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The observation took place during other data collection schedules (interview and questionnaire distribution).
4.4.2.5 Document analysis
Even though it is time-consuming, it is necessary to compile or accumulate items such as documents, archival records, and others linked to the researcher’s study topics while in the field, or from other sources such as libraries and electronically based sources (Yin 2011). Documents contain images, text, or words that have been documented without a researcher's intervention (Bowen, 2009; Morgan, 2022). They may be private or public documents (Creswell, 2014). Documents provide background and context, confirmation of findings from other data sources, supplementary data, further enquiries to be raised, follow-up development, and change when informants have forgotten the details of events (Bowen, 2009; Morgan, 2022). The document analysis was employed in this study to contextualise the guidelines, policies and regulations, and the actual practice of the leadership activities in assuring the quality of education.
This research studied the latest printed or electronic forms of secondary data such as annual reports, legislation, quality assurance policies, strategic plans, annual plans, students and staff statistics. Moreover, the web page of the four sampled HLIs and other documents and guidelines, which are assumed to be relevant sources of information for the study, were used and analysed to obtain detailed information that enhanced the data quality. Additionally, the Ethiopian Proclamations on Higher Education (351/2003, 650/2009, and 1152/2019) and the Ethiopian education development roadmap (2018–30) were analysed. The document analysis enriches results obtained through other data collection instruments and triangulates results with survey findings. Hard or soft copies of some documents were collected for further scrutiny with the consent of the institutions.
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to contemplate the data in various ways. The hallmark of thematic analysis is its flexibility regarding foundation and sample size, research question, method of data collection, and generating interpretation (Clarke & Braun, 2017). Analysing the data is interpreting the meaning because “data do not speak for themselves” (Braun & Clarke, 2012:67; Yin, 2011:207). Creswell and Plano Clark (2018) and Braun and Clarke (2012) clarify data analysis as a systematic search for meaning in a range of data collected through different data collection instruments. This implies that the achievement of research is highly reliant on data analysis.
In this study, the mixed methods analysis of data was employed, in which qualitative and quantitative data were gathered in different phases from different sources but merged after the separate data collection and analysis of the two approaches.
Qualitative and quantitative data analysis techniques were used to triangulate the data from interviews, observations, documents and survey questionnaires. Mixing of the qualitative and quantitative data happened during the collection, analysis, and interpretation of data, but considerable attention was given to mixing the data. The results of the two phases were incorporated during the discussion of the outcomes of the whole study.
After completion of data collection from all informants, the first stage in the research process was preparing data for analysis. Using mixed methods, quantitative and qualitative data analysis tools were utilised. Finally, the findings of the qualitative and quantitative data analysis were combined and interpreted to answer the research questions (Creswell & Plano Clark, 2018).
4.5.1 Quantitative data analysis
The quantitative data that was gathered through the closed-ended questionnaires was analysed by quantitative analysis using different statistical tools. To this end, according to Neuman (2014) and Babbie (2010), all completed survey questionnaires were manually cleaned and checked for completeness. The cleaned survey questionnaires were captured and analysed using the Statistical Package for Social Sciences IBM/SPSS (V28) computer software. Using the SPSS, different relevant descriptive and inferential statistical tools such as mean, frequency distributions, percentage, measures of variation, cross-tabulations, t‐tests, and chi‐square tests were employed
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to examine and interpret the findings. The topics for the data analysis were derived from the conceptual framework that is grounded in the basic research questions of the study. Finally, based on the statistical analysis, the results were presented in tables and interpreted based on the findings.
4.5.2 Qualitative data analysis
For the qualitative data, thematic analysis was carried out with open-ended questions.
The thematic analysis was undertaken by transcribing each response into one document, and classifying them into broad categories and subcategories by identifying repeated and unique views. Thematic analysis is a technique that lets researchers study the data in various ways to generate meaning for the data (Braun & Clarke, 2012;
Clarke & Braun, 2017). It is an appropriate analysis approach for qualitative research methods (Nowell, Norris, White & Moules, 2017). Based on the different phases of analysing qualitative data proposed by Babbie (2010), Nowell et al. (2017), Braun and Clarke (2012), and Yin (2011), the analysis of the qualitative data followed particular steps. First, compiling took place, organising or sorting the original data into a formal database; second, disassembling which involves grouping fragment themes and coding. Reassembling followed, creating data arrays, and then interpreting: description about the critical analytic section. The final phase was drawing conclusions based on the whole study. The phases could have recursive and iterative relationships by referring backward or forward for any modification until a comprehensive set of themes is established (Braun & Clarke, 2012; Yin, 2011; Creswell, 2014).
Qualitative data was also transformed into numeric data, where applicable, for further analysis (Bazeley, 2018). Based on these phases, the qualitative data analysis was done by transcribing interviews (audio recordings were translated) and typing transcripts in Microsoft Word format, edited and summarised. Atlas.ti5 Software was also used to code, classify, and sort qualitative data generated from text information such as interviews. With the support of the software, critical themes for each study question were identified and a coding system was established that allowed for summarising relevant information.
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Of the three basic mixed methods design identified by Creswell (2014), the convergent mixed methods approach was implemented in this study. Accordingly, the collection and analysis of qualitative and quantitative data were carried out separately and then brought together to see if the findings verified each other. The data were merged, based on the side-by-side comparison approach, by first reporting the quantitative statistical results and then discussing the qualitative findings that either complement or contradict the statistical results or vice versa (Creswell, 2014). The data transformation procedure was also applied by changing qualitative themes or codes into quantitative (numerical) variables and then merging the two quantitative databases (Bazeley, 2018; Creswell, 2014). A joint display of data procedure was also applied to combine the quantitative and qualitative data in a single visual or in a table form. Based on the findings of the analysis, the interpretation was included in the discussion section of the study, noting the divergence and convergence between the two sources of information for further corrective measures (Creswell, 2014).
Finally, quantitative data analysis was displayed first, and then the findings of the analysis were substantiated by the qualitative data analysis in the form of quotes and texts (Creswell & Plano Clark, 2018). King (2004), as cited in Nowell et al. (2017:11), suggests that “direct quotes from participants are an essential component of the final report”. The survey results were reported at a national level by aggregating the survey results for the corresponding HLIs.
4.6 QUALITY ASSURANCE OF THE RESEARCH