Positivist Approach: “What experiences might individuals with different levels of education have with long-term unemployment in the UK?”
  • Although positivism is often linked with quantitative methods, it could also be applied in qualitative research. The focus would likely remain on collecting observable data, but it might be recorded experiences and narratives in this case.
  • To explore this question, it could be suggested that one-on-one interviews are conducted with a diverse group of long-term unemployed individuals in the UK with varied levels of education. The interviews could be structured to maintain consistency, potentially allowing for comparison across different individuals’ experiences.
  • Data might be transcribed and coded for common themes and patterns. Despite the qualitative nature of the data, the analysis could be conducted systematically and rigorously, in line with the positivist philosophy.
  • The findings from this approach may provide a detailed understanding of how education level influences the experiences of long-term unemployment. These insights could inform interventions and policies that address the needs of the unemployed, providing a practical application for the research.
  • Reflecting on this methodological approach, one could argue that the strength of the positivist approach may be in its ability to provide systematic and comparable data when applied in a qualitative context. However, it is essential to note that the depth of individual experiences could be limited due to the structured nature of data collection, which might be a compromise for ensuring consistency in the data.
Interpretivist Approach: “How might individuals in the UK perceive the stigma associated with long-term unemployment?”
  • Interpretivism, as a research philosophy, appears to value individuals' subjective experiences and interpretations. To investigate this question, it could be suggested that qualitative methods that enable an in-depth exploration of individuals’ perceptions be used.
  • It could be proposed to conduct semi-structured interviews with a diverse group of long-term unemployed individuals in the UK. The interviews could be guided by open-ended questions, allowing participants to share their perceptions of the stigma associated with unemployment.
  • A thematic analysis of the interview transcripts might be possible upon data collection. This process could involve coding the data, identifying recurring themes, and interpreting these themes to gain insight into how unemployed individuals may perceive stigma.
  • The findings from this approach might provide a rich, nuanced understanding of the perceived stigma associated with long-term unemployment. These insights could inform interventions and policies that address the psychological and social needs of the unemployed, providing a practical application for the research.
  • Reflecting on this methodological approach, one could argue that the strength of the interpretivist approach may lie in its potential to provide a deep, human-centred understanding of long-term unemployment. However, the subjective nature of the data might limit the generalisability of the findings. Therefore, results should be interpreted within the context of the participants’ experiences and backgrounds.
Brainstorm all the aspects of Qual that make it different from Quant. Then, apply one of those aspects to an interview with a public figure and consider what we might better understand about their experiences if we consider that aspect as a part of our investigation. For example, how they may have felt towards a role model’s behaviour in the network they had when they were younger.
  1. Nature of Data: Qualitative (Qual) focuses on the qualities of entities, processes, meanings, and interpretations and is often subjective, while quantitative (Quant) deals with quantities, measurements, and numerical analysis, typically aiming for objectivity.
  2. Data Collection: Qual involves direct participant interaction through interviews, observations, and document analysis. Quant relies on structured data collection tools like surveys and tests.
  3. Data Analysis: Qual uses interpretive or critical methods to understand the data, where the context and subjectivity of interpretation are crucial. Quant employs statistical methods and numerical analysis.
  4. Outcome: Qual seeks to understand underlying reasons, opinions, and motivations, providing insights into problem setting. Quant quantifies data and generalises results from the sample to the population.

Applying the aspect of the ‘Nature of Data’ to an interview with a public figure, we might engage in a Qual approach to better understand their experiences, feelings, and interpretations instead of solely facts or numerical data. For instance, if we were to investigate a role model’s influence on this public figure during their formative years, a Qual approach could allow us to explore the nuances of how their relationship with this role model shaped their behaviour, values, and attitudes.

We could ask open-ended questions such as, “Can you describe an instance where your role model’s behaviour significantly impacted you?” or “How did your relationship with your role model influence your behaviour over time?” This approach might provide a deeper understanding of the public figure’s personal experiences and feelings and the complex interplay of influences, a depth that Quant methods might not achieve.

Share this post