The versatility of large scale textual data in self-forgiveness research: Integrative strategies for sequential mixed methods designs.

Introduction

This study on ‘big qual’ as a subfield of qualitative research aimed at the development and application of innovative analytic methods for large scale textual data. In the context of public health research there are few published examples of applied studies demonstrating the usefulness of large scale textual data for the qualitative initial and follow up phases of sequential mixed methods designs.

Goals and Methods

We demonstrate the versatility of large- scale textual data collected from 901 participants via an online self-forgiveness survey conducted in Trinidad and Tobago. Using dialectical pluralism as a philosophical guide we describe the mixing, merging, iterative, nesting, embedding, quantitizing, sequencing and meta integrative methodologies used to design integrative methodological frameworks and visual joint displays for exploratory and explanatory sequential mixed methods designs. In the exploratory sequential study, we applied maximum variation and theory focused sampling, open and theoretical coding and constant comparative analysis for nominal and ordinal variable classification during the first qualitative phase. Nested sampling, open coding, coding frequency analysis and thematic analyses were utilized during the follow up qualitative phase of the explanatory sequential mixed methods study.

Results

The application of these integrative methodologies led to theoretical and methodological innovation in four areas. First, theoretical expansion of the offense, hurt, self-condemnation and self-forgiveness dimensions, of the transactional stress and coping process theory, based on different groups, events, actions and processes. Second, a typology of offenses nested within multiple units of analyses. In addition to, analytic triangulation of multiple qualitative data collection and analysis strategies and convergence with latent class and hierarchical clustering, multivariate statistical analyses and results.

Conclusions

The integrative frameworks and findings from both exploratory and explanatory studies demonstrated the importance of qualitative focused mixed methods approaches for discovering overlooked areas of inquiry within quantitative focused self-forgiveness research.