Methodologists have offered general strategies for integration in mixed-methods studies through merging of quantitative and qualitative data. While these strategies provide researchers in the field general guidance on how to integrate data during mixed-methods analysis, a methodological typology detailing specific analytic frameworks has been lacking. The purpose of this article is to introduce a typology of analytical approaches for mixed-methods data integration in mixed-methods convergent studies. We distinguish three dimensions of data merging analytics: (1) the relational dimension, (2) the methodological dimension, and (3) the directional dimension. Five different frameworks for data merging relative to the methodological and directional dimension in convergent mixed-methods studies are described: (1) the explanatory unidirectional approach, (2) the exploratory unidirectional approach, (3) the simultaneous bidirectional approach, (4) the explanatory bidirectional approach, and (5) the exploratory bidirectional approach. Examples from empirical studies are used to illustrate each type. Researchers can use this typology to inform and articulate their analytical approach during the design, implementation, and reporting phases to convey clearly how an integrated approach to data merging occurred. KeywordsMixed methods, integration, convergent design, merging, analysis, theory research-article2017Original Article 2 Methodological InnovationsThe objectives of this article are three-fold. First, we describe integration at the different levels and the integration challenges specifically associated with merging quantitative and qualitative data in the analysis in convergent mixed-methods studies. Second, we define three dimensions of merging analytics at the relational, methodological, and directional levels, and describe the subcategories of each. Third, we introduce a typology including novel nomenclature for mixed-methods analysis procedures relative to the methodological and directional level in convergent MMR studies. We introduce a lens heuristic that can be used to illustrate the directional levels, unidirectional and bidirectional, to illustrate variations in the typology for informing integration approaches. Through these conceptual representations and examples, mixed-methods researchers can understand different approaches in merging of quantitative and qualitative data to enhance interpretation of merged data in convergent studies. Integration in mixed-methods research Integration at the philosophical levelThe utility of paradigms for MMR has been debated extensively by previous methodologists (Morgan, 2007; Plano Clark and Ivankova, 2016; Shannon-Baker, 2015). For purposes of this article, we use Morgan's (2007) definition of paradigm as "systems of shared beliefs among a community of scholars" that influence how researchers select both the questions they study and the methods that they use to study them (p. 65). The terms "quantitative" and "qualitative" are not synonymous with paradigms, but ...
BackgroundUndergoing diagnostic evaluation for cancer has been associated with a high prevalence of anxiety and depression and affected health-related quality of life (HRQoL). The aims of this study were to assess HRQoL, anxiety, and depression pre- and post-diagnosis in patients undergoing diagnostic evaluations for cancer due to non-specific symptoms; to examine changes over time in relation to final diagnosis (cancer yes/no); and to assess the predictive value of pre-diagnostic psychological, socio-demographic and clinical factors.MethodsA prospective, multicenter survey study of patients suspected to have cancer based on non-specific symptoms was performed. Participants completed the EORTC-QLQ-C30 quality of life scale, HADS, SOC-13 and self-rated health before and after completing diagnostic evaluations. Intra- and inter-group differences between patients diagnosed with cancer versus patients with non-cancer diagnoses were calculated. The impact of baseline psychological, socio-demographic, and medical factors on HRQoL, anxiety and depression at follow-up was explored by bootstrapped multivariate linear regression analyses and logistic regression analyses.ResultsA total of 838 patients participated in this study; 679 (81 %) completed the follow-up. Twenty-two percent of the patients received a cancer diagnosis at the end of the follow-up. Patients presented initially with a high burden of symptoms and affected role and emotional functioning and global health/QL, irrespective of diagnosis. The prevalence of clinical anxiety prior to knowledge of the diagnosis was 32 % in patients with cancer and 35 % in patients who received a non-cancer diagnosis. HRQoL and anxiety improved after diagnosis, and a larger improvement was seen in patients who received a non-cancer diagnosis. There were no intra- or inter-group differences in the depression scores. The strongest predictors of global QL, anxiety, and depression after a known diagnosis were baseline scores, co-morbidity and poor self-rated health.ConclusionsPatients undergoing diagnostic evaluations for cancer based on non-specific symptoms experience a high prevalence of anxiety and affected quality of life prior to knowledge of the diagnosis. The predictive value of the baseline scores is important when assessing the psychological impact of undergoing diagnostic evaluations for cancer.
AimUndergoing diagnostic evaluation for possible cancer can affect health-related quality of life (HRQoL). The aims of this study were to examine the HRQoL in patients undergoing a diagnostic evaluation for possible cancer due to non-specific symptoms and further to investigate the impact of socio-demographic and medical factors associated with HRQoL at the time of diagnosis.MethodsThis was a prospective, multicenter survey study that included patients who were referred for a diagnostic evaluation due to non-specific cancer symptoms. Participants completed the EORTC-QLQ-C30 quality of life scale before and after completing the diagnostic evaluation. The baseline and follow-up EORTC-QLQ-C30 scores were compared with reference populations. The impact of socio-demographic and medical factors on HRQoL at follow-up was explored by bootstrapped multivariate linear regression.ResultsA total of 838 patients participated in the study; 680 (81%) also completed follow-up. Twenty-two percent of the patients received a cancer diagnosis at the end of follow-up. Patients presented initially with a high burden of symptoms, less role and emotional functioning and a lower global health/QoL. Most domains improved after diagnosis and no clinically important difference between baseline and follow-up scores was found. Patients reported effects on HRQoL both at baseline and at follow-up compared with the Danish reference population and had similar scores as a cancer reference population. Co-morbidity, being unemployed and receiving a cancer diagnosis had the greatest effect on HRQoL around the time of diagnosis.ConclusionsPatients with non-specific symptoms reported an affected HRQoL while undergoing a diagnostic evaluation for possible cancer. Morbidity, being unemployed and receiving a cancer diagnosis had the greatest effect on HRQoL around the time of diagnosis.
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