44  Grounded Theory and Content Analysis

44.1 Introduction

In this section, we’ll explore two important methodologies in qualitative research: Grounded Theory and Content Analysis.

Both approaches offer distinct lenses through which researchers can interpret complex social phenomena, but differ significantly in their processes and applications.

44.2 Grounded Theory

Grounded theory is a research methodology developed by sociologists Barney Glaser and Anselm Strauss in their 1967 work “The Discovery of Grounded Theory” (Glaser & Strauss, 1967).

It diverges from traditional research methods that start with a hypothesis. Instead, grounded theory begins with data collection, without preconceived theories. The primary objective is to inductively derive theories about phenomena directly from the data gathered (Charmaz, 2006).

Data collection and analysis

Data collection in grounded theory is typically characterised by a process known as ‘theoretical sampling’. This is not random or representative sampling as seen in quantitative research but is guided by the emerging theory (Glaser & Strauss, 1967).

Researchers collect, code, and analyze data simultaneously. Initial data collection informs what data to collect next, leading to a continuous interplay between data gathering and analysis. This iterative process is crucial for the development of a theory that is deeply grounded in empirical evidence (Corbin & Strauss, 1990).

‘Coding’ in Grounded Theory

Coding in grounded theory occurs in several stages, primarily open, axial, and selective coding (Strauss & Corbin, 1990).

  • Open coding involves breaking down, examining, comparing, conceptualising, and categorising data.

  • Axial coding is about relating categories to their subcategories.

  • Selective coding is the process of integrating and refining the theory by focusing on the core category that represents the main theme of the research.

This systematic coding process allows for the emergence of patterns and themes that form the basis of the developed theory (Charmaz, 2006).

The role of the researcher

The role of the researcher in grounded theory is notably active. Researchers must engage deeply with the data, maintaining reflexivity about their role and potential biases.

‘Reflexivity’ involves acknowledging one’s perspectives, influences, and interactions with the research process (Birks & Mills, 2011). This self-awareness is crucial in grounded theory, as the researcher is the primary tool for data collection and analysis, and their insights and interpretations play a significant role in theory development (Charmaz, 2014).

Criticisms and challenges

Grounded theory is not without its criticisms. Some scholars argue that the method can be overly subjective due to the active role of the researcher in data analysis (Thomas & James, 2006).

Others point out the challenges in achieving theoretical saturation - the point at which no new information is being discovered (Glaser & Strauss, 1967).

Additionally, the flexibility of the method can lead to inconsistencies in its application, raising questions about the reproducibility of research findings (Seale, 1999).

References for Grounded Theory

  • Birks, M., & Mills, J. (2011). Grounded Theory: A Practical Guide. Sage.

  • Bryant, A., & Charmaz, K. (Eds.). (2007). The SAGE Handbook of Grounded Theory. Sage.

  • Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.

  • Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.

  • Corbin, J., & Strauss, A. (1990). Grounded Theory Research: Procedures, Canons, and Evaluative Criteria. Qualitative Sociology, 13(1), 3-21.

  • Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.

  • Seale, C. (1999). The Quality of Qualitative Research. Sage.

  • Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.

  • Thomas, G., & James, D. (2006). Re-inventing Grounded Theory: Some Questions about Theory, Ground and Discovery. British Educational Research Journal, 32(6), 767-795.

44.3 Content Analysis

Content analysis is a systematic, quantitative method for analyzing textual information to understand its context and significance. Initially used in communication studies, it has since been adopted across various social sciences.

Berelson (1952) defined content analysis as “a research technique for the objective, systematic, and quantitative description of the manifest content of communication” (Berelson, 1952). This method involves identifying and coding various elements of texts, such as words, themes, or concepts, to interpret the underlying context and meaning.

Process

The process of content analysis begins with defining the research question and selecting a sample of textual material. This could range from books, essays, and speeches to social media posts and news articles.

The next step involves developing a coding scheme, which is crucial for categorizing the content into manageable segments for analysis. This scheme should be both exhaustive and mutually exclusive to ensure comprehensive and non-overlapping coding (Krippendorff, 2013).

Researchers then systematically apply this coding scheme to the text, often using software tools to manage and analyse large datasets.

Quantitative and qualitative approaches

Content analysis can be both quantitative and qualitative. Quantitative content analysis focuses on counting and comparing the frequency, patterns, and trends of certain words or content in the text, leading to numerical and statistical analysis (Neuendorf, 2017).

Qualitative content analysis, on the other hand, involves interpreting the meaning of the content and its underlying context. This approach is more subjective and seeks to understand how and why certain themes or patterns emerge in texts (Schreier, 2012).

Reliability and validity

Just as in quantitative data analysis, ensuring reliability and validity is crucial in content analysis.

  • Reliability refers to the consistency of the coding scheme, while validity concerns whether the analysis truly reflects the data’s intended meaning. To enhance reliability, researchers often use intercoder reliability, where multiple coders independently code the text and then compare results (Lombard, Snyder-Duch, & Bracken, 2002).

  • Validity can be strengthened through pilot testing of the coding scheme and by using a well-defined theoretical framework to guide the analysis (Riffe, Lacy, & Fico, 2014).

Challenges and considerations

One of the main challenges in content analysis is managing subjectivity, especially in qualitative content analysis. Researchers’ biases and perspectives can influence the interpretation of texts.

To mitigate this, transparency in the coding process and a clear delineation of steps taken in the analysis are essential (Hsieh & Shannon, 2005). Additionally, the evolving nature of language, especially in rapidly changing mediums like social media, poses a challenge in maintaining the relevance and accuracy of coding schemes.

References

  • Berelson, B. (1952). Content Analysis in Communication Research. Free Press.

  • Hsieh, H. F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative Health Research, 15(9), 1277-1288.

  • Krippendorff, K. (2013). Content Analysis: An Introduction to Its Methodology (3rd ed.). Sage.

  • Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability. Human Communication Research, 28(4), 587-604.

  • Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.

  • Riffe, D., Lacy, S., & Fico, F. (2014). Analyzing Media Messages: Using Quantitative Content Analysis in Research (3rd ed.). Routledge.

  • Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.