8-step procedure to conduct qualitative content analysis in a research

By Priya Chetty on October 16, 2016

A study by Ary et al. (1996) categorized qualitative research/method into two distinct forms. Firstly participant observation, where the researcher is a participant of the study. Secondly non-participant observation, where the researcher observes but does not participate. It is in this non-participant observation where one can use the content analysis approach.

“A research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns”- Hsieh & Shannon (2005; p.1278).

The content analysis unlike statistical analysis does not measure or quantify patterns. It is based on interpreting opinions and perspectives of various subjects. Content analysis takes into following elements when analyzing issues:

Major elements to take into consideration while conducting a content analysis
Major elements of content analysis   (Source: Kohlbacher, 2005)

Steps of content analysis

Content analysis in qualitative research is carried out by recording the communication between the researcher and its subjects. One can use different modes such as transcripts of interviews/discourses, protocols of observation, video tapes and written documents for communication. Its strength lies in its stringent methodological control and step-by-step analysis of material. In other words every element in the data collected is categorized into themes which are identified through secondary literature. The method of the analysis comprises following 8 steps:

  1. Preparation of data: As discussed previously, there are several ways by which one can collect the data for qualitative content analysis. However one needs to be transform the data before the analysis can start. From the data set which the researcher has collected, choice of “content” need to clearly defined and justified. Before initiation of data preparation, researcher needs to know the answers to following questions:
    • All the data collected be transcribed or not.
    • Should verbalizations be transcribed literally.
    • Should observations be transcribed as well.

    Answers to these questions are dependent on the the objectives of the study. However,  everything should be transcribed at the start to save time during analysis.

  2. Defining the unit or theme of analysis: Unit or theme of analysis means classifying the content into themes which can be a word, phrase or a sentence. When deciding the unit of analysis, one theme should present an “idea”. This means the data related to the theme has to be added under that unit. Furthermore, unit or themes should be based on the objectives of the study.
  3. Developing categories and coding scheme: Next step is to develop sub-categories and coding scheme for the analysis. This is derived from three sources, the primary data, theories on similar topic and empirical studies. Since the qualitative content analysis can be based on both inductive and deductive approach, the categories and codes needs to be developed based on the approach adopted.In case of deductive approach, it is important to link the interpretations with the existing theories in order to draw inferences. However, in case of inductive approach the objective is to develop new theories. So, it is important to evaluate secondary sources in order to stimulate original ideas. In order to ensure consistency in the codes, the categories as per their properties with examples has to be defined.
  4. Pre-testing the coding scheme on sample: Like quantitative data, pre-testing qualitative data is also important. In order to ensure consistency, members of the research team need to code the sample of existing data. If the level of consistency is low across researchers then re-coding has to be done again.
  5. Coding all the text: After the coding consistency in the previous stage, it is important to apply the coding process to the data.
  6. Assessing the consistency of coding employed: After coding the whole data set validity and reliability should be checked.
  7. Drawing inferences on the basis of coding or themes: In this step, one has to draw inferences on the basis of codes and categories generated. It is important to explore the properties, dimensions and identify the relationship and uncover patterns in order to present the analysis.
  8. Presentation of results: To present the results under each theme with conclusions the results should be supported by secondary data and quotes from the developed code. Further, based on the analysis, the researcher can also present the results in the form of graphs, matrices, or conceptual frameworks. The results should be presented in such a way that the reader is able to understand the basis of interpretations.
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Computer-assisted qualitative content analysis

In conclusion ,qualitative data, like quantitative data can be huge. In such cases assistance from computer programs is required in order to reduce the complexity of analysis. Among various tools the most common are NVivo or Atlas. These tools have several features, which helps in coding and development of the nodes. This also enables visual presentation of interpretations drawn from the content.


  • Berg, B.L. (2001). Qualitative Research Methods for the Social Sciences. Boston: Allyn and Bacon.
  • Bradley, J. (1993). Methodological issues and practices in qualitative research. Library Quarterly, 63(4), 431-449.
  • Glaser, B.G., & Strauss, A.L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine.
  • Hsieh, H.-F., & Shannon, S.E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. Available on : http://qhr.sagepub.com/content/15/9/1277.short?rss=1&ssource=mfc
  • Miles, M., & Huberman, A.M. (1994). Qualitative Data Analysis. Thousand Oaks, CA: Sage Publications.
  • Patton, M.Q. (2002). Qualitative Research and Evaluation Methods. Thousand Oaks, CA: Sage.
  • Weber, R.P. (1990). Basic Content Analysis. Newbury Park, CA: Sage Publications.

Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

  • Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
  • Assessing customer satisfaction with in-house doctors of Jiva Ayurveda (a project executed for the company)
  • Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).



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