Expert guidance in merging quantitative and qualitative data to generate comprehensive, validated findings. We support doctoral researchers in designing integration strategies, creating joint displays, developing meta-inferences, and producing coherent mixed methods dissertations that leverage the full power of both paradigms.
Systematic approach to merging quantitative and qualitative findings
Identify points of interface, select integration approach, design joint display frameworks
Quantitizing qualitative data, qualitizing quantitative results, creating derived variables
Side-by-side comparison, data merging, joint display construction, weaving narrative
Convergence assessment, discordance handling, integrated conclusions and implications
Core methods for merging quantitative and qualitative data effectively
Present results separately then compare or contrast findings across datasets
Visual matrices combining QUANT and QUAL data using side-by-side, statistics-by-theme, or integrated formats
Converting qualitative themes into quantitative variables or quantitative results into qualitative narratives
Alternating QUAL and QUANT findings within a single narrative thread across sections
Comprehensive assistance for mixed methods data integration
Creating side-by-side, statistics-by-theme, integrated, and meta-matrix displays for dissertation chapters
Assessing agreement between QUANT and QUAL findings, explaining discordance, and enriching understanding
Transforming qualitative themes into quantitative scores, converting statistical results into narrative themes
Sample integration, inside-outside, weakness minimization, and sequential validity strategies
Crafting coherent mixed methods chapters, weaving narrative, presenting integrated findings
NVivo + SPSS workflows, MAXQDA mixed analysis, Dedoose cross-platform integration, R mixed package