Research project lecture by Dr.Ho Cao Viet

1. RESEARCH PROJECT
Data
Analysis
1Chapter 6_Data Analysis
Lecturer: Ho Cao Viet (PhD)
6

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. Classification of Quantitative Data
Categorical
3Chapter 6_Data Analysis
Quantifiable
Nominal Ordinal Discrete Continuous
Interval Ratio
Quantitative Data

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. 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. 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. Example: Graph for discrete data
Chapter 6_Data Analysis 7

8. Example: Graph for continuous
data
Chapter 6_Data Analysis 8

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. Example: Graph for ratio data
Chapter 6_Data Analysis 10
Ratio data of 1 & 2
Qtr is 1:9

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. 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. Insertion of data into a data matrix
Chapter 6_Data Analysis 13
Data
matrix
example

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. 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. 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. 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. 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. Lines graphs
Chapter 6_Data Analysis 19

20. Pie charts
Chapter 6_Data Analysis 20

21. Box plots
Chapter 6_Data Analysis 21
max
min
median
Lower limit of inter-quartile range
Upper limit of inter-quartile

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. 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. Multiple bar charts
Chapter 6_Data Analysis 24

25. Percentage component bar charts
Chapter 6_Data Analysis 25

26. Component bar charts
Chapter 6_Data Analysis 26

27. Graphical techniques –
Relationships
Graphical techniques
Relationships
Chapter 6_Data Analysis 27
• Scatter graphs
– Positive
correlation
– Negative
correlation

28. Scatter graphs
Chapter 6_Data Analysis 28
Engine Displacement (cu. inches)
5004003002001000-100
300
200
100
0
Positive correlation Negative correlation

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. 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. 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. Range, Percentiles & Quartiles
How to measure inter-quartiles ?
Chapter 6_Data Analysis 32

33. Range, Percentiles & Quartiles
What is box-plot ?
Chapter 6_Data Analysis 33

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. 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. 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. Standard deviation (STD)
Chapter 6_Data Analysis 37
• How to measure STD
– By excel: =STDEV(A1:Z99)
– By SPSS:
• Descritpive analysis function

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. 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. 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. 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. Linear & non-linear models
• Linear model
• Non-linear model
Chapter 6_Data Analysis 42

43. Chapter 6_Data Analysis 43
Linear & non-linear models

44. Chapter 6_Data Analysis 44
Linear & non-linear models
Transformation
Linear
Function

45. Chapter 6_Data Analysis 45
Linear & non-linear models
Linear
Function
Transformation

46. Chapter 6_Data Analysis 46
Linear & non-linear models
Transformation
Function
Linear