Procesos internos

The qualitative research aims to measure any trustworthiness in the study. Trustworthiness in qualitative research has to meet five key standards: Credibility, Transferability, Dependability, Conformability and Authenticity (Lincoln and Guba, 1985, Flick, 2014, Bryman, 2001, Seale et al, 2004). This section will evaluate the interviews in this study based upon the five standards aforementioned.

Credibility in qualitative research parallels internal validity of the quantitative approach. It gives an indication as to the degree of confidence in the research findings on whether they are believable (Lincoln and Guba, 1985, Flick, 1998, Bryman, 2001, Seale et al, 2004). Lincoln and Guba (1985), set up certain procedures to be followed to ensure the credibility of the research. First, all interviewees have to be informed of the purpose of the research and interviews. The interview seeks their honesty in answering the questions to establish a good understanding of the research problem. The interviewees in this study were encouraged to answer the questions as frankly as possible; however, they were granted the opportunity to skip any question they do not feel comfortable with.

Nevertheless, (Bryman, 2001) asserts that the fitness of the research method employed and the questions asked is one way to ensure the credibility of the study. Interview questions in this study were aimed at gathering students’ perceptions about their academic self-concept. For example, asking the interviewees how they thought they are doing at school would provide the researchers with a different perception as to what the students use to judge their actual academic performance at school. During the interviews, some students mentioned only one way of judging his academic ability, a further sub-question of would you think of any other way to judge your academic performance? Was raised.

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There are other ways of ensuring credibility in a study. Familiarizing the researcher himself with the context is one way to ensure credibility (Seale et al, 2004). The researcher has good knowledge and background about the context since he worked as a primary school teacher for two years. Furthermore, the interview recordings were replayed to the interviewees whereby confirmation and declaration was sought. In addition, the interviewees were given the opportunity to add or comment on any of the questions being asked, or the issues being raised (Bryman, 2001). To increase the credibility of the study, Lincoln and Guba (1985), suggested that the findings from the interviews will be compared with previous research. Since this study will compare its findings with previous studies, it would raise the question of the extent to which qualitative results can be applicable to other future situations. This will be discussed in the next section, transferability.

Lincoln and Guba (1985) and Bryan (2001), defined transferability as the applicability of a study’s findings to other contexts or situations. It indicates the extent to which results obtained in a qualitative research can be replicated in different contexts and compared with other studies. Bryan (2001) believes that transferability parallels the external validity in quantitative research, but the former claims the applicability of qualitative findings into other situations or contexts. The latter indicates the generalizability of quantitative results.

In order to achieve this condition of trustworthiness, researchers have to provide detailed information about the study, including the methods used, the sampling and the administration process and the time consumed during the data collection process (Flick, 1998). Lincoln & Guba (1985), warned that transferability would only be possible if the researcher provided rich data for the research, enabling him/her to decide whether transferability is applicable to his/her context. This study has achieved this condition, because the full details of the interviews, and the administration process, are being discussed in this chapter.

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Dependability, parallels the reliability which, according to Lincoln and Guba (1985), indicates that if a study is to be replicated in the same context using the same methods and the same participants, the study findings should be the same as the original one. They also claimed that dependability is tied to credibility, ensuring the latter would achieve the former. However, to achieve dependability of the data, researchers have to be as transparent as possible about the research design, the data gathering methods and the implication of the study by Lincoln and Guba (1985). In this study, piloting would allow the researcher to evaluate the dependability of the measurements by comparing the findings in the pilot study with those that emerge from the main study. The researcher, in this study, has been as transparent as possible in the methodology of this research, including the administration process.

Conformability, indicates that a research should be free from personal interference or bias and the research findings should only emerge from the participants’ experiences, ideas or opinions (Lincoln and Guba, 1985 and Bryan, 2001). Using semi-structured interviews help to reduce the probability of a researcher’s bias since it relies on planned guidelines and prepared questions. To avoid an interviewer’s bias s/he should use direct questions such as, Do you compare yourself with other students in the class? Indirect questions were assuming the social comparison by asking; How well do you do at school compared to other students?

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3.5 Translation and Adaptation Issues.

The academic self-description questionnaire, together with the PISA reading literacy test, has been adapted from the OECD (2001a) and Marsh (1990) and translated into Arabic (the language medium of Jordanian schools). The researcher has translated the measurements, but in consultation with an authorised translator. However, there was a need for slight modifications on the measurements.

The introductory instructions of the original questionnaire in Marsh (1990), and the PISA ability test, would be difficult to understand when they are translated into Arabic. Thus, the researcher wrote a new questionnaire introduction whereby the instructors would be more attuned with Arabic speaking students. The researcher has also added the school’s name and class to the heading of the instruction so schools’ data can be separated.

3.6 Sample

Sampling refers to a subset of participants within a large population which estimates the characteristics of that total population (Cohen et al, 2007). Bryan (2001) identified two different types of sampling, and depending on the selection of the sample, which are probability and non-probability samples. He explained that the probability sampling depends on a random selection of individuals, from the total of participants, ensuring an individual will have an equal chance of being selected. In the non-probability sampling the selection is non-random and the chances for an individual to be selected is unequal, thus, some individuals may have more chance to be selected than others. The probability sampling is considered representative of the total population, while the non-probability sampling is non-representative; its results cannot be generalised on the total population. However, the aim here is to conduct a probability

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representative sample; therefore, I will only discuss the probability sampling and focus on the type of probability sampling employed in this study.

Bryan (2001) identified four main types of probability sampling: simple random sample; systematic sample; stratified random sample and multi-cluster sample. A simple random sample is a basic form of probability sampling where the opportunity of selection among individuals is equal (Bryan, 2001). In this sample type, researcher randomly shortlist the participants to be chosen for a sample. The selection has to be random and in this type of sampling researchers would assign participants as numbers, before randomly drawing a set of numbers revealing the participants in the study. A systematic sample, on the other hand, does not differ much from the simple random sample. This system depends on randomly assigned participants in a table, or a list of numbers, where a systematic strategy of drawing the numbers would represent the study. This requires a systematic strategy, such as choosing the odd numbers, 1,3,5,7, and so on. However, those two random samplings lack the possibility of equal representation of a population, such as representing different departments in a university. In this case, stratified random sampling would be appropriate. A stratified random sample, according to Bryan (2001), refers to “stratifying the population by a criterion (such as departments, gender, race) and selecting either a simple random sample, or a systematic sample, from each of the resulting strata” (p. 90). In the university example, a researcher may identify 4 departments as 4 strata, randomly assigning the students in each department in a list, before selecting random numbers from the list. However, this type of sampling is only feasible when the strata are close to each other. Or in a wider context, where the population is spread in a wider area where conducting a random sample from all schools, in all regions, in a country would not be feasible. Thus, the multi-stage cluster sampling would bring the solution. A multi-stage cluster sample indicates the grouping of a large population into different groups or clusters and randomly choosing a sample that would represent each cluster, before finally

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selecting a random sample from the sample of each group (Bryan, 2001). For example, a researcher may seek to randomly select a sample of primary students in the UK. The context can be grouped into different regions such as South, South East, South West, and North etc. The researcher may then randomly choose a sample of five directorates from each region. He/she may then select a random sample of five schools from each of the five directorates.

However, based on the literature of Bryan (2001), Cohen et al (2007) and Neuman (2011), the decision of selecting a probability sampling depends on the population, feasibility and the strategies of conducting a sample. In conducting a probability sampling, the size of the total population can be either known or unknown. For example, it would not be possible to gain figures pertaining to the number of 10th grade students in a region (as in this study), so the size of population is unknown. On the other hand, a decision about selecting a sample has to consider how feasible conducting a sample is. For example, conducting a sample from different parts of a country would be expensive and time consuming. In this study it is not feasible to conduct a sample from all regions, in Jordan especially, because the researcher is located in the far north of the country. Furthermore, the sample has to be well representative of the population to allow generalizability; thus, a researcher may collect information about the population, identify its clusters and characteristics, before making a decision about the sample design.

A decision is being made in this study to conduct a systematic multi-stage cluster sample. The multi-cluster sample, consists of 10th grade basic education students in Jordanian single-sex schools and co-educational schools. The schools which took part in this study are mixed of private and public schools in rural and urban areas. Although the schools were selected from the northern regions of Jordan, the Jordanian context is homogenous. The vast majority of students share the same ethnic background, culture and language, thus, a larger sample size would not create large variation which would affect the generalizability of the results. Bryan

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(2001), supported this claim by suggesting that the sample be subject to some considerations as to time and cost, homogeneity and heterogeneity of the population.

Nevertheless, the reasons for choosing this sampling design is due to the lack of an exact population census. Also, the population, representing all 10th grade students in Jordan, is spread all over the country and it would not be feasible for one researcher to travel across Jordan collecting data consuming money and time. Further to this, a multi-stage cluster sample would allow a wide representation of the whole sample and increase the generalizability of the results.

The sampling process in this study is multi-staged. First, the researcher has identified the regions in the north of Jordan, by directorate. According to the Jordanian Ministry of Education’s official website, there are 14 directorates of education in the north regions of Jordan. The 14 directorates were assigned randomly with numbers from 1-14 and a clustered based on the first 6 odds numbers. The aim was to randomly select 12 single sex and co- educational schools from the 6 directorates. After selecting the 6 directorates, the researcher has obtained a list of schools in each directorate from the Ministry of Education website. Schools were assigned in 6 different lists:

1. Public, single-sex, schools for boys.

2. Public single sex-school for girls.

3. Private single-sex schools for boys

4. Private sex-schools for girls’

5. Public co-educational schools and

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The schools in each list were placed in a table with numbers and a random selection of 2 schools were made. This sampling process provided the study with a final sample of 12 schools. However, the number of 10th grade students varies from one school to another, but appropriate for the sample size required.

The sample size in this study started with 300 male and female students distributed over 10 single-sex and co-educational schools. The sample size was determined using the G-power statistical computer programme. Based on G-power analysis, each group has to have a minimum of 65 cases. In this study there are four groups: males in single-sex schools; males in co-educational schools; females in single-sex schools and females in co-educational schools. Therefore, there will be a need of a minimum sample of 260 participants for the current study. The output of the G*power (software v3.0.10 by Faul, Erdfelder, Buchner, and Lang, 2009) analysis is summarised below:

F tests - MANOVA: Special effects and interactions

Options: Pillai V, O'Brien-Shieh Algorithm

Analysis: A priori: Compute required sample size

Input: Effect size f²(V) = 0.25

α err prob = 0.05 Power (1-β err prob) = 0.95 Number of groups = 4 Number of predictors = 2 Response variables = 1

Output:

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Total Sample Size = 65

However, of the 300 male and female students who were asked to fill out the ASDQII, 31 invalid questionnaires were rejected. A total of 269 questionnaires were used in this study where 152 (56%) of the sample were males and 117 (44%) were females; both of whom attending single-sex or co-educational schools. In additional to the questionnaire, 8 interviews with 4 males and 4 females in single-sex and co-educational schools were conducted. The next section will reveal the administration of the study in more details.

3.7 The administration of the study

The administration of this study was carried out in two stages: pre-administration and post- administration. The pre-administration stage will reveal the ethical approval obtained, the arrangements for data collection processed and the contacts with the schools. The post- administration, on the other hand, will display the ability test administration, distribution of the questionnaires and conducting the interviews.