12 Qualitative Data, Analysis, and Design

1 34234212 OverviewQualitative Inquiry and Basic PrinciplesQualitative DataWorldviewGeneral ApproachesThe Qualitative MetaphorText as Data: Basic StrategiesRecap: The Qualitative ChallengeCodingRelational StrategiesHierarchyTypologyNetworksTable s and Cross TabulationsInseparable Data Collection and AnalysisEmergent MethodologyReliability and Validity: TrustworthinessCredibilityPattern MatchingResearch DesignsCase StudyPhenomenologyEthnographyNarrativeMi xed MethodsQualitative Research in the LiteratureClassroom ClimateThe Art of TeachingMinority TeachersLearning Disability Coping StrategiesDyslexiaParental InvolvementDetrackingImmigrant NewcomersScaffoldingData Analysis SoftwareSummaryKey TermsApplication ExercisesStudent study SiteReferencesOverview Recall from the two previous chapters that researchers seek the guidance of a research Design , a blueprint for collecting data to answer their questions.

2 Those chapters described experimen-tal and non-intervention designs, often incorporating statistical analysis, that are commonly used in educational research. This chapter continues a sampling of research designs with a Qualitative Data, Analysis, and DesignOutline Chapter 12: Qualitative Data, Analysis, and Design 343focus on common Qualitative research. The orientation of Qualitative researchers contrasts sharply with that of quantitative researchers on many dimensions. Their thinking generates questions that are answered with an emergent methodology, and their approach to rich sources of data requires creativity for its analysis.

3 Such divergent ( outside the box ) thinking is appar-ent in the tasks of designing and analyzing Qualitative research. This will become clear in this chapter when we focus on how researchers analyze Qualitative studies to extract the most meaning while ruling out alternative explanations. Emergent designs in the tradition of Qualitative research suggest a process that is not predetermined. A Design that emerges is one that is not finalized at the outset. Strategies for data collection are open and depend on context. Revisions are made until the researcher is satisfied that the direction taken affords the greatest potential for discovery, meaningful answers to questions posed, or the generation of new hypotheses (or questions).

4 Of course, Qualitative researchers begin with an interest or guiding question, but early decisions about what type of data should be collected and how it should be collected will undoubtedly be revised as the research progresses. A Qualitative research Design evolves and is likely not clarified until data collection ends. What may start as a case study may indeed develop into a Design that more closely resembles a phenomenological study (described later). For this reason, this chapter is organized somewhat differently. Qualitative research designs are described after types of Qualitative data and methods of analysis are described.

5 The type of data collected and the approach to its analysis are more relevant to a researcher s compelling argument and sound conclusion than a category name placed on a general approach to data describing Qualitative data and strategies for analysis, this chapter examines five broad classifications of designs: case study , phenomenological, ethnographic, narrative, and mixed methods. These designs require complex collection of data as sources of evidence for claims about the meaning of the data. Qualitative researchers become skilled at coding and pattern seeking using analytic induction. Making sense of data in the form of graphics, video, audio, and text requires clear thinking that is aided by theory, models, constructs, and perhaps metaphor.

6 Because Qualitative data analysis is less prescribed than statistical analysis and one goal is the discovery of new ideas and their associations, many would argue that it presents a greater challenge. Fortunately, techniques, strategies, and procedures have been developed to help Qualitative researchers extract meaning from their data (including software) and interpret it in ways that enhance our understanding of complex inQuiry and Basic PrinciPles While there is general consensus about classification systems among researchers who use quan-titative research designs how they are distinguished and what to call them there is less consensus among Qualitative researchers about designs.

7 The same can be said for quantitative and Qualitative worldviews. One leader in the field of Qualitative research in education, Sharan Merriam, notes that there is almost no consistency across writers in how [the philosophical] aspect of Qualitative research is discussed (2009, p. 8). She also adds that, in true Qualitative fashion, each writer makes sense of the field in a personal, socially constructed way. The field of Qualitative research is indeed fragmented with confusing language in regard to its orientation and methodological principles of data collection and analysis. Because there is little consensus 344 Part IV: Design anD analysIsabout the classification of Qualitative research, Merriam (2009) uses a term that guides the following general discussion: basic Qualitative research.

8 This chapter discusses the basic quali-ties of Qualitative research, followed by a description of common designs defined by these qualities. Despite the lack of consensus on types of Qualitative research, I believe all Qualitative research shares certain characteristics regarding making sense of data. Therefore, the chapter begins by examining how Qualitative researchers approach their DataMost Qualitative researchers would agree with Snider s (2010) observation that numbers impress, but unfortunately, also conceal far more than they reveal. They would also agree with Davis s (2007) observation that good Qualitative research has equaled, if not exceeded, quantitative research in status, relevance, and methodological rigor (p.)

9 574). Several principles guide the thinking and planning stages of most Qualitative researchers. Qualitative research, in all of its complex designs and methods of data analysis, is guided by the philosophical assumptions of Qualitative inquiry: To understand a complex phenomenon, you must consider the multiple realities experienced by the participants themselves the insider perspectives. Natural environments are favored for discovering how participants construct their own meaning of events or situations. The search for an objective reality, favored by quantitative researchers, is abandoned to the assumption that people construct their own personalized worlds.

10 For example, the experiences of high school dropouts, how beginning readers think about their comprehension, how an at-risk school trans-formed into a high-achieving school, what motivated first-generation women college graduates in Appalachia, how creativity is fostered in schools these are all topics suited for Qualitative inquiry. Questions like these yield complex data, although the sources and formats most common sources of Qualitative data include interviews, observations, and docu-ments (Patton, 2002), none of which can be crunched easily by statistical software. The descrip-tion of people s lived experiences, events, or situations is often described as thick (Denzin, 1989), meaning attention is given to rich detail, meaningful social and historical contexts and experiences, and the significance of emotional content in an attempt to open up the word of whoever or whatever is being studied.