1. Subject : Research
Methodology
QUANTITATIVE
RESEARCH METHODS
Group 5

2. Outline
1. An introduction to Quantitative research
method
2. 4 concepts of Quantitative Method :
 Population
 Sampling of Quantitative research
 Samples of Quantitative research
 Qualitative Scale
EXPERIMENTAL DESIGN

3. An introduction to Quantitative
research method
Presenter: Minh Sang

4. What is Quantitative research ?
 Quantitative research is an inquiry into an
identified problem, based on testing a theory,
measured with numbers, and analyzed using
statistical techniques.
 The goal of quantitative methods is to
determine whether the predictive
generalizations of a theory hold true.

5. Assumptions Underlying Quantitative
Methods
 Reality is objective, “out there,” and
independent of the researcher -- therefore
reality is something that can be studied
objectively.
 The researcher should remain distant and
independent of what is being researched.
 The values of the researcher do not interfere
with, or become part of, the research --
research is value-free.

6. Assumptions Underlying Quantitative
Methods
 Research is based primarily on deductive
forms of logic and theories and hypotheses are
tested in a cause-effect order.
 And the goal is to develop generalizations that
contribute to theory that enable the researcher
to predict, explain, and understand some
phenomenon.

7. Three general types of quantitative
methods:
 1. Experiments  True experiments are
characterized by random assignment of
subjects to experimental conditions and the
use of experimental controls.
 2. Quasi-Experiments  Quasi-experimental
studies share almost all the features of
experimental designs except that they involve
non-randomized assignment of subjects to
experimental conditions.

8. Three general types of quantitative
methods:
 3. Surveys  Surveys include cross-sectional
and longitudinal studies using questionnaires
or interviews for data collection with the intent
of estimating the characteristics of a large
population of interest based on a smaller
sample from that population.

9. Comparison of quantitative and qualitative
research approaches
 General framework
Quantitative Qualitative
Seek to confirm hypotheses about Seek to explore phenomena
Phenomena
Instruments use more rigid style Instruments use more flexible,
of eliciting and categorizing iterative style of eliciting and
responses to questions categorizing responses to questions
Use highly structured methods Use semi-structured methods such
such as questionnaires, surveys, as in-depth interviews, focus
and structured observation groups, and participant observation

10. Comparison of quantitative and qualitative
research approaches
 Analytical objectives
Quantitative Qualitative
To quantify variation To describe variation
To predict causal relationships To describe and explain
relationships
To describe characteristics of a To describe individual experiences
population
To describe group norms

11. Comparison of quantitative and qualitative
research approaches
 Question format
Quantitative Qualitative
Closed-ended Open-ended

12. Comparison of quantitative and qualitative
research approaches
 Data format
Quantitative Quanlitative
Numerical (obtained by assigning Textual (obtained from audiotapes,
numerical values to responses) videotapes, and field notes)

13. Comparison of quantitative and qualitative
research approaches
 Flexibility in study design
Quantitative Qualitative
Study design is stable from Some aspects of the study are
beginning to end flexible (for example, the addition,
exclusion, or wording of particular
interview questions)
Participant responses do not Participant responses affect how
influence or determine how and and which questions researchers
which questions researchers ask ask next
next
Study design is subject to Study design is iterative, that is,
statistical assumptions and data collection and research
conditions questions are adjusted according
to what is learned

14. VARIABLES

15. Outline
• Definitions
• Relating notions
• Types
– Independence
– Dependence
– Control
– Moderator
– Extraneous
– Correlation
• Cause

16. Definition
• A variable is something that can
change, such as 'gender' and are
typically the focus of a study

17. Relating notions
• Attributes: sub-values of a
variable, such as 'male' and 'female„
• Mutually exclusive attributes are those
that cannot occur at the same time.

18. • Quantitative data is numeric. This is
useful for mathematical and statistical
analysis  predictive formula.
• Qualitative data is based on human
judgement. You can also turn
qualitative data into quantitative data

19. • Units are the ways that variables are
classified. These include:
individuals, groups, social interactions
and objects.

20. Types
1. Independence
2. Dependence
3. Control
4. Moderator
5. Extraneous
6. Correlation

21. Independent
(Experimental, Manipulated, Treatment,
Grouping) Variable
• That factor which is measured, manipulated, or
selected by the experimenter to determine its
relationship to an observed phenomenon.
• In a research study, independent variables are
antecedent conditions that are presumed to
affect a dependent variable. They are either
manipulated by the researcher or are observed
by the researcher so that their values can be
related to that of the dependent variable.

22. Dependent (Outcome) Variable
• That factor which is observed and measured to
determine the effect of the independent
variable, i.e., that factor that
appears, disappears, or varies as the
experimenter introduces, removes, or varies the
independent variable.
• In a research study, the independent variable
defines a principal focus of research interest. It is
the consequent variable that is presumably
affected by one or more independent variables
that are either manipulated by the researcher or
observed by the researcher and regarded as
antecedent conditions that determine the value
of the dependent variable.
• The dependent variable is the outcome.

23. Control
• In an experiment there may be many
additional variables beyond the
manipulated independent variable
and the measured dependent
variables. It is critical in experiments
that these variables do not vary and
hence bias or otherwise distort the
results. There is control variable to
struggle to manage this.

24. Moderator
• That factor which is
measured, manipulated, or selected
by the experimenter to discover
whether it modifies the relationship of
the independent variable to an
observed phenomenon. It is a special
type of independent variable

25. Extraneous
• Those factors which cannot be
controlled.
• They may or may not influence the results.
One way to control an extraneous
variable which might influence the results
is to make it a constant (keep everyone
in the study alike on that characteristic).

26. Correlation
• With perfect correlation, the X-Y graph of
points (as a scatter diagram) will give a
straight line.
• Correlation can be positive (increasing X
increases Y), negative (increasing X
decreases Y) or non-linear (increasing X
makes Y increase or decrease, depending
on the value of X).
• Correlation can also be partial, that is
across only a range of values X. As all
possible values of X can seldom be
tested, most correlations found are at best
partial.

27. Cause
• When correlation is determined, a further
question is whether varying the
independent variable caused the
independent variable to change. This
adds complexity and debate to the
situation.
• Sometimes a third variable is the
cause, such as when a correlation
between ice-cream sales and drowning is
actually due to the fact that both are
caused by warm weather.

28. POPULATION
Presenter: Ngoc Cam

29. Population
1. What is a population?
2. When is a population identified?
3. Collecting data about a population

30. What is a population?
• A population is any complete group
with at least one characteristic in
common.
• Populations are not just people.
Populations may consist of, but are not
limited
to, people, animals, businesses, buildin
gs, motor vehicles, farms, objects or
events.

31. Populations

32. Identify the population
• When looking at data, it is important to clearly
identify the population being studied or
referred to, so that you can understand who
or what are included in the data.
• For example, if you were looking at some
Australian farming data, you would need to
understand whether the population the data
refers to is all farms in Australia, just farms that
grow crops, those that only have livestock, or
some other type of farm.

33. When is a population identified?
• The population needs to be clearly
identified at the beginning of a study.
• The study should be based on a clear
understanding of who or what is of
interest, as well as the type of information
required from that population.

34. Collecting data about a population

36. SAMPLing of
Quantitative reseach
Presenter: Huu Loc

37. Definition of 'Sampling'
• A process used in statistical analysis in which a
predetermined number of observations will be
taken from a larger population. The methodology
used to sample from a larger population will
depend on the type of analysis being
performed, but will include simple random
sampling, systematic sampling and observational
sampling.
• The sample should be a representation of the
general population.

38. Sampling Methods
• In most surveys, access to the entire population is
near on impossible, however, the results from a
survey with a carefully selected sample will
reflect extremely closely those that would have
been obtained had the population provided the
data.
• There are essentiality two types of sampling
o probability sampling
o non-probability sampling

39. Probability Sampling Methods
• Probability or random sampling gives all members of
the population a known chance of being selected for
inclusion in the sample and this does not depend upon
previous events in the selection process. In other
words, the selection of individuals does not affect the
chance of anyone else in the population being selected.
• Many statistical techniques assume that a sample was
selected on a random basis. There are four basic types
of random sampling techniques:
1) Simple Random Sampling
2) Systematic Sampling
3) Stratified Sampling
4) Cluster or Multi-stage Sampling

40. 1) Simple Random Sampling
• This is the ideal choice as it is a ‘perfect’
random method. Using this
method, individuals are randomly selected
from a list of the population and every single
individual has an equal chance of selection.
• This method is ideal, but if it cannot be
adopted, one of the following alternatives
may be chosen if any shortfall in accuracy.

41. 2) Systematic Sampling
• Systematic sampling is a frequently used variant of simple
random sampling. When performing systematic sampling, every
element from the list is selected (this is referred to as the
sample interval) from a randomly selected starting point. For
example, if we have a listed population of 6000 members and
wish to draw a sample of 2000, we would select every 30th
(6000 divided by 200) person from the list. In practice, we would
randomly select a number between 1 and 30 to act as our
starting point.
• The one potential problem with this method of sampling
concerns the arrangement of elements in the list. If the list is
arranged in any kind of order e.g. if every 30th house is smaller
than the others from which the sample is being recruited, there
is a possibility that the sample produced could be seriously
biased.

42. 3) Stratified Sampling
• Stratified sampling is a variant on simple random and systematic
methods and is used when there are a number of distinct
subgroups, within each of which it is required that there is full
representation. A stratified sample is constructed by classifying
the population in sub-populations (or strata), base on some well-
known characteristics of the population, such as age, gender or
socio-economic status. The selection of elements is then made
separately from within each strata, usually by random or
systematic sampling methods.
• Stratified sampling methods also come in two types –
proportionate and disproportionate.
• In proportionate sampling, the strata sample sizes are made
proportional to the strata population size. For example if the first
strata is made up of males, then as there are around 50% of
males in the UK population, the male strata will need to represent
around 50% of the total sample.

43. 4) Cluster or Multi-stage Sampling
• Cluster sampling is a frequently-used, and usually more
practical, random sampling method. It is particularly useful in
situations for which no list of the elements within a population
is available and therefore cannot be selected directly. As this
form of sampling is conducted by randomly selecting subgroups
of the population, possibly in several stages, it should produce
results equivalent to a simple random sample.
• The sample is generally done by first sampling at the higher
level(s) e.g. randomly sampled countries, then sampling from
subsequent levels in turn e.g. within the selected countries
sample counties, then within these postcodes, the within these
households, until the final stage is reached, at which point the
sampling is done in a simple random manner e.g. sampling
people within the selected households. The ‘levels’ in question
are defined by subgroups into which it is appropriate to
subdivide your population.

44. Non-probability Sampling Methods
• Non-probability sampling procedures are much less
desirable, as they will almost certainly contain sampling
biases. Unfortunately, in some circumstances such
methods are unavoidable.
• If you are forced into using a non-random method, you
must be extremely careful when drawing conclusions.
You should always be honest about the sampling
technique used and that a non-random approach will
probably mean that biases are present within the data.
In order to convert the sample to be representative of
the true population, you may want to use weighting
techniques.

45. • The importance of sampling should not be underestimated, as it
determines to whom the results of your research will be
applicable. It is important, therefore to give full consideration to
the sampling strategy to be used and to select the most
appropriate. Your most important consideration should be
whether you could adopt a simple random sample. If not, could
one of the other random methods be used? Only when you
have no choice should a non-random method be used.
• All to often, researchers succumb to the temptation of
generalizing their results to a much broader range of people
than those from whom the data was originally gathered. This is
poor practice and you should always aim to adopt an
appropriate sampling technique. The key is not to guess, but
take some advice.

46. SAMPLES of
Quantitative reseach
Presenter: Minh Dang

47. Definition
• A sample is a finite part of a statistical
population whose properties are
studied to gain information about the
whole.

48. • When dealing with people, it can be
defined as a set of correspomdents
(people) selected from a larger
population for the purpose of the
survey.

49. Sample size
• Sample size is important  must be large
enough
• Too big sample increases costs, too small
sample causes insufficient of data to
reach any meaningful conclusions

50. • Have as large a sample as possible
• Larger sample  more accurate results
• Take advice from a statistician who will
help you decide the numbers required
to give validity to your results.

51. Purpose of sampling (choosing a
sample)
1. Save time
2. Save money
3. Unable to survey some large population
4. Maybe only some parts of population
are accessible
5. Just observation is never enough

52. Qualitative Scale
Presenter: Ngan Giang

53. Measurement scales of
quantitative research
• There are four types :
• nominal
• ordinal
• interval
• ratio

54. • Nominal (categories): represents the
lowest level of measurement.
• When a nominal scale is used, the
data simply indicate how many
subjects are in each category.
• Category 4 and category 1 are not
different base on the number 4 and
1; 4 is not higher than 1 or more than
1.
• Example: Categories for IQ, types of
school…

55. • Ordinal (ranks): puts the subjects in
order from the highest to lowest, form
the most to least.
• Although ordinal scales indicate that
some subjects are higher, or
better, than other, they do not
indicate how much higher or better.

56. • Interval (scores): has all the characteristics of
a nominal and ordinal scale, in addition it is
based upon predetermined equal intervals.
• Examples: achievement tests, aptitude
tests, and intelligence tests …
• Interval scale, however, do not have a true
zero point.
• If an IQ test produces scores ranging from 0 to
200, a score of 0 does not indicate the
absence of intelligence, nor does a score of
200 dedicate possession of the ultimate
intelligence.
• We cannot say that a person scoring 90
knows twice as much as a person scoring 45

57. • Ratio: A ratio scale represents the
highest, most precise, level of
measurement.
• It has a meaningful, true zero point.
• Examples:
height, weight, time, distance, and
speed …

58. EXPERIMENTAL DESIGN

59. Outline
1. Definition of Experimental Design
2. Considerations in Design Selection
3. Experimental Design Terminology
4. Describing Experimental Designs
5. Basic Experimental Designs

60. 1. What is Experimental Design?
• Experimental design is a planned
interference in the natural order of events
by the researcher. He does something more
than carefully observe what is occurring. This
emphasis on experiment reflects the higher
regard generally given to information so
derived

61. • The importance of experimental design also
stems from the quest for inference about
causes or relationships as opposed to simply
description. Researchers are rarely satisfied
to simply describe the events they observe
 some form of experimental design is
ordinarily required

62. • The kinds of planned manipulation and
observation called experimental design
entail:
– selecting or assigning subjects to experimental
units
– selecting or assigning units for specific treatments
or conditions of the experiment (experimental
manipulation
– specifying the order or arrangement of the
treatment or treatments
– specifying the sequence of observations or
measurements to be taken

63. 2. Considerations in Design Selection
• The selection of a specific type of design
depends primarily on both the nature and
the extent of the information we want to
obtain

64. • Two ways of checking potential designs: ask
yourself
– What questions will this design answer
– What is the relative information gain/cost picture

65. 3. Experimental Design Terminology
• The group in an experiment which receives
the specified treatment is called the
Treatment Group or the experimental group.
However, the term Control Group refers to
another group assigned to the
experiment, but not for the purpose of being
exposed to the treatment.
• A variable refers to almost anything under
the sun. Two types: constants + variables

66. • Extraneous variables (external to the
experiment) are variables that may
influence or affect the results of the
treatment on the subject.
• Level refers to the degree or intensity of a
factor
• Randomness refers to the property of
completely chance events that are not
predictable (except in the sense that they
are random).

67. • Ex post factor refers to causal inferences
drawn “after the fact”
• Variance refers to the variability of any
event
• The inside logic of an experiment is referred
to as internal validity
• External validity, on the other hand, refers to
the proposed interpretation of the results of
the study
• Blocks usually refers to categories of
subjects with a treatment group

68. • The Hawthorne Effect refers to the behavior
of interest being caused by subject being in
the center of the experimental
stage, e.g., having a great deal of attention
focused on them
• The study is termed a blind experiment when
the subject does not know whether he or
she is receiving the treatment or a placebo
• The study is termed double blind when
neither the subject nor the person
administering the treatment/placebo knows
what is being administered knows either.

69. • Six major classes of information with which
an experimental designer must cope:
– post -treatment behavior or physical
measurement [P1]
– pre-treatment behavior or physical measurement
[P2]
– internal threats to validity [I]
– comparable groups [C]
– experiment errors [E]
– relationship to treatment [R]

70. • Post-Treatment Behavior or Physical
Measurement :
• In a typical experiment, this is the data, the
class of information of primary interest

71. • Pre-Treatment Behavior or Physical
Measurement:
• Information concerning pre-treatment
behavior or condition requires come
observation, a test, or measurement, to be
administered before the experimental
manipulation. Without such
observations, the design itself will not answer
any questions about the subjects before the
experimental conditions have been
introduced.

72. • Such information, however, may be
accrued from general knowledge or other
studies. Direct acquisition of this information
adds to the cost of an experiment.
Furthermore, it may have a confounding
effect, that is, sometimes the pre-treatment
observation or measurement influences the
subsequent behavior of the subject. When it
is over, it may not be clear whether the
behavior was due to the treatment, the pre-
treatment observation or measurement, or
both.

73. • Internal Threats to Validity:
• This class of information refers to some rival
hypothesis that threatens clear
interpretation of the experiment.

74. • Comparable Groups:
• This class of information, available only when
two or more experimental units or groups of
subjects are used, deals with whether the
subjects in the different units were about the
same in relevant attributes before the
treatment, and during the
treatment, except for the treatment
condition itself

75. • Experiment Errors:
• Experiment error refers to some unwanted
side effect of the experiment itself which
may be producing effect rather than the
treatment

76. • Relationship to Treatment:
• This class of information deals with the
possible interaction of the treatment effects
with: different kinds of subjects, other
treatments, different factors within a
complicated treatment, different degrees of
intensity, repeated applications or
continuation of the treatment, and different
sequences or orders of the treatment or
several treatments

77. 4. Describing Experimental Designs
The following letters will be used to describe
the various experimental design activities:
ACTIVITY LETTER(S)
Selection of the group or experimental unit GP
Random assignment to a group R
Blocking subjects, or other variables, into sets BK
Administering a treatment to a group T
Observing (measuring) results O

78. 5. Basic Experimental Designs
• Eleven commonly used experimental designs:
1. One-Shot
2. One-Group, Pre-Post
3. Static Group
4. Random Group
5. Pre-Post Randomized Group
6. Solomon Four Group
7. Randomized Block
8. Factorial
9. One-Shot Repeated Measures
10. Randomized Groups Repeated Measures
11. Latin Square

79. • One-Shot
• The One-Shot is a design in which a group of
subjects are administered a treatment and
then measured (or observed).

80. • Example
Ridgeway, G., Pierce, G.L., Braga, A.A., Tita, G., Wint
emute, G., and Roberts, W. (2008).Strategies for
Disrupting Illegal Firearms Markets: A Case Study of
Los Angeles. Santa Monica, CA: Rand.
This report details research and a program
development effort to understand the nature of
illegal gun markets operating in Los
Angeles, California. The primary goal of this project
was to determine whether a data-driven, problem-
solving approach could yield new interventions
aimed at disrupting the workings of local, illegal gun
markets serving criminals, gang members, and
juveniles in Los Angeles.

81. • One-Group, Pre-Post
• In this design, one group is given a pre-
treatment measurement or observation, the
experimental treatment, and a post-
treatment measurement or observation. The
post-treatment measures are compared
with their pre-treatment measures.

82. • Static Group
• In this design, two intact groups are
used, but only one of them is given the
experimental treatment. At the end of the
treatment, both groups are observed or
measured to see if there is a difference
between them as a result of the treatment

83. • Random Group
• This design is similar to the Static Group
design except than an attempt is made to
insure similarity of the groups before
treatment begins. Since it is difficult to have
exactly similar subjects in each of two
groups (unless you separate identical
twins), the design works toward a guarantee
of comparability between groups by
assigning subjects to groups at random. If
the researcher does this there is likely to be
reasonable comparability between the two
groups

84. • Pre-Post Randomized Group
• This design adds a pre-test to the previous
design as a check on the degree of
comparability of the control and
experimental groups before the treatment is
given

85. • Solomon Four Group
• The Solomon Four Group design attempts to
control for the possible "sensitizing" effects of
the pre-test or measurement by adding two
groups who have not been a part of the
pre-test or pre-measurement process.

86. • Randomized Block
• This design is of particular value when the
experimenter wishes to determine the effect
of a treatment on different types of subjects
within a group

87. • Factorial
• As you saw above in the blocking
design, the subjects were assigned to
different groups on the basis of some of their
own characteristics such as age, weight, or
some other physical characteristic.
Sometimes we wish to assign different
variations of the treatment as well, and the
procedure is similar

88. • One-Shot Repeated Measures
• This design, or variations of it, is used to
assess the effects of a treatment with the
same group or the same individual over a
period of time. A measure, or observation is
made more than once to assess the effects
of the treatment

89. • Randomized Groups Repeated Measures
• The Randomized Groups Repeated
Measures design is a variant of the previous
design in which two or more experimental
methods are compared and repeatedly
measured or observed.

90. • Latin Square
• A researcher may wish to use several
different treatments in the same
experiment, for example the relative effects
of an assortment of perhaps three or more
drugs in combination in which the sequence
of administration may produce different
results

91. The Question of External Validity
• Questions of a different sort than we have
faced arise from our need to generalize
from a limited set of observations. No one is
interested in observations than in no way
extend beyond this particular restricted set
of data. Generalizability depends on
whether the observed behavior
measurement [O] is representative of the
people, the surrounding conditions and the
treatments to which we now wish to extend
it

92. • Classes of questions include:
1. Did some of the early procedure in the research
affect the subjects so that their later
measurements were, in part a result of that?
2. Were the subjects themselves a representative
sample of the general population of people to
which it is desired to extend the research
findings?
3. Was there something in the research or setting
that would cause or influence the measurement
of the variable of interest?
4. Was the treatment accompanied by any
personal interaction that may be somewhat
peculiar to the research or to the subjects or the
experimenter involved?

93. • The important thing is to clarify where the
results of your observations may be
legitimately extended and where they can
not yet be legitimately extended. Helpful in
this regard is a comprehensive description of
the demographic characteristics of the
subjects of the research and a complete
and comprehensive description of the
methodology used so that the reader of the
research can judge for himself or herself
whether the results can be generalized to his
or her situation.