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Interpretive Research, Chapter 12, Source: Bhattacherjee, A. (2019).…
Interpretive Research
Differences between Interpretive vs. Positivist Research
Historical Context: Interpretive roots: Anthropology, sociology, psychology and linguistics
Origins: Early 19th Century viewed as erroneous and biased by positivist. Resurged in 1970s due to limits of positivism
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Paradigm Assumptions:
Positivist: reality is objective and context independent
Interpretive: Reality is socially constructed and context dependent. Also uses sense making not hypothesis testing
Data Types:
Qualitative: Non-numeric (e.g., interviews, observations)
Quantitative: Numeric (e.g. scores, metrics)
Mixed mode design combines both for better insights
Sampling Strategy:
Interpretive: Theoretical based on phenomena fit and characteristics
Positivist: Random sampling for generalizability and convenience sampling not acceptable
Role of Researcher:
Interpretive: May be involved or neutral but acknowledged
Positivist: External, independent and assumed unbiased
Analysis Approach:
Interpretive: Holistic, contextual and focuses on language, signs and meanings
Positivist: Reductionist, isolationists relying on statistical techniques
Rigor:
Interpretive: Systematic and transparent data collection and analysis
Positivist: Statistical benchmarks for validity and significance testing
Flexibility in data collection and analysis:
Interpretive: Iterative, simultaneous and questions can evolve during research
Positivist: Design is fixed, no midcourse detours without restarting study
Benefits:
--Explore hidden reasons behind complex, multi-faceted social processes
-Helpful for theory construction in areas lacking prior theory
-Appropriate for context-specific unique or idiosyncratic events
-Uncovers new and relevant research questions for follow-up research
Challenges:
-Time and resource intensive
-Requires well-trained researchers to interpret divers perspective objectively
-Participant credibility and trust issues
-Heavily contextual inferences limit replicability and generalizability
-May fail to answer research questions or predict future behaviors
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