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# Chapter 6 data analysis iec11

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Research project lecture by Dr.Ho Cao Viet

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### Chapter 6 data analysis iec11

1. 1. RESEARCH PROJECT Data Analysis 1Chapter 6_Data Analysis Lecturer: Ho Cao Viet (PhD) 6
2. 2. Student should be able to understand: How to prepare data for analysis 1 3 2 4 2Chapter 6_Data Analysis Learning objectives Type of qualitative data The use of graph in data analysis The use of statistical techniques in data analysis 5 How to analyze qualitative data
3. 3. Classification of Quantitative Data Categorical 3Chapter 6_Data Analysis Quantifiable Nominal Ordinal Discrete Continuous Interval Ratio Quantitative Data
4. 4. Nominal & Ordinal Data • Nominal data (Descriptive data): – Cannot be measured numerically – Can be categorized • Ordinal data (Ranked data): – Ex: results of class mathematics test  no individual scores  place students in rank order Chapter 6_Data Analysis 4
5. 5. Quantifiable Data • Can be measured numerically as qualities • Have individual numerical values • Discrete data: be measured accurately on a scale/whole numbers – Ex: number of illness person, number of goals • Continuous data: take on any value – Ex: temperature in HCMC, scores of students Chapter 6_Data Analysis 5
6. 6. Discrete & continuous data 1 2 3 4 5 6 7 8 9 10 11 12 Chapter 6_Data Analysis 6 26 27.5 28 28.2 29 30 30.5 30.8 29.5 29.2 27 25 Temperature on day Month Continuous data Discrete data Number of patients Day 1 2 3 4 5 6 7 8 9 10 11 12 26 27 28 28 29 30 30 30 29 29 27 25
7. 7. Example: Graph for discrete data Chapter 6_Data Analysis 7
8. 8. Example: Graph for continuous data Chapter 6_Data Analysis 8
9. 9. Example: Graph for interval data Chapter 6_Data Analysis 9 Interval data of 1 & 2 Qtr is 60% Interval data of 1 & 2 Qtr is 80%
10. 10. Example: Graph for ratio data Chapter 6_Data Analysis 10 Ratio data of 1 & 2 Qtr is 1:9
11. 11. Preparation of data analysis • 1st step: Data editing and cleaning • 2nd step: Insertion of data into a data matrix • 3rd step: data coding • 4th step: weighting of case Chapter 6_Data Analysis 11
12. 12. Data editing & data cleaning Chapter 6_Data Analysis 12 • Objectives of data editing: – Identify omissions, ambiguities, errors – Take place during and after data collection – Missing data • Missing data: – Available question – Respondent refused – Unable to answer – Omitted the question
13. 13. Insertion of data into a data matrix Chapter 6_Data Analysis 13 Data matrix example
14. 14. Data coding Chapter 6_Data Analysis 14 Code Description Variable 1 <15 yrs Variable 1 = AGE 2 15-<60 yrs 3 >60 yrs 4 Primary Variable 2 = EDU 5 Secondary 6 High school 7 University 8 Male Variable 3 = SEX 9 Female 10 Marriage Variable 4 = MAR STATUS 11 Divorce 12 Single
15. 15. Weighting of cases Chapter 6_Data Analysis 15 Stratum (*) Response rate (%) 1 90 2 75 3 60 • Stratum 1: 90/90 = 1.0 • Stratum 2: 90/75 = 1.2 • Stratum 3: 90/60 = 1.5 (*): using stratified random sampling
16. 16. Graphical techniques – Individual results Graphical techniques Individual Results Chapter 6_Data Analysis 16 • Frequency distributions • Bar charts & histograms • Line graphs • Pie charts • Frequency polygons • Box plots
17. 17. Frequency tables & graphs Chapter 6_Data Analysis 17 Frequency table of income per capita Code Frequency Percent Valid Percent Cumulative Percent 1 5 31,3 31,3 31,3 2 6 37,5 37,5 68,8 3 5 31,3 31,3 100,0 Total 16 100,0 100,0 Code: 1 : < 20,000 USD per month 2: 20,000 - < 40,000 3: > 40,000
18. 18. Frequency tables & histograms Chapter 6_Data Analysis 18 Frequency Percent Valid Percent Cumulative Percent Code 3 Cylinders 4 1,0 1,0 1,0 4 Cylinders 207 51,0 51,1 52,1 5 Cylinders 3 ,7 ,7 52,8 6 Cylinders 84 20,7 20,7 73,6 8 Cylinders 107 26,4 26,4 100,0 Total 405 99,8 100,0 Missing System 1 ,2 Total 406 100,0
19. 19. Lines graphs Chapter 6_Data Analysis 19
20. 20. Pie charts Chapter 6_Data Analysis 20
21. 21. Box plots Chapter 6_Data Analysis 21 max min median Lower limit of inter-quartile range Upper limit of inter-quartile
22. 22. Graphical techniques – comparisons Graphical techniques Comparison Chapter 6_Data Analysis 22 • Contingency tables • Multiple Bar charts • Percentage component bar charts • Multiple Line graphs • Multi-Box plots
23. 23. Contingency tables Chapter 6_Data Analysis 23 Number of Cylinder Japanese Germany Total 1 40 80 120 2 100 220 320 3 70 120 190 Total 210 420 630
24. 24. Multiple bar charts Chapter 6_Data Analysis 24
25. 25. Percentage component bar charts Chapter 6_Data Analysis 25
26. 26. Component bar charts Chapter 6_Data Analysis 26
27. 27. Graphical techniques – Relationships Graphical techniques Relationships Chapter 6_Data Analysis 27 • Scatter graphs – Positive correlation – Negative correlation
28. 28. Scatter graphs Chapter 6_Data Analysis 28 Engine Displacement (cu. inches) 5004003002001000-100 300 200 100 0 Positive correlation Negative correlation
29. 29. Statistical techniques Measures Chapter 6_Data Analysis 29 • Central tendency – Mean (Average) – Mode – Median • Dispersion – Range – Inter-quartile range – Quartiles – Deciles & percentiles – Standard deviation – Coefficient of variance
30. 30. Range, Percentiles & Quartiles How to measure quartiles ? Chapter 6_Data Analysis 30 • Quartile 1 (Q1) = 4 • Quartile 2 (Q2), which is also the Median, = 5 • Quartile 3 (Q3) = 8 Range of data
31. 31. Range, Percentiles & Quartiles How to measure quartiles ? Chapter 6_Data Analysis 31 • Quartile 1 (Q1) = 3 • Quartile 2 (Q2) = 5.5 • Quartile 3 (Q3) = 7
32. 32. Range, Percentiles & Quartiles How to measure inter-quartiles ? Chapter 6_Data Analysis 32
33. 33. Range, Percentiles & Quartiles What is box-plot ? Chapter 6_Data Analysis 33
34. 34. Range, Percentiles & Quartiles How to calculate inter-quartiles ? 3,4,4|4,7,10|11,12,14|16,17,18 Chapter 6_Data Analysis 34 • Quartile 1 (Q1) = (4+4)/2 = 4 • Quartile 2 (Q2) = (10+11)/2 = 10.5 • Quartile 3 (Q3) = (14+16)/2 = 15 • The Lowest Value is 3, • The Highest Value is 18 Q3 - Q1 = 15 - 4 = 11
35. 35. Standard deviation (STD) Chapter 6_Data Analysis 35 The standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. - examples are pretty tightly bunched together & bell-shaped curve is steep  the standard deviation is small. - examples are spread apart & bell curve is relatively flat  relatively large standard deviation.
36. 36. Standard deviation (STD) How to measure STD ? Chapter 6_Data Analysis 36 • xi = one value in your set of data • Avg (x) = the mean (average) of all values x in your set of data • N = the number of values x in your set of data
37. 37. Standard deviation (STD) Chapter 6_Data Analysis 37 • How to measure STD – By excel: =STDEV(A1:Z99) – By SPSS: • Descritpive analysis function
38. 38. Coefficient variation (Cv) Chapter 6_Data Analysis 38 • Why to measure Cv: – Compare spread of data around the mean of different distribution – High value of CV  more spread out of data • How to measure Cv: – Coefficient of Variation Cv = Standard Deviation / Mean
39. 39. Statistical techniques – Existence of relationships Measures Chapter 6_Data Analysis 39 • Chi-squared text • T-tests • Analysis of variance • Pearson’s product moment correlation coefficient • Coefficient of determination • Regression equations • Spearman’s rank correlation coefficient
40. 40. CORRELATION • Research quesion: are there relationship between “Age” & “Income” ? • Variables: Age and Income are 2 quantitative variables). • Null hypothesis : Age and Income have no relationship. Chapter 6_Data Analysis 40
41. 41. Statistical techniques – Existence of relationships Measures Chapter 6_Data Analysis 41 • Chi-squared text • T-tests • Analysis of variance • Pearson’s product moment correlation coefficient • Coefficient of determination • Regression equations • Spearman’s rank correlation coefficient CORRELATION
42. 42. Linear & non-linear models • Linear model • Non-linear model Chapter 6_Data Analysis 42
43. 43. Chapter 6_Data Analysis 43 Linear & non-linear models
44. 44. Chapter 6_Data Analysis 44 Linear & non-linear models Transformation Linear Function
45. 45. Chapter 6_Data Analysis 45 Linear & non-linear models Linear Function Transformation
46. 46. Chapter 6_Data Analysis 46 Linear & non-linear models Transformation Function Linear