1. The document discusses various techniques for quantitative data analysis, including statistical methods like correlation, t-tests, analysis of variance, regression analysis, and econometric modeling. 2. Both descriptive and inferential statistics are covered, with descriptive statistics used to describe or make abstractions about a population based on a sample, while inferential statistics allow inferences to be made about a population based on a sample. 3. Several principles of data analysis are provided, including the need to analyze rather than just narrate data, link the analysis back to research objectives and questions, and ensure findings are supported by data.

1. 1
SAJJAD KHUDHUR ABBAS
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
Episode 12 : Research
Methodology ( Part 2 )

2. Techniques of Data Analysis

3. Data analysis ??
• Approach to de-synthesizing data, informational,
and/or factual elements to answer research questions
• Method of putting together facts and figures
to solve research problem
• Systematic process of utilizing data to address
research questions
• Breaking down research issues through utilizing
controlled data and factual information

4. Qualitative & Quantitative Research
Qualitative Quantitative
"All research ultimately has
a qualitative grounding"
- Donald Campbell
"There's no such thing as
qualitative data.
Everything is either 1 or 0"
- Fred Kerlinger
The aim is a complete, detailed
description.
The aim is to classify features,
count them, and construct
statistical models in an attempt to
explain what is observed.
Researcher may only know roughly
in advance what he/she is looking
for.
Researcher knows clearly in
advance what he/she is looking
for.
Recommended during earlier
phases of research projects.
Recommended during latter
phases of research projects.
The design emerges as the study
unfolds.
All aspects of the study are
carefully designed before data is
collected. 4

5. 5
Qualitative Quantitative
Researcher is the data gathering
instrument.
Researcher uses tools, such as
questionnaires or equipment to
collect numerical data.
Data is in the form of words,
pictures or objects.
Data is in the form of numbers and
statistics.
Subjective - individuals�
interpretation of events is
important ,e.g., uses participant
observation, in-depth interviews
etc.
Objective seeks precise�
measurement & analysis of target
concepts, e.g., uses surveys,
questionnaires etc.
Qualitative data is more 'rich', time
consuming, and less able to be
generalized.
Quantitative data is more efficient,
able to test hypotheses, but may
miss contextual detail.
Researcher tends to become
subjectively immersed in the
subject matter.
Researcher tends to remain
objectively separated from the
subject matter.

6. In this lesson we look only into Quantitative
Data Analysis
Mathematical & Statistical analysis

7. Statistical Methods
Statistics: Analysis of “meaningful” quantities about a sample of
objects, things, persons, events, phenomena, etc. To infer
scientific outcome
MEANINGFUL???
I checked 3 Proton Saga 2008 model cars. In two of them the
gear box is not working properly.
Inference: Proton Saga 2008 model has a gear box defect!!!!!

8. Important Statistical processesImportant Statistical processes
Correlation and Dependence
Correlation and dependence are any of a broad class of statistical
relationships between two or more random variables or observed data values.
Correlations are useful because they can indicate a predictive relationship that
can be exploited in practice.
For example, an electrical utility may produce less power on a mild day based
on the correlation between electricity demand and weather.
Correlations can also suggest possible causal, or mechanistic relationships;
however, statistical dependence is not sufficient to demonstrate the presence
of such a relationship. 8

9. Student T-Test
A t-test is usually done to compare two sets of data. It is most
commonly applied when the test statistic would follow a
normal distribution.
For example, suppose we measure the size of a cancer
patient's tumour before and after a treatment. If the
treatment is effective, we expect the tumour size for many of
the patients to be smaller following the treatment.
9

10. Important Statistical processesImportant Statistical processes
Analysis of variance (ANOVA)
Analysis of variance is a collection of statistical models, and
their associated procedures, in which the observed variance is
partitioned into components due to different sources of
variation.
In its simplest form ANOVA provides a statistical test of
whether or not the means of several groups are all equal, and
therefore generalizes Student's two-sample t-test to more
than two groups.
10

11. ANOVAs are helpful because they possess a certain advantage over a two-sample t-
test.
Doing multiple two-sample t-tests would result in a largely increased chance of
committing a type I error.
For this reason, ANOVAs are useful in comparing three or more means
11

12. • Multivariate analysis of variance MANOVA
MANOVA is a generalized form of univariate analysis of
variance (ANOVA). I
It is used in cases where there are two or more dependent
variables.
As well as identifying whether changes in the independent
variable(s) have significant effects on the dependent variables,
MANOVA is also used to identify interactions among the
dependent variables and among the independent variables
12

13. Regression analysis
Regression analysis includes any techniques for modeling and
analyzing several variables, when the focus is on the
relationship between a dependent variable and one or more
independent variables.
More specifically, regression analysis helps us understand
how the typical value of the dependent variable changes
when any one of the independent variables is varied, while
the other independent variables are held fixed.
Most commonly, regression analysis estimates the conditional
expectation of the dependent variable given the independent
variables — that is, the average value of the dependent
variable when the independent variables are held fixed 13

14. Econometric modelling
Econometric models are statistical models used in econometrics.
An econometric model specifies the statistical relationship that is
believed to hold between the various economic quantities pertaining a
particular economic phenomena under study.
14

15. Important Statistical processesImportant Statistical processes
• Two main categories:
* Descriptive statistics
* Inferential statistics
15

16. Descriptive statistics
• Use sample information to explain/make
abstraction of population “phenomena”.
Common “phenomena”:
* Association
* Central Tendency
* Causality
* Trend, pattern, dispersion, range
• Used in non-parametric analysis (e.g. chi-
square, t-test, 2-way anova)

17. • Association is any relationship between two
measured quantities that renders them
statistically dependent
• central tendency relates to the way in which
quantitative data tend to cluster around some
value
• Causality is the relationship between an event
(the cause) and a second event (the effect),
where the second event is a consequence of
the first
17

18. Examples of “abstraction” of phenomena
Trends in property loan, shop house demand & supply
0
50000
100000
150000
200000
Year (1990 - 1997)
Loant oproperty sector (RM
million)
32635.8 38100.6 42468.1 47684.7 48408.2 61433.6 77255.7 97810.1
Demandf or shop shouses(units) 71719 73892 85843 95916 101107 117857 134864 86323
Supply of shophouses(unit s) 85534 85821 90366 101508 111952 125334 143530 154179
1 2 3 4 5 6 7 8
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
Batu
Pahat
JohorBahruKluang
Kota
TinggiM
ersing
M
uarPontianSegam
at
District
No.ofhouses
1991
2000
0
2
4
6
8
10
12
14
0-4
10-14
20-24
30-34
40-44
50-54
60-64
70-74
Age Category (Years Old)
Proportion(%)

19. Examples of “abstraction” of phenomena
Demand (% sales success)
12010080604020
Price(RM/sq.ft.builtarea)
200
180
160
140
120
100
80
1 0 . 0 0 2 0 . 0 0 3 0 . 0 0 4 0 . 0 0 5 0 . 0 0 6 0 . 0 0
1 0 . 0 0
2 0 . 0 0
3 0 . 0 0
4 0 . 0 0
5 0 . 0 0
- 1 0 0 . 0 0
- 8 0 . 0 0
- 6 0 . 0 0
- 4 0 . 0 0
- 2 0 . 0 0
0 . 0 0
2 0 . 0 0
4 0 . 0 0
6 0 . 0 0
8 0 . 0 0
1 0 0 . 0 0
DistancefromRakaia(km)
D i s t a n c e f r o m A s h u r t o n ( k m )
%
prediction
error

20. Inferential statistics
• Using sample statistics to infer some
“phenomena” of population parameters
• Common “phenomena”:
* One-way r/ship
* Multi-directional r/ship
* Recursive
• Use parametric analysis
Y1 = f(Y2, X, e1)
Y2 = f(Y1, Z, e2)
Y1 = f(X, e1)
Y2 = f(Y1, Z, e2)
Y = f(X)

21. Examples of relationship
Coefficientsa
1993.108 239.632 8.317 .000
-4.472 1.199 -.190 -3.728 .000
6.938 .619 .705 11.209 .000
4.393 1.807 .139 2.431 .017
-27.893 6.108 -.241 -4.567 .000
34.895 89.440 .020 .390 .697
(Constant)
Tanah
Bangunan
Ansilari
Umur
Flo_go
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: Nilaisma.
Dep=9t – 215.8
Dep=7t – 192.6

22. Which one to use?
• Nature of research
* Descriptive in nature?
* Attempts to “infer”, “predict”, find “cause-and-effect”,
“influence”, “relationship”?
* Is it both?
• Research design (incl. variables involved)
• Outputs/results expected
* research issue
* research questions
* research hypotheses
At post-graduate level research, failure to choose the correct data analysis
technique is an almost sure ingredient for thesis failure.

23. Common mistakes in data analysis
• Wrong techniques. E.g.
• Infeasible techniques. E.g.
How to design ex-ante effects of KLIA? Development occurs
“before” and “after”! What is the control treatment?
Further explanation!
• Abuse of statistics.
• Simply exclude a technique
Note: No way can Likert scaling show “cause-and-effect” phenomena!
Issue Data analysis techniques
Wrong technique Correct technique
To study factors that “influence” visitors to
come to a recreation site
“Effects” of KLIA on the development of
Sepang
Likert scaling based on
interviews
Likert scaling based on
interviews
Data tabulation based on
open-ended questionnaire
survey
Descriptive analysis based
on ex-ante post-ante
experimental investigation

24. Common mistakes (contd.) – “Abuse of statistics”
Issue Data analysis techniques
Example of abuse Correct technique
Measure the “influence” of a variable
on another
Using partial correlation
(e.g. Spearman coeff.)
Using a regression
parameter
Finding the “relationship” between one
variable with another
Multi-dimensional
scaling, Likert scaling
Simple regression
coefficient
To evaluate whether a model fits data
better than the other
Using coefficient of
determination, R2
Box-Cox χ2
test for
model equivalence
To evaluate accuracy of “prediction” Using R2
and/or F-value
of a model
Hold-out sample’s
MAPE
“Compare” whether a group is different
from another
Multi-dimensional
scaling, Likert scaling
two-way anova, χ2
, Z
test
To determine whether a group of
factors “significantly influence” the
observed phenomenon
Multi-dimensional
scaling, Likert scaling
manova, regression

25. How to avoid mistakes - Useful tips
• Crystalize the research problem → operability of it!
• Read literature on data analysis techniques.
• Evaluate various techniques that can do similar things
w.r.t. to research problem
• Know what a technique does and what it doesn’t
• Consult people, esp. supervisor
• Pilot-run the data and evaluate results
• Don’t do research?????????

26. Principles of analysis
• Goal of an analysis:
* To explain cause-and-effect phenomena
* To relate research with real-world event
* To predict/forecast the real-world
phenomena based on research
* Finding answers to a particular problem
* Making conclusions about real-world event
based on the problem
* Learning a lesson from the problem

27.  Data can’t “talk”
 An analysis contains some aspects of scientific
reasoning/argument:
* Define
* Interpret
* Evaluate
* Illustrate
* Discuss
* Explain
* Clarify
* Compare
* Contrast
Principles of analysis (contd.)

28. Principles of analysis (contd.)
• An analysis must have four elements:
* Data/information (what)
* Scientific reasoning/argument (what?
who? where? how? what happens?)
* Finding (what results?)
* Lesson/conclusion (so what? so how?
therefore,…)

29. Principles of data analysis
• Basic guide to data analysis:
* “Analyse” NOT “narrate”
* Go back to research flowchart
* Break down into research objectives and
research questions
* Identify phenomena to be investigated
* Visualise the “expected” answers
* Validate the answers with data
* Don’t tell something not supported by
data

30. Principles of data analysis (contd.)
Shoppers Number
Male
Old
Young
6
4
Female
Old
Young
10
15
More female shoppers than male shoppers
More young female shoppers than young male shoppers
Young male shoppers are not interested to shop at the shopping complex

31. Data analysis (contd.)
• When analysing:
* Be objective
* Accurate
* True
• Separate facts and opinion
• Avoid “wrong” reasoning/argument. E.g. mistakes in
interpretation.

32. Basic Concepts
• Population: the whole set of a “universe”
• Sample: a sub-set of a population
• Parameter: an unknown “fixed” value of population characteristic
• Statistic: a known/calculable value of sample characteristic representing that
of the population. E.g.
μ = mean of population, = mean of sample
Q: What is the mean price of houses in J.B.?
A: RM 210,000
J.B. houses
μ = ?
SST
DST
SD
1
= 300,000 = 120,000
2
= 210,000
3

33. Basic Concepts (contd.)
• Randomness: Many things occur by pure
chances…rainfall, disease, birth, death,..
• Variability: Stochastic processes bring in them
various different dimensions, characteristics,
properties, features, etc., in the population
• Statistical analysis methods have been
developed to deal with these very nature of
real world.

34. “Central Tendency”
Measure Advantages Disadvantages
Mean
(Sum of
all values
÷
no. of
values)
∗ Best known average
∗ Exactly calculable
∗ Make use of all data
∗ Useful for statistical analysis
∗ Affected by extreme values
∗ Can be absurd for discrete data
(e.g. Family size = 4.5 person)
∗ Cannot be obtained graphically
Median
(middle
value)
∗ Not influenced by extreme
values
∗ Obtainable even if data
distribution unknown (e.g.
group/aggregate data)
∗ Unaffected by irregular class
width
∗ Unaffected by open-ended class
∗ Needs interpolation for group/
aggregate data (cumulative
frequency curve)
∗ May not be characteristic of group
when: (1) items are only few; (2)
distribution irregular
∗ Very limited statistical use
Mode
(most
frequent
value)
∗ Unaffected by extreme values
∗ Easy to obtain from histogram
∗ Determinable from only values
near the modal class
∗ Cannot be determined exactly in
group data
∗ Very limited statistical use

35. Central Tendency – “Mean”,
• For individual observations, . E.g.
X = {3,5,7,7,8,8,8,9,9,10,10,12}
= 96 ; n = 12
• Thus, = 96/12 = 8
• The above observations can be organised into a frequency table
and mean calculated on the basis of frequencies
Thus, = 96/12 = 8
x 3 5 7 8 9 10 12
f 1 1 2 3 2 2 1
Σf 3 5 14 24 18 20 12

36. Central Tendency–“Mean of Grouped Data”
• House rental or prices in the PMR are frequently
tabulated as a range of values. E.g.
• What is the mean rental across the areas?
= 23; = 3317.5
Thus, = 3317.5/23 = 144.24
Rental (RM/month) 135-140 140-145 145-150 150-155 155-160
Mid-point value (x) 137.5 142.5 147.5 152.5 157.5
Number of Taman (f) 5 9 6 2 1
fx 687.5 1282.5 885.0 305.0 157.5

37. Central Tendency – “Median”
• Let say house rentals in a particular town are tabulated as
follows:
• Calculation of “median” rental needs a graphical aids→
Rental (RM/month) 130-135 135-140 140-145 155-50 150-155
Number of Taman (f) 3 5 9 6 2
Rental (RM/month) >135 > 140 > 145 > 150 > 155
Cumulative frequency 3 8 17 23 25
1. Median = (n+1)/2 = (25+1)/2 =13th
.
Taman
2. (i.e. between 10 – 15 points on the
vertical axis of ogive).
3. Corresponds to RM 140-
145/month on the horizontal axis
4. There are (17-8) = 9 Taman in the
range of RM 140-145/month
5. Taman 13th
. is 5th
. out of the 9
Taman
6. The interval width is 5
7. Therefore, the median rental can
be calculated as:
140 + (5/9 x 5) = RM 142.8

38. Central Tendency – “Median” (contd.)

39. Central Tendency – “Quartiles” (contd.)
Upper quartile = ¾(n+1) = 19.5th
.
Taman
UQ = 145 + (3/7 x 5) = RM
147.1/month
Lower quartile = (n+1)/4 = 26/4 =
6.5 th. Taman
LQ = 135 + (3.5/5 x 5) =
RM138.5/month
Inter-quartile = UQ – LQ = 147.1
– 138.5 = 8.6th
. Taman
IQ = 138.5 + (4/5 x 5) = RM
142.5/month

40. “Variability”
• Indicates dispersion, spread, variation, deviation
• For single population or sample data:
where σ2
and s2
= population and sample variance respectively, xi = individual
observations, μ = population mean, = sample mean, and n = total number
of individual observations.
• The square roots are:
standard deviation standard deviation

41. “Variability” (contd.)
• Why “measure of dispersion” important?
• Consider returns from two categories of shares:
* Shares A (%) = {1.8, 1.9, 2.0, 2.1, 3.6}
* Shares B (%) = {1.0, 1.5, 2.0, 3.0, 3.9}
Mean A = mean B = 2.28%
But, different variability!
Var(A) = 0.557, Var(B) = 1.367
* Would you invest in category A shares or
category B shares?

42. “Variability” (contd.)
• Coefficient of variation – COV – std. deviation as % of
the mean:
• Could be a better measure compared to std. dev.
COV(A) = 32.73%, COV(B) = 51.28%

43. “Variability” (contd.)
• Std. dev. of a frequency distribution
The following table shows the age distribution of second-time home buyers:
x^

44. “Probability Distribution”
• Defined as of probability density function (pdf).
• Many types: Z, t, F, gamma, etc.
• “God-given” nature of the real world event.
• General form:
• E.g.
(continuous)
(discrete)

45. “Probability Distribution” (contd.)
Dice1
Dice2 1 2 3 4 5 6
1 2 3 4 5 6 7
2 3 4 5 6 7 8
3 4 5 6 7 8 9
4 5 6 7 8 9 10
5 6 7 8 9 10 11
6 7 8 9 10 11 12

46. “Probability Distribution” (contd.)
Values of x are discrete (discontinuous)
Sum of lengths of vertical bars Σp(X=x) = 1
all x
Discrete values Discrete values

47. “Probability Distribution” (contd.)
2.00 3.00 4.00 5.00 6.00 7.00
Rental (RM/sq.ft.)
0
2
4
6
8
Frequency
Mean = 4.0628
Std. Dev. = 1.70319
N = 32
▪ Many real world phenomena
take a form of continuous
random variable
▪ Can take any values between
two limits (e.g. income, age,
weight, price, rental, etc.)

48. “Probability Distribution” (contd.)
P(Rental = RM 8) = 0 P(Rental < RM 3.00) = 0.206
P(Rental < RM7) = 0.972 P(Rental ≥ RM 4.00) = 0.544

49. “Probability Distribution” (contd.)
• Ideal distribution of such phenomena:
* Bell-shaped, symmetrical
* Has a function of
μ = mean of variable x
σ = std. dev. Of x
π = ratio of circumference of a
circle to its diameter = 3.14
e = base of natural log = 2.71828

50. “Probability distribution”
μ ± 1σ = ? = ____% from total observation
μ ± 2σ = ? = ____% from total observation
μ ± 3σ = ? = ____% from total observation

51. “Probability distribution”
* Has the following distribution of observation

52. “Probability distribution”
• There are various other types and/or shapes of
distribution. E.g.
• Not “ideally” shaped like the previous one
Note: Σp(AGE=age) ≠ 1
How to turn this graph into
a probability distribution
function (p.d.f.)?

53. “Z-Distribution”
• φ(X=x) is given by area under curve
• Has no standard algebraic method of integration → Z ~ N(0,1)
• It is called “normal distribution” (ND)
• Standard reference/approximation of other distributions. Since there are
various f(x) forming NDs, SND is needed
• To transform f(x) into f(z):
x - µ
Z = --------- ~ N(0, 1)
σ
160 –155
E.g. Z = ------------- = 0.926
5.4
• Probability is such a way that:
* Approx. 68% -1< z <1
* Approx. 95% -1.96 < z < 1.96
* Approx. 99% -2.58 < z < 2.58

54. “Z-distribution” (contd.)
• When X= μ, Z = 0, i.e.
• When X = μ + σ, Z = 1
• When X = μ + 2σ, Z = 2
• When X = μ + 3σ, Z = 3 and so on.
• It can be proven that P(X1 <X< Xk) = P(Z1 <Z< Zk)
• SND shows the probability to the right of any
particular value of Z.

55. Normal distribution…Questions
Your sample found that the mean price of “affordable” homes in Johor
Bahru, Y, is RM 155,000 with a variance of RM 3.8x107
. On the basis of a
normality assumption, how sure are you that:
(a) The mean price is really ≤ RM 160,000
(b) The mean price is between RM 145,000 and 160,000
Answer (a):
P(Y ≤ 160,000) = P(Z ≤ ---------------------------)
= P(Z ≤ 0.811)
= 0.1867
Using , the required probability is:
1-0.1867 = 0.8133
Always remember: to convert to SND, subtract the mean and divide by the std. dev.
160,000 -155,000
√3.8x107
Z-table

56. Normal distribution…Questions
Answer (b):
Z1 = ------ = ---------------- = -1.622
Z2 = ------ = ---------------- = 0.811
P(Z1<-1.622)=0.0455; P(Z2>0.811)=0.1867
∴P(145,000<Z<160,000)
= P(1-(0.0455+0.1867)
= 0.7678
X1 - μ
σ
145,000 – 155,000
√3.8x107
X2 - μ
σ
160,000 – 155,000
√3.8x107

57. Normal distribution…Questions
You are told by a property consultant that the
average rental for a shop house in Johor Bahru is
RM 3.20 per sq. After searching, you discovered
the following rental data:
2.20, 3.00, 2.00, 2.50, 3.50,3.20, 2.60, 2.00,
3.10, 2.70
What is the probability that the rental is greater
than RM 3.00?

58. “Student’s t-Distribution”
• Similar to Z-distribution:
* t(0,σ) but σn→∞→1
* -∞ < t < +∞
* Flatter with thicker tails
* As n→∞t(0,σ) → N(0,1)
* Has a function of
where Γ=gamma distribution; v=n-1=d.o.f; π=3.147
* Probability calculation requires information on
d.o.f.

59. “Student’s t-Distribution”
• Given n independent measurements, xi, let
where μ is the population mean, is the sample mean,
and s is the estimator for population standard
deviation.
• Distribution of the random variable t which is (very
loosely) the "best" that we can do not knowing σ.

60. “Student’s t-Distribution”
• Student's t-distribution can be derived by:
* transforming Student's z-distribution using
* defining
• The resulting probability and cumulative distribution
functions are:

61. “Student’s t-Distribution”
•
where r ≡ n-1 is the number of degrees of freedom, -∞<t<∞,Γ(t) is the gamma function,
B(a,b) is the beta function, and I(z;a,b) is the regularized beta function defined by
•
fr(t) =
=
Fr(t) =
=
=

62. Forms of “statistical” relationship
• Correlation
• Contingency
• Cause-and-effect
* Causal
* Feedback
* Multi-directional
* Recursive
• The last two categories are normally dealt with through regression

63. Correlation
• “Co-exist”.E.g.
* left shoe & right shoe, sleep & lying down, food & drink
• Indicate “some” co-existence relationship. E.g.
* Linearly associated (-ve or +ve)
* Co-dependent, independent
• But, nothing to do with C-A-E r/ship!
Example: After a field survey, you have the following
data on the distance to work and distance to the city
of residents in J.B. area. Interpret the results?
Formula:

64. Contingency
• A form of “conditional” co-existence:
* If X, then, NOT Y; if Y, then, NOT X
* If X, then, ALSO Y
* E.g.
+ if they choose to live close to workplace,
then, they will stay away from city
+ if they choose to live close to city, then, they
will stay away from workplace
+ they will stay close to both workplace and city

65. Correlation and regression – matrix approach

66. Correlation and regression – matrix approach

67. Correlation and regression – matrix approach

68. Correlation and regression – matrix approach

69. Correlation and regression – matrix approach

70. Test yourselves!
Q1: Calculate the min and std. variance of the following data:
Q2: Calculate the mean price of the following low-cost houses, in various
localities across the country:
PRICE - RM ‘000 130 137 128 390 140 241 342 143
SQ. M OF FLOOR 135 140 100 360 175 270 200 170
PRICE - RM ‘000 (x) 36 37 38 39 40 41 42 43
NO. OF LOCALITIES (f) 3 14 10 36 73 27 20 17

71. Test yourselves!
Q3: From a sample information, a population of housing
estate is believed have a “normal” distribution of X ~ (155,
45). What is the general adjustment to obtain a Standard
Normal Distribution of this population?
Q4: Consider the following ROI for two types of investment:
A: 3.6, 4.6, 4.6, 5.2, 4.2, 6.5
B: 3.3, 3.4, 4.2, 5.5, 5.8, 6.8
Decide which investment you would choose.

72. Test yourselves!
Q5: Find:
φ(AGE > “30-34”)
φ(AGE ≤ 20-24)
φ( “35-39”≤ AGE < “50-54”)

73. Test yourselves!
Q6: You are asked by a property marketing manager to ascertain whether
or not distance to work and distance to the city are “equally” important
factors influencing people’s choice of house location.
You are given the following data for the purpose of testing:
Explore the data as follows:
• Create histograms for both distances. Comment on the shape of the
histograms. What is you conclusion?
• Construct scatter diagram of both distances. Comment on the output.
• Explore the data and give some analysis.
• Set a hypothesis that means of both distances are the same. Make your
conclusion.

74. Test yourselves! (contd.)
Q7: From your initial investigation, you belief that tenants of
“low-quality” housing choose to rent particular flat units just
to find shelters. In this context ,these groups of people do
not pay much attention to pertinent aspects of “quality
life” such as accessibility, good surrounding, security, and
physical facilities in the living areas.
(a) Set your research design and data analysis procedure to address
the research issue
(b) Test your hypothesis that low-income tenants do not perceive “quality life” to be important in
paying their house rentals.

75. Summary

76. • Main Points
• Qualitative research involves analysis of data such as
words (e.g., from interviews), pictures (e.g., video), or
objects (e.g., an artifact).
• Quantitative research involves analysis of numerical
data.
• The strengths and weaknesses of qualitative and
quantitative research are a perennial, hot debate,
especially in the social sciences. The issues invoke
classic 'paradigm war'.
76

77. • The personality / thinking style of the researcher and/or the culture of the
organization is under-recognized as a key factor in preferred choice of methods.
• Overly focusing on the debate of "qualitative versus quantitative" frames the
methods in opposition. It is important to focus also on how the techniques can be
integrated, such as in mixed methods research. More good can come of social
science researchers developing skills in both realms than debating which method is
superior.
77

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