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Qualitative Data Analysis and Interpretation

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Qualitative data analysis is often a tough job and many researchers find it difficult to get comprehensive presentation on the topic. This seminar is an attempt to fulfil that purpose.

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Qualitative Data Analysis and Interpretation

  1. 1. SEMINAR ON: QUALITATIVE DATA ANALYSIS AND INTERPRETATION Presented by: Ms. Prekshya Thapa College of Nursing, B. P. Koirala Institute of Health Sciences, Dharan, Nepal
  2. 2. Introduction: • The purpose of data analysis is to organize, provide structure to, and elicit meaning from data. • In qualitative studies, data collection and data analysis often occur simultaneously rather than after data are collected. • The search for important themes and concept begins from the moment data collection gets underway. • Qualitative analysis is the labor-intensive activity that requires creativity, conceptual sensitivity and sheer hard work. 2 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  3. 3. Qualitative data analysis challenges: • Qualitative data analysis is particularly challenging enterprise for three major reasons. • First, there are no universal rules for analyzing qualitative data , and the absence of standard procedures makes it difficult to explain how to do such analyses. • The second challenge is the enormous amount of work required. Qualitative analyst must organize and make a sense of pages and pages of narrative materials. 3 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  4. 4. Qualitative data analysis challenges: • A final challenge comes in reducing data for reporting purposes. • Qualitative data must balance the need to be concise with the need to maintain the richness and evidentiary value of their data. 4 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  5. 5. Qualitative analysis process • Qualitative researchers typically scrutinize their data carefully and deliberately, often reading the data over and over again in a search for meaning and deeper understandings. • Insights and theories cannot emerge until researchers become completely familiar with data. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 5
  6. 6. Qualitative data analysis • Morse and Field (1995) note that qualitative data analysis is a “process of fitting data together, of making invisible obvious, of linking and attributing consequences to antecedents. It is a process of conjecture and verification, of correction and modification , of suggestion and defense.” • Morse and Field (1995) have identified four process of analysis: – Comprehending – Synthesizing – Theorizing – Recontextualizing QUALITATIVE DATA ANALYSIS AND INTERPRETATION 6
  7. 7. Qualitative data analysis process 1. Comprehending:  Early in analytic process, qualitative researchers strive to make sense of the data and to learn “ what is going on”  When comprehension is achieved , they are able to prepare thorough , rich description of phenomenon under study, and new data do not add much to that description.  Thus comprehension is completed when saturation is achieved. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 7
  8. 8. Qualitative data analysis process 2. Synthesizing  It involves a “sifting” of the data and putting pieces together.  At this stage, researchers get a sense of what is typical regard to the phenomenon , and what variation is like.  At the end of the synthesis, researcher can make some generalized statements about the phenomenon and about study participants. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 8
  9. 9. Qualitative data analysis process 3. Theorizing :  Theorizing involves a systematic sorting of a data.  During this process, researchers develop alternative explanations of the phenomenon and then hold these explanations up to determine their fit with the data.  Theorizing continues to evolve until the best explanation is obtained. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 9
  10. 10. Qualitative data analysis process 4. Recontextualizing  The process of recontextualizing involves further development of the theory to explore its applicability to other settings or groups.  In qualitative inquires whose ultimate goal is theory development, it is the theory that must be recontextualized and generalized. Although the intellectual processes in qualitative analysis are not linear in the same sense that quantitative analysis is, these four processes follow a rough progression over the course of the study. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 10
  11. 11. Qualitative data analysis process • Comprehension occurs primarily while in the field • Synthesis begins in the field but may continue well after the field work is done. • Theorizing and Recontextualizing are the processes that are difficult to undertake before synthesis has been completed. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 11
  12. 12. A. TRANSCRIBING QUALITATIVE DATA Qualitative Data Management And Organization QUALITATIVE DATA ANALYSIS AND INTERPRETATION 12
  13. 13. Qualitative Data Management and Organization Transcribing qualitative data – Audiotaped/videotaped interviews and field notes are major data sources in qualitative studies. – Verbatim transcription of the tapes is crucial step in preparing for data analysis and researchers need to ensure that the transcription are accurate and that they validly reflect the interview experience. 13 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  14. 14. Transcribing qualitative data • Transcription errors are almost inevitable, which means the researchers need to check the accuracy of transcribed data. That there are three categories of error: 1. Deliberate alterations of data:  Transcribers may intentionally “fix” data to make the transcription look more like what they “should” look like.  For e.g, transcriber may alter profanities, omit the sounds such as phone ringing, or “tidy up” the text by deleting “ums” and “uhs”.  It is crucial to impress on transcribers the importance of verbatim accounts. 14 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  15. 15. Transcribing qualitative data II. Accidental alterations of the data:  The insertion or omission of commas or question marks can alter the interpretation of the text.  Another error is the misinterpretation of the text. For example, the actual words might be, “ this was totally moot, "but the transcription might read “this was totally mute”.  Researchers should take place to verify accuracy before analysis gets underway. 15 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  16. 16. Transcribing qualitative data III. Unavoidable alterations:  Data are unavoidably altered by the fact that transcriptions capture only a portion of experience of an interview experience.  For example, transcriptions will inevitably miss nonverbal clues such as body language, intonations and so on. 16 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  17. 17. Qualitative data management and organization • Researcher should begin data analysis with the best possible quality data, which requires careful training of transcribers, ongoing feedback, and continuous efforts to verify accuracy. 17 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  18. 18. B. DEVELOPING A CATEGORY SCHEME QUALITATIVE DATA ANALYSIS AND INTERPRETATION 18
  19. 19. Qualitative data management and organization Developing a Category Scheme – Qualitative data analysis begins with data organization- by classifying and indexing the data. – This phase of data analysis is essentially reductionist- data must be converted to smaller, more manageable units that can be retrieved and reviewed. 19 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  20. 20. Developing a Category Scheme – The most commonly used procedure is to develop a category scheme and then to code data according to the categories. – Developing a high category scheme involves a careful reading of the data, with an eye to identifying underlying concepts and clusters of concepts. – Researcher whose aims are primarily descriptive tend to use categories that are fairly concrete. 20 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  21. 21. Coding scheme for food insecurity and hunger I hate being on welfare, it is a pain in the butt. I don’t need their cash, but the food stamps, they help a lot because it is hard, it is really hard. I got to live day by day for food for my kids. I have to call down to the shelter to get them to send you food, and you hate doing that because it is embarrassing, but I have to live day by day. I have to do things so my kids can eat. I don’t worry about me, just for my kids because I can go a day without eating, but as long as my kids eat. But I never have to worry about my kids starving because I have family. Excerpt from Polit et al. (2000) study for food insecurity and hunger in low income families A. Use of food service/programs 1. Food Stamps 2. Food Pantries 3. Soup Kitchens B. Food Inadequacy Experiences 1. Problems feeding family, having enough food 2. Having to eat undesirable food. C. Strategies to avoid hunger 1. Getting food from friends, relatives 2. Borrowing money D. Special issues 1. Mothers sacrificing for children 2. Effects of welfare reform on hunger 3. Stigma QUALITATIVE DATA ANALYSIS AND INTERPRETATION 21 A1 B1 C1 D3 D1 A2
  22. 22. Developing a Category Scheme • Studies that are designed to develop a theory are more likely to involve abstract ,conceptual categories. • In creating conceptual categories, researchers must break the data into segments, closely examine them, and compare them to other segments for similarities and dissimilarities to determine what the meaning of those phenomena are. 22 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  23. 23. • The researcher ask the questions such as the following about discrete events, incidents, or statements: – What is this? – What is going on? – What does it stand for? – What else is like this? – What is this distinct from? • Important concepts that emerge from close examination of the data are then given a label that forms a basis for a category. 23 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  24. 24. C. CODING QUALITATIVE DATA QUALITATIVE DATA ANALYSIS AND INTERPRETATION 24
  25. 25. Qualitative Data Management And Organization Coding Qualitative Data – Once the category scheme has been developed, the data are read in their entirety and coded for correspondence to the categories-a task that is seldom easy. – Researchers may have difficulty deciding the most appropriate code, or may not fully comprehend the underlying meaning of some aspect of the data. 25 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  26. 26. Coding of qualitative data – Researchers often discover during coding that the initial categories were incomplete. – It is common for categories to emerge that were not initially identified. – A concept might not be identified as salient until it has emerged a few times. – Making changes midway is often vexing, but a comprehensive category system is vital. 26 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  27. 27. Coding of qualitative data – Another issue is that narrative materials usually are not linear. – For example, paragraphs from transcribed interviews may contain elements relating to three or four different categories, embedded in complex fashion. – It is also recommended that a single person code the entire data set to ensure the highest possible coding consistency across interviews or observations. 27 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  28. 28. D. MANUAL METHODS OF ORGANIZING QUALITATIVE DATA QUALITATIVE DATA ANALYSIS AND INTERPRETATION 28
  29. 29. Qualitative data analysis process Manual methods of organizing qualitative data: – Traditional manual methods of organizing qualitative data are becoming less common as a result of widespread use of software that can perform indexing functions. – When a category is simple, researchers sometimes use colored paper clips or Post-It notes to code narrative content. 29 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  30. 30. Manual methods of organizing qualitative data: • For example, if we were analyzing interviews about women's concerns about the menopause, we might use blue paper clips for text relating to loss of fertility, red clips for the text on menopausal side effects, yellow clips for text relating to aging and so on. 30 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  31. 31. E. COMPUTER PROGRAMS FOR MANAGING QUALITATIVE DATA QUALITATIVE DATA ANALYSIS AND INTERPRETATION 31
  32. 32. Qualitative data management and organization Computer Programs For Managing Qualitative Data – Computer assisted qualitative data analysis software removes the work of cutting up pages of narrative material. – These programs allows the researchers to enter the entire data file into the computer , code each portion of the narrative and then retrieve and display the text for specified code for analysis. 32 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  33. 33. Computer Programs For Managing Qualitative Data • The software can also be used to examine the relationship between the codes. • Software cannot, however, do the coding, and it cannot tell researchers how to analyze the data. • Researchers must continue to be analysts and critical thinkers. 33 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  34. 34. Computer Programs For Managing Qualitative Data • The main types of software package that are available to handle and manage qualitative data include: – Text retrievers are the programs that help researchers locate text and terms in data bases and documents. – Code and retrieve packages permit the researchers to code text. 34 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  35. 35. Computer Programs For Managing Qualitative Data – Theory building software, permits the researchers to examine the relationships between the concepts, develop hierarchies of codes, diagram, and create hyperlinks to create nonhierarchical networks. – Software for concept mapping permits researchers to construct more sophisticated diagrams than theory building software. – Concept maps include concepts and relationships between them. – cmc2004-060.pdf 35 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  36. 36. Computer Programs For Managing Qualitative Data – Data conversion/collection software converts audio into text. – Voice recognition software can convert spoken voice into text and is attractive because of the time and expense needed to transcribe audiotape interviews. • Computer programs offer many advantages for managing qualitative data, but some prefer manual methods because they allow researchers to get closer to the data. • Proponents insists that it frees up time and permits to pay attention to important conceptual issues. 36 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  37. 37. ATLAS/ti (software for qualitative analysis) http://www.atlasti.de/ Computer Assisted Qualitative Analysis (CAQDAS) Networking Project http://caqdas.soc.surrey.ac.uk/ Ethnograph, The (software for qualitative analysis) http://www.qualisresearch.com/ Decision Explorer (software for mapping concepts) http://www.banxia.com/demain.ht ml Dragon Naturally Speaking (Voice recognition software bypassing transcription) http://www.nuance.com/ Freedom of Speech (Voice recognition software for bypassing transcription) http://www.freedomofspeech.com/f os/ HyperResearch (software for qualitative analysis) http://www.researchware.com/ Institute for Human and Machine Cognition (IHMC) http://www.ihmc.us International Institute for Qualitative Methodology http://www.uofaweb.ualberta.ca/iiq m/ NVivo (software for qualitative analysis) http://www.datasense.org/ QUALPAGE (Resources for qualitative researchers, ) http://www.qualitativeresearch.uga. edu/QualPage/ 37
  38. 38. Analytic procedures • Data management in qualitative research is reductionist in nature: it involves converting masses of data into smaller , manageable segments. • Qualitative data analysis involves pervasive ideas and searching for general concepts through an inductive process. 38 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  39. 39. Analytic procedures • The analysis of qualitative materials typically begins with a search for broad categories or themes. • According to desantis and ugarriza(2000) :" a theme is an abstract entity that brings meaning and identity to a current experience and its variant manifestations. As such, a theme captures and unifies the nature or basis of the experience into a meaningful whole" 39 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  40. 40. Analytic procedures • Thematic analysis relies on similarity and dissimilarity principle. • The similarity principle involves looking for units of information with the similar contents, symbols or meanings. • The contrast principle guides efforts to find out how content or symbols differ from other content or symbols- that is, to identify what is distinctive about the emerging themes or categories. 40 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  41. 41. Analytic procedures • During the analysis, qualitative researchers must distinguish between the ideas that apply to all people and aspects of experience that are unique to particular participants • The analysis of individual cases " enables the researcher to understand those aspects of experience that occur not as individual 'unit of meaning' but as part of the pattern formed by the confluence of the meaning within the individual accounts”. 41 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  42. 42. Analytic procedures • Thematic analysis involves not only discovering commonalities across participants but also seeking natural variation. • Researchers must attend not only to what themes arise, but also how they are patterned. – Does the theme apply only to certain types of people? – In certain contexts? – At certain periods? – What are the conditions that precede the observed phenomenon? And – What are the apparent consequences of it? 42 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  43. 43. Analytic procedures • In other words, the qualitative analyst must be sensitive to relationships within the data. • Researchers' search for themes and patterns sometimes can be facilitated by charting devices that enable them to summarize the evolution of the behaviors, events and processes. • For example, for qualitative studies that focus on dynamic experiences-such as decision making-it is sometimes useful to develop flow charts or timelines that highlight time sequences , major decision points and events factors affecting the decisions. 43 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  44. 44. Analytic procedures • berends.pdf • Two-dimensional matrices to array thematic material is another frequently used method of displaying thematic material. • Traditionally , each row of a matrix is allocated to individual participants and columns are used to enter either raw data or themes. 44 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  45. 45. Analytic procedures • Thematic Chart • Case Chart Case 1 Case 2 Case 3 etc… Theme Theme 1 Theme 2 Theme 3 etc… Case 45 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  46. 46. Analytic procedures • Some qualitative researchers -especially phenomenologist -use metaphors as an analytic strategy. • A metaphor is a symbolic comparison, using figurative language to evoke a visual analogy. • Metaphors can be powerfully expressive tool for qualitative analysts. • Moser.pdf 46 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  47. 47. Analytic procedures • Identifying key themes and categories is seldom a tidy, linear process- iteration is always necessary. • That is , researchers drive themes from the narrative materials , go back to the materials with the themes in mind to see if the materials really do fit and then refine the themes as necessary. • A further step involves validation. In this phase, the concern is whether the themes accurately represent the perspectives of participants. 47 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  48. 48. Analytic procedure • In the final analysis stage, researchers strive to weave thematic pieces together into an integrated whole . • The various themes need to be interrelated to provide an overall structure (such as theory or integrated description) to the data. • The integration task is a difficult one, because it demands creativity and intellectual rigor if it is to be successful 48 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  49. 49. Qualitative Data Analysis • It isn’t always necessary to go through all the stages, just as it isn’t always necessary to use multivariate modeling in statistics! • Let us take the example of the research question about the perceived health needs of carers. – What are the perceptions of carers living with people with learning disability, as regards their own health needs? 49 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  50. 50. Qualitative Data Analysis • We may simply be interested in finding out the community services that should be provided to meet these perceived needs or might want to know what sorts of services are valued or requested by the majority of carers. • Maybe several respondents mention that they struggle with depression and loneliness. 50 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  51. 51. Qualitative data analysis • There are three broad levels of analysis that could be pursued here: – One strategy would be to simply count the number of times a particular word or concept occurs (eg loneliness) in a narrative. – The qualitative data can then be categorized quantitatively, and subjected to statistical analysis. – This kind of analysis is sometimes called qualitative content analysis. 51 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  52. 52. Qualitative data analysis • For a thematic analysis we would want to go deeper than this. • All units of data(e.g. sentences or paragraphs) referring to loneliness could be given a particular code, extracted and examined in more detail. – Do participants talk of being lonely even when others are present? – Are there particular times of day or week when they experience loneliness? 52 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  53. 53. Qualitative data analysis – In what terms do they express loneliness? – Do men and women talk of loneliness in different ways? – Are those who speak of loneliness also those who experience depression? – Themes could eventually be developed such as ‘lonely but never alone’ or ‘these four walls’. 53 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  54. 54. Qualitative data analysis • For a theoretical analysis such as grounded theory we would want to go further still. • Perhaps we have developed theories if we have been analyzing data about depression being associated with perceived loss of a ‘normal’ child/spouse. • The disability may be attributed to an accident, or to some failure of medical care, without which the person cared for would still be ‘normal’. 54 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  55. 55. Qualitative data analysis • We may be able to test this emerging theory against existing theories of loss in the literature, or against further analysis of the data. • We may even search for ‘deviant cases’, that is data which seems to contradict theory, and seek to modify theory to take account of this new finding. • This process is sometimes known as ‘analytic induction’, and is used to build and test emerging theory 55 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  56. 56. Qualitative data analysis • So some decisions have to be made by the researcher as to the questions she or he is asking of the data, and the depth of analysis that is required. • It may even come down to the amount of time available, or ease of access to adequate resources. 56 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  57. 57. Analytic Procedures • Qualitative content analysis: – Qualitative content analysis is the analysis of the content of narrative data to identify prominent themes and pattern among the themes. – It involves breaking down the data into smaller units, coding and naming the unit according to the content they represent, and grouping coded material based on shared concepts. 57 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  58. 58. Ethnographic Analysis Ethnographers are continually looking for the patterns in the behavior and thoughts of participants, comparing one pattern against another, analyzing many patterns simultaneously As they analyze patterns of everyday life, ethnographers acquire deeper understandings of the culture being studied. 58 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  59. 59. Ethnographic analysis • Maps, flowcharts and organizational charts are useful tools that help to crystallize and illustrate the data. • Matrices(two dimension displays ) can also help to highlight a comparison graphically, to cross- reference categories and to discover emerging patterns. 59 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  60. 60. Locating an informant Interviewing an informant Making an ethnographic record Asking descriptive questions Analyzing ethnographic interviews Making a domain analysis Asking structural questions Making taxonomic analysis Asking contrast questions Making componential analysis Discovering cultural themes Writing the ethnography SPRADLEY’S METHOD 60
  61. 61. Ethnographic Analysis • Thus in Spradley's methods there is four levels of data analysis, the first of which is domain analysis. • Domains, which are the units of cultural knowledge, are the broad categories that encompass smaller ones. 61 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  62. 62. Ethnographic analysis • During this first level of data analysis, ethnographers identify the relational patterns among the terms in the domains that are used by members of the culture. • The ethnographers focuses on the cultural meaning of terms and symbols(objects and events) used in culture and their interrelationships. 62 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  63. 63. Ethnographic analysis • In taxonomic analysis, ethnographic decides how many domains the analysis will encompass. • After making this decision, a taxonomy-a system of classifying and organizing terms-is developed to illustrate the internal organization of a domain and the relationship among the subcategories of the domain. 63 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  64. 64. Ethnographic analysis • In componential analysis, relationships among the terms in the domains are examined. The ethnographer analyze the data for similarities and differences among the cultural terms in a domain. • Finally, in theme analysis, cultural themes are uncovered. 64 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  65. 65. Ethnographic analysis • Domains are connected in cultural themes, which help to provide holistic view of culture being studied. The discovery of cultural meaning is the outcome. 65 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  66. 66. The culture of general palliative nursing care in medical departments: an ethnographic study. • Bergenholtz H1, Jarlbaek L, Hølge- Hazelton B. • BACKGROUND: – In many countries, approximately half of the population dies in hospital, making general palliative nursing care (GPNC) a core nursing task. GPNC in the hospital setting is described as challenging, however little is known about its actual practice. AIM: – To explore the GPNC culture in medical departments. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 66
  67. 67. • METHODS: – An ethnographic study, using Spradley's 12- step method, with observational field studies and interviews with nurses from three medical departments in a Danish regional hospital. • FINDINGS: – Three cultural themes emerged from the analysis, focusing on the setting, the practice and the nurses' reflections on GPNC: – (1) GPNC provided in a treatment setting, – (2) transition to loving care and the licence to perform palliative care (PC) and – (3) potential for team improvement.QUALITATIVE DATA ANALYSIS AND INTERPRETATION 67
  68. 68. An ethnography: Understanding emergency nursing practice belief systems • An ethnography_ Understanding emergency nursing practice_LoBiondo.pdf QUALITATIVE DATA ANALYSIS AND INTERPRETATION 68
  69. 69. Phenomenological analysis • Three frequently used methods for phenomenology are the methods of Colaizzi(1978), Giorgi(1985), and vankaam (1966), all of whom are from the duquesne school of phenomenology. 69 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  70. 70. Phenomenological analysis • Phenomenological analysis using all three methods involves a search for common patterns , but there are some important differences among these approaches • The basic outcome of all three methods is the description of the meaning of an experience, often through the identification of essential themes. 70 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  71. 71. Phenomenological analysis • Colaizzi method is only one that calls for a validation of results by returning to study participants. • Giorgi analysis relies solely on researchers. His view is that it is inappropriate either to return to participants to validate findings or to use external judges to review analysis. • Van Kaam's method requires that intersubjective agreement be reached with other expert judges.71 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  72. 72. Step 1 Read written protocol Step 2 Extract Significant statements Step 3 Formulate meanings for each significant statement Step 4 Extract Significant statements Step 5 Integrate results into exhaustive description of the phenomeno n Step 6 Formulate exhaustive description into statement of identification of its fundamental structure Step 7 Return to participants for validation of Step 8 (if necessary )relevant new data are worked into final product of research Repeat Steps 1-3 for each protocol Refer back to original protocols 72
  73. 73. Example of Phenomenological Study using Colaizzi’s Method • Supporting hemodialysis patients_ A phenomenological study.epub QUALITATIVE DATA ANALYSIS AND INTERPRETATION 73
  74. 74. Step 1 Statements of patients read Step 2 Key phrases were extracted Step 3 Each significant statements were written in scientific language Step 4 Concepts were organized into thematic categories Step 5 Findings were integrated into comprehensi ve description of the desired phenomenon Step 6 Description of the investigated phenomenon was presented in the form of an explicit and clear statement Step 7 Findings were returned to the participants and were evaluated Supporting Hemodialysis Patients: A phenomenological Study 74
  75. 75. Grounded theory analysis • Grounded theory methods emerged in the 1960s in connection with Glaser and Strauss's (1967) research program on dying in hospitals. • The two co-originators eventually split and developed divergent school of thought , which have been called the "Glaserian" and "Straussian" version of grounded theory. 75 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  76. 76. Glaser and Strauss's grounded theory method • Grounded theory in both systems of analysis uses the constant comparative method of analysis. • This method involves a comparison of elements present in one data source(e.g. In one interview) with those in another to determine if they are similar. 76 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  77. 77. Glaser and Strauss's grounded theory method • The process continues until the content of each source has been compared to the content in all sources. In this fashion, commonalities are identified. • The concept of fit is an important element in the Glaserian grounded theory analysis. • By fit, Glaser meant that the developing categories of the substantive theory must fit the data. 77 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  78. 78. Glaser and Strauss's grounded theory method • Fit enables the researcher to determine if data can be placed in the same category or if they can be related to one another. • Coding in the Glaserian approach is used to conceptualize data into patterns. • The substance of the topic under study is conceptualized through substantive codes, while the theoretical codes provide insights into how codes relate to each other. 78 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  79. 79. Coding Substantiv e Open Level I Level II Level III Selective Theoretica l QUALITATIVE DATA ANALYSIS AND INTERPRETATION 79 Core Categorie s
  80. 80. Glaser and Strauss's grounded theory method • Substantive codes are either open or selective. • Open coding used in the first stage of constant comparative analysis captures what is going on in the data. • Open codes may the actual words used by the participants. 80 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  81. 81. Glaser and Strauss's grounded theory method • Through the open coding , data are broken down into incidents and their similarities and differences are examined. • During the open coding, researcher might ask" what category or the property of the category does this incident indicate?“ • There are three level of open coding that vary in the degree of abstraction. 81 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  82. 82. Glaser and Strauss's grounded theory method • Level 1 codes(in vivo codes) are derived directly from the language of the substantive area and have vivid imagery. • Researchers constantly compare new level I codes to previously identified ones, and then condense them into broader level II codes. e.g. : These 5 level I codes were collapsed into level II code as “Reaping the Blessing” 82 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  83. 83. QUALITATIVE DATA ANALYSIS AND INTERPRETATION 83
  84. 84. Glaser and Strauss's grounded theory method • Level III codes (theoretical constructs) are the most abstract. Collapsing level II codes aids in identifying constructs. • Open coding ends when the core category is discovered and then selective coding begins. 84 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  85. 85. Glaser and Strauss's grounded theory method • The core category is a pattern of behavior that is relevant and/or problematic for participants. • In the selective coding, researcher code only those data that are related to the core variable. 85 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  86. 86. Glaser and Strauss's grounded theory method • Glaser (1978) provided nine criteria to help researchers decide on a core category: – It must be central, meaning that it is related to many categories. – It must reoccur frequently in the data. – It takes more time to saturate than other categories. – It relates meaningfully and easily to other categories. 86 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  87. 87. Glaser and Strauss's grounded theory method – It has clear and grabbing implications for formal theory. – It is completely variable. – It is a dimension of the problem. – It can be kind of theoretical code. 87 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  88. 88. Glaser and Strauss's grounded theory method • Glasers' grounded theory method is concerned with the generation of categories and hypothesis rather than testing them. • The product of typical grounded theory analysis is a theoretical model that endeavors to generate " a theory of continually resolving the main concern, which explain most of the behavior in an area of interest." 88 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  89. 89. Glaser and Strauss's grounded theory method • Once the basic problem or central concern emerges, the grounded theorists goes on to discover the process these participants experience in coping with or resolving this problem. 89 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  90. 90. Strauss and Corbin's approach • The Strauss and Corbin approach to grounded theory analysis, differs from the Glaser and Strauss method with regard to method, processes, and outcomes. • Glaser stressed that to generate a grounded theory, the basic problem must emerge from the data-it must be discovered. 90 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  91. 91. Strauss and Corbin's approach • The theory is , from the very start, grounded in the data, rather than starting with a preconceived problem. • Strauss and Corbin stated that research itself is only one of the possible sources of a research problem. • Research problem can come from literature or a researcher's personal and professional experience 91 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  92. 92. Strauss and Corbin's approach • The Corbin and Strauss method involves two types of coding: open and axial coding. • In open coding, data are broken down into parts and concepts are identified and their properties and dimension are delineated. • In axial coding, the analyst relates concepts to each other. 92 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  93. 93. Strauss and Corbin's approach • The first step in integrating the findings is to decide on the central category(sometimes called the core category), which is the main theme of the research. • Techniques to facilitate the central category are writing the storyline, using diagrams, and reviewing and organizing memos. 93 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  94. 94. • Grounded Theory_Evolving Self-Care in Individuals with Schizophrenia and Diabetes Mellitus.pdf QUALITATIVE DATA ANALYSIS AND INTERPRETATION 94
  95. 95. Focus Group Data Analysis: • Focus group interviews yield rich and complex data that pose special analytic challenges. • Focus group interviews are especially difficult to transcribe , partly because of technical problems. 95 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  96. 96. Focus Group Data Analysis: • For example, it is difficult to place microphones so that the voices of all group members are picked up with equal clarity , particularly because the participant tend to speak at different volumes. • An additional issue is the inevitability that several participants will speak at once, making it impossible for the transcriptionist to discern everything being said. 96 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  97. 97. Focus group data analysis A controversial issue in the analysis of the focus group data is whether the unit of analysis is individual or group. Some writer maintain that group is the proper unit of analysis. Analysis of group-level data involves scrutiny of themes, interactions and sequences within and between groups. 97 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  98. 98. Focus group data analysis • Others, however argued that analysis should occur at both the group and individual level. • Those who insist on only group level analysis argue that what individuals say in focus group cannot be treated as personal disclosures because they are inevitably treated influenced by the dynamics of the group. 98 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  99. 99. Focus group data analysis • For those who wish to analyze data from individual participants, it is essential to maintain information about what each person did-a task that is not possible if researcher rely solely on audiotapes • Videotapes, as supplements to audiotapes, are sometimes used to identify who said what in focus group sessions. 99 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  100. 100. Focus group data analysis • Transcription quality is especially important in focus group interviews. • Emotional content as well as words must be faithfully recorded because participants are responding not only to questions being posed, but also to the experience of being in the group. 100 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  101. 101. Focus group data analysis • Field notes, debriefing notes and verbatim transcripts ideally must be integrated to yield a comprehensive transcript for analysis. • Because of group dynamics, focus group analysts must be sensitive to both the thematic content of these interviews, and also to how, when, and why themes are developed. 101 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  102. 102. Focus group data analysis • Some of the issues that could be central to focus group analysis are the following: – Does an issue raised in a focus group constitute a theme or merely a strongly held viewpoint of one or two members? – Do the same issues or themes arise in more than one group? 102 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  103. 103. Focus group data analysis – If there are group differences, why might this be the case-were participants different in characteristics and experiences or did group processes affect the discussions? 103 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  104. 104. Focus group data analysis – Are some issues sufficiently salient that not only are they discussed in response to specific questions posed by the moderator , but also spontaneously emerge at multiple points in the session? – Do group members find certain issues both interesting and important? 104 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  105. 105. Interpretation of Qualitative findings • Interpretation and analysis of qualitative data occur simultaneously, in an iterative process. • That is, researcher interpret the data as they read and re-read them , categorize and code them, inductively develop a thematic analysis, and integrate the themes into unified whole. 105 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  106. 106. Interpretation of Qualitative findings • Incubation is the process of living the data in which the researchers must try to understand their meanings , find their essential patterns , and draw legitimate , insightful conclusions. • Another key ingredient in interpretation and meaning making is researchers' self awareness and ability to reflect on their own world view and perspectives- that is reflexivity. 106 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  107. 107. Interpretation of Qualitative findings • Creativity also plays important roles in uncovering meaning of the data. • Efforts to validate analysis are necessarily efforts to validate the interpretations as well. • Prudent qualitative researchers hold their interpretations up for closer scrutiny as well as review by peers and outside reviewers. 107 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  108. 108. Critiquing qualitative Analysis • Evaluating a qualitative analysis in a report is not easy to do, even for experienced researchers. • The main problem is that readers do not – have access to the information they would need to determine whether the researchers exercised good judgment and critical insight in coding the narrative materials – developing the thematic analysis and integrating materials into meaning whole. 108 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  109. 109. Critiquing qualitative Analysis • Researchers are seldom able to include the handful of data in a journal article. • Moreover, the process they used to abstract meaning from the data is difficult to describe and illustrate. • In a critique of qualitative analysis, a primary task usually is assessing whether researchers took sufficient steps to validate inferences and conclusions. 109 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  110. 110. Critiquing qualitative Analysis • A major focus of critique, then, is whether the researchers adequately documented the analytic process. • The report should provide information about the approach used to analyze the data. 110 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  111. 111. Critiquing qualitative Analysis • One aspect of a qualitative analysis that can be critiqued, however is, whether the researchers documented that they have used one approach consistently and have been faithful to the integrity of its procedures. • Thus, for example, if researchers say they are using the Glaser and Strauss approach to grounded theory analysis, they should not also include elements from Strauss and Corbin method. 111 QUALITATIVE DATA ANALYSIS AND INTERPRETATION
  112. 112. • Which of the following best describes the term themes? (Select all that apply.) a) A theme is a label. b) Themes must be determined before data analysis. c) Themes describe large quantities of data in a condensed format. d) Themes predict relationships among variables. Ans: a, c QUALITATIVE DATA ANALYSIS AND INTERPRETATION 112
  113. 113. • The nurse researcher identifies that saturation has occurred in a research study. On the basis of this, what does the researcher conclude? a. That additional subjects should be interviewed b. That a new category of subjects should be interviewed c. That no additional subjects need to be identified d. That additional data can emerge from current interviews Ans: c QUALITATIVE DATA ANALYSIS AND INTERPRETATION 113
  114. 114. References • (2012)Qualitative data analysis. In: Polit, D.F., Beck, C.T. (eds.). Nursing Research: Generating and Assessing Evidence for Nursing Practice. 9th Edition. New Delhi: Wolters Kluwer India Pvt. Ltd. • United Kingdom. National Health Services. (2007) Qualitative Research Analysis. London: The NIHR RDS for the East Midlands / Yorkshire & the Humber. • www.pubmed.com • www.googlescholar.com 114 QUALITATIVE DATA ANALYSIS AND INTERPRETATION

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