1. Research Skills 6: Data Analysis

2. Quantitative Data Analysis Data and evidence based on numbers Simplest analysis is use tables, graphs and charts and see a pattern Next descriptive statistical techniques provide more mathematical basis for discerning patterns Finally inferential statistical techniques can establish whether patterns actually exist or are the result of chance

3. Types of Quantitative Data 1 Nominal data (aka categorical data) –Describes categories and has no actual numeric value –Categorise by a number but number has no meaning (1 for male, 2 for female) –Frequency analysis is the only possible analysis Ordinal data –Allocated to a quantitative scale (an order to the data so can rank outcomes, Agree is 4, Agree strongly is 5) –Cannot know how much better different outcomes are, because differences or intervals between ranks are not known –Sometimes called ranked data

4. Types of Quantitative Data 2 Interval data –Similar to ordinal data, but measurements made against a quantitative scale where differences or intervals between scale points are of consistent size –Proportionate ranking of categories, e.g. time units –Addition and subtraction available Ratio data –Similar to interval data, but now has a true zero, e.g. height, weight, profit, turnover –Addition, subtraction, multiplication and division now possible

5. Types of Quantitative Data 3 Discrete data –Each measurement leads to a whole number, not a fraction of a number, e.g. number of children Continuous data –Measurements are not limited to whole numbers but can be measured with greater accuracy depending upon what is possible and what is appropriate

6. Data Coding E.g. in questionnaires, where responses are not already discrete or continuous data, then each predefined answer option needs a numeric code and each theme appearing in answers to open questions needs a code Coding schemes must be: –Mutually exclusive (no overlap in codes) –Exhaustive (cover every pre-defined option and every theme) –Consistently applied Create a code book: –Note codes used and data they apply to, codes for missing data Check for errors: –Unlikely values (too old), non-existent codes, illogical relationships, filtering rules not followed

7. Visual Aids Use tables and charts: –Specific values, highest and lowest values, frequencies, proportions, distributions, and trends –Bar charts for displaying frequencies –Scatter graph for relationships between two variables (plot one set as x, other set as y, and see if trendlines can be found) –Line graphs for showing trends

8. Using Statistics Describing the central tendency: –Mean (arithmetic mean, average): only use with real numbers, not nominal data –Median (mid-point): use with ordinal, interval, and ratio data but not nominal data –Mode (most common): use with nominal, ordinal, interval, ratio Describing the dispersion: –Range: highest to lowest distance –Fractiles (including quartiles) –Standard deviation: average distance of data from the mean

9. Significance and Correlation A calculation of apparent link between two variables Spearman’s rank correlation coefficient (for ordinal data) Pearson’s product moment correlation coefficient (for interval and ratio data) Calculations will have value between -1 and +1 –Positive value means as one goes up, other goes up –Negative coefficient means as one goes up, other goes down –Zero means no relationship –0.3 to 0.7 (+ or -) suggests reasonable correlation –Closer to -1 or +1, more perfect. Also rare, but not impossible Null hypothesis and tests of significance

10. Quantitative Evaluation Advantages: –Provides (seeming) scientific respectability –Based on well-established techniques –Tests of significance provide confidence in findings –Based on measurable quantities and checkable by others –Large volumes can be analysed by software Disadvantages: –Sophisticated statistical tests run without understanding may not lead to understanding of data –Analysis is only as good as the data –Need to be clear on statistical tests and data they required before data gathered –Researchers can influence results (scales, groupings, etc.) Computer support available (Excel, SPSS, Matlab, R)

11. Qualitative Data Analysis Includes all non-numeric data (text, images, audio, video, etc.) Can employ quantitative analysis on qualitative data –Frequency counts on words/phrases, percentage allocations Qualitative data analysis is abstracting from data to themes Need to be able to provide sufficient information on analysis to provide credibility to results

12. Analysing Textual Data 1 Data needs to be prepared for analysis Transcribed into double-margin sheets Always work on duplicates (electronic or paper)

13. Analysing Textual Data 2 Read through all of data to gain a general impression Divide material into: –Segments that seem irrelevant for current study –Segments that provide research context –Segments that seem relevant to current study Categorise each unit of relevant data –May be word, phrase, sentence or longer –Needs a label –May be derived from existing theories or those of own devising (deductive approach) –May be observed in data (from respondents, occur from reading), letting the data speak (inductive approach)

14. Analysing Textual Data 3 Refine categories – some need to be combined, some to be fissioned. Will require multiple readings of the data Look for themes and inter-connections between elements and categories May need to copy-and-paste data together Then need to explain what the data says by linking to theories, to the literature, to the context Do not settle on first explanation Keep notes on process, stages and ideas

15. Analysing Non-textual Data Audio, videos, images, etc. need to be prepared for analysis Make duplicates, location coding (time-coding for video, sound and animations) and enough room for notes Analyse the data looking for themes: –Go beyond what can be seen to: –Cultural context, messages it conveys, and symbols and signs Video or animation: –Denotation: what or who is depicted, what genre or style –Connotation: what ideas and values expressed? –Production: camera, angles, lighting, colour, rendering, etc –Author: who produced, what circumstances, why –Viewer: how do viewers interpret

16. Grounded Theory 1 Particular approach to qualitative research: do field research, analyse data to see what theory emerges, so theory grounded in the data Inductive approach, and is concerned with generating theories

17. Grounded Theory 2 Start with one person or instance, generate data, analyse it, and on basis of their first emerging ideas from data then decide who or what to look at next Data generation leads to data analysis to data generation. Discovery trail using theoretical sampling of successive site, peoples, or sources, chosen to test or refine (new) theories, ideas or categories as they emerge Process only finishes when further data no longer triggers changes to data categories and emerging theory - known as the point of theoretical saturation

18. Grounded Theory 3 Data analysis has three phases of coding: 1.Open coding: initial process of labelling units of data, based on terms and concepts found in data 2.Axial coding: as code list emerges, move to a higher / more abstract analysis and looks for relationships between codes – some will be incorporated under broader headings, some will be more important (axial) 3.Selective coding: researcher focuses on core codes – those that are seen as vital for any explanation (theory) of the phenomenon under investigation Uses constant comparative method: –As any new code is identified, revisit all coded material to see if it can be better coded. New theory checked against all data

19. Qualitative Evaluation Advantages: –Data and analysis can be rich and detailed –Possibility of alternative explanations Disadvantages: –Volume of data can be overwhelming –Interpretation more closely tied to investigators, so more tentative conclusions –Non-textual data and analysis does not easily fit written formats Computer support available