Chapter 8: Analysis and interpretation of data

Analysis and interpretation of data are critical skills for broadcast journalism professionals who manage projects involving audience research, content evaluation, and impact assessment. In Kenya’s dynamic media landscape, journalists must select and apply appropriate qualitative data analysis tools to extract meaningful insights from interviews, focus groups, and viewer feedback. This chapter equips broadcast journalists with practical knowledge to choose suitable qualitative analysis techniques that enhance storytelling, improve programming decisions, and support evidence-based reporting.

8.1 Choosing appropriate qualitative data analysis tools

Qualitative data analysis tools are the programs and methods used to organise, code and interpret non-numerical data, such as interview transcripts, discussion recordings, field notes and audience comments. Choosing the right tool decides how quickly the material can be sorted and how well the conclusions can be traced back to what people said. Example: a documentary team has recorded thirty interviews with residents of a fishing community. Reading and colour-coding every transcript on paper would take weeks, so the team considers a coding tool that lets them tag passages, group them under themes and retrieve every quotation on a theme in seconds.

Where resources are limited, an informed choice lets a team get good value from its data. A tool that is too complex wastes learning time, and one that is too simple may not handle the volume.

Qualitative data types and what they require

• Interview transcripts: word-for-word records of conversations. They need tools that allow text coding and theme development so that recurring topics and attitudes can be found.
• Focus group discussions: several speakers produce data on shared views. The tool must keep track of who said what and show where the group agreed or differed.
• Field notes and observations: these are often unstructured and descriptive. A flexible tool that handles free text and allows notes on the context is needed.
• Audience feedback and social media comments: these are many in number and informal in tone. The tool needs filtering, categorising and, where useful, simple sentiment tagging.
• Audio and video recordings: these must be transcribed first, or analysed in software that can code directly on the recording.

Criteria for selecting a tool

• Ease of use: an interface that a journalist can learn quickly, without long technical training.
• Compatibility: the tool opens the formats in which the data is stored, such as text files, audio recordings or video clips.
• Analytical features: it provides the functions the project needs, such as coding, memo writing, theme identification and charts or maps of themes.
• Cost: the licence or subscription is affordable, or a free or open-source tool is available that covers the essential functions.
• Support and training: tutorials, help pages or a user community exist, which shortens the learning period.
• Data security: sensitive interviews need a tool that keeps data confidential and allows it to be stored securely, for example on an encrypted drive.
• Scalability: the tool can hold the amount of data in the project, whether a handful of interviews or several hundred comments.
• Collaboration: if several people will code the same material, the tool must allow shared work or the merging of separate files.

Common qualitative analysis tools

Licence terms and prices of commercial programs change, so a team checks the current terms with the supplier before buying. Free and open-source tools suit a team with a small budget, and commercial programs suit larger projects that need advanced functions. For a few dozen short comments, a spreadsheet with one column for the comment and one for its code may be enough.

Steps in choosing a tool

1. Describe the data: its type, its amount and the language or languages used.
2. State what the analysis must produce, such as a list of themes with quotations or a count of categories.
3. List the team's skills and the number of people who will code the data.
4. Set a budget and a time limit, including time to learn the tool.
5. Shortlist two or three tools and check each against the criteria above.
6. Test the shortlisted tools on a small piece of the real data.
7. Choose the tool that handles the test data best within the budget, and record the reasons.

Worked examples of choosing a tool

• Interviews with a sensitive subject: a team is analysing interviews with sources who fear reprisals. Security comes first, so the team chooses a tool that stores files on the researcher's own computer, protects them with a password, and does not upload them to an outside server.
• A community radio station with a small budget: the station wants to analyse listener feedback and notes from interviews. A free tool such as Taguette meets the need for coding and retrieval, and costs nothing.
• Comments under a debate video: a digital desk has several hundred comments in a spreadsheet. A spreadsheet can sort and count comments by category, while a coding program may be used if the team wants to link the codes to themes.
• A large documentary project: a team with hundreds of pages of transcripts and video interviews, and several analysts, needs a program that supports shared coding, memos and multimedia, which points to a commercial package if the budget allows.

Chapter Summary

This chapter explored how to select suitable qualitative data analysis tools, emphasizing the importance of matching tools to the nature and goals of the project data. It then examined the criteria for choosing appropriate quantitative data analysis tools, focusing on statistical methods and software that best fit numerical datasets. The discussion progressed to common challenges encountered during data analysis, such as data quality issues and bias, and presented strategies to address these difficulties effectively. Finally, the chapter covered the interpretation of data, highlighting techniques for drawing meaningful conclusions and making informed decisions based on the analyzed information. Understanding these elements is crucial for project managers to ensure accurate insights that guide project success. Throughout, the focus remained on practical application within project management contexts.