This document outlines key concepts for analyzing qualitative and quantitative data. It discusses preparing data through editing, coding and inserting into a matrix. Graphical techniques like histograms, scatter plots and box plots are presented for depicting individual, comparative and relational data. Measures of central tendency, dispersion, relationships and models are explained including mean, median, standard deviation, correlation, and linear and non-linear models. The goal is for students to understand how to analyze data using appropriate statistical techniques and data visualization.

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