Evidence map›Paper›PMID 23801237›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2014

Using mixed methods effectively in prevention science: designs, procedures, and examples.

Wanqing Zhang, Shinobu Watanabe-Galloway

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Article in Prevention science : the official journal of the Society for Prevention Research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. A Primer on Mixed Methods for Pediatric Researchers.Journal of pediatric psychology · 2019
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wanqing ZhangDepartment of Health Services Research and Administration, College of Public Health, University of Nebraska Medical Center, 984350 University of Nebraska Medical Center, Omaha, NE, 68198-4350, USA, wazhang@unmc.edu.
Shinobu Watanabe-Galloway

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is growing interest in using a combination of quantitative and qualitative methods to generate evidence about the effectiveness of health prevention, services, and intervention programs. With the emerging importance of mixed methods research across the social and health sciences, there has been an increased recognition of the value of using mixed methods for addressing research questions in different disciplines. We illustrate the mixed methods approach in prevention research, showing design procedures used in several published research articles. In this paper, we focused on two commonly used mixed methods designs: concurrent and sequential mixed methods designs. We discuss the types of mixed methods designs, the reasons for, and advantages of using a particular type of design, and the procedures of qualitative and quantitative data collection and integration. The studies reviewed in this paper show that the essence of qualitative research is to explore complex dynamic phenomena in prevention science, and the advantage of using mixed methods is that quantitative data can yield generalizable results and qualitative data can provide extensive insights. However, the emphasis of methodological rigor in a mixed methods application also requires considerable expertise in both qualitative and quantitative methods. Besides the necessary skills and effective interdisciplinary collaboration, this combined approach also requires an open-mindedness and reflection from the involved researchers.

Indexed as

Preventive MedicineResearch DesignHumans

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.