observations on how changes in legal information resources and use are influencing current legal practice in Nigeria.
In selecting the level of analysis to be used, the research design, the type of data collected and the methods used for data collection, determine the specific quantitative technique to be employed and the type of analysis that can be performed.
The analyses of both questionnaires were undertaken on two levels:
The first part is a combined presentation of the profiled information of the law firms and the aspirant barristers, the imperative of which is to establish the relationship between activities of the firms and the skills of the aspirant barristers and to contextualise the issue of work related competencies.
The second portion of the analysis concentrated on the responses by the aspirant
barristers with regard to issues of transferability of information literacy skills to the legal workplace which enabled the researcher to ascertain the areas and the degree to which legal information literacy is especially needed.
Details of the analyses are provided in chapter five of the study.
4.7.2. Qualitative data analysis
Gray (2004: 319) defines qualitative data analysis as a rigorous and logical process through which data are given meaning. The challenge of qualitative analysis is the ability of the
researcher to make sense of a large amount of data which requires skills, knowledge, experience and creativity (Patton, 2002: 432). There is usually no outlined formula by which qualitative analysis can be undertaken. According to Sarantakos (1993: 301) methods of qualitative data analysis are not uniform or integrated into any specific model but are based on the notion of the subjectivity of the researcher. However, the processes of qualitative data analysis involve identifying patterns in the data which depend very much on the focus of the study and the different ways in which the data sources relate to each other (Darlington & Scott, 2002: 145).
Findings from qualitative data are often developed from in-depth interviews, direct observation, written documents and so on which are often in large amounts. The task for the researcher therefore in obtaining qualitative data is to provide a framework within which the study
population can respond in ways that reflect their point of view of the context in which they live (Patton, 2002: 4, 22).
Qualitative data analysis employs inductive reasoning by making specific observations and drawing inferences about general phenomena (Leedy & Ormrod, 2005: 96). The preliminary processes in qualitative analysis involves reducing masses of data to meaningful and manageable portions or data reduction which helps to bring out the themes or patterns of behaviour that will help in the process of interpretation (Polit & Beck, 2004: 578; Gorman & Clayton, 2005: 207).
According to Gorman and Clayton (2005: 210) and Miles and Huberman (1994: 9), in order to meaningfully and contextually analyse and interpret qualitative data the following steps are important:
Affixing codes to a set of field notes.
Noting reflections or remarks in the margins.
Sorting and sifting to identify key events, phrases, patterns, variables or themes.
Confirming patterns and themes through additional data collection and analysis.
Developing new theories or contributing to existing theories.
These stages involve reducing the amount of the data by teasing out patterns, themes and groupings of the data which is then followed by the process of breaking down the data into smaller units in order to reveal their characteristic elements and structure. The kind of patterns identified depends very much on the focus of the study (Dey, 1993: 30; Lancaster, 2005: 307).
One of the most flexible approaches for identifying themes and patterns in qualitative research is thematic analysis. As defined by Braun and Clarke, thematic analysis is:
A method of identifying, analysing and reporting patterns (themes) within the data. It minimally organises and describes your data set in (rich) detail. It helps to interpret various aspects of the research topic (2006: 79).
Unlike theories such as grounded theory and discourse analysis, thematic analysis cannot be said to be aligned to any pre-existing theoretical framework and can thus be used within different theoretical frameworks. However, the interpretations of the data must be consistent with the chosen theoretical framework. Its processes allow the researcher to determine what constitutes a theme and to capture important elements within the data (Braun & Clarke, 2006: 79-80). The application of this method of analysis to the study helped the researcher to identify patterns of development within the legal profession in Nigeria that incorporate concepts of information literacy.
Most case study research methods are aligned to qualitative approaches and the analysis of data in case studies involve organising data by specific units of cases for in-depth analysis and study (Putney, 2010: 118; Patton, 2002: 447). According to Patton (2002: 447-448), qualitative data analysis in case study research constitutes a specific way of collecting, organising and analysing data, the purpose of which is to gather comprehensive, systematic and in-depth information about the case. The analysis process is vital to the outcome of the study as it highlights both the processes and the final outcome or product of the study.
Unlike quantitative data, qualitative data are open to multiple interpretations due to their subjective nature and they also require a processing stage which involves editing of notes and transcribing of tape recordings. The processes of analysing quantitative data on the other hand offers the advantage of increased objectivity in analysing and interpreting large volumes of data and ensuring the validity and reliability of findings (Patton, 2002: 432). For the purpose of analysis in both qualitative and quantitative analysis however, it is important to select the appropriate methods for the type of data collected and the techniques aimed at achieving the research objectives.
4.7.3. Mixed methods data analysis
Data analysis in mixed methods research is gathered through qualitative and quantitative
approaches. According to Teddlie and Tashakkori (2009: 8, 263), data analysis in mixed methods involves a process whereby quantitative and qualitative strategies are combined, connected, or integrated in a research study. Creswell (2009: 218) also points out that in mixed methods research, analysis can occur both within and between the quantitative and qualitative approaches and must also relate to the type of research strategy chosen for the procedure. Some of the ways in which data analysis using mixed methods can be done include parallel mixed data analysis, conversion mixed data analysis, sequential mixed data analysis and so on (Teddlie & Tashakkori, 2009: 266-269). The dimension of mixed methods employed in this study was sequential; the phases of the data collection and analysis were planned and conducted to ensure that related aspects of the research questions were answered in an integrated manner in order to support the quantitative findings with the qualitative ones and to achieve convergence of findings (Creswell
2003: 218; Creswell and Plano-Clark 2007: 118). The rationale for this choice as suggested by Brannen (2005 12) is complementarity by which findings from the application of both methods are used to enhance each other and generate complementary insights on the objectives of the study (Creswell, 2008: 527). According to Pinto (2010: 813), through the use of inductive and deductive reasoning, data analysis in mixed methods integrates techniques from qualitative and quantitative paradigms which help in tackling the outlined research questions thereby enhancing the validity of the study. Ngulube, Mokwatlo and Ndwandwe (2009: 115) and Ngulube (2010:
254), also pointed out that the major goal of the researcher in conducting mixed methods
research in a study is the successful integration and interpretation of qualitative and quantitative data either concurrently or sequentially in such a way as to ensure that the combination of both paradigms provide complementary insights in addressing the research questions of the study.
4.7.4. Analysing data using SPSS
For the purposes of this study, the software package SPSS, version 18 was used in analysing the findings of the study. SPSS is one of the major computer packages for analysing quantitative data and can be used to create a data file containing the figures to be analysed. As a statistical package, SPSS is widely used and has enormous data processing power which allows the researcher to establish correlations between different variables (Foster, 2006: 287). Software packages such as SPSS, allow large quantities of data to be entered into the computer for the organisation and interpretation of collected data (Polit & Beck, 2004: 576; Leedy & Ormrod, 2005: 152). Analysed data collected from findings of the study were presented using tabulated frequencies which assisted in the interpretations of the outcomes from the study (Cohen, Manion
& Morrison, 2000: 77).