Data Analysis Qualitative Data Quantitative DataIt is a systematic approach to investigations duringwhich numerical data is collected and/or theresearcher transforms what is collected orobserved into numerical data. It often describes asituation or event, answering the ‘what’ and ‘howmany’ questions you may have about something.This is research which involves measuring orcounting attributes (i.e. quantities).What does this mean?Many of you will have seen adverts which say ‘8 outof 10 people prefer Android over iphone’.We’re saying that in a survey of 10 people, 8 ofthem preferred Android.We’ve taken the people (sample size), counted whatthey prefer and report the findings as numbers.A quantitative approach is often concerned withfinding evidence to either support or contradict anidea or hypothesis you might have.A hypothesis is where a predicted answer to aresearch question is proposed, for example, youmight propose that if you give a student training inhow to use a search engine it will improve theirsuccess in finding information on the Internet.Quantitative data analysis enables you to makesense of data by: organising them summarising them doing exploratory analysisTo communicate the meaning to others bypresenting data as: tables graphical displays summary statistics where responses are similar if there are differences if there is a relationship Allow for a broader study, involving a greaternumber of subjects, and enhancing thegeneralisation of the results Can allow for greater objectivity and accuracy ofresults. Generally, quantitative methods are designed toprovide summaries of data that supportgeneralisations about the phenomenon understudy. In order to accomplish this, quantitative researchusually involves few variables and many cases,and employs prescribed procedures to ensurevalidity and reliability using standards means that the research can bereplicated, and then analysed and compared withsimilar studies. personal bias can be avoided by researcherskeeping a ‘distance’ from participating subjectsand employing subjects unknown to them Results are limited as they provide numericaldescriptions rather than detailed narrative andgenerally provide less elaborate accounts ofhuman perception the research is often carried out in an unnatural,artificial environment so that a level of controlcan be applied to the exercise. This level ofcontrol might not normally be in place in the realworld yielding laboratory results as opposed toreal world results Preset answers will not necessarily reflect howpeople really feel about a subject and in somecases might just be the closest match. the development of standard questions byresearchers can lead to ‘structural’ bias and falserepresentation, where the data actually reflectsthe view of them instead of the participatingsubject. Qualitative data does not simply count things, butis a way of recording people’s attitudes, feelingsand behaviours in greater depth. Provides depth and detail : looks deeper thananalysing ranks and counts by recordingattitudes, feelings and behaviours Creates openness: encouraging people to expandon their responses can open up new topic areasnot initially considered Simulates people’s individual experiences: adetailed picture can be built up about why peopleact in certain ways and their feelings about theseactions Attempts to avoid pre-judgements: if usedalongside quantitative data collection, it canexplain why a particular response was given Usually fewer people studied: collection ofqualitative data is generally more time consumingthat quantitative data collection and thereforeunless time, staff and budget allows it is generallynecessary to include a smaller sample size. Less easy to generalise: because fewer people aregenerally studied it is not possible to generaliseresults to that of the population. Usually exactnumbers are reported rather than percentages. Difficult to make systematic comparisons: forexample, if people give widely differing responsesthat are highly subjective. Dependent on skills of the researcher: particularlyin the case of conducting interviews, focusgroups and observation. 1. Transcription of notes – This is usually the firstaction after you have collected your data fromquestionnaires, interviews, observation etc 2. Initial processing – This is usually done onceyou transcribed you notes, it usually involvedreading and re-reading your notes looking forcategories and themes 3. Return to observe or ask further questions –This is usually done after you have done someinitial processing, and can then be done at anytime during your study if and when required 4. Summary sheets for each response – This isusually done after you have transcribed yournotes, summaries can then be used as a memoryjogger when your are looking for categories andthemes, or if you need to return to observe or askfurther questions 5. Identify categories relating to patterns orthemes identified – This is usually done after youhave identified the core categories of your study,which are found in your transcribed notes 6. Coding – This is usually done after you haveidentified categories relating to the patterns orthemes identified. 7. Discussion – This usually takes place after youhave done some analysis of your data, when youhave found out if any interesting patterns orthemes have emerged 8. Conclusions – Conclusions sum up the analysisyour have done of your data and any interestingdiscussions 9. Recommendations – Recommendations tend tocome at the end of you study, they may includespecific recommendations relating to the findingsof your study or may suggest where extra datacollection and analysis activities are required Data can be coded according to categories andsub-categories identified by reading and rereading the data collected. Descriptive coding Analytic or Theoretical coding Coding usually starts with a summary of the textyou are examining. This kind of coding is called descriptivecoding because it essentially forms a summarydescription of what is in the transcript or text.Analysis of data on the basis of Themes, Topics Ideas, Concepts Terms, Phrases Keywords Coded into categories and sub-categories canhelp to find patterns and themes Simple counts to see if patterns, themes andtrends can be grouped to see stronger themesand patterns according to frequency ofoccurrence Establish similarities and differences between thedata groups and looked at interrelationshipsbetween different parts of the data. Build a logical chain of evidence which now needsto be written up and presented. This will depend on what you have been studying,the type of methods used and the best way topresent your findings.
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