chapter 6 data analysis iec11

RESEARCH PROJECT

Data Analysis

1 Chapter 6_Data Analysis

Lecturer: Ho Cao Viet (PhD)

6

Student should be able to understand:

How to prepare data for analysis

1

3

2

4


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

Classification of Quantitative Data

Categorical


Quantifiable

Nominal Ordinal Discrete Continuous

Interval Ratio

Quantitative Data

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

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


Discrete & continuous data

1 2 3 4 5 6 7 8 9 10 11 12


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

Example: Graph for discrete data


Example: Graph for continuous data


Example: Graph for interval data


Interval data of 1

& 2 Qtr is 60%

Interval data of 1

& 2 Qtr is 80%

Example: Graph for ratio data


Ratio data of 1 & 2

Qtr is 1:9

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


Data editing & data cleaning


• 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

Insertion of data into a data matrix


Data matrix

example

Data coding


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

Weighting of cases


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

Graphical techniques – Individual results

Graphical techniques

Individual

Results


• Frequency distributions

• Bar charts & histograms

• Line graphs

• Pie charts

• Frequency polygons

• Box plots

Frequency tables & graphs


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

Frequency tables & histograms


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

Lines graphs


Pie charts


Box plots


max

min

median

Lower limit of inter-quartile range

Upper limit of inter-quartile

Graphical techniques – comparisons


Comparison


• Contingency tables

• Multiple Bar charts

• Percentage component bar charts

• Multiple Line graphs

• Multi-Box plots

Contingency tables


Number of Cylinder

Japanese Germany Total

1 40 80 120

2 100 220 320

3 70 120 190

Total 210 420 630

Multiple bar charts


Percentage component bar charts


Component bar charts


Graphical techniques – Relationships


Relationships


• Scatter graphs

– Positive correlation

– Negative correlation

Scatter graphs


Engine Displacement (cu. inches)

5004003002001000-100

Hors

epow

er

300

200

100

0

Positive correlation Negative correlation

Statistical techniques

Measures


• Central tendency

– Mean (Average)

– Mode

– Median

• Dispersion – Range

– Inter-quartile range

– Quartiles

– Deciles & percentiles

– Standard deviation

– Coefficient of variance

Range, Percentiles & Quartiles

How to measure quartiles ?


• Quartile 1 (Q1) = 4

• Quartile 2 (Q2), which is also the Median, = 5


Range of data

http://www.mathsisfun.com/median.html


How to measure quartiles ?



• Quartile 2 (Q2) = 5.5



How to measure inter-quartiles ?



What is box-plot ?



How to calculate inter-quartiles ?

3,4,4|4,7,10|11,12,14|16,17,18


• 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

Standard deviation (STD)


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.


How to measure STD ?


• 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



• How to measure STD

– By excel: =STDEV(A1:Z99)

– By SPSS:

• Descritpive analysis function

Coefficient variation (Cv)


• 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

Statistical techniques – Existence of relationships

Measures


• 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

• Research quesion: are there relationship

between “Age” & “Income” ?

• Variables: Age and Income are 2

quantitative variables).

• Null hypothesis : Age and Income have no

relationship.


Statistical techniques – Existence of relationships

Measures


• 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

Linear & non-linear models • Linear model

• Non-linear model



Linear & non-linear models



Transformation

Linear

Function



Linear

Function

Transformation



Transformation

Function

Linear

chapter 6 data analysis iec11

Education

range of data

quantifiable data

insertion of data

set of data

data analysis lecturer

preparation of data

discrete data chapter

continuous data chapter