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Posted by / 07-Jan-2020 04:56

Validating quantitative data triangulation model

We then recruited a new set of 16 participants for 1-on-1 sessions in which participants interacted with the sites used earlier and discussed their attitudes toward those sites.This enabled us to look more closely into trends we observed in the larger sample.In a previous article we explored ‘bias’ across research designs and outlined strategies to minimise bias.1 The aim of this article is to further outline rigour, or the integrity in which a study is conducted, and ensure the credibility of findings in relation to qualitative research.Concepts such as reliability, validity and generalisability typically associated with quantitative research and alternative terminology will be compared in relation to their application to qualitative research.Triangulation facilitates validation of data through cross verification from more than two sources.It tests the consistency of findings obtained through different instruments and increases the chance to control, or at least assess, some of the threats or multiple causes influencing our results.While it might seem unorthodox to mix seemingly different fields, it turns out to be a common practice.

We often talk of qualitative OR quantitative research.

You needn’t think of this as an either-or situation.

You can often optimize customer research with a mix of the two.

It can lead to multi-perspective meta-interpretations.

Evaluating the quality of research is essential if findings are to be utilised in practice and incorporated into care delivery.

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