Dedoose Publications

PUBLICATIONS

Dedoose has been field-tested and journal-proven by leading academic institutions and market researchers worldwide. Thousands of prominent researchers across the US and abroad have benefited from early versions of Dedoose in their qualitative and mixed methods work and have laid an outstanding publication and report trail along the way.

Education Based Publications

Intercoder Reliability for Validating Conclusions Drawn from Open-Ended Interview Data

Kurasaki, Karen S. (2000)

Field Methods, 12(3): 179-194

Intercoder reliability is a measure of agreement among multiple coders for how they apply codes to text data. Intercoder reliability can be used as a proxy for the validity of constructs that emerge from the data. Popular methods for establishing intercoder reliability involve presenting predetermined text segments to coders. Using this approach, researchers run the risk of altering meanings by lifting text from its original context, or making interpretations about the length of codable text. This article describes a set of procedures that was used to develop and assess intercoder reliability with free-flowing text data, in which the coders themselves determined the length of codable text segments. Discusses procedures for developing and assessing intercoder reliability with free-flowing text.
Education Based Publications

Toward a Definition of Mixed Methods Research

Johnson, R. Burke, Onwuegbuzie, Anthony J., & Turner, Lisa A. (2007)

Journal of Mixed Methods Research, 1(2), 112-133

Examines the definition of the emerging mixed methods research field. Surveyed major authors in the mixed method literature with regard to definition for the field and key issues that need to be addressed as the field advances. Results show a consensus of mixed methods as an emerging ‘research paradigm’ and a breadth of opinion around definition for the field.
Medical Based Publications

Clustering Methods with Qualitative Data: a Mixed-Methods Approach for Prevention Research with Small Samples

David Henry, Allison B. Dymnicki, Nathaniel Mohatt, James Allen, James G. Kelly (2015)

Qualitative methods potentially add depth to prevention research but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed-methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed-methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-means clustering, and latent class analysis produced similar levels of accuracy with binary data and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a “real-world” example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities.
Sociology Based Publications

Integrating Survey and Ethnographic Methods for Systematic

Pearce, L. D. (2002)

Sociological Methodology, 32(1): 103-132

How the salience of research findings can be enhanced by combining survey and ethnographic methods to draw insight from anomalous cases. Using examples from a research project examining the influence of religion on childbearing preferences in Nepal, the author illustrates how survey data can facilitate the selection of ethnographic informants and how semistructured interviews with these deviant cases leads to improved theory, measures, and methods.
Education Based Publications

Barriers to Integrating Quantitative and Qualitative Research

Bryman, A. (2007)

Journal of Mixed Methods Research, 1(1): 8-22

This article is concerned with the possibility that the development of mixed methods research is being hindered by the tendency that has been observed by some researchers for quantitative and qualitative findings either not to be integrated or to be integrated to only a limited extent. It examines findings from 20 interviews with U.K. social researchers, all of whom are practitioners of mixed methods research. From these interviews, a wide variety of possible barriers to integrating mixed methods findings are presented. Challenges to integrating mixed methods data and strategy for writing mixed methods research articles.
Education Based Publications

EthnoNotes: An Internet-Based Fieldnote Management Tool

Lieber, Eli, Weisner, Thomas S., & Presley, Matthew (2003)

Field Methods, 15(4): 405-425

This report describes a field notes database management tool, EthnoNotes. EthnoNotes makes the process of writing, sharing, and analyzing field notes easier and more systematic. Text can be indexed, coded, and integrated with quantitative data or images, all accessed from the same database system. EthnoNotes can be used by individual researchers or be fully Internet-based, accessible online by teams collaborating in empirical studies. Field notes are easily entered on the Web, then are immediately accessible to other researchers for interpretation and analyses.
Medical Based Publications

"I speak a different dialect": Teen explanatory models of difference and disability

Daley, Tamara, & Weisner, Thomas S. (2003)

Medical Anthropology Quarterly, 17(1): 25-48

After eras of “blaming” parents for their children’s disabilities and relying on biomedical labels as both correct and sufficient to explain and name various conditions, research and practice today recognize the significance of the meaning and understanding of disabilities held by family members and children themselves. Elicited explanatory models from adolescents with varied cognitive disabilities and delay to better understand their personal experiences.
Education Based Publications

Qualitative Interviewing

Patton, Michael Quinn (1980)

Thousand Oaks: Sage Publications, In Michael Quinn Patton, Qualitative Evaluation Methods, pp. 195-263

We interview people to find out from them those things we cannot directly observe. This issue is not whether observational data are more desireable, valid, or meaningful than self-report data. The fact is tahtw e cannot observe everything. We cannot observe felings, thoughts, intentions, behaviors that took place at some previous point in time, situations that preclude the presence of an observer, or how people have organized the world and the meanings they attach to what goes on in the world. We have to ask people questions about those things. Thus, the purpose of interviewing is to allow us to enter into the other person's perspective. Qualitative interviewing begins with the assumption that the perspective of others is meaningful, knowable, and able to be made explicit. We interview to find out what is in and on someone else's mind, to gather their stories. Program evaluation interviews, for example, aim to capture the perspectives of program participants, staff, and others associated with the program. What does the program look and feel like to the people involved? What are their experiences? What thoughts do people knowledgeable about the program have concerning the program? What are their expectations? What changes do participants perceive in themselves as a result of their involvement in the program? It is the responsibility of the evaluator to provide a framework within which people can respond comfortably, accurately, and honestly to these kinds of questions. Evaluations can enhance the use of qualitative data by generating relevant and high quality findings. As Hermann Sudermann said in Es Lebe das Leben I, ‘I know how to listen when clever men are talking. That is the secret of what you8 call my influence.’ Evaluators must learn how to listen when knowledgeable people are talking. That may be the secret of their influence. An evaluator or qualitative or mixed method research interviewer faces the challenge of making it possible for the person being interviewed to bring the interviewer into his or her world. The quality of the information obtained during an interview is largely dependent on the interviewer. This chapter discusses ways of obtaining high-quality information by talking with people who have that information. We’ll be delving into the ‘art of hearing’ (Rubin and Rubin 1995). This chapter presents three different types of interviews. Later sections consider the content of interviews: what questions to ask and how to phrase questions. The chapter ends with a discussion of how to record the responses obtained during interviews. This chapter emphasizes skill and technique as ways of enhancing the quality of interview data, but no less important is a genuine interest in and caring about the perspectives of other people. If what people have to say about the world is generally boring to you, then you will never be a great interviewer. On the other hand, a deep and genuine interest in learning about people is insufficient without disciplined and rigorous inquiry based on skill and technique.
Education Based Publications

Managing Data in CAQDAS

Fielding, Nigel & Lee, Ray M. (1998)

Chapter 4 in Fielding & Lee, Computer Analysis and Qualitative Research, pp. 86-118

from COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS SOFTWARE: A PRACTICAL PERSPECTIVE FOR APPLIED RESEARCH, JOSEPH B. BAUGH, ANNE SABER HALLCOM, and MARILYN E. HARRIS Computer assisted qualitative data analysis software (CAQDAS) holds a chequered reputation to date in academia, but can be useful to develop performance metrics in the field of corporate social and environmental responsibility and other areas of contemporary business. Proponents of using CAQDAS cite its ability to save time and effort in data management by extending the ability of the researcher to organize, track and manage data. Opponents decry the lack of rigor and robustness in the resultant analyses. Research reveals that these opinions tend to be divided by “the personal biography and the philosophical stance of the analyst” (Catterall & Maclaran, 1998, p. 207), as well as “age, computer literacy, and experience as a qualitative researcher” (Mangabeira, Lee & Fielding, 2004, p. 170). A more recent article (Atherton & Elsmore 2007) discussed the continuing debate on CAQDAS in qualitative research: The two perspectives both indicate that CAQDAS should be used with care and consideration; in ways that explicitly demonstrate a “fit” between the ethos and philosophical perspective(s) underpinning a research study, on the one hand, and the means of ordering and manipulating the data within CAQDAS on the other. (p. 75) Despite the ongoing literary debate on the merits of CAQDAS, the use of computer-aided qualitative data analysis has become acceptable to most qualitative researchers (Lee & Esterhuizen; Morison & Moir, 1998; Robson, 2002). However, writers advise that researchers avoid the trap of letting the software control the data analysis (Catterall & Maclaran, 1998). Morison and Moir counseled that CAQDAS is merely one tool in the qualitative data analysis toolbox. No tool should replace the researcher's capacity to think through the data and develop his or her emergent conclusions (Atherton & Elsmore, 2007). On the other hand, Morison and Moir among others (e.g., Blank, 2004; Catterall & Maclaran, 1998; Mangabeira et al., 2004) found the use of qualitative data analysis software can also free up significant amounts of time formerly used in data management and encoding allowing the researcher to spend more time in deeper and richer data evaluation. Qualitative research studies to develop performance metrics can create huge amounts of raw data (Miles & Huberman, 1994; Robson, 2002). Organizing, tracking, encoding, and managing the data are not trivial tasks and the effort should not be underestimated by the applied researcher. Two methodologies exist to handle these activities and manage the data during the data analysis phase. The first methodology is a manual process, which must be done at times to avoid missing critical evidence and provide trustworthiness in the process (Malterud, 2001), while the second methodology indicates the use of technology for managing the data and avoid being overwhelmed by the sheer amount of raw data (Lee & Esterhuizen, 2000). It is the experience of the authors that some manual processing must be interspersed with CAQDAS. This provides an intimacy with the data which leads to the drawing of credible and defensible conclusions. Thus, a mixed approach that melds manual and automated data analyses seems most appropriate. A basic approach for applying traditional qualitative research methodologies lies in the ability of CAQDAS to support data reduction through the use of a “provisional start list” (Miles & Huberman, 1994, p. 58) of data codes that are often developed manually from the research question. A rise in the use of CAQDAS for applied research and other nonacademic research fields has been identified (Fielding & Lee, 2002). Since CAQDAS is becoming more prevalent in nonacademic researcher populations and can be useful for developing performance metrics for corporate social and environmental responsibility and solving other complex business issues, it seems prudent at this juncture to discuss how to use the software appropriately rather than rehash the argument for or against using CAQDAS. Selection of and training with an appropriate CAQDAS package can help the researcher manage the mountains of data derived from qualitative research data collection methods (Lee & Esterhuizen, 2000).
Medical Based Publications

Codebook Development for Team-Based Qualitative Analysis

MacQueen, Kathleen M., McLellan, Eleanor, Kay, Kelly, & Milstein Bobby (1998)

Cultural Anthropology Methods, 10(2): 31-36

One of the key elements in qualitative data analysis is the systematic coding of text (Strauss and Corbin, 1990; 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. On the one hand, we must often explain basic methods such as this in clear terms to a wide range of scientists who have little or no experience with qualitative research and who may express a deep skepticism of the validity of our results. On the other, our codebook development strategy must be responsive to the teamwork approach that typifies the projects we undertake at CDC, where coding is generally done by two or more persons who may be located at widely dispersed sites. We generally use multiple coders so that we can assess the reliability and validity of the coded data through intercoder agreement measures (e.g., Carey et al. 1996) and, for some projects, as the only reasonable way to handle the sheer volume of data generated. The standardized structure and dynamic process used in our codebook development strategy reflects these concerns. This paper describes (1) how a structured codebook provides a stable frame for the dynamic analysis of textual data; (2) how specific codebook features can improve intercoder agreement among multiple researchers; and (3) the value of team-based codebook development and coding. Origins of the Codebook Format Our codebook format evolved over the course of several years and a variety of projects. The conceptual origins took shape in 1993 during work on the CDC-funded Prevention of HIV in Women and Infants Project (WIDP) (Cotton et al. 1998), which generated approximately 600 transcribed semistructured interviews. One research question pursued was whether women’s narratives about their own heterosexual behavior could help us understand general processes of change in condom use behavior (Milstein et al. 1998). The researchers decided to use the processes of change (POC) constructs from the Transtheoretical Model (Prochaska 1984; DiClemente and Prochaska 1985) as a framework for the text analysis. However, the validity of the POC constructs for condom-use behavior was unknown, and a credible and rigorous text coding strategy was needed to establish their applicability and relevance for this context. To do this, the analysts had to synthesize all that was known about each POC construct, define what it was, what it was not, and, most importantly, learn how to recognize one in natural language. Several years earlier, O’Connell (1989) had confronted a similar problem while examining POCs in transcripts of psychotherapy sessions. Recognizing that "coding processes of change often requires that the coder infer from the statement and its context what the intention of the speaker was," O’Connell (1989:106) developed a coding manual that included a section for each code titled "Differentiating (blank) from Other Processes." Milstein and colleagues used O’Connell’s "differentiation" section in a modified format in their analysis of condom behavior change narratives. They conceptualized the "differentiation" component as "exclusion criteria," which complemented the standard code definitions (which then became known as "inclusion criteria"). To facilitate on-line coding with the software program Tally (Bowyer 1991; Trotter 1993), components were added for the code mnemonic and a brief definition, as well as illustrative examples. Thus, the final version of the analysis codebook contained five parts: the code mnemonic, a brief definition, a full definition of inclusion criteria, a full definition of exclusion criteria to explain how the code differed from others, and example passages that illustrated how the code concept might appear in natural language. During the code application phase, information in each of these sections was supplemented and clarified (often with citations and detailed descriptions of earlier work), but the basic structure of the codebook guidelines remained stable.
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