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Index de Enfermería

On-line version ISSN 1699-5988Print version ISSN 1132-1296

Abstract

GONZALEZ-GARCIA, Lorena et al. Triangulation of a qualitative research by logistic regression. Index Enferm [online]. 2014, vol.23, n.1-2, pp.80-84. ISSN 1699-5988.  http://dx.doi.org/10.4321/S1132-12962014000100017.

Background and objectives: Qualitative research seeks to enrich its vision of reality through triangulation. Binary logistic regression is a prediction tool in analytical epidemiology. Our aim was to complement a qualitative study by logistic regression models. Methods: On gathered information by a previous focus group, we organized the data into three variables: Aphorism / short phrase (dependent), Professor and Type (predictive) and built two models with binary logistic regression. The alpha error was 5 and 10%. The sample size was imposed by the previous focus group task (qualitative saturation). Routines were implemented to work with the program R. Results: With 127 elements (44 aphorisms and 83 short sentences) we obtained a 10% raw signification for two of the ten teachers with relevant information for the focus group (odds ratios of 0.42 and 2.33 respectively; Brier scaled =0.06 and area under ROC curve = 0.63) and significations less than 5% for four the five sections in which we divided the variable "Type" (epidemiological, epistemological, statistical, pragmatic or heuristic). The heading "Statistics" was significant with respect to "Epistemological" (OR = 5.00, CI 95% = 14.431-1.743) and with respect to "Pragmatic" (OR = 4.80, CI 95% = 14.602-1.577). The label "Scientific Spread" was not significant. Conclusions: In an environment of qualitative and pedagogical research on aphorisms and short phrases, binary logistic regression has been shown effective in identifying original teachers for focus group (p<0.1) and to identify qualifying entries with interest (p<0.05). The predictive capability of models has been low and acceptable the discriminative capacity.

Keywords : Qualitative research; Knowledge; Triangulation; Logistic models; Teaching; Epidemiology; Statistics; Mixed methods.

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