Data Analysis Stages

Data analysis may be simple, involving a summary of major themes, or may call for more complex content analysis and comparisons of groups (Goldenkoff, 2004).

2.3.3.1 Quantitative Data Analysis

Reliability and validity are positivist epistemology tools (Golafshani, 2003), where reliability can be defined as when the results of a study are harmonious over time, and can be reproduced under a similar methodology; an accurate representation of the study population is referred to as reliability (Joppe, 2000). The reliability of the findings depends on the likely repetition and the interpretation of the original data (Ritchie and Lewis, 2003). Kirk and Miller (1986) determined three types of reliability in quantitative research: the degree to which a measurement, given repeatedly, remains the same, the stability of a measurement over time, and the similarity of measurements within a given time period.

Validity is concerned with two main issues: whether the instruments used for measurement are accurate and whether they are actually measuring what they want to measure (Winter, 2000). Ritchie and Lewis (2003) indicated that the validity of research is conceived as the precision or correctness of the research finding. Arksey and Knight (1999) and Winter (2000) identified two different dimensions of the validity concept: internal validity that ensures that the researcher investigates what he claims to be investigating, and external validity that is concerned with the extent to which the research findings can be generalised to the wider population. The validity in quantitative research is a ‘construct validity’, which represents the initial concept, notion, question, or hypothesis that decides which data is to be collected and how. Quantitative researchers affect the interaction between construct and data so as to validate their investigation; therefore the involvement of the researchers in the research process would entirely diminish the validity of a test (Wainer and Braun, 1998).

It can be concluded that, in quantitative research, reliability cares about whether the results are replicable, and validity cares about whether the means of measurement are accurate and are measuring what was intended to be measured (Golafshani, 2003). Reliability and validity are

considered as very important measurements for judging the questionnaires’ data quality (Guddemi, 2003).

Multi-stage sampling is a technique that fits the mass estimation of populations living in habitats with complicated structures; it is an easy technique, which is widely applicable and convenient (Kuno, 1976). This technique is more complex than cluster sampling, in which larger groups are subdivided into smaller and more targeted groups for surveying purposes, as it creates a more representative sample of the population. It can be used in cases of initial constructions due to the low cost of large- scale survey research, and because such a technique limits population aspects, which need to be included within the frame for sampling (Kaplan, 2013). The technique of cluster sampling is designed to generate statistics about particular populations by dividing the full population into significant groups, but this might not reflect the diversity of the population and accordingly not be as accurate as simple random samples (Ahmed, 2009). Cluster sampling is used in cases when it is impractical to collect an intensive list of elements that structure the target population (Crossman, 2012).

Demographic variables represent characteristics such as age, gender, and defined variables that can diagnose socioeconomic status (Department of Media and Communication, 2004).

‘Pearson’s Correlation’ is usually about different variables of the quantitative approach that measure all parts of a sample, for instance, by considering a couple of these variables, often it is figured as a relation between those two variables in order to measure if they are related. This relation can be assorted as one variable, which promotes other variables (statstutor, 2012).

• ‘Positive correlation’, where other variables also tend to increase

• ‘No correlation’, where the opposite variable seems to have null influence, where there is no up or down.

2.3.3.2 Qualitative Data Analysis

Patton (2002) states that validity and reliability are two factors, which any qualitative researcher should be concerned about while designing a study, analysing results, and judging the quality of the study. While the terms reliability and validity are an essential criteria for quality in quantitative paradigms, in qualitative paradigms the terms Credibility, Neutrality or Confirm-ability, Consistency or Dependability, and Applicability or Transferability are to be the essential criteria for quality (Lincoln and Guba, 1985).

Marshall and Rossman (1999) and Seale (1999) argued that the absolute repetition of qualitative studies is very difficult to achieve since they reflect realities at the time they were collected, especially in a situation which is likely to change. It is often referred to as an unrealistic demand. Phenomenological research may be difficult to repeat because it depends generally on unstructured data collection methods (Gray, 2004).

Reliability and validity represent trustworthiness, rigour, and quality in the qualitative paradigm. Achieving research validity and reliability is affected by qualitative researchers’ perspectives, which are to eliminate bias and increase the researcher’s truthfulness of a proposition about some social phenomenon (Denzin, 1978) using triangulation. Then triangulation can be defined as a validity procedure where researchers search for convergence among multiple and different sources of information to form themes or categories in a study (Creswell and Miller, 2000).

Denscombe (1998) reported that using multi-methods to examine one issue strengthens the research findings and increases the validity of the data.

A ‘theme’, as Braun and Clarke (2006) explained, captures something important about the data in relation to the research questions, and represents response or meaning within the data set. A rich description of the data set is important to determine the type of analysis, so the reader gets a sense of the predominant or important themes. The researcher’s judgement is necessary to determine what a theme is. Thematic analysis is a method of analysing qualitative data that can be used within a number of theoretical frameworks and is specifically matched with a study that uses a mixed methods approach. This method of analysis is suitable for identifying issues in areas of research where little is known about the topic being discussed (Binnersley, 2010).

Themes can also be identified in one of two levels: a semantic level or latent level. Themes in the semantic approach are identified within the surface meaning of the data, and the data does not go beyond what a participant has said or has written. The analytic process involves a progression from description, where the data has simply been organised and summarised, to interpretation. In contrast, a thematic analysis at the latent level goes beyond the semantic level and identifies the underlying ideas, assumptions, and ideologies that are theorised as reforming the semantic content of the data (Braun and Clarke, 2006).

Thematic analysis consists of reading data and noting down items of interest according to quality criteria that comprises the interview quality, which requires the extent of spontaneous, rich, specific, and relevant answers from the interviewee, and the degree to which the interviewer clarifies the meanings of the relevant aspects of the answers (Aronson, 1994). Kvale (1996) reported that the ideal interview subjects don’t exist, the interviewer should be expert in the subject of the interview, and the interviewer should be capable of selecting the appropriate data to be allocated into themes which can be analysed easily.