Stages of qualitative data analysis

Related slideshares at ative data n nigatu haregu, phd hed on mar 6, presentation summarizes qualitative data analysis methods in a brief manner. Read and use for your qualitative you sure you want message goes professional training year tutor faculty of arts and human sciences at university of surrey & doctoral useful thank you so much for you sure you want message goes you sure you want message goes er, university of technology and education, ho chi minh city, viet sity of presentation is definitely helpful for my knowledge of conducting a qualitative research project. You sure you want message goes ic consultant and technical advisor at expertsmind and chegg; visiting technical expert at you sure you want message goes consumer products ion specialist _unicef nutrition specialist _ ative data e of the presentationqualitative researchqualitative dataqualitative analysisqualitative softwarequalitative reporting ative research is qualitative research? Pope & mays bmj 1995;311:42-45 ions of qualitative methodsunderstanding context• how economic, political, social, cultural, environmental and organizational factors influence healthunderstanding people• how people make sense of their experiences of health and diseaseunderstanding interaction• how the various actors involved in different public health activities interact each other vs quan: basic differences qualitative quantitativepurpose to describe a situation, to measure magnitude-how gain insight to particular widespread is a practice... No pre-determined pre-determined response response categories categories, standard measuresdata in-depth explanatory data wide breadth of data from large from a small sample statistically representative sampleanalysis draws out patterns from tests hypotheses, uses data to concepts and insights support conclusionresult illustrative explanation & numerical aggregation in individual responses summaries, responses are clusteredsampling theoretical statistical vs quan: analytic approaches quantitative qualitativeresearch question fixed/focused broader, contextual, flexibleexpected outcome identified in usually not predefined, advance emergent research questionhierarchy of phases linearity circularconfounding factors controlled during searched in the field design & analysistime dimension slower rapid to slower vs quan: data collection method quantitative qualitativesampling random sampling open ended and less structured protocols (flexible)tools structured data depend on interactive collection instruments interviewsresults produce results that produce results that give generalize, compare and meaning, experience and views summarize for combining qual-quan methods qual-quan combining models sequential use model concurrent use modelqual-quan quan-qual quan qual quan qual model model model model ant concepts in designing qualitative researchconcept descriptionnatural setting participants are free from any control & data are collected in their natural environmentholism the whole is more than the sum, take magnitude of contextual factors in to accounthuman as a researcher is involved in every step being responsive,research flexible, adaptive and good listenerinstrumentemergent design study design emerges as further insights are gained through data collection and analysissaturation or a stage where additional interview or observation is notredundancy believed to add new information-enough is enough! Qualitative study designsstudy design descriptionethnography portrait of people- study of the story and culture of a group usually to develop cultural awareness & sensitivityphenomenology study of individual’s lived experiences of events-e. The experience of aids caregrounded theory going beyond adding to the existing body of knowledge-developing a new theory about a phenomenon-theory grounded on dataparticipatory action individuals & groups researching their own personalresearch beings, socio-cultural settings and experiencescase study in-depth investigation of a single or small number of units at a point (over a period) in time.

Data analysis of qualitative data

Evaluation of s service ng in qualitative research • to generate a sample which allows understanding the social process aim of interest • purposive sampling- selection of the most productive sample to answer the research questiontechnique • ongoing interpretation of data will indicate who should be approached, including identification of missing voices • the one that adequately answers the research question-until new size categories, themes or explanations stop emerging from the data • depend on available time and resources ng techniques in qualitative research snow ball/chain  extreme/deviant  homogeneous  sampling case sampling sampling maximum  convenience  opportunistic variation sampling sampling sampling ative data of qualitative datastructured text, (writings, stories, survey comments,news articles, books etc)unstructured text (transcription, interviews, focusgroups, conversation)audio recordings, musicvideo recordings (graphics, art, pictures, visuals). Data collection methodsmethods brief explanationobservation the researcher gets close enough to study subjects to observe (with/without participation) usually to understand whether people do what they say they do, and to access tacit knowledge of subjectsinterview this involves asking questions, listening to and recording answers from an individual or group on a structured, semi-structured or unstructured format in an in-depth mannerfocus group focused (guided by a set of questions) and interactivediscussion session with a group small enough for everyone to have chance to talk and large enough to provide diversity of opinionsother methods rapid assessment procedure (rap), free listing, pile sort, ranking, life history (biography) ons for qualitative interviewstypes of examplesquestionshypothetical if you get the chance to be an hiv scientist, do you think you can discover a vaccine for hiv? Of qualitative questions• experience: when you told your manager that the project has failed, what happened? Ing transcripttranscribe word by word (verbatim)consider non-verbal expressionstry to do the transcribing yourselfbe patient-time consuming ing metadata(log)project/research titledate of data collectionplace of data collectionid-code of informant(s)research teammethod of data collectiondocumentation type: tape recorder, notesand observations ative analysis is qualitative data analysis? Data analysis (qda) is the range ofprocesses and procedures whereby we move from thequalitative data that have been collected into some formof explanation, understanding or interpretation of thepeople and situations we are is usually based on an interpretative idea is to examine the meaningful and symboliccontent of qualitative data http:///intro_qda/what_is_ ches in analysisdeductive approach – using your research questions to group the data and then look for similarities and differences – used when time and resources are limited – used when qualitative research is a smaller component of a larger quantitative studyinductive approach – used when qualitative research is a major design of the inquiry – using emergent framework to group the data and then look for relationships ative vs quantitative data analysisqualitative quantitative• begins with more general • key explanatory and open-ended questions, outcome variables moving toward greater identified in advance precision as more • contextual/confounding information emerges variables identified and• pre-defined variables are controlled not identified in advance • data collection and• preliminary analysis is an analysis distinctly inherent part of data separate phases collection • analysis use formal statistical procedures for helping the analytical processsummaries: should contain the key points thatemerge from undertaking the specific activityself memos: allow you to make a record of theideas which occur to you about any aspect ofyour research, as you think of themresearcher used in qualitative data analysistheory: a set of interrelated concepts, definitions and propositionsthat presents a systematic view of events or situations by specifyingrelations among variablesthemes: idea categories that emerge from grouping of lower-leveldata pointscharacteristic: a single item or event in a text, similar to anindividual response to a variable or indicator in a quantitativeresearch. It is the smallest unit of analysiscoding: the process of attaching labels to lines of text so that theresearcher can group and compare similar or related pieces ofinformationcoding sorts: compilation of similarly coded blocks of text fromdifferent sources in to a single file or reportindexing: process that generates a word list comprising all thesubstantive words and their location within the texts entered in to aprogram ples of qualitative data analysis1. Exceptional cases may yield insights in to a problem or new idea for further inquiry es of qualitative data analysis• analysis is circular and non-linear• iterative and progressive• close interaction with the data• data collection and analysis is simultaneous• level of analysis varies• uses inflection i.

Qualitative analysis of questionnaire data

This was good”• can be sorted in many ways• qualitative data by itself has meaning, i. Apple” ng, collecting and thinking model think  collect  about  things things notice things process of qualitative data analysisstep 1: organize the datastep 2: identify frameworkstep 3: sort data in to frameworkstep 4: use the framework for descriptive analysisstep 5: second order analysis 2: identify a framework• read, read, read... Identify a framework – explanatory – guided by the research question – exploratory-guided by the data• framework will structure, label and define data• framework=coding plan 3: sort data in to framework• code the data• modify the framework• data entry if use computer packages http:///intro_qda/how_what_to_ 4: use framework in descriptive analysis• descriptive analysis – range of responses in categories – identify recurrent themesstop here if exploratory research 5: second order analysis• identify recurrent themes• notice patterns in the data• identify respondent clusters – search for causality – identify related themes• build sequence of events• search data to answer research questions• develop hypothesis and test of qualitative analysis• content analysis• narrative analysis• discourse analysis• framework analysis• grounded theory http:/// t analysis• content analysis is the procedure for the categorization of verbal or behavioural data for the purpose of classification, summarization and tabulation• the content can be analyzed on two levels – descriptive: what is the data? Http:///guides/research/content/ ive analysis• narratives are transcribed experiences• every interview/observation has narrative aspect-the researcher has to sort-out and reflect up on them, enhance them, and present them in a revised shape to the reader• the core activity in narrative analysis is to reformulate stories presented by people in different contexts and based on their different experiences http:///garson/pa765/ gies for analyzing observations• chronology: describe what was observed chronologically overtime, to tell the story from the beginning to the end• key events: describing critical incidents or major events, not necessarily in order of occurrence but in order of importance• various settings: describe various places, sites, settings, or locations in which events/behaviours of interest happen• people: describing individuals or groups involved in the events• process: describing important processes (e. Control, recruitment, decision-making, socialization, communication)• issues: illuminating key issues – how did participants change y in qualitative studiescriteria issues solutioncredibility truth value prolonged & persistent observation,(=internal validity) triangulation, peer-debriefing, member checks, deviant case analysistransferability applicability thick description, referential adequacy,(=external validity) prevention of premature closure of the data, reflexive journaldependability consistency dependability audit(=reliability) reflexive journalconformability neutrality conformability audit(=objectivity) reflexive journal http:///intro_qda/qualitative_ ative software ng and using computer software• it is possible to conduct qualitative analysis without a computer• concerns: relying too much on computers shortcuts will impede the process by distancing the researcher from the text• advantages: ease the burden of cutting and pasting by hand, and produce more powerful analysis by creation and insertion of codes in to text files, indexing, construction of hyperlinks, and selective retrieval of text segments ative analysis with softwares• with qualitative softwares, your workflow will be similar, but each step will be made easier by the computer’s capability for data storage, automated searching and display. You can use text, picture, audio and video source files directly• you can assign codes manually (autocode) to any section of text, audio or video or part of a picture• analysis is easy with the report feature, where you can select a subset of cases and codes to work with, choose what data to use, and sort your reports automatically http:/// of computer software in qualitative studies1) transcribing data2) writing/editing the data3) storage of data4) coding data (keywords or tags)5) search and retrieval of data6) data linking of related text7) writing/editing memos about the data8) display of selected reduced data9) graphic mapping10) preparing reports http:///intro_caqdas/what_the_sw_can_ to choose software - key questionstype and amount of datatheoretical approach to analysistime to learn vs time to analyzelevel of analysis (simple or detailed)desired “closeness” to the dataany desired quantification of resultsindividual or working as a teampeer software support availableany cost constraints (weitzman and miles 1995; lewins and silver 2005). G a qualitative report g qualitative reportqualitative research generates rich information- thus deciding where to focus and the level of sharing is very challenging.

Focus – academic: conceptual framework/theories, methodology and interpretation – practitioners: concrete suggestions for better practice, policy recommendations – lay readers: problem solving, reform on practice/policy ions in the report format• problem-solving approach (problem-based)• narrative approach (chronological)• policy approach (evidence-based)• analytic approach (theory/conceptual framework based) ing qualitative research• typically use quotes from data – descriptive – direct link with data – credibility• ways to use quotes – illustrative – range of issues – opposing views ing without quotes• list range of issues• rank or sequence issues• describe types of behaviour, strategies, experiences• report proportions (most, many, the majority)• flow diagrams: decision-making, event sequencing etc retation• interpretation is the act of identifying and explaining the core meaning of the data• organizing and connecting emerging themes, sub-themes and contradictions to get the bigger picture-what it all means – think how best to integrate data from multiple sources and methods• make generalization-providing answers to questions of social and theoretical significance• ensuring credible or trustworthy interpretations rd report format1. References ional technology for student course - linkedin oint 2016: tips and course - linkedin ing learning course - linkedin tative data ative data analysis (steps). Data analysis r 10-data analysis & mae nalzaro,bsm,bsn, analysis analysis tation, analysis and interpretation of sent successfully.. References course - linkedin core: exploring k-12 course - linkedin ng techniques: classroom course - linkedin tative data ative data analysis (steps). Now customize the name of a clipboard to store your can see my pell institute and pathways to college , organize, & clean unit of e quantitative e qualitative ces & icate & e qualitative ative data analysis involves the identification, examination, and interpretation of patterns and themes in textual data and determines how these patterns and themes help answer the research questions at ative analysis is (nsf, 1997):Not guided by universal a very fluid process that is highly dependent on the evaluator and the context of the to change and adapt as the study evolves and the data ore, this section will provide a loosely structured guide for the steps you should take when analyzing qualitative is important to note that qualitative data analysis is an ongoing, fluid, and cyclical process that happens throughout the data collection stage of your evaluation project and carries over to the data entry and analysis stages. Although the steps listed below are somewhat sequential they do not always (and sometimes should not) happen in isolation of each ons to ask yourself throughout the qualitative analysis analyzing your qualitative data it is important that you continuously ask yourself the following types of questions:What patterns/common themes emerge around specific items in the data? The patterns that emerge support the findings of other corresponding qualitative analyses that have been conducted?

Chapter 4 in user friendly handbook for mixed methods 1step 2step 3step 4step 5step s and record data soon as data is collected it is critical that you immediately process the information and record detailed notes could include:Things that stuck out to ghts from the is important to do this while the interaction is still fresh in your mind so that you can record your thoughts and reactions as accurately as is helpful to make a reflection sheet template that you carry with you and complete after each interaction so that it is standardized across all data collection analyzing as data is being ative data analysis should begin as soon as you begin collecting the first piece of moment the first pieces of data are collected you should begin reviewing the data and mentally processing it for themes or patterns that were exhibited. It is important to do this early so that you will be focused on these patterns and themes as they appear in subsequent data you ative studies generally produce a wealth of data but not all of it is meaningful. After data has been collected, you will need to undergo a data reduction process in order to identify and focus in on what is meaningful. This is the process of reducing and transforming your raw is your job as the evaluator to comb through the raw data to determine what is significant and transform the data into a simplified format that can be understood in the context of the research questions (krathwohl, 1998; miles and huberman, 1994; nsf, 1997). When trying to discern what is meaningful data you should always refer back to your research questions and use them as your framework. You are already reducing data by not recording every single thing that occurs in your data collection interaction but only recording what you felt was most meaningful, usable, and relevant. This process helps you hone in on specific patterns and themes of interest while not focusing on other aspects of the process of data reduction, however, must go beyond the data collection stage.

Evaluators must take time to carefully review all of the data you have collected as a fying meaningful patterns and order for qualitative data to be analyzable it must first be grouped into the meaningful patterns and/or themes that you observed. This process is the core of qualitative data process is generally conducted in two primary ways:The type of analysis is highly dependent on the nature of the research questions and the type(s) of data you collected. Sometimes a study will use one type of analysis and other times, a study may use both t analysis is carried out by:Coding the data for certain words or fying their reting their type of coding is done by going through all of the text and labeling words, phrases, and sections of text (either using words or symbols) that relate to your research questions of interest. After the data is coded you can sort and examine the data by code to look for ic analysis – grouping the data into themes that will help answer the research question(s). These themes may be (taylor-powell and renner, 2003):Directly evolved from the research questions and were pre-set before data collection even began, lly emerged from the data as the study was your themes have been identified it is useful to group the data into thematic groups so that you can analyze the meaning of the themes and connect them back to the research question(s). Identifying themes or content patterns, assemble, organize, and compress the data into a display that facilitates conclusion drawing. The display can be a graphic, table/matrix, or textual less of what format you chose, it should be able to help you arrange and think about the data in new ways and assist you in identifying systematic patterns and interrelationships across themes and/or content (miles and huberman, 1994; nsf, 1997).

For example, using our summer program study, you could examine patterns and themes both within a program city and across program sion drawing and sion drawing and verification are the final step in qualitative data draw reasonable conclusions, you wil need to (krathwohl, 1998; miles and huberman, 1994; nsf, 1997):Step back and interpret what all of your findings ine how your findings help answer the research question(s). Implications from your verify these conclusions, you must revisit the data (multiple times) to confirm the conclusions that you have drawn. 2017 the pell institute for the study of opportunity in higher education, the institute for higher education policy, and pathways to college need to analyse the data from our qualitative research study in order sense of it and to make accessible to the researcher (and people who report of the research) the large amount of rich textual data that has evidence obtained from the ned with the organisation and the interpretation of information ( numerical information, which is generally the preserve of ch) in order to discover any important underlying patterns and is involves such processes as coding (open,Axial, and selective), categorising and making sense of the essential meanings of the researcher works/lives rich descriptive data, then common themes stage of analysis es total immersion for as long as it is needed in order to a pure and a thorough description of the this is concerned with sation and the interpretation of information (other than ation, which is generally the preserve of quantitative research] to discover any important underlying patterns and qualitative research requires slightly different methods of data analysis:The constant is the process that we use in qualitative research in which any ted data is compared with ted data that was collected in an earlier is a continuous ure, because theories are formed, enhanced, confirmed, or even a result of any new data that emerges from the study. Way in which data can ntly compared throughout a research study is by means of coding:Coding - open coding is the first organisation of the data to try some sense of - axial coding is a way of interconnecting the - selective coding is the building of a story that the end of these processes, it that one has achieved the production of a set of theoretical propositions. Data analysis is the process in which we move raw data that have been collected as part of the research study and use provide explanations, understanding and interpretation of the phenomena,People and situations which we are aim of analysing qualitative data is to examine gful and symbolic content of that which is found within. This, of course, many ways be dictated by the methodology and data collection methods that already decided to look at the data analysis that is described in the e we are using as a ’ve collected a crazy load of information during your in-depth interviews. From your research supervisor or boss you are expected to do something with that data.

Can imagine you look up to doing analysis, you expect it to be boring and difficult. Data must be put together in tables, figures and maps and the like, to be able to compare it and see new ic content of the interesting ways to do qualitative analysis, especially in social science, is thematic content analysis. Out of the many supercomplex descriptions of how to conduct such analysis, i finally found an easy to go guideline that i would like to share with you. It is merely one of the ways to go, but for each analysis counts this: always keep your research objective and research questions in the back of your mind! Transcribing will quadruple the quality of your analysis, but sometimes there is just not enough time to do all the writing as it can take up to -in the worst cases- six hours to transcribe just one hour of interviewing. Make summaries per interviewed respondent or per topic and don’t forget to add demographic data such as age, gender, marital status and other relevant 2. Coding your your developed coding scheme, you can now identify the rest of your data and put all useful information into this coding scheme.

You can interpret this as literally as cutting out data extracts and putting them together under each code that you wrote on a post it. This goes beyond the thematic content analysis, which brings you to other forms of and thorogood describe this method very clearly and give good examples in their book ‘qualitative methods for health research’ (3rd edition). They dig deeper into the matter by for example describing how to conduct open coding and axial coding, differences between emic and etic levels, deductive and inductive analysis et cetera. They also describe more forms of analysis, in case you want to dig a little you.