 Quantitative research designs use numbers in stating
generalizations about a given problem or inquiry in
contrast to qualitative research that hardly uses statistical
treatment in stating generalizations.
 These numbers are the results of objective scales of
measurements of the units of analysis called variables.
 Research findings are subjected to statistical treatment to
determine significant relationships or differences between
variables, the results of which are the bases for
generalization about phenomena.
 We research people and their behaviour, opinions, attitudes,
trends and patterns, also politics, animals, health and illness.
Research can be conducted either informally for our own
benefit, through asking questions, watching, counting or
reading and formally, for medical or academic purposes, as a
marketing strategy, to inform and influence politics and policy.
 Research may be carried out in our own lives, through the
media, in our place of work, with our friends and family or
through reading past research.
 Our views - personal, social, community and worldwide and
our own identities are socially constructed through our own
theorizing.
 The data is usually gathered using structured research instruments.
 The results are based on larger sample sizes that are representative of the
population.
 • The research study can usually be replicated or repeated, given its high
reliability.
 • Researcher has a clearly defined research question to which objective
answers are sought.
 All aspects of the study are carefully designed before data is collected.
 Data are in the form of numbers and statistics, often arranged in tables,
charts, figures, or other non textual forms.
 Project can be used to generalize concepts more widely, predict future
results, or investigate causal relationships.
 Researcher uses tools, such as questionnaires or computer software, to
collect numerical data
 Allows for a broader study, involving a greater number of
subjects, and enhancing the generalization of the results;
 Allows for greater objectivity and accuracy of results.
Generally, quantitative methods are designed to provide
summaries of data that support generalizations about the
phenomenon under study. In order to accomplish this,
quantitative research usually involves few variables and
many cases, and employs prescribed procedures to ensure
validity and reliability;
 Applying well established standards means that the
research can be replicated, and then analyzed and
compared with similar studies;
 You can summarize vast sources of information and make
comparisons across categories and over time; and,
 Personal bias can be avoided by keeping a 'distance' from
participating subjects and using accepted computational
techniques.
 Quantitative research can be costly, difficult and time-
consuming difficult because most researchers are
nonmathematicians.
 Quantitative studies require extensive statistical
treatment, requiring stringent standards, more so with
confirmation of results. When ambiguities in some
findings surface, retesting and refinement of the
design call for another investment in time and
resources to polish the results.
Quantitative methods also tend to turn
out only proved or unproven results,
leaving little room for uncertainty, or grey
areas.
For the social sciences, education,
anthropology and psychology, human
nature is a lot more complex than just a
simple yes or no response.
 Research design refers to the overall strategy that you
choose in order to integrate the different components of
the study in a coherent and logical way, thereby ensuring
you will effectively address the research problem.
Furthermore, a research design constitutes the blueprint
for the selection, measurement and analysis of data. The
research problem determines the research design you
should use. Quantitative methods emphasize objective
measurements and the statistical, mathematical, or
numerical analysis of data collected through polls,
questionnaires, and surveys, or by manipulating
preexisting statistical data using computational techniques.
Using the survey research as the method of
research, an organization conducting survey
ask different survey questions from the
respondents using the various types like
online surveys, online polls paper
questionnaires, etc. then collect data and
analyze collected data in order to produce the
numerical results.
Causal-Comparative Research method is
used to draw conclusions with respect to
the cause-and-effect equation between
the two or more than two variables, where
the one variable will be dependent on
other variables which will be
independent.
This analysis is done for the purpose of
proving or for disproving the statement. It
is generally used in the field of natural
sciences or in the field of social sciences
as in those areas various statements are
there which required to be proved as
right or wrong.
Correlation Research conducted for
establishing a relationship between the two
closely associated entities for knowing the
impact of one on other and the changes which
eventually observed. It is carried for giving
value to the naturally occurring relationships.
For this research minimum, two different
groups will be required.
1.More reliable and objective
2.Can use statistics to generalize a finding
3.Often reduces and restructures a complex problem to a
limited number of variables
4.Looks at relationships between variables and can establish
cause and effect in highly controlled circumstances
5.Tests theories or hypotheses
6.Assumes sample is representative of the population
7.Subjectivity of researcher in methodology is recognized
less
8.Less detailed than qualitative data and may miss a desired
response from the participant
 The term ‘variable’ has been mentioned several times so that it is
necessary to define it here. In research, a variable refers to a
“characteristics that has two or more mutually exclusive values or
properties” (Sevilla and Other, 1988). Sex, for instance, has two
properties which are maleness and femaleness. The ages of different
persons have different values; so with their size, height, weight and
income. The phenomenon of variety is what makes life interesting; it
is one of the motivating factors of the research undertaking.
 The root word of the word variable is “vary” or simply “can change”.
These variables are among the fundamental concepts of research,
alongside with measurement, validity, reliability, cause and effect;
and theory. Bernard (1994) defines a variable as something that can
take more than one value, and values can be words or numbers.
 A variable specifically refers to characteristics, or attribute of an
individual or an organization that can be measured or observed and
that varies among the people or organization being studied
(Creswell, 2002).
A variable that can take infinite number
on the value that can occur within the
population. Its values can be divided into
fractions. Examples of this type of
variable include age, height, and
temperature.
INTERVAL VARIABLES
 It have values that lie along an evenly dispersed range of
numbers. It is a measurement where the difference
between two values does have meaning. Examples of
interval data include temperature, a person’s net worth
(how much money you have when you subtract your debt
from your assets), etc. In temperature, this may illustrate as
the difference between a temperature of 60 degrees and 50
degrees is the same as difference between 30 degrees and
20 degrees. The interval between values makes sense and
can be interpreted.
RATIO VARIABLES
 It have values that lie along an evenly dispersed range of
numbers when there is absolute zero. It possesses the
properties of interval variable and has a clear definition of
zero, indication that there is none of that variable. Examples
of which are height, weight, and distance. Most scores
stemming from response to survey items are ratio-level
values because they typically cannot go below zero.
Temperature measured in degrees Celsius and degrees
Fahrenheit is not a ratio variable because 0 under these
temperatures scales does not mean no temperature at all.
This is also known as categorical or
classificatory variable. This is any
variable that has limited number of
distinct values and which cannot be
divided into fractions like sex, blood
group, and number of children in family.
NOMINAL VARIABLE
 It represent categories that cannot be ordered in any
particular way. It is a variable with no quantitative value. It
has two or more categories but does not imply ordering of
cases. Common examples of this variable include eye
color, business type, religion, biological sex, political
affiliation, basketball fan affiliation, etc. A sub-type of
nominal scale with only two categories just like sex is
known as dichotomous.
ORDINAL VARIABLE
 It represent categories that can be ordered from greatest to
smallest. This variable has two or more categories which can be
ranked. Examples of ordinal variable include education level,
income brackets, etc. An illustration of this is, if you asked people if
they liked listening to music while studying and they could answer
either “NOT VERY MUCH”, “MUCH”, “VERY MUCH” then you have
an ordinal variable. While you can rank them, we cannot place a
value to them. In this type, distances between attributes do not
have any meaning. For example, you used educational attainment
as a variable on survey, you might code elementary school
graduates = 1, high graduates = 2, college undergraduate = 3, and
college graduate = 4. In this measure, higher number means
greater education. Even though we can rank these from lowest to
highest, the spacing between the values may not be the same
across the levels of the variables. The distance between 3 and 4 is
not the same with the distance between 1 and 2.
INDEPENDENT VARIABLES
 Those that probably cause, influence, or affect outcomes.
They are invariably called treatment, manipulated,
antecedent or predictor variables. This is the cause
variable or the one responsible for the conditions that act
on something else to bring about changes.
DEPENDENT VARIABLES
 those that depend on the independent variables; they are
the outcomes or results of the influence of the independent
variable. That is why it is also called outcome variable.
INTERVENING OR MEDLING VARIABLES

Variables that “stand between” the independent and
dependent variables, and they show the effects of the
independent variable on the dependent variable.
CONTROL VARIABLES
 A special types of independent variables that are measured in the
study because they potentially influence the dependent variable.
Researchers use statistical procedures (e.g. analysis of
covariance) to control these variables. They may be demographic
or personal variables that need to be “controlled” so that the true
influence of the independent variable on the dependent variable
can be determined.
CONFOUNDING VARIABLES
 Variables that are not actually measured or
observed in a study. They exist but their influence
cannot be directly detected in a study. Researchers
comment on the influence of confounding variables
after the study has been completed, because these
variables may have operated to explain the
relationship between the independent variables
and dependent variable, but they were not or could
not be easily assessed