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Resource Methods Toolkit wp6-tuni Published: 12 May 2022

Manual analysis

The analysis process in manual analyses involves preparing, organising, representing, and interpreting the data

Preparing the Data

Before Transcribing

  1. Organise the data by type. 

  2. Go through/review collected data: listening/watching recordings, reading through observation notes etc.

  3. Decide which parts of data should be analysed. Decide whether to use only manifest content or also latent content (Elo & Kyngäs, 2008; Vaismoradi, Turunen & Bondas, 2013).

Transcribing the Data (following Gibbs, 2007)

Qualitative data (this includes but is not limited to video and audio recordings, notes, and observations, as well as documents and photographs) is usually transcribed into the form of typed text because this format is easiest to work with. 

  1. Transcribe into text form. Decide what extent and type of transcription to use. Also decide whether to use detailed rendering (naturalised transcription) including pauses, stress, intonation, pitch, overlapping speech etc. or less detailed rendering (denaturalised transcription) in transcribing video and audio data. Naturalised transcription (Neale, 2016) captures every utterance and is best for discourse or conversation analysis, for example. Denaturalised transcription, instead, only  focuses on informational content and is suitable for thematic, content, and framework analysis. For field notes, observations, documents, photographs etc., decide whether to summarise or give detailed descriptions. 

  2. Decide what labels to give the individual bodies’ data (including fields notes, documents, photos etc.) as well as the participants and interviewers. Consider factors like anonymity and the privacy of participants. 

  3. Transcription is best done by the interviewer or researcher themselves because of familiarity and understanding. However, one may also use third-party transcribers or voice-to-text software. Accuracy is paramount regardless of the choice of transcriber, and transcripts should be checked against the original form of data collected to ensure that it is accurate.

  4. Read through transcription for an overall understanding, then read through again and look for emerging patterns, ideas, and themes. This is called the ‘exploratory approach’ (Guest, Macqueen & Namey, 2012).

Organising the Data

Coding and Categorising the Data

Coding is a method of organising data in the qualitative analysis process (Gibbs, 2007). Coding and categorising are analysis methods that are applicable to many different forms of data. This is the most common analysis form of data collected from interviews, focus groups, and observations (Flick, 2007). Coding can be done manually or through the use of computer-assisted qualitative data analysis software (CAQDAS) like NVivo, Atlas ti 6.0, HyperRESEARCH 2.8, Max QDA, and others. 

Example of Protocol in Manual Coding

  1. Review the textual data line by line. Identify key themes (codes) and attach or assign segments of the data to these codes. Add new codes as new themes emerge from the data, thus creating a hierarchical code tree (Neale, 2016). Some experts recommend using multiple open codes of an exploratory nature and then collapsing these into a fewer number of more focused codes, which are in turn merged into an even smaller number of broader conceptual codes (Neale, 2016). Other experts recommend starting with broader descriptive codes and then breaking them down into smaller coding units in order to make comparisons across the data (Neale, 2016).

  2. One way to do the former is to begin by grouping together ideas, words, phrases etc., which belong together. These are subcategories. Give preliminary names/titles to the groups. Examine to see if some groups or clusters of data can be further grouped under larger themes or ideas. These are generic categories. Collapse these generic categories further into overarching themes, ideas, or meanings. These are the main categories or themes. Ideally, there should be about 5 to 7 main categories at the end of this process. This entire process is known as ‘Abstraction’ (Elo &Kyngäs, 2008).

  3. In this manner, work systematically through the entire data corpus until all lines of transcribed data have found a place in a category. Lines that do not have any clear place can be grouped under a code/category for miscellaneous data. Lines that could be grouped under more than one code/category can be placed in more than one code/category, or then a decision should be made as to which category is the best representation of the data.

Representing the Data

As part of the analysis, data can be represented in the form of narratives or visuals. Narratives are rich textual descriptions of the data, while visuals are a way of presenting the data in image or graphic form.

  1. Narratives 

  • Rich interpretive descriptions

  1. Visuals

  • Tables

  • Charts

  • Trees

  • Diagrams 

  • Drawings 

Interpreting the Data

All analysis processes require some level of interpretation by the researcher. This is an understandable, accepted, and even necessary part of qualitative research (e.g. Willig, 2014). However, the decisions and interpretations made should be explained and justified in order to ensure the robustness and validity of the research. Through the process of interpretation, knowledge is generated. This knowledge ranges from the straightforward translations of surface meanings into deeper meanings to the explanations of meanings, which add layers to the original account without altering it (Willig, 2014). There are several forms of interpretation. Some forms are based on hunches, insights, and intuition, some are done within certain social sciences constructs or ideas, and some are a combination of personal views as contrasted with social science constructs or ideas (Creswell, 2007). In a nutshell, interpretation involves the following: 

  1. Searching for patterns and connections, looking for the relative importance of the data, and identifying relationships between data sets or themes.

  2. Drawing conclusions, summaries, and theories from the data in relation to the theoretical and conceptual framework as well as research aims and questions.

  1. Creswell, J. W. (2007). Qualitative Inquiry & Research Design: Choosing Among Five Approaches (2nd ed.). Thousand Oaks, Calif: Sage Publications.

  2. Elo, S., & Kyngäs, H. (2008). The Qualitative Content Analysis Process. Journal of Advanced Nursing, 62(1), 107-115. doi:10.1111/j.1365-2648.2007. 04569.x

  3. Flick, U. (2007). Analyzing Qualitative Data. In Flick, U. (2007). Qualitative Research Kit: Designing Qualitative Research (pp. 100-108). London: SAGE Publications, Ltd. doi: 10.4135/9781849208826

  4. Gibbs, G. R. (2007). The Nature of Qualitative Analysis. In Gibbs, G. R. (2007). Qualitative Research Kit: Analyzing Qualitative Data (pp. 1-10). London, England: SAGE Publications, Ltd. doi: 10.4135/9781849208574

  5. Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied Thematic Analysis. Thousand Oaks, CA: SAGE Publications, Inc. DOI: 10.4135/9781483384436

  6. Neale, J. (2016). Iterative Categorization (IC): A Systematic Technique for Analysing Qualitative Data. Addiction (Abingdon, England), 111(6), 1096–1106.

  7. Willig, C. (2014). Interpretation and Analysis 1. In Flick, U.(2014). The SAGE Handbook of Qualitative Data Analysis (pp. 136-150). London: SAGE Publications Ltd doi: 10.4135/9781446282243

  8. Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content Analysis and Thematic Analysis: Implications for Conducting A Qualitative Descriptive Study. Nursing & Health Sciences,15(3), 398-405. doi:10.1111/nhs.12048

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Vallery Michael

Tampere University
Tampere University

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