Due to differing research traditions and philosophical positions, qualitative and epidemiological research are rarely integrated. Therefore, here, we reflect on how we integrated epidemiological methods into a purposive sampling approach to obtain a highly diverse study sample for our in-depth qualitative study ( N = 37) from the Dutch Lifelines Cohort Study ( N = 152,728). This study contributes to mixed methods research, as we first reflect on benefits and challenges related to interdisciplinarity among researchers, also in relation to open science practices. Second, we discuss the integration of quantitative epidemiological elements into a purposive sampling strategy and how this resulted in an information-rich and varied study sample. Last, we provide hands-on recommendations and lessons learned on integrating epidemiological and qualitative techniques within an interdisciplinary setting.
This methodological article advances guidance for implementing qualitatively-driven mixed methods convergent designs. A convergent design includes the concurrent collection and analysis of qualitative and quantitative data, followed by their integration, allowing researchers to examine a research problem by drawing on different methods and perspectives. Despite the widespread use of concurrent designs, few examples illustrate how qualitative approaches can drive the integration process. Reflecting on their efforts to prioritize the qualitative component within a convergent design as researchers new to MMR, the authors identify key lessons about missed opportunities. Their experiential account highlights the value of concurrent analysis and iterative integration while addressing limitations in prevailing design guidance and building capacity for qualitatively-driven convergent designs.
Crossover analyses are ways of mixing data across study strands from differing traditions, for example, transforming and analyzing qualitative strand data quantitatively together with the quantitative strand data. In response to a recent call for innovation with crossover analyses in mixed methods, this article contributes to the field of mixed methods research by presenting a method of crossover analysis in convergent mixed methods designs. Using survey data, contingency tables were used to merge dichotomized quantitative and qualitative strand results for mixed analysis and joint interpretation using Chi-square and odd ratio analyses. Ratings and open-ended comments about different aspects of the same phenomenon were integrated and explicitly linked with the rationale as means to achieve complementarity in the meta-inference.
Community-engaged research (CER) in disaster risk reduction requires novel approaches that move beyond raising awareness and instead employ methods that support and measure actual impacts on household risk reduction. This study introduces Community Engagement for Disaster Risk Reduction (CEDRR), a longitudinal CE-MMR approach that involves an initial dialogic engagement with a 6-month follow-up to examine how participants learned about risk, adopted protective actions, and shared insights within their social networks. By integrating quantitative indicators with qualitative impact narratives expressed during the follow-up in a joint display, CEDRR demonstrates how impacts emerge and evolve across time and place. CEDRR contributes to MMR by extending interventionalist traditions with a longitudinal design that captures nuanced participant-level change and traces its diffusion through communities.
Mixed methods research has the potential to address wicked problems that are complex, multifaceted, and require the integration of diverse perspectives and data sources. Yet integrating perspectives of multiple parties of interest remains difficult. This article describes how a Delphi consensus technique can help refine meta-inference in complex research problems. We illustrate this through a project using an explanatory sequential design aimed at developing recommendations for the organization of services delivered by specialized professionals in childcare settings. This study contributes to the mixed methods research literature by demonstrating how a Delphi consensus technique can help produce meta-inferences that may serve as actionable recommendations for decision-makers. We also discuss the advantages and considerations for its use in mixed methods research.
There are limited studies on the application of exploratory sequential design as a mixed methods approach to intervention development. Accordingly, the methodological purpose of this paper, using an agricultural education example, is to illustrate how the mixed methods exploratory sequential design can be used in the development of a continuous professional development framework. The paper incorporates 35 semi-structured interviews, 70 survey responses, an additional qualitative-plus-quantitative step (including two focus groups), and a transnational perspective to the exploratory sequential design process. The paper signifies a novel methodological contribution to the traditional exploratory sequential design process as it is widely understood. The approach applied in this paper can be used by other researchers/educators/training providers/interest holders/etc., to inform future intervention development.
As evaluation methodologies evolve, there is increasing emphasis on understanding not only whether a programme works but also how and why its outcomes are achieved. Yet existing literature offers limited guidance on integrating multiple qualitative and quantitative data sets. This article introduces the Adapted Extended Pillar Integration Process (Adapted ePIP) as a structured approach for integrating complex mixed methods data. Using an evaluation of inquiry-based learning in speech-language pathology education, the article illustrates how the Adapted ePIP supports systematic integration across more than three data sources, thereby overcoming a key limitation of the original ePIP. This contribution advances mixed methods research by demonstrating a replicable process for developing meta-themes and meta-inferences that strengthen legitimation and enhance the interpretive depth of programme evaluations.
This mixed methods study investigates the use of natural language processing (NLP) to analyze sentiment and predict interpretation errors in bilingual court interpreting, focusing on Mandarin-English remote hearings. We assembled a dataset of 3,250 minutes of courtroom recordings and 192,465 utterances, annotated for sentiment and error types. Our methodology combines transformer-based sentiment analysis with Conditional Random Fields and Support Vector Machines for error prediction, leveraging linguistic features such as discourse markers, hedging devices, and textual tonal cues. Sentiment analysis reveals that domain-specific fine-tuning of transformer models captures subtle emotional shifts in lawyer questioning with high accuracy, yet interpreters frequently omit critical markers and tonal signals, diluting intended rhetorical force, particularly during cross-examination. Error prediction shows that simultaneous interpretation incurs significantly higher omission and distortion rates than consecutive interpreting, and that aggressive questioning markedly increases error likelihood. This research integrates quantitative NLP modeling with qualitative discourse analysis, offering insights into cognitive and pragmatic factors that affect interpretation fidelity. By highlighting patterns of sentiment shift and interpreter error, our findings inform targeted training and technological interventions aimed at promoting linguistic equity and procedural fairness in multilingual judicial contexts. This article contributes to mixed methods methodology by (i) specifying an explanatory sequential design in which quantitative error metrics and sentiment modeling purposively inform reflexive thematic analysis of interpreter accounts, and (ii) offering a joint display that integrates Quantitative patterns (e.g., omission rates by mode & times; tone) with Qualitative themes (e.g., cognitive load management and discourse-marker decisions), thereby illustrating how integration at interpretation yields inferences unattainable by monomethod designs.
This article contributes to the mixed methods literature by proposing a critical realist philosophy of science to resolve the apparent, and ultimately unfruitful, trade-off between internal and external validity. It is argued that this trade-off is a false dichotomy rooted in contestable philosophical assumptions about causality. Thanks to its stratified ontology and epistemological focus on causal mechanisms, critical realism allows for reconceptualizing internal and external validity as interdependent components of explanatory adequacy. Identifying causal mechanisms is a prerequisite for understanding the scope conditions under which those mechanisms operate. Testing these scope conditions, in turn, strengthens confidence in the initial explanation. The article thus provides mixed methods research with a philosophical foundation for an integrated understanding of validity.
Emerging studies from low-and-middle-income countries (LMIC) have applied mixed methods to investigate gender-based violence (GBV) syndemics. There is currently no guidance on how to appropriately combine qualitative and quantitative methods when testing syndemic theory. This research introduces a mixed methods framework for testing GBV syndemics in LMIC. A multi-step approach was used for framework development. The framework centers on the distinct strengths of the population perspective (quantitative) and locally grounded perspective (qualitative) and informs the development of (i) measurement tools, (ii) causal models, and (iii) contextual interventions. This article contributes an application of mixed methods to inform the design of studies that robustly test theoretical constructs. Further, this article demonstrates how a sensitive/hidden exposure like GBV contributes to the patterning of chronic and infectious diseases, leading to population health disparities.
A surge in social media research recruitment has led to increased fraudulent participation, impacting studies such as our sequential mixed methods research (MMR) study on peer loneliness among adolescents with chronic pain during COVID-19. The purpose of this paper is to describe the challenges and subsequent strategies implemented to prevent and identify fraudulent participants during a MMR study that used online data collection methods. The results of the various mitigation strategies implemented are provided along with recommendations for future research. This article makes a valuable contribution to MMR literature by highlighting the threat to data integrity and detailing various mitigation strategies for different phases of MMR. These strategies should be proactively implemented by researchers to increase data integrity.
The diverse culture, identities, and practices across the Caribbean provide rich insight into the contextual nature of "knowing" and "being" within the region. While this diversity is rooted in the shared colonial history, it transcends to embrace Indigenous strengths and ways from the region. These qualities are essential in defining how research should be conducted with Caribbean people and communities. Traditional mixed methods approaches have evolved to include Indigenous methodologies but remain inadequately adapted for Caribbean-specific research contexts. This paper advocates for a qualitatively driven mixed methods approach that is culturally responsive to the Caribbean context and accounts for the region's colonial legacy, decolonization efforts, and defining qualities, such as spirituality. Key methodological tenets include researcher positionality and cultural responsiveness.
Design-based research (DBR) systematically investigates and develops innovative solutions to complex educational problems. We argue that DBR intervention studies intrinsically employ mixed methods to collect, analyze, and interpret data. Drawing on a 3-year DBR project (2015-2018) implementing a social studies/history curriculum in self-contained special education classrooms, we conceptualize Transformative Mixed Methods Design-Based Research (Transformative MM-DBR). The central contribution of this methodological article is to articulate Transformative MM-DBR as an equity-oriented mixed methods typology that systematizes multi-phase integration of qualitative and quantitative strands. We outline the rationale for mixed methods in special education, illustrate the design of a disciplinary literacy intervention for students with learning differences, and offer core tenets for researchers adopting this paradigm.
Clearly articulated research questions shape the overall methodology and design in mixed methods research. To date, only one classification of mixed methods research questions has been proposed which includes methods-focused, content-focused, and combination mixed methods research questions. This research note proposes three additional types of mixed methods questions that explicitly address the integration procedures and relationships among the quantitative and the qualitative strands in a mixed methods study. The newly proposed questions are integration-explicating questions, relationship-elucidating questions, and three-way mixed methods questions. This paper adds to the field of mixed methods research by offering new insights into the types of questions that can be formulated to articulate the mixed methods rationale and integration intent when using this research approach.
