mixed-methods-research

Mixed methods research integrates qualitative coding with quantitative statistical results by applying convergent or sequential designs to triangulate evidence. It uses joint displays to compare and converge qualitative themes with quantitative outcomes for credible conclusions.

Inter-rater reliability for content analysis coding is calculated using metrics like kappa and Krippendorff’s alpha. This validates coding quality by measuring agreement between coders working from a structured codebook within reproducible Python workflows.

Yes, you can use Python with pandas and statsmodels for QCA-style configuration evaluation. This approach enables qual-quant integration by evaluating configurational reasoning alongside traditional statistical modeling to produce triangulated interpretations.

Mixed methods research fully supports sequential explanatory, sequential exploratory, and convergent study designs. It aligns qualitative and quantitative phases systematically, allowing quantitative results to explain qualitative themes or vice versa.

You create a joint display to triangulate data from multiple sources by systematically mapping qualitative themes against quantitative outcomes. This integration technique highlights convergence or differences, supporting structured interpretation across mixed data strands.

You need scipy and scikit-learn alongside pandas, numpy, statsmodels, and matplotlib to run reproducible mixed methods workflows. These dependencies provide the statistical modeling and data integration foundation required for comprehensive qual-quant analysis.