Questionnaires

The questionnaires are used in the conduct of educational, psychological and social research. Verma and Mallick (1999) asserted that ―the questionnaire is often a vital tool in the collection of research data, and that, if it is well- constructed, it can provide data economically and in a form that lends itself perfectly to the purposes of the study‖(p.117). The use of questionnaires has

increased to become the most common research tool for collecting quantitative data because they can be distributed to the large samples (Assaf, 1992).

The questionnaire is a tool used to collect data from individuals or groups and includes a set of questions or statements which enable the researcher to access qualitative or quantitative information which may be used alone or with other research tools so as to reveal the aspects determined by the researcher. Babbie (1990) mentioned that the questionnaire is ―a document comprising questions and other kinds of items designed to solicit suitable information to analysis‖ (p. 377).

Questionnaires have been used in this study because: ―Such a survey could be designed as part of a case study and produce quantitative data as part of the case study evidence‖ (Yin, 2003, p.91).

Questions have been used with a five-point Likert scale (see Table 4.3 as an example), together with open-ended questions, to allow the researcher to learn a lot about the issues related to the research topic and answer the research questions. The open-ended questions will allow freedom of expression for the participants and should highlight specific issues which can be investigated more fully through the interviews; participants will not be restricted to answering on specific issues only. Consequently, information has been obtained from each element of Activity Theory. Peterson (2000) indicated to that: ―The primary benefit of an open-end question is that its answers can provide extremely insightful information, because study participants provide answers in their own words, no researcher bias is introduced by presenting or predetermining answers‖ (p.33).

To ensure good questionnaire construction to cover all aspects of Activity Theory, the dimensions of the questionnaire were identified as:

‗Subject‘ is science student teachers, the perceptions about a good science teacher, background that helped to shape the views of participants about science

teachers, and expectations about science teaching and education science teaching.

‗Object‘ is the science student teachers‘ preparation through the partnership between the school and university and what it is hoped to achieve of the goals, the support from university and school to improve student teachers‘ learning, and focusing on science student teachers during teaching practice at school.

‗Tools‘ are academic tools available to science student teachers in both parties at the university and school that could help science student teachers in their learning about science teaching.

‗Community‘ is the partnership community at the university and school which is relevant to the preparation of science student teachers.

‗Rules‘ are the regulations organizing the science student teachers‘ education at the university and teaching practice at school.

‗Division of labour‘ is the roles and responsibilities of university supervisors, coordinators at the university, teacher collaborators, school headteachers, and science student teachers.

Table 4.3: Examples of Likert scale questions

NO. Questions

SA A N D SD

1

The main reason for the involvement of the students in science teacher preparation programme at university and practice at school is:

1-1 To become good science teachers.

1-2 Because of their interest in science.

1-3 Because teaching science is very easy.

1-4 Because it is an enjoyable occupation. 1-5 To find a good job with a good salary.

NO. Questions

SA A N D SD

2 There are ways for learning to teach science from your expectations. The most important of these for science student teachers is:

2-1 The science student teacher learns from lectures in the teacher education programme.

2-2 The cooperating teacher helps science student teachers

address gaps in subject knowledge in the school context.

2-3 Please add any other reasons not covered above.

Table 4.3 shows how area 1 of the questionnaire was designed. The Lickert scale questions help the respondents to understand what I am interested in. The open-ended part allows them to add their own ideas as I did not want to constrain their thinking. This can also be done in the interview, but having this element in the questionnaire too means that I can gather such information from a larger number of people.

Some of the questions appear to be stated as a report and not as a question. This is the result of the translation from Arabic into English. Some of the sentences such as, ―The main reason for the involvement of the students in science teacher preparation programme at university and practice at school‖ are understood as a question in the Arabic context.

For more information about the questionnaires, see Appendix 4.1, 4.2 and 4.3.

Procedures for applying the questionnaires

The questionnaires were translated from English into Arabic and then applied as a pilot study to two of each category of participants; the time to complete the questionnaire was measured. The comments received from the participants

about the questionnaire were addressed. It was then re-drafted, making the completion time approximately 20 minutes. In the final step, the questionnaires were distributed to the main sample, which was composed of 53 science student teachers, 11 university staff, and 27 school staff, as shown in Table 4.4.

Table 4.4: Meta-data of questionnaire participants

University staff School staff Number of questionnaires Science student teachers University supervisors University coordinators School headteachers School teachers

Males Females Males Females Males Females Males Females Males Females Distributed _{18 75 5 6 3 3 18 40 18 40}

Incoming _{16 53 3 3 3 2 12 14 8 11}

Actual number of

completed 14 39 3 3 3 2 9 8 5 5

The sample of university staff was identified in order to provide data that represent the perspective of people from all the main roles in the team, and to compare the data. Similarly, in the school sample, categories representing all roles within the school team were chosen so that I could describe the school perspective and make the comparisons that were useful for the reader. Where the questionnaires included free answers through open questions and, therefore could be added to the analysis of qualitative data with the appropriate category. The original population of the science student teacher was 18 males who were distributed to 18 schools, and 75 females distributed to 40 schools. The questionnaires were distributed to all the science students teachers in the cohort, to all the supervising teachers and head teachers who worked with them in the schools where they were placed for school experience and to all the science education staff in the university.

The questionnaires received from the total of original population were as follows: 16 males and 53 females of science student teachers, 3 males and 3 females of

university supervisors, 3 males and 2 females of coordinators in the university. For cooperating teachers 12 males and 14 females, as well as 8 males and 11 females of the school headteachers. The completed and valid questionnaires were for science student teachers, 14 for males and 39 for females. For science supervisors at the university, 3 males, and 3 females and, the university coordinators were 3 males and 2 females. The collaborating teachers were 9 males and 8 females, and the school headteachers were 5 males and 5 females. The completed and valid questionnaires were analyzed where these respondents demonstrated the commitment to the project by doing all that was asked of them, so I felt their responses might be a more valid reflection of what they were thinking, while incomplete questionnaires were ignored and were not used in the analysis, also the completely blank questionnaires were considered as questionnaires that were not received.

An overview of the process of quantitative data analysis

1. In the beginning, the completed questionnaires answered by the participants were collected and numbered from 1 to 91 in preparation for the introduction of data into the Statistical Package for the Social Sciences (SPSS) Software, version 21. SPSS is a powerful software package to manage and analyse quantitative data. The variables were coded to transform the data into numerical data suitable for SPSS programme; for example, codes given for the variable ‗job‘ were (1) science student teacher, (2) university staff, (3) school staff. Codes for the variable ‗gender‘ were (1) male and (2) female; and for the variable ‗years of experience‘ (0) 0 years, (1) less than 10 years, (2) from 10 to 20 years, and (3) more than 20 years. The SPSS software enables one to obtain descriptive tables as well as comparison tables between variables in different groups. Bryman and Cramer (2011) indicate that: ―The great advantage of using a package like SPSS is that it will enable you to score and to analyze quantitative data very quickly and in many different ways‖ (p.21).

Figure 4.3:An example of the quantitative data inserted to the SPSS software

2. The appropriate analytical tests were selected according to the normality of distribution of the data as identified through the skewness and kurtosis tests, and through the shapes of the histograms for each question. These revealed that most of the data followed a normal distribution, although some followed a non- normal distribution.

Distribution of data: skewness and kurtosis

Skewness and kurtosis tests were used to find out whether the responses of the participants were normally distributed, in order to decide whether a parametric or a non-parametric statistical technique was appropriate (Pallant, 2007).

Table 4.5: Descriptive statistics

Q= Question 1,2,3,… A= Sub question 1,2,3,…

Table 4.5 shows example of the results of the skewness and kurtosis tests on the variables all sub-questions from question one. Most values of skewness and kurtosis were close to zero suggesting that the related data approximated to a normal distribution. However, one item (Q1A1) did not have a normal distribution

Q1 Reason for the

involvement of the students in science teacher preparation programme at university and practice at school participation in the science teacher preparation programme.

N Minimum Maximum Mean Std.

Deviation

Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error Q1A1To become good science teachers. 91 1.00 5.00 4.3736 .72493 -1.782 .253 5.667 .500 Q1A2 Because of their interest in science. 91 1.00 5.00 3.6593 1.03516 -.623 .253 -.582 .500 Q1A3 Because teaching science is very easy. 91 1.00 5.00 2.3956 1.16313 .735 .253 -.440 .500 Q1A4 Because it is an enjoyable occupation. 91 1.00 5.00 3.9011 .90744 -.896 .253 .669 .500

Q1A5 To find a good job with a good salary.

so it was dealt with through nonparametric tests. Tables of skewness and kurtosis for all items are shown in Appendix 5.A.

Distribution of data: histograms

The form of a histogram provides information on the distribution of scores on a continuous variable. Pallant (2007) confirmed that normality ―can be checked by inspecting the histograms of scores on each variable‖ (p.124). Figure 4.4: shows examples of the shapes of histograms for all responses to the items in question one. The shapes of the histograms for all items are shown in Appendix 5.A.

Figure 4.4: The shapes of histograms for all responses to the items in question one

By looking at the histogram for each variable, it is seen that most of the actual responses lie within the bell-shaped curves). This is consistent with the indications from the skewness and kurtosis tests. These tests and the histograms together indicate that it is appropriate to treat most of the data (except Q1A1) as normally distributed.

There are statistical tests that can be used to find out whether there are statistically significant differences on a continuous dependent variable among a number of groups. The parametric versions of these tests, suitable for interval scaled data with a normal distribution, are the t-tests and one way ANOVA (Pallant, 2007). Since it has been argued (Jaccard and Wan 1996) that it is acceptable to treat data from Lickert items as interval data, and most variables in this study followed a normal distribution, a t-test was used when there were two categorical independent variables, and a one-way ANOVA when there were more than two categorical independent variables.

Non-parametric tests should be used if the data are not interval, or not normally distributed: the Mann-Whitney test, can be used to test for differences between two independent groups in place of the t-test; the Kruskal–Wallis test can be used in place of one way ANOVA to test for differences between several independent groups. (Field, 2009). These tests were used in my study where normality testing showed them to be necessary.