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.

Draws on a content analysis of methods and design from 232 articles using combined methods. Examine and discusses the rationales provide for employing mixed-methods and whether they correspond to actual practice.

Discusses mixed methods sampling techniques in creative and effective ways.

Research involving adolescents from same-sex parent families provides an important contribution to the evidence base on their health, well-being and the impact of stigma. To date reports on the perspectives of adolescents with same-sex attracted parents have been limited. This study aimed to describe the multidimensional experiences of physical, mental and social well-being of adolescents living in this context. A mixed methods study of adolescents with same-sex attracted parents comprising of an adolescent-report survey of 10- to 17-year-olds and family interviews with adolescents and their parents. Data were collected in 2012 and 2013 as part of the Australian Study of Child Health in Same-Sex Families. The findings from qualitative interviews with seven adolescents and responses to an open-ended survey question (n = 16) suggest four themes: perceptions of normality, positive concepts of health, spheres of life (including family, friends and community) and avoiding negativity. The quantitative sample of adolescents with same-sex attracted parents (n = 35) reported higher scores than population normative data on the dimensions general health and family activities within the Child Health Questionnaire (CHQ) as well as higher on the peer problems scale on the Strengths and Difficulties Questionnaire (SDQ). Perceived stigma correlates with lower health and well-being overall. Positive health outcomes are informed by the ways adolescents conceptualize health and how they construct their spheres of life. Peer relationships, and community perspectives of same-sex families, inform perceived stigma and its correlation with poorer health and well-being. Although adolescents see their families as essentially normal they are negatively affected by external societal stigma.

The use of qualitative data analysis software has been increasing in recent years. A number of qualitative researchers have raised questions concerning the effect of such software in the research process. Fears have been expressed that the use of the computer for qualitative analysis may interfere with the relationship between the researcher and the research process itself by distancing the researcher from both the data and the respondent. Others have suggested that the use of a quantitative tool, the computer, would lead to data dredging, quantification of results, and loss of the "art" of qualitative analysis. In this study of 12 qualitative researchers, including both faculty members and graduate students, we have found that these fears are exaggerated. Users of qualitative data analysis software in most cases use the computer as an organizational, time-saving tool and take special care to maintain close relationships with both the data and the respondents. It is an open question, however, whether or not the amount of time and effort saved by the computer enhance research creativity. The research findings are mixed in this area. At issue is the distinction between creativity and productivity when computer methods are used. Computer packages targeted at qualitative and mixed methods research data are readily available and the methodology sections of research articles indicate that they are being utilised by some health researchers. The purpose of this article is to draw together concerns which have been expressed by researchers and critics and to place these within the perspective of 'framing' (MacLachlan & Reid, 1994). Here, the focus becomes the frame that these computer programs impose on qualitative data. Inevitably, all data sets are disturbed by the techniques of collection and the conceptual and theoretical frames imposed, but computer framing not only distorts physically but also imposes an often minimally acknowledged frame constructed by the metaphors and implicit ideology of the program. This frame is in opposition to most of the recent changes in qualitative data interpretation, which have emphasized context, thick description and exposure of the minimally disturbed voices of participants.

How do you decide whether to use a qualitative or a quantitative approach for the design of a research study? How do you write up the results of a study for a scholarly journal article or dissertation? This book addresses these issues by providing a guide to major design decisions, such as deciding a paradigm, stating a purpose for the study, identifying the research questions and hypotheses, using theory, and defining and stating the significance of the study. Research Design is aimed at upper division to graduate level research methods courses that are taught to prepare students to plan and write up independent research studies. In the past two decades, research approaches have multiplied to a point at which investigators or inquirers have many choices. For those designing a proposal or plan, I recommend that a general framework be adopted to provide guidance about all facets of the study, from assessing the general philosophical ideas behind the inquiry to the detailed data collection and analysis procedures. Using an extant framework also allows researchers to lodge their plans in ideas well grounded in the literature and recognized by audiences (e.g., faculty committees) that read and support proposals for research. What frameworks exist for designing a proposal? Although different types and terms abound In the literature, I will focus on three: quantitative, qualitative, and mixed methods approaches. 'The first has been available to the social and human scientist for years, the second has emerged primarily during the last three or four decades, and the last is new and still developing in form and substance. This chapter introduces the reader to the three approaches to research. I suggest that to understand them, the proposal developer needs to consider three framework elements: philosophical assumptions about what constitutes knowledge claims; general procedures of research called strategies of inquhy and detailed procedures of data collection, analysis, and writing. called methods. Qualitative, quantitative, and mixed methods approaches frame each of these elements differently, and these defiances are identified and discussed in this chapter. 'Then typical scenarios that combine the three elements are advanced, followed by the reasons why one would choose one approach over another in designing a study. 'This discussion will not be a philosophical treatise on the nature of knowledge, but it will provide a practical grounding in some of the philosophical ideas behind research.

Written by a long-time authority on focus group, presents a brief history of focus group application up to, and including, information on the variety of current uses across many disciplines. Great section on the uses of focus groups in combination with other methods with a full compare/contrast discussion. Finally, goes into the specifics on ‘how to’ plan and conduct effective group data collection. My own preference (Morgan, 1996) is for a more inclusive approach that broadly defines focus groups as a research technique that collects data through group interaction on a topic determined by the researcher. In essence, it is the researcher's interest that provides the focus, whereas the data themselves come from the group interaction. One reason for favoring an inclusive approach is that the exclusive approaches do not really exclude very much. Other than focus groups, the primary categories of group interviews in the existing typologies are things that are manifestly different from focus groups. On the one hand, there are nominal groups and Delphi groups (Stewart & Shamdasani, 1990), which do not involve actual group interaction. On the other hand, there is the observation of naturally occurring groups, which typically do not involve the researcher in determining the topic of discussion. Thus, little is gained by excluding these categories of data collection because they already fall outside the broad definition of focus groups offered here. Among the more specific criteria that could be used to distinguish focus groups from other types of group interviews, both Frey and Fontana (1989) and Khan and Manderson (1992) assert that focus groups are more formal. In particular, they argue that focus groups are likely to involve inviting participants to the discussion and they also stress the distinctive role of the moderator. Although there is no doubt that group interviews vary along a continuum from more formally structured interaction to more informal gatherings, I do not believe it is possible to draw a line between formal and informal group interviews in a way that defines some as focus groups and others as something else. Instead, I find it more useful to think that the degree of formal structure in a focus group is a decision that the research makes according to the specific purposes of the research project. In particular, the use of either a more formal or a less formal approach will depend on the researcher's goals, the nature of the research setting, and the likely reaction of the participants to the research topic. Among the other criteria that have been offered as distinguishing features of focus groups are their size and the use of specialized facilities for the interview (McQuarrie, 1996). Again, however, these supposedly exclusive criteria are mostly a matter of degree. Who is to say when a group is too large or too small to be called a focus group or when a setting is too casual to qualify? Rather than generate pointless debates about what is or is not a focus group, I prefer to treat focus groups as a "broad umbrella" or "big tent" that can include many different variations. Of course, this approach requires researchers to make choices about doing focus groups one way rather than another. Fortunately, this need to make explicit decisions about data collection strategies is a familiar concern to social scientists, and it comes under the heading of "research design." As social scientists have gained increasing experience with focus groups, we also have produced insights into the situations in which different research designs are either more or less likely to be effective (e.g., Krueger, 1993; Morgan, 1992.a, 1995).

Discusses issues, types, and calculations for inter-rater reliability. Seventeen measures of association for observer reliability (interobserver agreement) are reviewed and computational formulas are given in a common notational system. An empirical comparison of 10 of these measures is made over a range of potential reliability check results.

Systematic reviews conducted using either meta-analysis or meta-synthesis are well established methodological procedures for combining data and results across different quantitative or qualitative studies. Recently, a third option for systematic reviews has emerged. Systematic mixed studies reviews combine data and results across quantitative, qualitative and mixed method studies. An important challenge is how to integrate the quantitative, qualitative and mixed method studies. Here, we introduce the concept of “meta-integration”. Our overarching aim is to define and illustrate the novel concept of meta-integration as applied to convergent systematic mixed studies reviews using examples from our research on parental self-perception and autism spectrum disorder. Specifically, we present a typology for meta-integration procedures at two levels, both basic and advanced meta-integration. Three models, namely, basic convergent meta-integration, basic convergent qualitative meta-integration, and basic convergent quantitative meta-integration, combine quantitative and qualitative studies. Three additional models, namely, advanced convergent qualitative meta-integration, advanced convergent qualitative meta-integration, and advanced convergent quantitative meta-integration, combine quantitative, qualitative and mixed method studies. The models generally follow six steps: (1) categorize data sources; (2) transform the data; (3) conduct intra-method synthesis; (4) conduct inter-method synthesis and/or integration; (5) organize results and assess fit; and (6) draw final conclusions. One basic and one advanced model do not involve data transformation. These models for conducting convergent meta-integration in systematic mixed studies reviews provide guidance for researchers to apply rigorous and coherent methodology. Following these procedures can substantively improve the quality of systematic reviews seeking to use quantitative, qualitative and mixed method studies.

The purpose of this study was to compare the impact of music therapy (MT) versus music medicine (MM) interventions on psychological outcomes and pain in cancer patients and to enhance understanding of patients’ experiences of these two types of music interventions. This study employed a mixed methods intervention design in which qualitative data were embedded within a randomized cross-over trial. Thirty-one adult cancer patients participated in two sessions that involved interactive music making with a music therapist (MT) and two sessions in which they listened to pre-recorded music without the presence of a therapist (MM). Before and after each session, participants reported on their mood, anxiety, relaxation, and pain by means of visual analogue and numeric rating scales. Thirty participants completed an exit interview. The quantitative data suggest that both interventions were equally effective in enhancing target outcomes. However, 77.4 % of participants expressed a preference for MT sessions. The qualitative data indicate that music improves symptom management, embodies hope for survival, and helps connect to a pre-illness self, but may also access memories of loss and trauma. MT sessions helped participants tap into inner resources such as playfulness and creativity. Interactive music making also allowed for emotional expression. Some participants preferred the familiarity and predictability of listening to pre-recorded music. The findings of this study advocate for the use of music in cancer care. Treatment benefits may depend on patient characteristics such as outlook on life and readiness to explore emotions related to the cancer experience.