What are some common methodological limitations in qualitative research?

1 Sampling

Sampling is the process of selecting a subset of participants or cases from a larger population for your study. In qualitative research, sampling is usually purposive, meaning that you choose participants or cases based on their relevance, diversity, or richness of information for your research question. However, this approach can also bring about certain limitations, such as a sample size that is too small or too large to capture the complexity or variability of the phenomenon under study. A sample that is generalizable to the wider population or context of interest, or a sampling criteria or strategy that is biased or unclear, leading to questions about the credibility or transferability of the findings. To address these issues, it is important to explain and justify your sampling decisions, provide a detailed description of your sample characteristics, and recognize the potential implications or limitations of your sampling approach for your findings and conclusions. (This section has been updated by LinkedIn editors based on member feedback.)

2 Data collection

Data collection is the act of obtaining information from participants or cases through a variety of means, such as interviews, observations, documents, or artifacts. In qualitative research, data collection is often flexible, iterative, and responsive to the topics that arise during the study. However, there are some drawbacks to data collection, such as the potential for incomplete, inaccurate, or inconsistent data due to the availability, accessibility, or quality of the sources or methods. Additionally, the data may be influenced by the researcher's subjectivity, bias, or expectations, or by the participants' self-presentation, social desirability, or recall. Furthermore, managing, organizing, or storing the data can be difficult, especially if it is voluminous, diverse, or sensitive. To combat these issues, it is important to carefully select and prepare data sources and methods, build a rapport and trust with participants, and use multiple sources or methods to triangulate and validate the data. Additionally, it is essential to document and reflect on the data collection process, and ensure the confidentiality, security, and ethical treatment of the data.

3 Data analysis

Data analysis is the process of interpreting, synthesizing, and presenting your data in relation to your research question. In qualitative research, this usually involves inductive reasoning, meaning that patterns, themes, or categories are derived from the data, rather than testing predefined hypotheses or theories. However, this approach can be subject to certain limitations, such as the researcher's perspective, skills, or criteria potentially influencing the analysis, as well as preconceptions, assumptions, or values. Additionally, the analysis may be difficult to communicate, illustrate, or verify, particularly if it is complex, abstract, or nuanced. To mitigate these issues, it is important to follow a systematic and transparent approach to data analysis, using appropriate tools, techniques, or frameworks. Additionally, it is essential to acknowledge and bracket the researcher's positionality, compare and contrast findings with other studies or perspectives, and use quotes, examples, or visual aids to support and illustrate the analysis. Finally, feedback or confirmation from participants, peers, or experts should be sought to ensure accuracy.