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VARIABLE
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Anything that can varyalong some dimension. This
means that it must have
more than one value or levele.g. “sex of subject” is a
variable as it has two valuesi.e. male and female.
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A Variable is a characteristic of aperson, object or phenomenon that
can take on different values.
A simple example of a variable is aperson’s age. The variable age can
take on different values because
a person can be 20 years old, 35years old, and so on.
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Other examples of variables are
– Weight (expressed in kilograms or in
pounds);
– Distance between homes and clinic
(expressed in kilometers or in minuteswalking distances
– Monthly income ( expressed in dollars,
rupees,or Kwachas.)
– Because the values of all these variables are
expressed in numbers, we call them
Numerical Variables.
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The different values of a variable may also be
expressed in categories. For example, the variable
sex has two values, male and female, which are
distinct categories. Other example are:
VARIABLES CATEGORIES
Red
Blue
Colour
Green etc.
Recovery
Chronic illness
Outcome of disease
DeathSince the value of these variables are expressed in categories, we
call them CATEGORICAL VARIABLES.
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Variables
Dependent Variables Independent Variable
Confounding Variable
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Dependent and independent variables
Because in health system research you often look
for causal explanations, It is important to makedistinction between dependent and independent
variables.
The variable that is used to describe or measure theproblem under study is called the DEPENDENT
variable.
The variables that are used to describe or measure
the factors that are assumed to cause or at least toinfluence the problem are called the INDEPENDENT
variables.
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For example, in a study of the
relationship between smoking and
lung cancer, “suffering from lungcancer”(with the values yes, no )
would be the dependent variable
and “smoking” ( varying from notsmoking to smoking more than
three packets a day) the
independent variables.
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The statement of the problem and the objectives
of the study determine whether a variable is
dependent or independent. It is, therefore,
important when designing a study to clearly
state which variable is the dependent and which
are the independent ones.
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If a research investigates why people smoke,
“smoking “is the dependent , and “pressure
from peers to smoke”could be an independent
note that in the lung cancer study “smoking “
was the independent variable.
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Although in everyday language we may speak of possible
CAUSES of problems, in scientific language we prefer to
speak of ASSOCIATIONS between variables, unless a
casual relationship can be proven If we find an
association between smoking and cancer,we can conclude that
smoking causes cancer only if we can both demonstrate that the
cancer was developed after the patient started smoking
and that there are no other factors that may have caused both
the cancer and the habit of smoking. Nervous people, for
example, may both smoke more and suffer more from cancer,
than persons who are not nervous.
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Certain variables may produce changes in
the DV which are mistakenly interpreted as
representing effects of the IV. When thishappens, the variable is called a
confounding variable because its effects are
confused with IV effects.
A variable that is associated with the
problem and with a possible cause of the
problem is a potential CONFOUNDINGVARIABLE.
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A confounding variable may either strengthen
or weaken the
apparent relationship between the problem and
a possible cause.
Cause Effect/outcome
(independent variable) (dependent
variable)
Other factors
(confounding variables)
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Therefore, to give a true picture of cause
and effect, the confounding variables
must be considered, either at planning
stage or while doing data analysis.
For example:A relationship is shown between the low
level of the mother’s education and
malnutrition in under--5’s However, familyincome may be related to the mother’s
education as well as to malnutrition.
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Mother education Malnutrition
(independent variable) (dependentvariable)
Family income
confounding variable)
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Family income is therefore a potential
confounding variable. To give a true picture
of
the relationship between mother’s education
and
malnutrition, family income should also be
considered and measured.
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This could either be incorporated into the
research design, for example by selecting mothers
with a specific level of family income, or it can be
taken into account in the analysis of the findings,
with mother’s education and malnutrition
among their children being analyzed for families
with different categories of income.
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Confounding
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The concept of confounding is a central
one in the interpretation of any
epidemiological study.Confounding can be thought of as
mixing of the effect of the exposure
under study on the disease with that of
an extraneous factor.
This external factor (other than the
exposure and the disease under study)
must be associated with the exposureand,independent of the exposure must
be a risk factor for the disease.
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Table 1. Relation of Myocardial infarction
(MI)to Recent Oral Contraceptive (OC) Use.
MI Control Estimated
relative risk
OC
Yes 29 135 =1.68No 205 1607
Total 234 1742
Source: Shapiro et al. 1979.
age- use spec c re a ve
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age- use spec c re a verisk
25-29 Yes 04 62 7.2
No 02 224
30-34 Yes 09 33 8.9
No 12 390
35-39 Yes 04 26 1.5
No 33 330
40-44 Yes 06 09 3.7
No 65 362
45-49 Yes 06 05 3.9
No 93 301Total 234 1742
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Confounding can be controlled in
study design through:
Restriction
Matching exposure
Randomization of
Confounding can be controlled in analysis
through:
Stratification
Multivariate analysis
h t
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have to
be evaluated in several clinical trials and
other
epidemiological investigation where
appropriate
selection of the population to be studied is
made
and with proper study design, so that theresults
can be applied to other population I.e., they
are
esearc s u es an var a es:
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esearc s u es an var a es:
Experiments and research studies are
designed
around variables. Variables are measured,
variables are manipulated by the researcher to
see how they affect other variables. Two or
more
variables are measured at the same time to
see if
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CHARACTERISTICS OF DIFFERENT
TYPES OF MEASUREMENT:1- A measure scale must consist of at
least two different possible
measurement categories or
values.
2- The usable scale of measurement
should be complete or
exhaustive i.e. Muslim,Hindus,etc.
3- The measures we define along our
scale of measurement should be
mutually exclusive.
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SCALE OF MEASUREMENT:
A scale of measurement is defined
before
any observation is done or any
measurements are taken by the
experimenter.
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Measures vary from crude to
precise:
Some aspects of person such asheight, age
or income can be measured in a very
precise fashion,other aspects such an
attractiveness of appearance do not
lend
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TAKING MEASURES
“ I have met a wonderful young man.
He is handsome, six footer withengaging
smile,long brown hair and a guitar.
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If a measurement scale has:
• (I) two or more possible measures.
• (ii) is exhaustive so that each and every
subject can be assigned.
• (iii) Is mutually exclusive, so that no
subject can be assigned two different
measures on the same scale.
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It is nominal scale e.g. Sex is a
;nominal scale.However, if in addition to above, the
scale also has a natural order, then the
scale is ordinal.And if the scale also has equal sized
steps, then the scale is an interval
scale only measures assigned
according to an interval scale can be
added and subtracted.
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ORDINAL SCALES OF MEASUREMENT:
The measurement scale may possess
ordered categories but the possible
measures along the scale may not be
truly
equidistant.
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Strongly Disagree
Disagree agree NEGATIVE
Mildly disagree
No opinion NEUTRAL
Mildly agree
Agree POSITIVE
Strongly agree
Designing an experiment:
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Designing an experiment:
The design of a research project is the
overall
plan for how the research will be
performed.
When you develop your design you have to
specify how many IVs you are going to
investigate and how many levels of each IV
you
intend to use..
Y d i d t i h ill t
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Your design determines how you will sort
your
subjects into groups and how many groups you
will need. Your design must specify when
and
how often you will take measures on your
DVS
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ASSOCIATION AND CAUSATION
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Association refers to the statistical
dependence
between two variables I.e., the degree to which
the
rate of a disease is higher or lower with a
specific
exposure than the rate of disease among those
without the exposure.
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The judgement about an association
rests on two elements:
That the association is:Valid : Look for alternative explanations
including, confounding, chance and
bias.
Repeatable: the evidence taken from a
number of sources supporting the judgement of causality.
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Chance
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Evaluation of the role of chance
consists of two components:-
Hypothesis testing: performing a testof statistical significance with the p-
value to determine the likelihood that
sampling variability can beconsidered as an explanation.
Estimation of the confidence
interval :to indicate the range withinwhich the true estimate of effect is
likely to have with a given degree of
assurance.
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BIAS
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Any systematic error that results
in an incorrect estimate of theassociation between the exposure
and disease outcome. Usually
introduced by the experimenter or the researcher himself due to non-
standardized measuring
techniques.
T f Bi
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Type of Bias:-
Selection Bias Observation/Information Bias:-
Recall Bias
Interviewers Bias
Lost-to-follow up
Misclassification
C C t l Bi
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Can Control Bias:-
In study design
Choice of study population
Data collection:-
Questionnaire
Instruments
BlindingStandardization of instruments
Source of information.
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Risk factors:
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Are ascertainable characteristics or circumstances of a
person or group of persons AND are associated with a
disease outcome.
* They have a temporal relationship with thedisease outcome.
* One or a combination of factors will increase the
risk
* They are statistically associated with a diseaseoutcome.
* They may have casual relationship with a
disease outcome but are not the cause of
a disease.* Removal of a risk factor may decrease the risk
of a disease outcome.
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Risk:
Is defined as the probability of an
adverse consequence/s in thepresence
of one or more characteristic/s or