2.
What are variables?
• Variables are the characteristics of person,
object or phenomenon that can be
measured or take in different values.
Examples : height, weight, age, blood
pressure, Hb level, number of deaths,
parity, apgar score, gender, gestational
age etc
Dr. RS Mehta, MSND
3.
Examples
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Blood Pressure
Sex
Gender
Age
Extraversion
Patient Satisfaction
Heart rate
Political Party
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Time
Weight
Height
Anxiety
Pleasure
Fear
Aggression
Attractiveness
Dr. RS Mehta, MSND
4.
Why variables?
• Variables help to present and analyze the
data in convenient way
• Identification of variables helps in the
presentation of data
• Variables help to achieve the objective of
research
• Variables help to prove hypotheses
Dr. RS Mehta, MSND
5.
Variables are classified as
qualitative and quantitative
• Qualitative variables are usually un –
measurable i.e. only can be categorized
such as, gender as male and female,
colour as red or white or blue or green.
Birth weight as low, high and normal etc.
• Quantitative variables are measurable or
can be expressed them numerically such
as apgar score, gestational age, birth
weight, height, age, parity etc.
Dr. RS Mehta, MSND
6.
Conceptualization of quantitative variables
as discrete and continuous
• Continuous variable: Any variable that is continuous and
which can be expressed in fractions is known as continuous
variable. e.g: age, temperature.
• Discrete variable: Any variable that can not be expressed
in fractions is known as discrete variable and divided into:
i) Dichotomous discrete: when one has to choose one
from the two alternatives. e.g: dead/alive, M/F.
Ii) Polytomous discrete: When it cannot be expressed in
fractions or cannot be divided into smaller parts. e.g football
score, parity, gravida etc.
Dr. RS Mehta, MSND
7.
Classification of variables in showing
relationship
• Dependent variable
• Independent variable
Dr. RS Mehta, MSND
8.
Dependent variable
• It describes or measures the problem, depends
upon the independent variable and generates the
data.
• It is expected to change during the result of the
research.
• The changed or effected variable is referred to as
the dependent variable cause it’s value depends
on the value of the independent variable.
• Some examples of dependent variables are
performance, fitness, learning, health
knowledge, achievement and behaviour etc
Dr. RS Mehta, MSND
9.
Independent variable
• It describes or measures the factor that is assumed to
cause or at least influence the problem.
• The independent variable is known as the treatment and
will not change during the research or as a result of the
research.
• It is expected to cause some effect on the dependent
variables.
• Some examples, exercise, intelligence, attitudes etc.
Dr. RS Mehta, MSND
10.
Examples of dependent and independent
variables in a hypothesis
• Hypothesis:
A vegetarian diet produces stronger and
healthier people than does a non-vegetarian.
• Independent variable: Type of diet (quantitative )
• Dependent variable: Strength and health score
( quantitative )
Dr. RS Mehta, MSND
11.
• Hypothesis:
There is a difference in self-confidence of female
adults who exercise program and the female
adults, who dropout of the exercise programs.
• Independent variable: exercise programs,
( quantitative, discrete )
• Dependent variable: Self confidence score
(quantitative, continuous)
Dr. RS Mehta, MSND
12.
Confounding variable
• A variable that is associated with the problem
and with the possible cause of the problem is a
confounding variable.
• It must be associated with the exposure and
independent of that exposure be a factor.
• It interacts with the dependent variable to make
the independent variable extremely effective or
ineffective. e.g
– Mother’s education ( Independent variable)
– Malnutrition ( Dependent variable)
– Family income ( Confounding variable)
Dr. RS Mehta, MSND
13.
Confounder
Confounder
Family Income
Mother’s Education
Malnutrition
Independent
Dependent
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14.
Confounding
(from the Latin confundere, to mix together)
Dr. RS Mehta, MSND
15.
Confounding refers to the
mixing of the effect of on
extraneous variable with the
effects of the exposure and
disease of interest.
Dr. RS Mehta, MSND
16.
Confounding……
In a study of the association between
exposure to a cause (or risk factor) and
the occurrence of disease, confounding
can occur when another exposure
exists in the study population and is
associated both with the disease and
the exposure being studied.
Dr. RS Mehta, MSND
17.
“A CONFOUNDING FACTOR is an
independent variable that distorts the
association between another independent
variable and the problem under study, as it
is related to both.”
“For a variable to be confounding, it must
be associated with the first risk factor and
be an independent risk factor for the
problem.”
Dr. RS Mehta, MSND
18.
Criteria for confounders:
1. It is a risk factor of the study disease
(but is not the consequence)
2. It is associated both with the disease and the
exposure being studied
3. It is out of interest of current study
(an extraneous variable)
4.In the absence of exposure it independently able
to cause disease (outcome)
Dr. RS Mehta, MSND
19.
Some common confounders:
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Age
Sex
Religion
Educational level
Social status
Family income
Marital status
Employment
Obesity
Smoking……..
Dr. RS Mehta, MSND
20.
For a factor to be a potential confounding variable there has to be a
triangular relationship between the first risk factor, the potential
confounding factor and the problem under investigation, as shown in
Figure
A
B
Cause
Effect
(Independent variable)
(Dependent variable)
C
Other factors
(Confounding Variable)
(The apparent association between A and B may be due to a third variable, C which associates
with both A and B)
Dr. RS Mehta, MSND
21.
S
N
Independent
variable
1 Coffee
drinking
Dependent
variable
Confounding variable
Coronary Cigarette smoking
a.
It is known that coffee consumption is associated
heart
with cigarette smoking; people who drink coffee are
disease
•
2
High
blood
pressure
more likely to smoke than people who do not drink
coffee.
It is also well known that cigarette smoking is a
cause of coronary heart disease.
Coronary Increasing age
heart
Increasing age may be associated with high blood
disease
pressure as well as to coronary heart disease.
Dr. RS Mehta, MSND
22.
Inter-relationship between smoking (factor), mining (confounding
factor) and lung cancer (problem) in a cohort study
smoking is related to lung cancer, mining is related to smoking as well as to lung
cancer. Therefore, there is a triangular relationship between smoking, mining and
lung cancer,
Dr. RS Mehta, MSND
24.
cause
Effect
Myocardial infarction
Total cholesterol
Obesity
Confounding variable
Dr. RS Mehta, MSND
25.
What is the effect of confounding?
• Confounding can result in the association
between a risk factor and the outcome
appearing smaller (under-estimated) or
appearing bigger than it is (over-estimated).
• It can even change the direction of the
observed effect, resulting in a harmful factor
appearing to be protective or vice versa.
Dr. RS Mehta, MSND
26.
The control of confounding
a. At the research designing stage:
1. Randomization
2. Restriction
3. Matching
b. At the data analysis stage:
1. Stratification
2. Statistical modeling
Dr. RS Mehta, MSND
27.
1. Randomization:
• Applicable only to experimental studies
• Ensuring that potential confounding
variables are equally distributed among the
groups being compared
• Random allocation of individuals to groups
e.g., for the experimental and control
groups, by chance.
Dr. RS Mehta, MSND
28.
2. Restriction:
- Can be used to limit the study to people who
have particular characteristics.
for exampleIn a study on the effects of coffee on coronary
heart disease, participation in the study could be
restricted to nonsmokers.
Coronary heart disease
Coffee drinking
Cigarette smoking
Dr. RS Mehta, MSND
29.
3.Matching:
The study participants are selected so as to ensure
that potential confounding variables are evenly
distributed in the two groups being compared.
For exampleIn a case-control study of exercise and
coronary heart disease, each patient with heart
disease can be matched with a control of the
same age group and sex
(to ensure that confounding by age and sex does not
occur).
Dr. RS Mehta, MSND
30.
B.1. Stratification:
- For control of confounding in the analytical phase (in
large studies)
- Measurement of the strength of association in welldefined and homogenous categories (strata) of the
confounding variable.
For examplea. If age is confounder, the association may be measured
in, say, 10 year age group.
b. If sex is a confounder, the association is measured in
men and women.
c. If ethnicity is a confounder, the association is measured
in the different ethnic groups.
B.2. Statistical modeling : Various statistical Tests
Dr. RS Mehta, MSND