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Abstract

In recent years, the field of qualitative data collection, management, and analysis has undergone significant evolution, with researchers worldwide increasingly leveraging technological advancements to streamline and simplify their practices. Despite the advantages of using qualitative data coding software, several challenges persist, particularly in resource-constrained settings. Issues such as the high cost of software licenses, the time required to learn and use these tools, and the complexity of advanced features often hinder researchers, especially students and early career researchers in low-income contexts, and contribute to the generation of poor-quality research evidence. This paper aims to provide a simplified, step-by-step guide to manual qualitative data coding using Microsoft Word, a widely accessible tool in low-income contexts. It is designed to support students and early-career researchers in low-income settings, helping them to conduct effective qualitative data management and analysis without the need for expensive software. We provide a five-step detailed guide for qualitative data coding using Microsoft Word, based on a practical example from previous research on maternal and child health in rural Tanzania. We also present a hypothetical example of a student research study focusing on the drivers behind pursuing a Bachelor of Science in Nursing degree. The process begins with examining the research objectives and questions to develop preliminary themes and sub-themes, identifying relevant data sources within study interview guides, and creating guided transcription and coding templates tailored to each interview guide, followed by data coding by integrating interview data into a structured template. Practical insights are provided on transforming coded data into meaningful interpretations and analyses. This systematic approach aims to facilitate effective qualitative data management and analysis using accessible tools like Microsoft Word, thereby supporting students and early career researchers in conducting small-scale studies effectively.

Keywords

qualitative research manual coding students early career researchers resource-constrained settings low income

Introduction

In recent years, the field of qualitative data management and analysis has evolved significantly. Researchers worldwide are increasingly leveraging technological advancements to streamline and simplify their data management and analysis practices (Abdekhodaie et al., 2018; Brailas et al., 2023; Cypress, 2019; Dalkin et al., 2021). Coding, a critical stage in qualitative data analysis, has particularly benefited from these technological advancements, with many researchers transitioning from manual to technology-based coding methods. The introduction of various software tools has revolutionized the coding process in qualitative research (NYU, 2024). Widely used programs such as NVivo, ATLAS. ti, MAXQDA, Taguette, Dedoose, and others have simplified the process, allowing researchers to efficiently label, merge, or split codes and to export and import coding schemes (NYU, 2024). The technology-based coding and qualitative analysis are expected to further benefit from the innovation of artificial intelligence (AI)-powered coding and data analysis. This advancement can facilitate rapid coding, processing, and recognition of data patterns (Friese, 2023; Morgan, 2023). This will continue to challenge the traditional methods of coding and analysing qualitative data, particularly in resource-constrained settings where the application of these new technologies is still in its early stages.

It is important to acknowledge that, despite advancements in technology, coding will remain an essential activity within the qualitative research process. Coding involves the organization and categorization of data to uncover patterns, themes, and meanings (Brailas et al., 2023; Coates et al., 2021). Through coding, researchers apply labels (codes) to segments of data, transforming raw information into a structured format that can be analysed effectively (Coates et al., 2021). This process is crucial for making sense of qualitative data and for identifying meaningful interpretations and insights.

Coding softwares have become essential in handling large volumes of qualitative data, offering increased flexibility, and improving the validity and auditability of research findings (Basit, 2003). The use of technology-based coding software not only simplifies qualitative analysis through improved data management but also bridges geographical barriers by facilitating collaboration among researchers in different contexts, thereby enhancing the rigor of qualitative research (Basit, 2003; Chris, 2008; Church et al., 2019; LaPelle, 2004). Despite the advantages of using qualitative data coding software, several challenges persist, particularly in resource-constrained settings. Issues such as high costs of software licenses, the time required to learn and use these tools, and the complexity of advanced features often hinder researchers, especially students and early career researchers in low-income regions, and contribute to generation of poor-quality research evidence (Basit, 2003; Berthet et al., 2023; Church et al., 2019; Cypress, 2019; NYU, 2024; St John & Johnson, 2000). For example, Ose et al. (2016) affirms that computer-assisted qualitative data coding and analysis software is overly advanced and complex while the goal (common among students and early career researchers) is simply to organize and structure the data. Some authors have deliberated on the necessity of reevaluating Western methodological approaches to qualitative data management when applied in low-income countries, acknowledging the financial and technical constraints that researchers in this context face (Cypress, 2019; Halme et al., 2024). Halme et al. (2024, p. 36) affirms that “research in low-income settings, however, poses significant challenges for qualitative scholars… their contextual richness cannot easily be conveyed with methodological conventions originating from Western contexts”. As a result, there is an unmet need for alternative, simplified methods of qualitative coding that can be utilised by students and early career researchers in low-income contexts without heavily relying on expensive or complex software.

Cypress (2019) asserts that although there is a growing reliance on software-powered coding, there’s recognition that qualitative data coding and analysis can still be carried out manually employing diverse techniques for data categorization and theoretical development. Cypress (2019) concluded that early career researchers undertaking small studies would be advised to opt for a manual approach initially to grasp the intuitive facets of analysis, which may in the long run (we argue that) form the fundamental basis of any analytical method, including computerized methods. In support, Basit (2003, p. 152) acknowledges that “the use of software may not be considered feasible to code only a few interviews”. Obviously, students and early career researchers in resource-constrained settings often conduct few interviews due to inability to manage the software license costs and the time required to learn their application amidst other academic/work demands. In such scenarios, manual coding offers several pedagogical advantages among students and early career researchers including eliminating the need for specialized software, reduces costs, and provides a straightforward method for organizing and analysing qualitative data (Abdekhodaie et al., 2018; Basit, 2003; Bree & Gallagher, 2016; Chris, 2008; LaPelle, 2004; Ose, 2016). Manual coding may also offer more engagement with data as the students and early career researchers transition to technology-assisted coding. This is in line with Basit (2003, p. 152) who suggests that coding “allow [s] the researcher to communicate and connect with the data to facilitate the comprehension of the emerging phenomena”. Compared to technology-assisted coding, manual coding may therefore allow students and early career researchers to immerse themselves more deeply in their data, fostering a nuanced understanding of the material that can be critical for developing analytical skills. Manual coding encourages meticulous data examination, which can be particularly beneficial for students and early career researchers who are still learning the intricacies of qualitative analysis. By engaging directly with the data, they can develop a stronger grasp of the coding process, which serves as a foundation for more advanced analytical techniques (Ose, 2016; St John & Johnson, 2000). As these students and early career researchers advance in their studies and research careers, the transition to technology-assisted coding becomes smoother, enabling them to better appreciate the efficiencies and capabilities of technology-based coding software due to their solid grounding in manual coding.

Manual coding, using tools like Microsoft (MS) Word and MS Excel, offers a viable solution for students and early career researchers in resource-constrained settings. Unlike qualitative data coding software, MS Office is often preinstalled on computers sold in local outlets in resource-constrained settings. This practice makes MS Office accessible to students and early career researchers, despite the high cost of licenses (Ghosh, 2003), thereby providing a viable alternative for qualitative data management. It is important however, to acknowledge that the use of MS Excel presents a drawback of duplicated effort, as researchers are required to navigate through numerous cells to transfer data to MS Word for interpretive writing, a process that can be both time-consuming and cumbersome (Bree & Gallagher, 2016; Ose, 2016). By using MS Word as a manual coding tool, students and early career researchers can easily employ techniques such as ‘cut and paste’ and the use of coloured fonts to categorize data and develop theoretical explanations (Cypress, 2019; Ose, 2016). This paper aims to provide a simplified, step-by-step guide to manual qualitative data coding using MS Word. It is designed to support students and early career researchers in low-income settings, helping them to conduct effective qualitative data analysis without the need for expensive software.

Methods

This paper draws on reflections of years of experience in conducting qualitative research and teaching in higher learning institutions in low-income African countries to provide a detailed, step-by-step guide for qualitative data coding using MS Word, utilizing a modified example from previous research (Isangula et al., 2024) and a hypothetical example of a student research. The process begins with a thorough examination of the research objectives and questions, which is essential for developing preliminary themes and sub-themes . This foundational step ensures that the analysis is aligned with the study’s goals preferably taking a deductive approach. Deductive coding is a top-down approach that begins with the development of themes and subthemes through an initial set of codes. Researchers then read through the data, assigning excerpts to these predefined codes. This method contrasts with inductive coding, a bottom-up approach where themes and subthemes emerge directly from the data itself (Braun & Clarke, 2006; Chandra & Shang, 2019). The second step involves identifying the data sources within the study interview guide(s). This requires a careful review of the study interview guide to pinpoint relevant sections and responses that align with the preliminary themes and subthemes.

The third step involves developing guided transcription template(s) tailored to each interview guide, depending on the number of guides used. These templates facilitate the organization of data and ensure consistency in how information is captured during transcription for easy coded. In the fourth step, data coding or ‘mining’ is performed after conducting interviews and developing written transcripts. This involves extracting pertinent information from the individual interview transcripts and integrating it into a single template. This consolidation helps in managing the data more effectively and lays the groundwork for comprehensive analysis. However, it may be necessary to separate different themes into distinct MS Word documents, creating a flexible structure that facilitates the logical organization of interview data (Cypress, 2019). This approach is crucial for researchers using computers with lower technical specifications, such as limited random-access memory (RAM), which can cause frequent crashes with lengthy MS Word documents. By mining or coding text into specific themes on separate MS documents, with codes corresponding to headings, researchers can manage the data efficiently and later combine it into a final MS Word document. The approach of coding by directly mining data from transcripts has been endorsed by some researchers. For instance, Hacking et al. (2023) recently observed that manual coding using text mining techniques achieves commendable accuracy compared to other manual coding approaches. It has been observed that interviews coded with text mining shows greater consistency than those coded using traditional manual methods (Hacking et al., 2023). The fifth and final step is data cleaning and interpretative writing. This step is crucial for refining the data, removing any inconsistencies, and interpreting the findings in a coherent narrative. By following these steps, students and early career researchers can systematically analyse qualitative data using MS Word, making the process accessible and manageable, especially for those conducting small-scale studies. Figure 1 summarises these steps.

Figure 1.

Summary of manual coding steps.

A Practical Case

To assist students and early career researchers in effectively applying the strategies described here, we will utilize a modified example from previous study on maternal and child healthcare conducted by the primary author in rural Tanzania (Isangula et al., 2024). This study will serve as an example to deductively develop a manual coding framework by examining the study objectives and key questions in the two interview guides. Note that the questions on demographic information in the interview guides are truncated, as they may need to be managed separately when developing a table of participants’ demographic characteristics to be presented as part of the results chapter. The key study details are summarised below (Table 1).

Table 1.

Study Objectives, Participants and Interview Guides.

Sub-Study: Exploration of the Drivers of Antenatal Care (ANC) Attendance Among Pregnant Women in Rural Tanzania

Specific Objectives:

1. To identify the socioeconomic factors influencing ANC attendance among pregnant women in rural Tanzania.

2. To examine the cultural beliefs and practices that impact ANC attendance among pregnant women in rural Tanzania.

3. To explore the availability and accessibility of ANC services and their effect on attendance among pregnant women in rural Tanzania.

Research Participants

• Pregnant women attending ANC clinics

• Healthcare providers in reproductive and child health clinics

Data Collection Tools and Sample Size

1. Focus Group Discussion (FGD) guide for pregnant women attending ANC clinics (4 FGDs)

2. Key informant Interview (KII) guide for healthcare workers (8 KIIs)

FGD Guide with Pregnant Women (Key Questions)

1. What are the advantages of attending ANC clinics?

2. What are the costs associated with attending antenatal care for you (e.g., transportation, clinic fees)?

3. How do you manage these costs? Are there any financial barriers that prevent you from attending antenatal care?

4. How does your partner’s or family’s support influence your attendance at antenatal care?

5. What are the cultural beliefs or practices influencing your decision to attend or not attend antenatal care?

6. What are the issues at healthcare facilities that discourage you from seeking antenatal care services? (Probe: distance, equipment, staff quantity, staff behaviors, clinic hours etc.)

7. What improvements do you think are necessary to make antenatal care services more accessible and convenient for you?

KII Guide with Healthcare Workers (Key Questions)

1. Based on your experience, what social barriers do pregnant women in rural Tanzania face when trying to attend antenatal care appointments

2. How do you think the financial status of pregnant women affects their attendance at antenatal care clinics?

3. What strategies have you implemented to help pregnant women overcome financial barriers to attending antenatal care?

4. What cultural beliefs or practices have you encountered that affect pregnant women’s decisions to attend antenatal care?

5. How do you address or incorporate cultural beliefs and practices when providing antenatal care to ensure better attendance and compliance?

6. What are the most common logistical challenges pregnant women face when trying to access antenatal care services in this area?

7. Have there been any initiatives or programs implemented to improve the accessibility and availability of antenatal care services? If so, how effective have they been?

8. In your opinion, what improvements are necessary to make antenatal care services more accessible for pregnant women in rural Tanzania?

Steps for Developing Manual Coding Framework in MS Word

Step 1: Develop Preliminary Themes and Subthemes

The broad objective of this study is to explore the drivers of ANC attendance among pregnant women. To enhance the rigor of the research (Carter et al., 2014), the perspectives of pregnant women are triangulated with those of healthcare workers (HCWs). The specific objectives reveal three preliminary deductive themes: (1) socioeconomic factors, (2) cultural beliefs and practices, and (3) ANC service availability and accessibility. Additionally, a range of subthemes can be generated from interview guides corresponding to each of these deductive themes (Table 2).

Table 2.

Themes and Subthemes From Objectives and Interview Guides.

Deductive subthemes (from specific objectives)	Corresponding subthemes from FGD Guide	Corresponding subthemes from KII Guide
Socio-economic factors	• Perceived advantages	• Social barriers
	• Cost of attendance^a	• Financial status^a
	• Partner and family support	• Overcoming financial barriers
Cultural beliefs and practices	• Cultural beliefs and practices	• Cultural beliefs and practices
ANC service availability and accessibility	• Health facility challenges	• Logistical challenges
ANC service availability and accessibility	• Health facility challenges	• Initiatives and programs
^b improvements needed	• Improvements needed	• Improvements needed

^aThese can be combined into financial barriers.

^bA new theme from interview guide.

Several considerations should be noted at this stage. First, students and early career researchers may choose to generate themes and subthemes from the research questions rather than the objectives, depending on their preference. Second, there may be additional themes or subthemes emerging from the interview guides that do not fit into those generated from specific objectives or research questions. These should be accommodated as additional themes, see for example, the ‘improvement needed’ theme (above). Third, some themes may evolve into subthemes and vice versa. Fourth and finally, consensus building between students, early career researchers, and their supervisors/mentors on the final list of preliminary themes and subthemes from research objectives and interview guides is crucial for ensuring rigor through investigator triangulation (Carter et al., 2014). We recommend that students, early career researchers, and supervisors/mentors examine the objectives and interview guides separately and then come together to generate a consensual list of initial subthemes, a critical step in qualitative research (Cascio et al., 2019; Raskind et al., 2019). This collaborative approach is essential for maintaining the integrity and validity of the research findings.

Step 2: Identifying the Data Sources Within the Study Interviews Guides

Now that we have identified the deductive themes and subthemes, the next step is to clearly link these themes and subthemes to relevant data sources. Data sources include specific question numbers from the interview guides (Table 1), whose responses will provide data related to the generated themes and subthemes. This process requires a careful review of the interview guides to pinpoint relevant sections and responses that align with the preliminary themes and subthemes (Table 3). It is important to note that all subthemes related to a similar theme will be considered together. This meticulous mapping ensures that data coding is systematic and that each theme and subtheme is thoroughly explored, thereby enhancing the study’s overall rigor and coherence (Table 3).

Table 3.

Themes and Subthemes and Their Data Sources From Interview Guides.

Theme	Subthemes	Data Source
Socio-economic factors	Perceived advantages	FGD with pregnant women Qn.1
	Social barriers	KII with healthcare workers Qn.1
	Financial barriers	FGD with pregnant women Qn.2&3
	Financial barriers	KII with healthcare workers Qn. 2
	Overcoming financial barriers	KII with healthcare workers Qn.3
	Partner and family support	FGD with pregnant women Qn. 4
Cultural beliefs and practices	Cultural beliefs and practices	FGD with pregnant women Qn.5
Cultural beliefs and practices	Cultural beliefs and practices	KII with healthcare workers Qn.5
ANC service availability and accessibility	Health facility challenges	FGD with pregnant women Qn. 6
	Logistical challenges	KII with healthcare workers Qn.6
	Initiatives and programs	KII with healthcare workers Qn.7
Improvements needed	Suggestions for improvements	FGD with pregnant women Qn. 7
Improvements needed	Suggestions for improvements	KII with healthcare workers Qn.8

Step 3: Developing Guided Transcription and Coding Templates

Transcription is a vital part of qualitative research because it converts raw audio or video data into written text, facilitating easier coding and interpretative analysis (McMullin, 2023; Oliver et al., 2005). This process ensures that researchers can systematically examine and interpret the data, maintaining accuracy and enhancing the overall rigor of the study. Now that we have mapped themes and subthemes to their corresponding data sources, the next step is to develop guided transcription templates for each interview guide. In the context of this paper, guided transcription refers to the process of converting audio or video responses into written text using a standardized template. The purpose of guided transcription templates is to facilitate easy coding and ensure that each coded text corresponds accurately to its original data source. This is crucial because any addition, omission, or misnumbering of questions during transcription could disrupt the linkage between the text and its relevant themes and subthemes. Using guided templates helps maintain the integrity of the data, ensuring that the responses remain aligned with their designated themes and subthemes. This alignment is essential for accurate data analysis and interpretation. By systematically structuring the transcription process, we can prevent errors and ensure that the thematic analysis remains robust and reflective of the participants’ true responses. This step is also vital for maintaining the rigor and validity of the research findings.

To facilitate guided transcription, we will add spaces/text boxes to each question in the FGD (Table 4) and KII (Table 5) guides. These spaces/text boxes will be used to record the transcribed text corresponding to each question. These transcription guides will be utilized by the students and early career researchers to transcribe each interview. For instance, since four FGDs and eight KIIs were conducted, the same transcription guide will be applied to each relevant interview, ensuring that we have twelve separate transcripts by the end. This structured approach ensures that responses are accurately linked to specific questions, making the coding and analysis process more efficient and reliable.

Table 4.

Guided Transcription Template for FGD With Pregnant Women.

Table 5.

Guided Transcription Template for KII With Healthcare Workers.

The final template we need to develop is the data coding template (Table 6). This template is essential for manual coding using MS Word as it allows for the consolidation of all transcribed texts from individual interview guides into a single unified document, facilitating interpretative analysis. This template can take either a ‘linked’ form, where themes and subthemes are placed under each specific objective or research question within the template, or a ‘free’ form, where themes and subthemes are organized without considering the objectives. The latter approach is recommended because the deductive themes and subthemes are already structured to respond to each objective. Creating a data coding template involves carefully listing all identified themes and subthemes from Table 3, including data sources and providing space to insert transcribed text from all guided transcription templates. This structured format enables efficient data mining and organization, ensuring that all responses from guided transcription templates related to a particular theme or subtheme are grouped together. By doing so, the coding process becomes more systematic and the interpretative analysis more coherent, allowing researchers to draw meaningful insights and conclusions from the data.

Table 6.

Data Coding Template.

Step 4: Guided Data Coding

The fourth step involves data coding, a crucial process that helps organize and structure data from individual transcripts into meaningful categories within the coding template. The process begins by extracting pertinent information from individual interview guides and integrating it into a single template or set of ‘themes’ and ‘subthemes’. For example, transferring responses from the four FGD and eight KII transcripts into a coding template. This consolidation aids in effective data management and lays the groundwork for comprehensive analysis.

Students and early career researchers should acknowledge three main issues related to coding. First, almost everyone has engaged in some form of coding at some point in their lives. For example, religious individuals often mark and underline phrases in religious books, assigning a ‘code’ or summary word or phrase to represent what the passage signifies. Similarly, students and researchers highlight sentences in books or journals and assign ‘codes’ or summary words or phrases to represent their meanings. The difference with guided data coding is that codes, which may translate to categories, subthemes, or themes, are deductively generated from the research objectives and interview guides (a top-down thematic analysis). As noted previously, this contrasts with the inductive code generation process, where codes are derived directly from the data and then linked to study objectives (a bottom-up approach). These methods have been detailed by Chandra and Shang (2019) and Braun and Clarke (2006) among other qualitative researchers. Second, manual coding using MS Word can be done in any language. Both transcriptions and coding templates, as well as the coding process, can be developed in the local language in which the interviews were conducted. Translation can be performed during the interpretative analysis of the coded data. This approach is beneficial because students and early career researchers often perform ‘targeted translation’ by only translating the quotes used in the report, saving time and cost that would be needed to translate all transcripts. Most of the transcripts will be used only in interpretative analysis, with only a few quotes selected for reporting justifying a need for targeted translation. Third and finally, it is important to clearly indicate some demographic information of the initiator of the coded text/quotes at the beginning or end of the sentence. This practice is essential as researchers often reference individual participant demographics when using their quotes or categorizing data during interpretative analysis. Including non-identifying demographic details, such as age, gender, or role, provides context and enhances the depth of the analysis. This information helps in understanding the perspectives of different participant groups and ensures that interpretations are grounded in the demographic realities of the study population (Hammer, 2011; Li et al., 2017). However, students and early career researchers may find it beneficial to maintain a separate sheet detailing demographic characteristics of all participants. This practice ensures that different participant categories are considered collectively, thereby enriching the interpretation in the development of the final report. By consistently incorporating demographic identifiers, researchers can more accurately attribute insights and findings to specific segments of their study population, thereby improving the overall quality and relevance of the analysis. To offer more insights on the process, let us consider the coded data for theme 2: cultural beliefs and practices (Table 7).

Table 7.

Coded Data for Cultural Beliefs and Practice’s Theme and Subtheme.

Step 5: Coded Data Cleaning and Interpretative Writing

It is crucial to conduct data cleaning after guided coding. This process involves meticulously cross-checking whether data from individual transcripts are correctly categorized under relevant themes and subthemes. This consideration becomes even more crucial when individuals other than students and early career researchers are involved in the coding process. Additionally, it includes adding or modifying key demographic details of participants that may enhance interpretative analysis. This may involve reviewing a separate sheet of participants’ demographic characteristics to identify demography influencing responses that should be integrated into coded texts, and then incorporating them into corresponding quotes or statements to enrich interpretative analysis. Eliminating template labels, such as ‘data sources’ and ‘insert responses from all transcripts’, is also part of this process. These steps ensure that the transcribed text is prepared and optimized for interpretative analysis. By ensuring data accuracy and relevance, researchers can confidently proceed with the qualitative analysis, focusing on deriving meaningful insights and drawing valid conclusions from the coded data. This meticulous approach enhances the overall integrity and reliability of the research findings, supporting robust interpretations and contributing to the study’s overall credibility.

After coding and data cleaning, the next and final step is interpretative analysis. This phase involves thoroughly examining all the coded data to uncover underlying patterns, relationships, and even new themes within the participants’ perspectives. Rather than just organizing data into categories, interpretative analysis seeks to understand the nuanced experiences, beliefs, and behaviors expressed in the coded text (Gopaldas, 2016; Mays & Pope, 2000Mays & &ope, 2000). Researchers engage in comparing and contrasting perspectives, identifying contradictions or confirmations, and synthesizing findings to develop a comprehensive understanding of the research phenomenon.

A robust interpretative analysis is crucial for generating rich, contextually grounded interpretations that can inform practice, policy recommendations, or future research directions (Drisko, 2005; Gopaldas, 2016; Mays & Pope, 2000Mays & &ope, 2000). It requires thoughtful reflection and critical thinking to ensure that findings are valid, credible, and contribute meaningfully to the broader field of study (Anderson, 2010; Drisko, 2005). It is important to note that effective interpretative analysis should be supported by participant quotes. However, selecting quotes to include in a report can be challenging, but according to Lingard (2019), it is essential that the chosen quotes are authentic, illustrative, representative, and reflect strong patterns in the data. These quotes should support the researcher’s claims, illustrate ideas clearly, evoke emotions, illuminate participants’ experiences, and stimulate thoughtful responses (Corden & Sainsbury, 2006; Lingard, 2019; Thorne, 2021). Nevertheless, rather than overwhelming the analysis with numerous quotes (“quote dumping”), a strong analysis includes thoughtful reflection and critical analysis of issues emerging from the coded data, supported by select quotes that illustrate key points or insights. This approach enhances the clarity and depth of interpretation, enriching the research findings. Below, we present a sample of analysis from manually coded data using MS Word (Table 8).

Table 8.

Sample Analysis From Manual Coded Data Using MS Word.

Additional Example

Consider a Bachelor's degree student at Aga Khan University conducting a study to explore the reasons why students choose to pursue a Bachelor of Nursing degree instead of other courses (Table 9). The student has collected qualitative data through interviews with several nursing students and needs to manually code the interview transcripts using MS Word to analyze the responses.

Table 9.

Research About Drivers Choosing Bachelor of Nursing Degree.

Manual Coding Process

Step 1: Examination of the research objectives and questions for developing preliminary themes and sub-themes : The specific objectives of the study identify three primary deductive themes: (1) Personal Factors, (2) Professional Factors, and (3) Societal Factors. Each theme is further elaborated through subthemes derived from the interview guides. For instance, Personal Factors includes a subtheme focused on personal experiences that shape students’ decisions, while Professional Factors encompasses a subtheme on career aspirations and professional goals that inform their choice of nursing. Societal Factors is expanded by examining the influence of family members and friends in guiding educational paths. Although Health System Factors, which reflect the demand for healthcare professionals and available resources, could be considered part of Societal Factors, it is more appropriate for it to stand alone as a distinct theme. Students may choose to rename the themes—such as changing Personal Factors to Personal Experiences and Societal Factors to Influence of Social Networks—based on consensus with their supervisors. In summary, the deductive themes identified in the first step are: Personal Drivers, Professional Drivers, Social Network Drivers, and Health System Drivers.

Step 2: Identifying the data sources within the study interview guide(s) by pinpointing relevant sections and responses that align with the preliminary themes and subthemes: It is important to note that we have utilized a single interview format in this example—KII—which simplifies manual coding and makes the process much easier and more straightforward compared to using multiple interview formats. This means that, based on the KII guide, the data sources for each of the deductive themes are as follows: Question 1 corresponds to Personal Drivers, Question 2 relates to Professional Drivers, Question 3 addresses Social Network Drivers, and Question 4 pertains to Health System Drivers.

Step 3: Developing guided transcription template(s) tailored to each interview guide, depending on the number of guides: The guided transcription template must be designed by including “insert responses here” under each question of the interview guide. This allows the student to simply listen to the audio transcript and insert the corresponding responses under the relevant questions. To create a coding template, we can build on the deductive themes established in step one and the identified data sources for each theme in step two. Then, the coding template will incorporate these preliminary themes (including any subthemes from step one if desired) along with the data sources, followed by the phrase “Insert responses from all transcripts here” to facilitate the inclusion of responses corresponding to the themes from all ten interviews (Table 10).

Table 10.

Guided Transcription Guide and Coding Template for a Study of Drivers of Choosing Bachelor of Nursing Degree.

Step 4: Data coding or mining by extracting pertinent information from the individual interview and integrating it into a single template after conducting interviews and developing written transcripts: This process involves compiling all responses from the ten interviews according to their corresponding themes (and subthemes). By organizing the data in this manner, students and early-career researchers can better analyse and interpret the information. Below, we provide examples of coding for two interviews for each theme (Table 11). It’s essential for students to integrate responses from all ten interviews into a comprehensive coding template, ensuring that each theme is adequately represented. This systematic approach will facilitate a deeper understanding of the data and help identify patterns and insights relevant to the research objectives.

Table 11.

Coded Data for a Study on Study of Drivers of Choosing Bachelor of Nursing Degree.

Theme	Sample coded data
Personal drivers	Aisha, 21: “Growing up, I watched my grandmother struggle with chronic illness, and I often accompanied my mother to the hospital to visit her. Those experiences had a profound impact on me. I Saw how compassionate and dedicated the nurses were, always providing support not just to the patients but also to the families. It made me realize how vital nurses are in the healthcare system. After my grandmother passed away, I felt a strong urge to help others in similar situations. This personal experience sparked my interest in pursuing a bachelor of nursing degree, as I want to provide care and support to patients and their families during their most vulnerable moments.” John, 22: “I have always been interested in science and healthcare, but it was my experience volunteering at a local health clinic that truly solidified my decision. During my time there, I interacted with patients from diverse backgrounds and witnessed firsthand the challenges they faced in accessing healthcare. I Found it incredibly rewarding to assist the nurses and learn about patient care. One particular incident stood out to me—a young mother came in with her sick child, and I saw the relief on her face when the nurses were able to provide immediate care and reassurance. That moment made me realize the direct impact I could have on people’s lives as a nurse, and I knew I wanted to pursue this path instead of other programs focused solely on theory.”
Professional drivers	Emmanuel, 23: “My career aspirations have always revolved around making a meaningful contribution to healthcare. Initially, I considered degrees in fields like biology and public health, but I realized that my true passion lies in direct patient care. I Want to be on the front lines, helping people when they need it most. My goal is to become a registered nurse and eventually specialize in critical care. The bachelor of nursing program offers a comprehensive education that equips me with the necessary skills and knowledge to achieve that goal. I Appreciate the hands-on training and clinical experience integrated into the program, which I believe is essential for my development as a competent healthcare professional. Other degrees might provide a broader understanding of health issues, but I want to be directly involved in patient care and advocacy, which is why nursing is the perfect fit for me.” Leila, 21: “My professional goals are centered around community health and addressing healthcare disparities, particularly in underserved areas. When I was exploring my options, I recognized that a bachelor of nursing degree would allow me to gain both the clinical expertise and the cultural competence necessary to work effectively in diverse communities. My aspiration is to become a community health nurse, focusing on education and preventive care. I Believe that nursing is unique in that it combines medical knowledge with a holistic approach to patient care. While other degrees, such as health administration or social work, offer valuable insights into healthcare systems, I felt that nursing would provide me with the hands-on experience needed to implement change directly at the community level. This alignment between my career aspirations and the practical nature of nursing solidified my decision to pursue this degree.”
Social network drivers	Samuel, 24: “ My family has played a crucial role in this decision. My sister is a nurse, and her stories about the impact she has on her patients have inspired me greatly. She always emphasized how rewarding nursing can be, not just in terms of career stability but also in making a genuine difference in people’s lives. Her encouragement and guidance helped me see nursing as a pathway that aligns with my aspirations to serve and lead in healthcare.” Zainab, 22: “My family has significantly influenced my decision; they have always emphasized the importance of following a career that not only provides financial stability but also allows for personal fulfillment. My mother often shared her experiences in the healthcare field and how she found joy in helping others, which resonated with me deeply. Additionally, my friends have been supportive, sharing their perspectives on job security and career growth in nursing. Their positive outlook on the nursing profession helped reassure me that I was making the right choice by enrolling in this program instead of pursuing degrees that may not offer the same level of direct impact on people’s lives.”
Health system drivers	Michael, 22: “One of the primary factors that influenced my decision to pursue a bachelor of nursing degree was the critical shortage of healthcare professionals in Tanzania. During my high school years, I volunteered at a local clinic and witnessed firsthand how overwhelmed the nurses were with the number of patients needing care. This experience highlighted the urgent need for qualified nurses in our healthcare system, which motivated me to choose nursing as a career path. Additionally, I learned about various government initiatives aimed at improving healthcare access and quality in rural areas, which further reinforced my desire to contribute to these efforts as a nurse. I Believe that by choosing this degree, I can play a vital role in addressing the healthcare challenges our country faces.” Amani, 21: “My decision to pursue a bachelor of nursing degree was largely influenced by the healthcare policies and programs in Tanzania that prioritize nursing as a vital profession. I Attended a seminar on healthcare delivery where experts discussed the significant role nurses play in improving patient outcomes, especially in maternal and child health. This made me realize that pursuing nursing would allow me to be at the forefront of making a difference in people’s lives, particularly in underserved communities. Moreover, I noticed that the government is investing in nursing education and infrastructure, which reassured me that there would be ample opportunities for professional growth and job security in the field. This alignment of personal aspirations with the national healthcare agenda ultimately guided my choice toward nursing over other academic paths.”

It is important to note that more advanced qualitative researchers may code these interviews differently and develop themes that differ from those presented here. We should acknowledge that these themes are designed to facilitate learning among students and early-career researchers rather than advanced qualitative experts.

Step 5: Data cleaning and interpretative writing by refining the data, removing any inconsistencies, and interpreting the findings in a coherent narrative: After completing data cleaning and incorporating respondents’ demographics, we can proceed with an interpretative analysis. This analysis enables us to draw meaningful insights from the data. Below, we provide an example of analysing the theme of “Career Aspirations and Professional Drivers.” This theme encompasses various motivations and goals that influenced respondents’ decisions to pursue a Bachelor of Nursing degree. This analysis can be further enriched by incorporating selective quotes from participants that highlight the central theme of career aspirations and professional goals as driving forces behind their decision to pursue a Bachelor of Science in Nursing.

“The theme of career aspirations and professional goals emerges as a significant driver for individuals pursuing a Bachelor of Science in Nursing. Many students express a deep desire to make meaningful contributions to healthcare, particularly through direct patient care. Aspirants often recognize that while other fields, such as biology or public health, provide valuable insights, they lack the hands-on clinical experience integral to nursing. The Bachelor of Nursing program is viewed as a pathway that equips students with essential skills and knowledge, enabling them to specialize in areas such as critical care or community health. Additionally, the holistic approach to patient care inherent in nursing resonates with those aiming to address healthcare disparities, especially in underserved communities. This alignment between personal aspirations and the practical training offered by nursing solidifies the choice for many, making it the ideal field for achieving their professional goals.”

Conclusions and Discussion

Strategies aimed at facilitating rapid qualitative research methods are gaining significant recognition in scholarly articles. Most of these studies focus on developing rapid practices, such as employing targeted transcription methods, to ensure smooth and rapid generation of qualitative insights (Lewinski et al., 2021; Vindrola-Padros et al., 2020; Vindrola-Padros & Johnson, 2020). For example, Vindrola-Padros et al. (2020) employed rapid assessment procedures during COVID-19 by training field researchers to use daily summary sheets for swift synthesis and continuous reporting to stakeholders during the study. This signifies that qualitative methods are continuously evolving to ensure both simplicity and systematic generation of evidence across various contexts, needs, and circumstances.

Recognizing the necessity for a simplified yet systematic approach to generating qualitative evidence in resource-constrained settings, this paper systematically outlined a methodical approach to manual coding of qualitative data using MS Word, emphasizing its significance as a foundational skill for students and early career qualitative researchers. Manual coding, akin to computer-assisted methods, involves organizing and categorizing textual data to uncover patterns, themes, and relationships crucial for deriving insights and interpretations from qualitative datasets (Basit, 2003; Berthet et al., 2023; Coates et al., 2021; St John & Johnson, 2000). The key distinction lies in the simplicity, applicability and accessibility of MS Word for manual coding, requiring less specialized training, time and resources (Ose, 2016).

Manual coding using MS Word offers distinct advantages over automated software for novice researchers. Firstly, students and early career researchers in low-income settings often face significant challenges due to limited financial resources for their research projects. The high cost and time demand for learning the use computerized coding software (Basit, 2003; Berthet et al., 2023; Church et al., 2019; Cypress, 2019; NYU, 2024; St John & Johnson, 2000) necessitates alternative tools that can simplify and systematically aid in generating evidence, making them highly preferred. Therefore, manual coding using MS Word empowers students and early career researchers to adapt coding frameworks to fit specific research questions and contextual constraints, such as limited time and financial resources. Unlike rigid algorithms in automated software (Abdekhodaie et al., 2018; Brailas et al., 2023; Cypress, 2019; Dalkin et al., 2021), MS Word allows flexibility in coding to capture emergent themes and unexpected findings encountered during analysis. This adaptability is particularly valuable in qualitative research, where iterative refinement of coding frameworks is often necessary to explore diverse perspectives and experiences (Braun & Clarke, 2006). Nevertheless, while computerized techniques are faster and more efficient in managing and analysing data compared to manual methods, both manual and computerized software often yield similar results in analysis (Owan & Bassey, 2018). Therefore, researchers should feel confident using either technique without hesitation guided by available resources, time and expertise. Second, manual coding may foster methodological rigor and reflexivity by involving students and early career researchers in the coding process from start to finish. The development and application of transcription and coding templates empower students and early career researchers to actively engage in data management and analysis under mentorship of their supervisors, thereby gaining insights that are valuable as they transition to technology-assisted research processes. This active engagement under mentorship enables critical reflection on assumptions and biases, thereby enhancing the transparency and validity of research findings before exposure to automated software.

Third, students and early career researchers directly engage with data through user-friendly MS Word templates. Unlike automated tools, manual coding encourages students and early career researchers to develop templates, perform guided coding, meticulously review and analyse each piece of data, gaining a deeper understanding of the dataset’s complexities and nuances without a need to own an expensive license for a coding software (Church et al., 2019; Hacking et al., 2023; Ose, 2016; St John & Johnson, 2000). This inexpensive hands-on approach allows for the discovery of insights within MS Word that automated tools may overlook. Moreover, manual coding with MS Word may cultivates essential analytical skills among students and early career researchers, including systematic data analysis, pattern recognition, integration of diverse viewpoints, and synthesis of findings into coherent narratives (Basit, 2003; Ose, 2016). These skills are transferable across disciplines, enabling researchers to conduct rigorous qualitative analyses and contribute substantively to academic discourse.

In conclusion, proficiency in manual coding using MS Word is essential for students and early career researchers engaged in qualitative research. It may enhance engagement with data, strengthen methodological rigor, encourages flexibility in coding frameworks, and develops critical analytical skills. Mastering manual coding techniques with MS Word may equips students and early career researchers in resource-constrained settings to navigate qualitative data complexities effectively, generate robust research outcomes, and make meaningful contributions to their fields of study. Therefore, investing in the development of manual coding skills using MS Word is pivotal for achieving high-quality qualitative research outcomes and advancing knowledge across diverse academic disciplines, particularly in settings with limited resources.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

ORCID iD

Kahabi Ganka Isangula

Data Availability Statement

All data supporting the descriptions of this paper are included within the article.*

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Manual Qualitative Data Coding Using MS Word for Students and Early Career Researchers in Resource-Constrained Settings

Abstract

Keywords

Introduction

Methods

A Practical Case

Steps for Developing Manual Coding Framework in MS Word

Step 1: Develop Preliminary Themes and Subthemes

Step 2: Identifying the Data Sources Within the Study Interviews Guides

Step 3: Developing Guided Transcription and Coding Templates

Step 4: Guided Data Coding

Step 5: Coded Data Cleaning and Interpretative Writing

Additional Example

Manual Coding Process

Conclusions and Discussion

Footnotes

Declaration of Conflicting Interests

Funding

Ethical Statement

ORCID iD

Data Availability Statement

References