How to make data analysis in research

A research g the proposal - data your research proposal, you will also discuss how you will conduct an analysis of your data. By the time you get to the analysis of your data, most of the really difficult work has been done. It's much more difficult to define the research problem, develop and implement a sampling plan, develop a design structure, and determine your measures. If you have done this work well, the analysis of the data is usually a fairly straightforward you look at the various ways of analyzing and discussing data, you need to review the differences between qualitative research/quantitative research and qualitative data/quantitative do i have to analyze data? The analysis, regardless of whether the data is qualitative or quantitative, may:Describe and summarize the fy relationships between fy the difference between r, you distinguished between qualitative and quantitative research. It is highly unlikely that your research will be purely one or the other – it will probably be a mixture of the two example, you may have decided to ethnographic research, which is qualitative. In your first step, you may have taken a small sample (normally associated with qualitative research) but then conducted a structured interview or used a questionnaire (normally associated with quantitative research) to determine people’s attitudes to a particular phenomenon (qualitative research). Source of confusion for many people is the belief that qualitative research generates just qualitative data (text, words, opinions, etc) and that quantitative research generates just quantitative data (numbers). Sometimes this is the case, but both types of data can be generated by each approach. For instance, a questionnaire (quantitative research) will often gather factual information like age, salary, length of service (quantitative data) – but may also collect opinions and attitudes (qualitative data). It comes to data analysis, some believe that statistical techniques are only applicable for quantitative data.

How to make a data analysis in research

There are many statistical techniques that can be applied to qualitative data, such as ratings scales, that has been generated by a quantitative research approach. Even if a qualitative study uses no quantitative data, there are many ways of analyzing qualitative data. For example, having conducted an interview, transcription and organization of data are the first stages of analysis. Manchester metropolitan university (department of information and communications) and learn higher offer a clear introductory tutorial to qualitative and quantitative data analysis through their analyze this!!! In additional to teaching about strategies for both approaches to data analysis, the tutorial is peppered with short quizzes to test your understanding. The site also links out to further te this tutorial and use your new knowledge to complete your planning guide for your data are many computer- and technology-related resources available to assist you in your data general ing research (lots of examples of studies, and lots of good background, especially for qualitative studies). Data tative data analysis rice virtual lab in statistics also houses an online textbook, hyperstat. The site also includes a really useful section of case studies, which use real life examples to illustrate various statistical sure which statistical test to use with your data? The diagram is housed within another good introduction to data statistical analysis and data management computer-aided qualitative data analysis are many computer packages that can support your qualitative data analysis. The following site offers a comprehensive overview of many of them: online r package that allows you analyze textual, graphical, audio and video data. No free demo, but there is a student has add-ons which allow you to analyze vocabulary and carry out content analysis.

How to make data analysis in research paper

Use these questions and explanations for ideas as you complete your planning guide for this common worries amongst researchers are:Will the research i’ve done stand up to outside scrutiny? Questions are addressed by researchers by assessing the data collection method (the research instrument) for its reliability and its ility is the extent to which the same finding will be obtained if the research was repeated at another time by another researcher. The following questions are typical of those asked to assess validity issues:Has the researcher gained full access to the knowledge and meanings of data? Procedure is perfectly reliable, but if a data collection procedure is unreliable then it is also invalid. The other problem is that even if it is reliable, then that does not mean it is necessarily ulation is crosschecking of data using multiple data sources or using two or more methods of data collection. The many sources of non-sampling errors include the following:Researcher error – unclear definitions; reliability and validity issues; data analysis problems, for example, missing iewer error – general approach; personal interview techniques; recording dent error – inability to answer; unwilling; cheating; not available; low response section was discussed in elements of the proposal, where there are many online resources, and you have reflective journal entries that will support you as you develop your ideas for reliability and validity in your planning guide. In addition this writing tutorial specifically addresses the ways in which this can be explained in your research to writing the proposal - different ibility version | skip to content | change text university > learning support > research students > efine your goalstrack your path: your projectcreate your working tand the process of graduate your thesis your thesis into your your yourselfyour learning you want from p your personal p research e for life after the research questionsdeveloping research tical approachconceptual methods will you use? Methods in the social p a rganise the thesis writing processplan the started, keep ge your a research with writer's ul readingread to manage the quantity of reading te your argumentcritically t your work in the story in your ic writing presence in the ating quotationsforms of rase or quotation? Writing stylethe language of thesis tical e and styles of and ting your n your writing skillsdeveloping a good g and ptalk to your supervisorsestablish expectations and ng to your g good to conduct interviews and focus groupshow to conduct an to conduct a focus s your t with confidencepresent a formal at sample thesesguide to analysing sample a research ure your thesiscomponents of a l thesis the s your the literaturewhat are the examiners looking for? Yourself in relation to previous lling the dinner ting your own ng introductions and up your data analysisreport your s your your phd thesis examiners g for publicationwhat to publish, and g an article for ng and resubmitting. Write your data section and the next, on reporting and discussing your findings, deal with the body of the thesis.

This is where you present the data that forms the basis of your investigation, shaped by the way you have thought about it. This section is concerned with presenting the analysis of the this part of research writing there is a great deal of variation. For example, a thesis in oral history and one in marketing may both use interview data that has been collected and analysed in similar ways, but the way the results of this analysis are presented will be very different because the questions they are trying to answer are different. In all cases, though, the presentation should have a logical organisation that reflects:The aims or research question(s) of the project, including any hypotheses that have been research methods and theoretical framework that have been outlined earlier in the are not simply describing the data. You need to make connections, and make apparent your reasons for saying that data should be interpreted in one way rather than chapter needs an introduction outlining its e from a chemical engineering phd thesis:In this chapter, all the experimental results from the phenomenological experiments outlined in section 5. The new data may be found in appendix e from a literature phd thesis:The principal goal of the vernacular adaptor of a latin saint's life was to edify and instruct his audience. In this chapter i shall try to show to what extent our texts conform to vernacular conventions of a well-told story of a saint, and in what ways they had to modify their originals to do so, attempting also to identify some of the individual characteristics of the three that, the organisation will vary according to the kind of research being reported. Below are some important principles for reporting experimental, quantitative (survey) and qualitative data will be presented in the form of tables, graphs and diagrams, but you also need to use words to guide readers through your data:Explain the tests you performed (and why). Show any negative results too, and try to explain te what results are meaningful any immediate tative (survey) are generally accepted guidelines for how to display data and summarize the results of statistical analyses of data about populations or groups of people, plants or animals. However, this display needs to be presented in an informative the reader of the research question being addressed, or the hypothesis being the reader what you want him/her to get from the which differences are ght the important trends and differences/te whether the hypothesis is confirmed, not confirmed, or partially analysis of qualitative data cannot be neatly presented in tables and figures, as quantitative results can be. Try to make your sections and subsections reflect your thematic analysis of the data, and to make sure your reader knows how these themes evolved.

Headings and subheadings, as well as directions to the reader, are forms of signposting you can use to make these chapters easy to all types of research, the selection of data is important. You will not include pages of raw data in your text, and you may not need to include it all in an appendix e what you need to support the points you want to your selection criteria and gruba (2002) offer some good advice about how much to put in an appendix: 'include enough data in an appendix to show how you collected it, what form it took, and how you treated it in the process of condensing it for presentation in the results chapter. Send us your feedback and suggestions: current students/staff | public ght © 2003 monash university abn 12 377 614 012 - caution - privacy - cricos provider number: updated: 02 april 2009 - maintained by lsweb@ - accessibility ibility version | skip to content | change text university > learning support > research students > efine your goalstrack your path: your projectcreate your working tand the process of graduate your thesis your thesis into your your yourselfyour learning you want from p your personal p research e for life after the research questionsdeveloping research tical approachconceptual methods will you use? Send us your feedback and suggestions: current students/staff | public ght © 2003 monash university abn 12 377 614 012 - caution - privacy - cricos provider number: updated: 02 april 2009 - maintained by lsweb@ - accessibility analysis – analyzing data in qualitative is data analysis? Is more than coding, sorting and gh some researchers suggest that disassembling, coding, and then sorting and sifting through your data, is the primary path to analyzing data / data analysis. But as other rightly caution, intensive data coding, disassembly, sorting, and sifting, is neither the only way to analyze your data nor is it necessarily the most appropriate strategy. It has been argued that they also fit the notice, collect, and think process invariably also belonging to the data analysis the thinking process the researcher examines the things that have been collected. The goals of the data analysis are:To make some type of sense out of each data look for patterns and relationships both within a collection, and also across collections, make general discoveries about the phenomena you are use an analogy: after sorting the pieces of a jigsaw puzzle into groups, it is important to inspect individual pieces to determine how they fit together and form smaller parts of the picture (e. When analyzing data, one compares and contrasts each of the things that have been noticed in order to discover similarities and differences, build typologies, or find sequences and patterns. In the process one might also stumble across both “wholes” and, quite literally, holes in the the jigsaw puzzle approach to analyzing data is frequently productive and fruitful, it also entails some risks and problems that also translate to qualitative data analysis. For example, when coding data, the simple act of breaking down data into its constituent parts can distort and mislead the analyst and distort the final data analysis.

A serious problem is sometimes created by the very fact of organizing the material through coding or breaking it up into segments in that this destroys the totality of philosophy as expressed by the interviewee-which is closely related to the major goal of the study that informs the data analysis. A proper data analysis acknowledges this problem and, in fact, takes precautions already when first analyzing ative data analysis hare uses cookies to improve functionality and performance, and to provide you with relevant advertising. 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 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! 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. 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? 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. 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. Http:///michael/qual_ g ready to write• must come close to the point of maturation – be aware of resource constraints and sponsors interests• organize your materials – list of codes – summary device: tables, thematic structure• writing a chronicle (“writing it out of your head”) ng a style and focus• format • research report • scientific research article • report to donor • field report • evaluation report...

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 cation for interactive course - linkedin writing for course - linkedin oint: designing better 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..