Field Methods 

About: Field Methods is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Computer science & Population. It has an ISSN identifier of 1525-822X. Over the lifetime, 672 publications have been published receiving 47471 citations.

How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability

Abstract: Guidelines for determining nonprobabilistic sample sizes are virtually nonexistent. Purposive samples are the most commonly used form of nonprobabilistic sampling, and their size typically relies on the concept of “saturation,” or the point at which no new information or themes are observed in the data. Although the idea of saturation is helpful at the conceptual level, it provides little practical guidance for estimating sample sizes, prior to data collection, necessary for conducting quality research. Using data from a study involving sixty in-depth interviews with women in two West African countries, the authors systematically document the degree of data saturation and variability over the course of thematic analysis. They operationalize saturation and make evidence-based recommendations regarding nonprobabilistic sample sizes for interviews. Based on the data set, they found that saturation occurred within the first twelve interviews, although basic elements for metathemes were present as early as six...

Techniques to Identify Themes

Abstract: Theme identification is one of the most fundamental tasks in qualitative research. It also is one of the most mysterious. Explicit descriptions of theme discovery are rarely found in articles and reports, and when they are, they are often relegated to appendices or footnotes. Techniques are shared among small groups of social scientists, but sharing is impeded by disciplinary or epistemological boundaries. The techniques described here are drawn from across epistemological and disciplinary boundaries. They include both observational and manipulative techniques and range from quick word counts to laborious, in-depth, line-by-line scrutiny. Techniques are compared on six dimensions: (1) appropriateness for data types, (2) required labor, (3) required expertise, (4) stage of analysis, (5) number and types of themes to be generated, and (6) issues of reliability and validity.

Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice

Abstract: This article discusses some procedural issues related to the mixed-methods sequential explanatory design, which implies collecting and analyzing quantitative and then qualitative data in two consecutive phases within one study. Such issues include deciding on the priority or weight given to the quantitative and qualitative data collection and analysis in the study, the sequence of the data collection and analysis, and the stage/stages in the research process at which the quantitative and qualitative data are connected and the results are integrated. The article provides a methodological overview of priority, implementation, and mixing in the sequential explanatory design and offers some practical guidance in addressing those issues. It also outlines the steps for graphically representing the procedures in a mixed-methods study. A mixed-methods sequential explanatory study of doctoral students’ persistence in a distance-learning program in educational leadership is used to illustrate the methodological dis...

Codebook Development for Team-Based Qualitative Analysis:

Abstract: One of the key elements in qualitative data analysis is the systematic coding of text (Strauss and Corbin 1990:57%60; Miles and Huberman 1994:56). Codes are the building blocks for theory or model building and the foundation on which the analyst’s arguments rest. Implicitly or explicitly, they embody the assumptions underlying the analysis. Given the context of the interdisciplinary nature of research at the Centers for Disease Control and Prevention (CDC), we have sought to develop explicit guidelines for all aspects of qualitative data analysis, including codebook development.

Comparing response rates from Web and mail surveys: A meta-analysis.

Abstract: This study meta-analyzes thirty-nine study results published within last ten years that directly compared Web and mail survey modes. Although considerable variation exists across the studies, the authors’ findings show that mail surveys have higher response rates than Web surveys in general. Two study features (i.e., population types and follow-up reminders) are shown to contribute statistically to the variation of response rate differences between Web and paper surveys in the comparative studies. College respondents appear to be more responsive to Web surveys, while some other respondents (e.g., medical doctors, school teachers, and general consumers) appear to prefer traditional mail surveys. Follow-up reminders appear to be less effective for Web survey respondents than for mail survey respondents. Other study features (i.e., implementation of random assignment of survey respondents, incentives, and publication year) are not statistically useful in accounting for the variation of response rate differences between Web and mail surveys.