Afectación al principio de proporcionalidad

Content analysis is a ‘strict and systematic set of procedures for the rigorous analysis, examination and verification of the contents of the written data’ (Flick 1998, p. 192 and Mayring 2004, p. 266, both cited in Cohen et al. 2007, p. 475). In conducting content analysis, the researcher generates or tests a theory by taking texts, analysing them, reducing them and interrogating them into a summary form

through the use of both pre-existing categories and emergent themes (Cohen et al. 2007, p. 476).

Content analysis has several strengths which make it a popular analysis technique among researchers. Krippendorp finds that it is an unobtrusive technique (2004, p. 40, cited in Cohen et al. 2007, p. 475). This is because, in content analysis, one can observe without being observed (Robson 1993, p. 280, cited in Cohen et al. 2007, p. 475). Also, because the data are in a permanent, written form, it is possible to reanalyse it and replicate it at any given time (Cohen et al. 2007, p. 475).

A weakness of this approach is the degree of subjectivity it brings with it. Krippendorp maintains that texts do not have one objective interpretation or meaning, but can rather sustain multiple readings and interpretations (2004, pp. 22- 24, cited in Cohen et al. 2007, p. 476). For this reason, when interpreting qualitative data, meanings should be drawn while taking into consideration the specific contexts, discourses and purposes (2004, pp. 22-24, cited in Cohen et al. 2007, p. 476).

Cohen et al. advise researchers to follow eleven steps when conducting content analysis (2007, p. 476). I will refer to some of these with reference to my own research.

Step 1: Define the research questions to be addressed by the content analysis

1. In what ways can the arts, and drama in particular, contribute to the following aspects of children’s well-being:

 To their happiness and pleasure?

 To their sociability, social skills and skills of working with others?

 To their self-esteem, self-confidence and sense of achievement?

2. What pedagogy should a teacher who is interested in enhancing her students’ well-being follow? What are the challenges that this pedagogy conveys?

3. What are the theoretical and methodological limitations of this approach?

During my fieldwork, I conducted direct and indirect observation, transcribed the interviews, and input data from the questionnaires. In doing these, I realised that further aspects of well-being sprang from my data; play, beauty and children’s voice. Therefore my research questions were reformed as follows:

1. In what ways can the arts, and drama in particular, contribute to the following aspects of children’s well-being:

 To their happiness and pleasure?

 To their sociability, social skills and skills of working with others?

 To their self-esteem, self-confidence and sense of achievement?

 To them acquiring experiences of beauty and aesthetic quality?

 To opportunities for them to express their voice and to have this taken into consideration?

2. What pedagogy should a teacher who is interested in enhancing her students’ well-being follow? What are the challenges that this pedagogy conveys?

3. What are the theoretical and methodological limitations of this approach?

These questions served as my guidance in the analysis of my data.

Step 2: Define the population from which sampling units occur

Cohen et al. perceive population to be both people and texts that constitute domains of analysis (2007, p. 477). In my case, the people that offered data were the students, their parents, teachers and head teachers, drama practitioners, as well as certain ‘knowledgeable people’; these are defined by Ball as individuals ‘who have in-depth knowledge about particular issues, maybe by virtue of their professional role, power, access to networks, expertise or experience’ (1990, cited in Cohen et al. 2007, p. 115). In my research, the ‘knowledgeable people’ were the General Co-ordinator for Health Education Programmes, and the president of the organisation funding the Youth Theatre. I employed content analysis for the interviews, the questionnaires’ open-ended questions, the observation-based fieldnotes and the journal notes. The drama conventions that I used as research tools yielded data that were self-contained and did need to be further analysed.

Step 3: Define the sample to be included

Cohen and Holliday argue that there are two main methods of sampling (1979; 1982; 1996, cited in Cohen et al. 2007, p. 110). In the probability sampling, the researcher selects her sample from the wider population by chance (Cohen et al. 2007, p. 110). In this way, every member has equal chances of being selected to participate in the research (Cohen et al. 2007, p. 110). In the non-probability or purposive sampling the researcher handpicks the cases based on whether the individuals possess the particular characteristics that respond to the research theme (Cohen et al. 2007, p. 115). Within these two categories lie various subcategories, which Cohen et al. refer to in detail in their book (2007, pp. 110-117).

I employed a non-probability sampling strategy, whereby my targets were particular groups of children (Cohen et al. 2007, p. 113). Any conclusions drawn should take into consideration the specific contexts, locations and discourses.

The type of non-probability sampling I employed was convenience sampling (Cohen et al. 2007, p. 114). This involves ‘choosing the nearest individuals to serve as respondents and continuing that process until the required sample size has been obtained’ (Cohen et al. 2007, pp. 114-115). Both institutions were accessible to me because of my previous teaching experience in these.

Step 4: Define the units of analysis

Krippendrop distinguishes among three kinds of units (2004, pp. 99-101, cited in Cohen et al. 2007, p. 477). Sampling units are those that are selected to be included in, or excluded from, the analysis. Recording/coding units are those that are contained within sampling units and are therefore smaller and less complex. Context

units are ‘units of textual matter that set limits on the information to be considered in the description of recording units’ (Krippendorp 2004, pp. 101, 103, cited in Cohen et al. 2007, p. 477).

My sampling units were particular interviews, questionnaires and notes from my observation and journal that were useful in providing data responding to my research questions. From these units, I extracted phrases and sentences as my recording/coding units. In order to provide integrity of meaning, I offered the larger texts these were found in; these were my context units. For example, for the purposes of my analysis I chose a particular interview that the YT participants gave (sampling unit), offered a particular phrase a child used to support my theoretical argument (recording/coding unit) and embedded this in a larger text (context unit) to ensure clarity of meaning.

Step 5: Decide the codes to be used in the analysis

Following the advice of Hammersley and Atkinson, I went through all of my data numerous times so as to become familiar with them (1983, pp. 177-178, cited in Cohen et al. 2007, p. 478). I then noted down interesting patterns that emerged, as well as unexpected features, inconsistencies and contradictions (1983, pp. 177-178, cited in Cohen et al. 2007, p. 478).

As I re-visited my data, I ascribed codes to each piece of datum (Cohen et al. 2007, p. 478). A code is ‘a word or an abbreviation sufficiently close to that which it is describing form the researcher to see at a glance what it means’ (Cohen et al. 2007, p. 478). For example, I used the phrase ‘sense of achievement’ to describe the data that referred to children’s activities or responses suggesting this. Whereas I had

created this particular code pre-ordinately, others emerged in my revisiting of the data. For example, I used ‘stories for w.b.’ to refer to children’s responses as to what stories they expressed interest in. The process of coding enabled me to detect the codes which occurred more often and which occurred together (Cohen et al. 2007, p. 478).

Step 6: Construct the categories for analysis

In this stage, I formed categories of key features of the text in order to highlight the links between the units of analysis (Cohen et al. 2007, p. 478). I came up with several different categories, some of which I had created pre-ordinately, such as ‘pleasure in drama’. Other categories emerged from the data, such as ‘children liking scary stories’. Hammersley and Atkinson stress out that some items of data can be assigned to more than one category (1983, cited in Cohen et al. 2007, p. 479). In their view, this is desirable because it maintains the richness of the data (1983, cited in Cohen et al. 2007, p. 479). This was the case with my data as well.

Step 7: Conduct the coding and categorising of the data

Having decided on the codes and categories I would use, I was ready to commence the analysis (Cohen et al. 2007, p. 480). I returned to the texts and ascribed codes and categories to each piece of datum (Cohen et al. 2007, p. 480). This process is called coding and is defined by Kerlinger as ‘the translation of question responses and respondent information to specific categories for the purpose of analysis’ (1970, cited in Cohen et al. 2007, p. 480). An example from the coding process can be found in Appendix 2.

Because of the large amount of data available, it was necessary for it to undergo the process of summarising content analysis which reduced it to manageable proportions while remaining faithful to the essence of the content (Mayring 2004, pp. 268-269, cited in Cohen et al. 2007, p. 480). After performing the first round of assigning codes and categories to the data, I was able to detect emerging patterns and themes and to begin to make generalisations (Cohen et al. 2007, p. 481).

Step 8: Conduct the data analysis

With the data coded and categorised, I composed a 30-page document in which I noted the thematic categories and their subcategories, along with the pieces of data that responded to each one. Composing this document was helpful, not only because it offered a general overview of the data, but also because it highlighted the associations between the various codes and categories (Cohen et al. 2007, p. 481). An example from the document of the categorising of data can be found in Appendix 2.

Step 9: Summarising

After gaining a general sense of the data, I wrote a summary of the key issues that had emerged and needed subsequent investigation (Cohen et al. 2007, p. 482). I included interesting incidents in the drama workshops and intriguing responses in the interviews and questionnaires (Cohen et al. 2007, p. 482). I was then in position to start forming the theories that responded to my data. Patton sets out the preliminary stages of theory generation:

 Finding a focus for the research and analysis

 Writing a qualitative description or analysis

 Inductively developing categories, typologies and labels

 Analysing the categories to identify where further clarification and cross- clarification are needed

 Expressing and typifying these categories through metaphors

 Making inferences and speculations about relationships, causes and effects (1980, cited in Cohen et al. 2007, p. 483).

Step 10: Making speculative inferences

In the last stage, the data moved from description to inference (Cohen et al. 2007, p. 483). I sought possible explanations for the situations, and generated working hypotheses that fed into my data and theory (Cohen et al. 2007, p. 483). An example of a hypothesis is the following:

‘The process of working towards the performance benefited children in terms of their self-confidence because they felt that their ideas and suggestions mattered in shaping the final product.’