Anything That Can Vary Along Some Dimension. This Means That

8/14/2019 Anything That Can Vary Along Some Dimension. This Means That

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VARIABLE



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.



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.



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.



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.



Variables

Dependent Variables Independent Variable

Confounding Variable



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.



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.



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.



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.



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.



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.



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)



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.



Mother education Malnutrition

(independent variable) (dependentvariable)

Family income

confounding variable)



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.



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.



Confounding



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.





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



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



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



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:



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



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.



SCALE OF MEASUREMENT:

A scale of measurement is defined

before

any observation is done or any

measurements are taken by the

experimenter.



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



TAKING MEASURES

“ I have met a wonderful young man.

He is handsome, six footer withengaging

smile,long brown hair and a guitar.



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.



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.



ORDINAL SCALES OF MEASUREMENT:

The measurement scale may possess

ordered categories but the possible

measures along the scale may not be

truly

equidistant.



Strongly Disagree

Disagree agree NEGATIVE

Mildly disagree

No opinion NEUTRAL

Mildly agree

Agree POSITIVE

Strongly agree

Designing an experiment:



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



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



ASSOCIATION AND CAUSATION



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.





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.



Chance



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.



BIAS



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



Type of Bias:-

Selection Bias Observation/Information Bias:-

Recall Bias

Interviewers Bias

Lost-to-follow up

Misclassification

C C t l Bi



Can Control Bias:-

In study design

Choice of study population

Data collection:-

Questionnaire

Instruments

BlindingStandardization of instruments

Source of information.



Risk factors:



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.



Risk:

Is defined as the probability of an

adverse consequence/s in thepresence

of one or more characteristic/s or

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Anything That Can Vary Along Some Dimension. This Means That

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