Bayesian Approaches to the Synthesis of Qualitative and Quantitative Research Findings

Crandell, Jamie; Voils, Corrine I.; Sandelowski, Margarete

doi:10.1002/9781119959847.ch7

Cited by 10 publications

(15 citation statements)

References 34 publications

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“…We chose Bayesian meta‐analysis (Crandell et al . ) and realist synthesis (Pawson ) methods to achieve the aims of our study because of their inclusiveness and flexibility in managing methodologically diverse and incomparable data sets and sample sizes and because they allow qualitative and quantitative findings to play equally important roles in all phases of a research synthesis study. Bayesian methods are directed largely towards the synthesis of findings by aggregation, thereby yielding summative statements on relationships among variables; realist methods are directed largely towards the synthesis of findings by configuration, thereby yielding explanations for how they are related (Sandelowski et al .…”

Section: Data Sourcementioning

confidence: 99%

Text‐in‐context: a method for extracting findings in mixed‐methods mixed research synthesis studies

Sandelowski

Leeman

Knafl

et al. 2012

Journal of Advanced Nursing

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Aim Our purpose in this paper is to propose a new method for extracting findings from research reports included in mixed-methods mixed research synthesis studies. Background International initiatives in the domains of systematic review and evidence synthesis have been focused on broadening the conceptualization of evidence, increased methodological inclusiveness and the production of evidence syntheses that will be accessible to and usable by a wider range of consumers. Initiatives in the general mixed-methods research field have been focused on developing truly integrative approaches to data analysis and interpretation. Data source The data extraction challenges described here were encountered and the method proposed for addressing these challenges was developed, in the first year of the ongoing (2011–2016) study: Mixed-Methods Synthesis of Research on Childhood Chronic Conditions and Family. Discussion To preserve the text-in-context of findings in research reports, we describe a method whereby findings are transformed into portable statements that anchor results to relevant information about sample, source of information, time, comparative reference point, magnitude and significance and study-specific conceptions of phenomena. Implications for nursing The data extraction method featured here was developed specifically to accommodate mixed-methods mixed research synthesis studies conducted in nursing and other health sciences, but reviewers might find it useful in other kinds of research synthesis studies. Conclusion This data extraction method itself constitutes a type of integration to preserve the methodological context of findings when statements are read individually and in comparison to each other.

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Section: Data Sourcementioning

confidence: 99%

Text‐in‐context: a method for extracting findings in mixed‐methods mixed research synthesis studies

Sandelowski

Leeman

Knafl

et al. 2012

Journal of Advanced Nursing

Self Cite

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“…However, as we want unbiased opinions, we separate the qualitative evidence from the prior probability. The previous methods implemented in health research do not differentiate between the types of evidence [9]- [11]. It leaves it up to the researchers/practitioners to decide how they want to evaluate the evidence.…”

Section: Discussionmentioning

confidence: 99%

“…Two approaches called "quantitizing" [11] and "qualitizing" [9] have been proposed in health research. However, these approaches do not address the EBSE recommendation of incorporating subjective opinion into the synthesis hence, not discussed further.…”

Section: B Bayesian Synthesis In Health Researchmentioning

confidence: 99%

“…Posterior probability is the refinement of the prior probability given the likelihood. The equation of posterior probability is stated in [9] as shown in Equation 1.…”

Section: Step 3: the Posterior Probabilities -Refining Prior Probamentioning

confidence: 99%

“…the practitioners' or decisionmakers' and policy-makers' perspective into consideration [7]. It is more flexible and efficient in using evidence from all available sources [8] and can synthesize findings from methodologically diverse studies [9]. Hence, the Bayesian approach is an attractive method to synthesize evidence and support KT as it provides interpretations of what evidence mean in a particular context by incorporating subjective opinions of the potential users of synthesized data.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Synthesis for Knowledge Translation in Software Engineering: Method and Illustration

Badampudi

Wohlin

2016

2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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Systematic literature reviews in software engineering are necessary to synthesize evidence from multiple studies to provide knowledge and decision support. However, synthesis methods are underutilized in software engineering research. Moreover, translation of synthesized data (outcomes of a systematic review) to provide recommendations for practitioners is seldom practiced. The objective of this paper is to introduce the use of Bayesian synthesis in software engineering research, in particular to translate research evidence into practice by providing the possibility to combine contextualized expert opinions with research evidence. We adopted the Bayesian synthesis method from health research and customized it to be used in software engineering research. The proposed method is described and illustrated using an example from the literature. Bayesian synthesis provides a systematic approach to incorporate subjective opinions in the synthesis process thereby making the synthesis results more suitable to the context in which they will be applied. Thereby, facilitating the interpretation and translation of knowledge to action/application. None of the synthesis methods used in software engineering allows for the integration of subjective opinions, hence using Bayesian synthesis can add a new dimension to the synthesis process in software engineering research.

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Quantitizing findings from qualitative studies for integration in mixed methods reviewing

Grootel

Nair

Klugkist

et al. 2020

Research Synthesis Methods

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In mixed methods reviewing, data from quantitative and qualitative studies are combined at the review level. One possible way to combine findings of quantitative and qualitative studies is to quantitize qualitative findings prior to their incorporation in a quantitative review. There are only a few examples of the quantification of qualitative findings within this context. This study adds to current research on mixed methods review methodology by reporting the pilot implementation of a new four‐step quantitizing approach. We report how we extract and quantitize the strength of relationships found in qualitative studies by assigning correlations to vague quantifiers in text fragments. This article describes (a) how the analysis is prepared; (b) how vague quantifiers in text fragments are organized and transformed to numerical values; (c) how qualitative studies as a whole are assigned effect sizes; and (d) how the overall mean effects size and variance can be calculated. The pilot implementation shows how findings from 26 primary qualitative studies are transformed into mean effect sizes and corresponding variances.

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Bayesian Approaches to the Synthesis of Qualitative and Quantitative Research Findings

Cited by 10 publications

References 34 publications

Text‐in‐context: a method for extracting findings in mixed‐methods mixed research synthesis studies

Text‐in‐context: a method for extracting findings in mixed‐methods mixed research synthesis studies

Bayesian Synthesis for Knowledge Translation in Software Engineering: Method and Illustration

Quantitizing findings from qualitative studies for integration in mixed methods reviewing

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