Chapter 6.pptx Data Analysis and processing
- 2. QUANTITATIVE DATA ANALYSIS • Quantitative analysis deals with data in the form of numbers and uses mathematical operations to investigate their properties. • The levels of measurement used in the collection of the data i.e. nominal, ordinal, interval and ratio, are an important factor in choosing the type of analysis that is applicable, as is the numbers of cases involved. 2
- 3. . • Some of the primary purposes of quantitative analysis are to: measure make comparisons examine relationships make forecasts test hypotheses construct concepts and theories explore control explain. 3
- 4. .example Case Gender Age Education Income 1 1 4 3 4 2 2 5 3 6 3 2 3 4 4 4 2 5 5 3 5 1 6 3 5 6 1 4 2 2 7 1 3 5 4 • Key: Gender; Male = 1, Female = 2 • Age; 0–18 = 1, 19–25 = 2, 26–35 = 3, 36–45 = 4, 46–55 = 5; 56–65 = 6, 66 and above = 7 • Education level; None = 1, GCSE = 2, A-level = 3, Degree = 4, Masters = 5, Research degree = 6 • Income (Annual); 0–5k = 1, +5−10k = 2, +10−20K = 3, +20−40K = 4, +40−60k = 5, Over 60k = 7 4
- 5. STATISTICAL TESTS (PARAMETRIC) • There are two classes of parametric statistical tests: descriptive and inferential. • Descriptive tests will reveal the ‘shape’ of the data in the sense of how the values of a variable are distributed. • Inferential tests will suggest (i.e. infer) results from a sample in relation to a population. 5
- 6. . Distinction is also made between the number of variables considered in relation to each other: Univariate analysis – analyses the qualities of one variable at a time. Only descriptive tests can be used in this type of analysis. Bivariate analysis – considers the properties of two variables in relation to each other. Inferences can be drawn from this type of analysis. Multivariate analysis – looks at the relationships between more than two variables. Again, inferences can be drawn from results. 6
- 7. 1. Univariate analysis Frequency distribution Measures of central tendency (mean, median and mode) Measures of dispersion (standard deviation, variance, range, ….) Charts and Diagrams Normal and Skewed distribution 7
- 8. 2. Bivariate analysis Correlation coefficient Analysis of variance (standard deviation- common T-test, ANNOVA) 8
- 9. 3. Multivariate analysis Multiple regression analysis Logistic regression analysis 9
- 10. QUALITATIVE DATA ANALYSIS • This type of research analysis is based on data expressed mostly in the form of words- descriptions, accounts, opinions, feelings etc. – rather than on numbers. • This type of data is common whenever people are the focus of the study, particularly in social groups or as individuals. 10
- 11. . • Qualitative analysis is the analysis of qualitative data such as text data from interview transcripts. • Unlike quantitative analysis, which is statistics driven and largely independent of the researcher, qualitative analysis is heavily dependent on the researcher’s analytic and integrative skills and personal knowledge of the social context where the data is collected. • The emphasis in qualitative analysis is “sense making” or understanding a phenomenon, rather than predicting or explaining. 11
- 12. Steps in analysing the data • Qualitative data, represented in words, pictures and even sounds, cannot be analysed by mathematical means such as statistics. • So how is it possible to organize all this data and be able to come to some conclusions about what they reveal? • Unlike the well-established statistical methods of analysing quantitative data, qualitative data analysis is still in its early stages. 12
- 13. . • Miles and Huber man (1994, pp. 10–12) suggest that there are three concurrent flows of action: I. data reduction II. data display III. conclusion drawing/verification 13
- 14. Distinctions between quantitative and qualitative data analysis Quantitative analysis • Based on meanings derived from numbers • Collection results in numerical and standardised data • Analysis conducted through the use of diagrams and statistics Qualitative analysis • Based on meanings expressed through words • Collection results in non- standardised data requiring classification into categories • Analysis conducted through the use of conceptualisation 14