How to analyze data of a case study research?: A practical example of an exploratory study

In this blog post, I briefly explain how I analyzed data for my Ph.D. dissertation, titled- An Exploratory Study of Recruitment and Selection System of Administrative Staff in the Public Sector Universities of Khyber Pakhtunkhwa, Pakistan.

Introduction:

Using General Systems Theory as a theoretical lens, my Ph.D. research investigated the system of recruitment and selection in public sector universities in Khyber Pakhtunkhwa, Pakistan with focus on administrative staff.

I selected qualitative research paradigm by virtue of its nature and assumptions that suits more to the studies which are context-based and having limited back ground literature. This is basically a multiple case study research. Being one of the most challenging of all the social sciences research endeavours, case study method digs deeper into the situation for a better understanding of the prevailing nuances and dynamics of a particular case.

 The study involved probing a small number of key informants through extensive and prolonged engagements in order to develop patterns and relationships of key themes and subthemes for further analysis. In this blog post, I explain how I analyzed data for this multiple-case study research. But first of all I elaborate how I collected data for my study.

Data Collection:

• For the study, I purposefully selected a total of 06 public universities (out of the total 19 public sector universities in Khyber Pakhtunkhwa at that time). To maintain anonymity these universities were designated as University-A, University-B, University-C, University-D, University-E and University- F. Moreover, attribute coding was used for the participants from these universities as participants’ code.
• For data collection, I used three sources of information: i) Interview; ii) Non-participant observations, and iii) Documents and archival records.
• I conducted semi-structured interviews with low, middle and top management as multiple key informants. Since, the Establishment and the Meetings Sections are the two main HR sections/departments in the universities, therefore, the top, mid and low career level manager/administrative officers working in these sections (i.e. Registrar, Additional Registrar, Deputy Registrar and Assistant Registrar) were selected for the purpose.
• These units of analysis were highly pertinent for obtaining the type of data required for the study, and this is one of the most important considerations for selection of respondents for the study.
• And wherever these positions were vacant, then, the available key informants were further requested to identify the relevant members of the staff from the administrative cadre who were appropriate to be interviewed subjected to the following criteria:

1. Qualification in HR at least MBA
2. Having at least 05 years of experience in dealing with HR issues at the university level, and
3. Having deep understanding of the rules/regulations governing HR in the universities

• A total of 28 semi-structured interviews (in-depth with open-ended questions) having a minimum of four participants from each case university were conducted.
• The interview guide was designed and finalized based on initial literature review, theoretical framework and pilot interview conducted for the study.
• Each interview session spanned over 02 to 03 hours of duration. The interviews were tape-recorded with due permission.
• Notes were taken during interview sessions and data was analyzed once this stage of data collection was completed.
• I also made non-participant observations as I visited the university campuses for interviews several times and I observed their official settings, a way of disposing of official business and working environment, including the university campuses and their infrastructures which helped in the triangulation of data.
• In addition archival records and official documents were also consulted for collection of data for triangulation purposes. These sources include annual reports of the universities and their official websites, as well as the official website of HEC.
• I transcribed all of the interviews and systematically organized relevant documents for analysis and also shared them with the respondents to ensure that the essence of their comments was properly reordered.
• I thoroughly read through transcriptions, documents, and notes to gain a deep understanding of the recruitment and selection process of the case Universities.

Data Analysis

The data analysis process involved determining codes, categories, subthemes, main themes and the essence from the participants’ descriptions. I used Braun & Clarke (2006) pragmatic six steps, to analyze data to arrive at the sub-themes and main themes from the data as listed below:

1. Becoming familiar with the data;
2. Generating initial codes;
3. Searching for themes;
4. Reviewing themes;
5. Defining and naming themes, and
6. Producing the report.

These various steps are explained here one by one:

Familiarization with the data:

• In this very 1^st step of data analysis, I familiarized myself with the data and comprehended the nature and meaning of the data.
• This was done by reading the transcripts again and again in an iterative manner such as interview transcripts, field notes, and all available qualitative material.
• Here I immersed myself in the raw data to understand its content, context, and nuances.
• I took notes on initial impressions, interesting patterns, or recurrent ideas and annotated the data with initial thoughts or questions.

Generating Initial Codes: 

• Once I collected data and transcribed it into a common format, I began the process of coding by carefully, reading and re-reading the raw data.
• This is the 2^nd step of data analysis to generate initial codes by identifying meaningful units.
• Here I coded the specific segments of the data that capture important concepts, ideas, or phenomena using participants’ own words or descriptive codes to capture the essence of the content allowing codes to emerge organically.
• Here, specific codes were identified during repetitive readings of the data. The actual words and phrases of the respondents were used to create coding categories.
• This is in-vivo coding considered as a lower level categorizing of codes. This is further categorized into more summarized codes called the upper-level categories or themes.
• Here I generated initial codes from interviews and document analysis such as “Poor linkages with internal factors”, “External forces overlooked, “Global trends ignored”, “Detached body and parts”, “Missing links in internal structure” and “Misaligned HR functions” given in the following table:

• In this step interesting features of the data were coded in a systematic fashion across the entire data set, hence, generating initial line-by-line codes called in-vivo codes.
• Then data relevant to each code was organized. This exercise was done
• In the process, all the potential themes were coded. These codes were predominantly context-based. Sometimes, data extracts were multiply Hence, patterns were established in the various codes.

Searching for Sub-Themes and Themes:

• In the 3^rd step of data analysis, I searched for sub-themes and themes.
• This is a broader level of analysis.
• In this step, codes become categories, subthemes and central themes. This is an iterative, as well as intuitive process:
• In the first phase, of this step I grouped codes into categories as given below:

• In the Second phase of this step, I begin grouping categories into preliminary themes that reflect the overarching ideas or concepts
• I group categories into sub-themes as given below to reflect the main ideas or concepts of the categories:

• In the third phase of this step subthemes become overarching themes as given below.
• Here I refined the sub-themes, ensuring they accurately represent the data.
• I checked if each sub-theme tells a coherent and meaningful story about the data. I consider relationships between sub-themes and their relevance to each other.

Reviewing the Themes:

• In the 4^th step of data analysis, I reviewed the themes.
• Here, I reviewed and refined the identified themes by revisiting the data, codes, and categories.
• Once I found ‘candidate themes’, those were reviewed in this phase of data analysis.
• ‘Candidate themes’ were those which did not have sufficient data to support them.
• Some of those were merged or separated and some were even removed.

Developing and naming the themes

• Defining and naming themes was the 5^th step of data analysis.
• In this step, the emerging themes were described in a way that captured the essence of the theme.
• Here I clearly articulated and labeled each theme, ensuring they are distinct and represent specific aspects of the data.
• I clearly defined the boundaries of each theme and ensure it captures a unique aspect of the data.
• I provided a clear and concise name for each theme that encapsulates its essence.
• Here, the themes were defined and were ultimately keyed out.
• Here, data was further abridged and internal coherence in a theme or strong distinctions between certain themes were established.

Producing the report

• Translating the data analysis in black and white and producing the report was the last and final step.
• Here, I produced analytic narrative of the entire data and presented findings in a narrative format, describing the nuances of the recruitment and selection system in KP public sector universities included direct quotations and examples to illustrate key points.
• Here, a balance was maintained by looking at the overall meaning and each individual part of the data.
• This required moving back and forth several times to confirm the whole and the context given to each part, and it matched the general theme and the overall data.
• I presented the analysis in a coherent and meaningful manner, providing an account of the identified themes. I wrote a comprehensive narrative discussing each theme, providing examples and supporting quotes. I also considered the implications of the themes for the research question or objectives.
• Resultantly, several central themes emerged and based on the same the overall skeleton of the report was developed. I ensured the report is clear, well-organized, and aligned with the research goals.
• For instance, while delineating various loopholes in the staffing function in the universities, the following two central themes emerged.

1. Strategic loopholes
2. Operational loopholes

Similarly, while explaining various factors responsible for these loopholes in staffing function the following two main themes emerged:

1. Internal factors
2. External factors

While discussing various recommendations to address these loopholes in staffing function, the following major themes emerged:

1. Policy-level recommendations
2. Procedural level recommendations

These overarching themes were discussed threadbare and the process of arriving at these was illustrated in minute detail in the respective parts of the thesis.

Conclusion:

Data analysis is a crucial and integral part of the qualitative research process. It involves the systematic examination and interpretation of qualitative data to derive meaningful insights, patterns, and themes. To work out actual coding and categories is a vital stage of data analysis for any research study. I performed this by identifying patterns, themes and repeated ideas by meticulously reading the interview transcripts, over and over again. At this stage, the underlying ideas as well the perennial patterns were identified across the raw data which was linked with other data and the literature facilitating the transition of raw data and the conceptual analysis on the basis of which conclusions were drawn and the report was produced.

In summary, this exploratory case study provides valuable insights into the recruitment and selection system of administrative staff in public sector universities of Khyber Pakhtunkhwa, Pakistan. The findings inform policy revisions, improve the efficiency of the system, and contribute to enhancing the overall organizational effectiveness of these institutions.