Independent Studies Resource 3: Qualitative and Quantitative Analysis

1. Independent StudiesResource 3: Qualitative and Quantitative Analysis Dr Jill Hanson N509 J.Hanson@derby.ac.uk

2. Aims • To look at different types of qualitative analysis • To look at thematic analysis in more depth • To introduce you to descriptive statistics • To enable you to choose the correct inferential statistical test

3. QualitativeAnalysis • What is it? • It is the analysis of words or actions measured through • Interview transcripts • Field notes (notes taken in the field being studied) • Video • Audio recordings • Images • Documents (reports, meeting minutes, e-mails)

4. What Is It’s Goal? • Qualitative Data Analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that have been collected into some form of explanation, understanding or interpretation of the people and situations we are investigating. • The process of QDA usually involves two things, writing and the identification of themes.

5. Writing • Involves writing about the data and what you have found. • Often what you write may be analytic ideas. • In other cases it may be some form of précis or summary of the data, though this usually contains some analytic ideas.

6. Coding into themes • Looking for themes involves coding. • This enables researchers to retrieve and collect together all the text and other data that they have associated with some thematic idea

7. Example “When you move into your own home, you're alone. There is no bustle of people around the house. I miss having someone to chat to when I get home. I put the TV or some music so there’s some background noise, the silence makes me feel so alone. Sometimes I will be sat watching trash TV and thinking I should be out doing something rather than watching this rubbish. I read a lot but sometimes I am too tired and just want to veg out. But it's been good to move out of mum and dad’s as it's not healthy to rely on them as they won't last forever. I become independent and made my own decisions. It's good they still there when I need them.  It's good to have some distance as when I was at home I was arguing a lot with my dad and that was what made me decide it was time to go.”

9. Interpreting • It is easy to write and code in ways that are nothing more than descriptive summaries of what participants have said or done. • But you need to trick is to move towards explaining why things are as you have found them.

10. Organising • Researchers tend to approach this organisation in one of two ways. • Manual methods • Notes and interviews are transcribed and transcripts and images etc. are copied. The researcher then uses folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. 2. Computer based • Many analysts now also use dedicated computer assisted qualitative data analysis (CAQDAS) packages. Can be tricky to learn and no point unless your sample size is large

11. Aspects of QA that you should consider • Are you interested in interpreting the data in terms of themes / concepts / ideas / interactions / processes? Then YOU have to do the thinking, the analysis. There is no software that can actually do the thinking for you • Data may be messy • You need to give thought to efficient data management. • You need to find out what literature there is around your research topics. • Qualitative data usually cannot be reduced to numbers. • If you ARE just trying to reduce the data to numbers, have you properly understood the reasons for doing qualitative research? • Will the sample size and/or sampling method be telling you anything of value at all? (Many qualitative samples are small and not proper random samples). • If you are generating numbers then you should see the numbers only as pointers to more thinking and researching about where and why there are anomalies or exceptions. This may mean more data collection, more thinking, more testing

12. Action research Field Research Memory work Analytic InductionFramework analysis Mixed methods Biographical research Grounded theory Narrativeanalysis EthnomethodologyMatrix Analysis/Logical Analysis ConversationbAnalysis PhenomenographyComparative analysis Interpretative Phenomenological Analysis (IPA) PhenomenologyDiscourse analysis Life HistoryQCA - Qualitative Comparative Analysis EthnographyLife-world analysis Symbolic interactionism Approaches to analysing QD

13. Thematic Analysis • We will only look at a simple Thematic Analysis which is likely to be appropriate for the majority of you given your experience and the nature of the data you will be collecting. • See Braun & Clarke (2006) for extensive detail

14. Braun & Clarke (2006) • “Thematic analysis is a method for identifying, analysing and reporting patterns (themes) within data” (p.79) • It differs from other analytic methods such as IPA and grounded theory because it does not require the researcher to subscribe to the implicit theoretical commitments of other approaches

15. What is a theme? • “A theme captures something important about the data in relation to the research question, and represents some level of patterned response or meaning within the data set” (Braun & Clarke, 2006, p. 82) • Will be evidenced in a number of participants responses

16. Focus? • You can focus on: • A rich description of themes emerging from the ENTIRE data set • OR • Provide a detailed account of one particular aspect (in relation to a particular question)

17. Inductive versus theoretical thematic analysis • Themes can be identified in • Bottom up way (inductive) • Top down way (theoretical and deductive) • Inductive – themes are strongly linked to data itself, may not bear a strong relation to the actual questions asked • Deductive – themes are driven by the researchers theoretical or analytic interest in the area • Choice usually maps onto how and why you are coding the data • Code for a specific research question (maps onto theoretical approach) • Or specific research question can evolve through the coding process (maps onto the inductive approach)

18. Semantic or Latent Themes • Semantic approach, the themes are identified within the explicit or surface meanings of the data, and the analyst is not looking for anything beyond what a participant has said or what has been written. • In contrast, a thematic analysis at the latent level goes beyond the semantic content of the data, and starts to identify or examine the underlying ideas, assumptions, and conceptualizations / and ideologies /that are theorized as shaping or informing the semantic content of the data. • If we imagine our data three-dimensionally as an uneven blob of jelly, the semantic approach would seek to describe the surface of the jelly, its form and meaning, while the latent approach would seek to identify the features that gave it that particular form and meaning. • Thus, for latent thematic analysis, the development of the themes themselves involves interpretative work, and the analysis that is produced is not just description, but is already theorized

19. How to conduct and write up Thematic Analysis • Make sure you explicitly state the theoretical position you are taking in your write up. • Familiarise yourself with your data • Generate initial codes • Search for themes • Review themes • Define and name themes • Produce report

21. Pitfalls • Failure to actually analyse the data – you must include analytic narrative as well as extracts and these must be directly relevant to your objective • Using the data collection questions as the themes (here no analysis has been done) • Weak or unconvincing analysis – themes do not work, there is too much overlap, themes are not internally coherent/consistent • Mismatch between data and claims made – claims are not supported by data and report does not consider alternative interpretations of data

22. Quantitative Analysis: NUMBERS • When? • If you have used structured interviews or questionnaires • Your data takes the form of numbers, ranks or categorical responses

23. What is Quantitative Analysis • 1. Descriptive statistics – used to describe what your data looks like. NOT ENOUGH AT MASTERS LEVEL • 2. Inferential statistics • Use these to confirm differences between groups or relationships between variables. YOU MUST CONDUCT THESE AT MASTERS LEVEL

24. Types of Variables/Data Forms • Nominal/categorical/group • Where your data takes the form of groups – you have asked the respondent to check a box • e.g. gender, job type • Ratio/Interval/Scale/numeric • Where your data is a number • e.g. Likert scale ratings, age in specific years, weight, height • Ordinal/ranked • Where you have asked respondents to rank in order of preference e.g. 1st, 2nd, 3rd

25. ways to describe data • Measures of central tendency • Mean, mode, median • Measures of dispersion • SD, variance, skewness, kurtosis • You should always report both measures but careful to choose the statistic that is correct for YOUR form of data

26. Descriptive Statistics

27. Mean AAARRRGGGGHHHH! All that says is add up all the scores in the sample and divide by the number of cases! i.e. work out the average….

28. Variance, Standard Deviation Again, ignore the silly sum. Variance means looking at the way the scores in your sample vary around the mean. The standard deviation is a sum which calculates that. The bigger the standard deviation, the further away your scores are from the mean. What does that mean?

29. The normal curve for numeric data 34% 34% 47% 47% 49% 49%

30. The z-score or the“standardized score” A calculation you perform when you want your scores to be normalised and put into a format where they can be compared to scores that have been calculated using different scales. A z score distribution has a mean of 0 and a standard deviation of 1

31. SkewnessSymmetrical distribution • IQ • SAT • “No skew” • “Zero skew” • Symmetrical

32. SkewnessAsymmetrical distribution • “Negative skew” • “Left skew”

33. Skewness(Asymmetrical distribution) • “Positive skew” • “Right skew”

34. Kurtosis leptokurtic mesokurtic platykurtic

35. How do you present descriptive statistics? • Tables • Graphs: • Line • Histogram • Bar • Scatterplots • Box plots • Make sure you use tables and graphs only to ILLUSTRATE WHAT YOU HAVE WRITTEN • Always give them a Table or Figure number and a title

36. Three words about pie charts: don’t use them

37. So, what’s wrong with them • Hard to get a comparison among groups - the eye is very bad in judging relative size of circle slices

38. example

39. The worst graph ever published

40. Conventions for using graphs and tables • ALWAYS give the graph or table a clear title and table or figure number • Make sure the axes in graphs are clearly labelled • Don’t make them over complicated • Never use them as analyses in their own right. They are there to illustrate what you say in your write up only.

41. Inferential statistics • Descriptive statistics and the graphs you can draw using them will often suggest that there are meaningful differences or relationships in your data. • However, how can you tell if those differences are real or if the relationship really exists? Perhaps it is just an artefact. • Inferential tests are used to confirm that what you think you see is real, or not. • At UG and PG level it is not enough to just use descriptive statistics

42. What can you do with inferential statistics? • Identify significant relationships between variables (e.g. see if age is related to weight) • Compare groups for significant differences (e.g. compare males and females on commitment…) • Identify groups of similar cases (e.g. can you cluster people based on their personality and IQ?) • Identify groups of similar variables (e.g. do the items in a scale only represent one factor or do they actually represent two or three?)

43. Deciding which test to use • How is your objective worded? • What kind of data have you collected? • How many variables do you have? • How many samples do you have? • Does your data meet the test requirements? • Parametric tests require your data to meet certain requirements (e.g. normally distributed, numeric data) • Non-parametric tests do not. (There is always a non-parametric equivalent)

44. Identifying Significant Relationships

45. Regression • An extension of correlation where you work out the extent to which one or more variables can predict changes in another variable • Not too difficult if you use the right book!

46. Comparing Groups for Differences • T-tests (where you have parametric data, 1 sample, or two groups) • ANOVA (where you have parametric data and you want to compare more than 2 groups) • Non-parametric equivalents of the above (when your data does not meet requirements) • Mann-whitney/wilcoxon/kruskal-willis/friedman’s ANOVA • Chi-square (for categorical data) • See your SPSS guide for more information

47. Identifying similar cases/variables • Similar cases – cluster analysis • Similar variables – Factor analysis

48. Most likely for you? • Correlations • Maybe regression if you are model testing • T-tests or ANOVA (to compare groups on a numeric/scale/interval variable e.g. do females have a higher verbal IQ than men?) • Chi square (to compare groups on a nominal/categorical variable e.g. do more men than women smoke?) • Good news is that these are all easy to run using SPSS and easy to interpret using Pallant’s fab book!