Most rejected manuscripts fail not because the study is “weak,” but because the claims are not licensed by the paradigm the author claims to use. Reviewers today are trained to identify mismatch between a researcher’s worldview and their methodological choices.
A research paradigm combines four elements:
- Ontology – what reality is
- Epistemology – how we know reality
- Methodology – the strategy of inquiry
- Methods – tools for collecting and analysing data
If these elements do not logically align, your findings appear incoherent, regardless of statistical power or qualitative richness. Several studies show that paradigm–method mismatch is one of the top three reasons qualitative and mixed-method papers are rejected. (Mertens, 2020; Tracy, 2019)
Common Paradigm Alignment Failures and Why They Matter
Below are the most frequent—and most costly—misalignments researchers make.
1. Constructivist Stance, Positivist Tests
Constructivism assumes multiple, co-constructed realities. Positivist statistics assume an objective, measurable reality.
Mismatch example:
Claiming “constructed meaning” while running ANOVA to “prove” reality.
This confuses reviewers because your worldview and your tools measure different versions of reality.
2. Causal Claims Using Cross-Sectional Data
Correlation is not causation. Cross-sectional snapshots lack temporal movement, counterfactuals, and mechanisms.
Reviewers immediately note the mismatch between:
- Causal language (“X influences Y”)
- Non-causal design (single-wave surveys)
Leading journals explicitly list this as a rejection factor. (Maxwell, 2021)
3. “Grounded Theory” Using a Pre-Defined Codebook
Grounded Theory requires emergent codes. Using a priori codes violates its foundational logic.
Reviewers look for:
- iterative coding
- theoretical sampling
- emergence, not imposition
If these are missing, your claim collapses.
4. Interpretivist Aims, SEM Fit Worship
Interpretivism prioritizes meaning, context, and reflexivity. SEM prioritizes statistical fit.
Fit ≠ meaning.
If you claim to produce “deep interpretive insight” but rely on latent variable modeling, reviewers see the contradiction.
5. fsQCA Mixed with Linear Net Effects
fsQCA assumes causal complexity, configurations, and equifinality.
Linear models assume additive, net effects.
When authors blend these without justification, reviewers reject the paper due to incompatible causal logics.
6. RCTs Without a Theory of Change
A randomized controlled trial isolates effects, but without a theory of change, you only know tha something happened—not how or why.
Journals now expect mechanism-driven causal models, not “black box” results.
7. Mixed Methods Without Integration
Most mixed-method papers fail due to:
- parallel tracks
- no triangulation
- no convergence
- no meta-inference
Mixed methods requires integration, not “two separate studies packaged together.”
8. Validity Claims from the Wrong Paradigm
You cannot claim internal validity in an interpretivist study or credibility/transferability in a purely experimental study.
Each paradigm has its own validity standards.
How Research & Report Consulting Helps
Our Rapid Paradigm Alignment Audit evaluates:
- your worldview
- your methodology
- your data structure
- your analytical logic
- your validity claims
- your write-up
This ensures your research is publishable and defensible.
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