SAGE Journal Articles
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(10.1). In this article, the writers explore a nonrandom approach to sampling in online survey research.
Abstract: Probability-based sampling is the gold standard for general population surveys. However, when interested in more specific populations (e.g., consumers of a particular brand), a lot of research uses data from non-probability-based online panels. This article investigates different ways to select a sample in an opt-in panel: without previous information, using profiling information, or using passive data from a tracker installed on the panelists’ devices. Moreover, it investigates the effect of sending the survey closer to the “moment-of-truth,” which is expected to reduce memory limitations in recall questions. Using additional information (profiling or passive) to select the sample leads to clear improvements in terms of levels of participation and fieldwork efficiency, but not in terms of data quality (measured by the proportion of don’t know answers and the length of answers to open narrative questions) or accuracy (measured by comparing the answers to 14 questions to an external source of information). Doing the survey closer to the “moment-of-truth” further improves the fieldwork efficiency; however, there are still many challenges to implement true “in-the-moment” surveys. We also observed differences across the different samples in respondents’ socio-demographic characteristics and in the survey evaluation.
(10.2). Many groups of people are very hard to locate. This article is how to deal with this situation. It is relevant for both qualitative and quantitative research.
Abstract: Certain social groups are often difficult for researchers to access because of their social or physical location, vulnerability, or otherwise hidden nature. This unique review article based on both the small body of relevant literature and our own experiences as researchers is meant as a guide for those seeking to include hard-to-reach, hidden, and vulnerable populations in research. We make recommendations for research process starting from early stages of study design to dissemination of study results. Topics covered include participant mistrust of the research process; social, psychological, and physical risks to participation; participant resource constraints; and challenges inherent in nonprobability sampling, snowball sampling, and derived rapport. This article offers broadly accessible solutions for qualitative researchers across social science disciplines attempting to research a variety of different populations.
(10.3). This article explains the idea of theoretical saturation as a criterion for knowing when you have obtained enough data in a qualitative research study.
Abstract: Saturation remains a problematic concept within the field of qualitative research, particularly with regard to issues of definition and process. This article sets out some of the common problems with saturation and, with reference to one research study, assesses the value of adopting a range of ‘conceptual depth criteria’ to address problems of definition and process when seeking to establish saturation within a grounded theory approach. It is suggested that the criteria can act as a test to measure the progress of the theoretical sampling and thus ascertain the readiness of the research for the final analytical stages and theory building. Moreover, the application of ‘conceptual depth criteria’ provides the researcher with an evaluative framework and a tool for producing a structured evidence base to substantiate choices made during the theoretical sampling process.
(10.4). This article presents a perspective about sampling in mixed methods research. It’s a little different from ours but it is still useful.
Journal Article 4: Teddlie, C., & Yu, F. (2007). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1, 77–100.
Abstract: This article presents a discussion of mixed methods (MM) sampling techniques. MM sampling involves combining well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs. Several issues germane to MM sampling are presented including the differences between probability and purposive sampling and the probability-mixed-purposive sampling continuum. Four MM sampling prototypes are introduced: basic MM sampling strategies, sequential MM sampling, concurrent MM sampling, and multilevel MM sampling. Examples of each of these techniques are given as illustrations of how researchers actually generate MM samples. Finally, eight guidelines for MM sampling are presented.