their English reading abilities and use of reading skills, the facilities available at Zawia University and the strategies that their teachers implement in reading classes. The first part of the questionnaire analysis concerned the demographic data of the participants such as gender, age group, and academic level of study. For the reliability of the questionnaire and consistency of all items in it, the data from the questionnaires were imported into SPSS to find out Cronbach’s Alpha, which was found to be 0.925. Descriptive analysis of the results provided the frequency and percentages, and mean scores from the data collected to show students’ level of agreement and disagreement with the statements of the questionnaire. To achieve the research objectives for the current study and answer the research questions, the variables were compared through three tests: independent t-test, one-way Anova, and Pearson correlation. An independent-samples T-test was conducted to compare the mean scores of the participants according to their gender (males and females). A one-way Anova between-groups analysis of variance was run to explore the impact of the academic levels of students on their reading comprehension performance and determine some factors that influenced the quality of learning of reading comprehension. To find out any differences, Duncan test was run to identify where the differences were. Pearson Correlation analysis was used to quantify the strength and direction of the relationships between each two variables.

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4.14.2 Procedures of data analysis of interviews

To verify the results from the questionnaire data analysis, semi-structured interviews were conducted to investigate the lecturers’ view about the process of teaching and learning of reading comprehension at the Department of English at Zawia University. After providing the background of the participants, there was an assessment of the lecturers’ perspectives regarding their teaching of English reading comprehension module, their perceptions about the teaching environment, and their views concerning some issues related to their roles in motivating students to read. Content analysis was employed. According to Jankowicz (2005), content analysis is suitable for analysing data obtained from interviews in which the categories reflect the major perspectives arising in the interview process.

4.11 Pilot study

According to Bernard (2013) and Saunders et al. (2009), a pilot study should be conducted before running a larger study. Kumar (2014) calls it a feasibility study. The pilot study must be done with individuals similar to the intended participants as the purpose of it is to check for poorly prepared items, ambiguity, or confusion (Wiersma and Jurs, 2009) and to refine the questionnaire so that the participants will face no problem in answering the questions (Saunders et al., 2009). However, Bryman (2012:263) argues that “the desirability of piloting such instruments is not solely to do with

trying to ensure that survey questions operate well; piloting also has a role in ensuring that the research instrument as a whole functions well”.

Therefore, it also helps researchers discover weaknesses in their methodology and methods.

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To make sure that the wording and meanings of the questions are understandable in order to identify the students’ perceptions about reading comprehension skills, a preliminary questionnaire was designed and distributed to 30 students at the Department of English at Zawia University. All of the 30 students answered all of the questions although some of them reported that the questionnaire was slightly long. All of the students agreed that the language used in the questionnaire was understandable. The data from the pilot questionnaire were analysed using the Statistical Package for Social Sciences (SPSS) version 23, and the Cronbach Alpha of the questionnaire in this research was found to be ‘0.747’. consequently, the overall reliability co-efficient is more than 0.70, which implies that there is a good internal consistency of scale. According to Gorard (2003), the instrument in a pilot study should be delivered and analysed in the same way as intended for the final study. Wiersma and Jurs (2009) indicate that as soon as the measurement instrument is evaluated to be satisfactory, the researcher can begin collecting data. Based on the feedback received from the pilot group and the positive Cronbach Alpha, the study instrument gained the initial reliability and it was valid for the full-scale data collection.