Dataset Information

Evaluating survey quality in health services research: a decision framework for assessing nonresponse bias.

ABSTRACT:

Objective

To address the issue of nonresponse as problematic and offer appropriate strategies for assessing nonresponse bias.

Study design

A review of current strategies used to assess the quality of survey data and the challenges associated with these strategies is provided along with appropriate post-data collection techniques that researchers should consider.

Principal findings

Response rates are an incomplete assessment of survey data quality, and quick reactions to response rate should be avoided. Based on a five-question decision making framework, we offer potential ways to assess nonresponse bias, along with a description of the advantages and disadvantages to each.

Conclusions

It is important that the quality of survey data be considered to assess the relative contribution to the literature of a given study. Authors and funding agencies should consider the potential effects of nonresponse bias both before and after survey administration and report the results of assessments of nonresponse bias in addition to response rates.

SUBMITTER: Halbesleben JR

PROVIDER: S-EPMC3681236 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Publications

Evaluating survey quality in health services research: a decision framework for assessing nonresponse bias.

Halbesleben Jonathon R B JR Whitman Marilyn V MV

Health services research 20121010 3

<h4>Objective</h4>To address the issue of nonresponse as problematic and offer appropriate strategies for assessing nonresponse bias.<h4>Study design</h4>A review of current strategies used to assess the quality of survey data and the challenges associated with these strategies is provided along with appropriate post-data collection techniques that researchers should consider.<h4>Principal findings</h4>Response rates are an incomplete assessment of survey data quality, and quick reactions to res ...[more]

PMID: 23046097

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Project description:BackgroundResearch co-production is an umbrella term used to describe research users and researchers working together to generate knowledge. Research co-production is used to create knowledge that is relevant to current challenges and to increase uptake of that knowledge into practice, programs, products, and/or policy. Yet, rigorous theories and methods to assess the quality of co-production are limited. Here we describe a framework for assessing the quality of research co-production-Research Quality Plus for Co-Production (RQ+ 4 Co-Pro)-and outline our field test of this approach.MethodsUsing a co-production approach, we aim to field test the relevance and utility of the RQ+ 4 Co-Pro framework. To do so, we will recruit participants who have led research co-production projects from the international Integrated Knowledge Translation Research Network. We aim to sample 16 to 20 co-production project leads, assign these participants to dyadic groups (8 to 10 dyads), train each participant in the RQ+ 4 Co-Pro framework using deliberative workshops and oversee a simulation assessment exercise using RQ+ 4 Co-Pro within dyadic groups. To study this experience, we use a qualitative design to collect participant demographic information and project demographic information and will use in-depth semi-structured interviews to collect data related to the experience each participant has using the RQ+ 4 Co-Pro framework.DiscussionThis study will yield knowledge about a new way to assess research co-production. Specifically, it will address the relevance and utility of using RQ+ 4 Co-Pro, a framework that includes context as an inseparable component of research, identifies dimensions of quality matched to the aims of co-production, and applies a systematic and transferable evaluative method for reaching conclusions. This is a needed area of innovation for research co-production to reach its full potential. The findings may benefit co-producers interested in understanding the quality of their work, but also other stewards of research co-production. Accordingly, we undertake this study as a co-production team representing multiple perspectives from across the research enterprise, such as funders, journal editors, university administrators, and government and health organization leaders.

Project description:ObjectiveTo systematically survey existing definitions, concepts, and criteria of clinical research quality, both developed by stakeholder groups as well as in the medical literature. This study serves as a first step in the development of a comprehensive framework for the quality of clinical research.Study design and settingWe systematically and in duplicate searched definitions, concepts and criteria of clinical research quality on websites of stakeholders in clinical research until no further insights emerged and in MEDLINE up to February 2015. Stakeholders included governmental bodies, regulatory agencies, the pharmaceutical industry, academic and commercial contract research organizations, initiatives, research ethics committees, patient organizations and funding agencies from 13 countries. Data synthesis involved descriptive and qualitative analyses following the Framework Method on definitions, concepts, and criteria of clinical research quality. Descriptive codes were applied and grouped into clusters to identify common and stakeholder-specific quality themes.ResultsStakeholder concepts on how to assure quality throughout study conduct or articles on quality assessment tools were common, generally with no a priori definition of the term quality itself. We identified a total of 20 explicit definitions of clinical research quality including varying quality dimensions and focusing on different stages in the clinical research process. Encountered quality dimensions include ethical conduct, patient safety/rights/priorities, internal validity, precision of results, generalizability or external validity, scientific and societal relevance, transparency and accessibility of information, research infrastructure and sustainability. None of the definitions appeared to be comprehensive either in terms of quality dimensions, research stages, or stakeholder perspectives.ConclusionClinical research quality is often discussed but rarely defined. A framework defining clinical research quality across stakeholders' individual perspectives is desirable to facilitate discussion, assessment, and improvement of quality at all stages of clinical research.

Dataset Information

Evaluating survey quality in health services research: a decision framework for assessing nonresponse bias.

Objective

Study design

Principal findings

Conclusions

Publications

Evaluating survey quality in health services research: a decision framework for assessing nonresponse bias.

Similar Datasets

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