Variables

Dr. RS Mehta, MSND

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

Examples• Blood Pressure• Sex• Gender• Age• Extraversion• Patient Satisfaction• Heart rate• Political Party

• Time• Weight• Height • Anxiety• Pleasure• Fear• Aggression• Attractiveness

Dr. RS Mehta, MSND

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

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

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

Classification of variables in showing relationship

• Dependent variable • Independent variable

Dr. RS Mehta, MSND

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

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

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

• 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

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

Confounder

Malnutrition

Confounder

Independent Dependent

Mother’s Education

Family Income

Dr. RS Mehta, MSND

Confounding

(from the Latin confundere, to mix together)

Dr. RS Mehta, MSND

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

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

“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

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 studied3. 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

Some common confounders:• Age• Sex• Religion• Educational level• Social status• Family income• Marital status• Employment• Obesity• Smoking……..

Dr. RS Mehta, MSND

(The apparent association between A and B may be due to a third variable, C which associates with both A and B)

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

Cause Effect (Independent variable) (Dependent variable)

Other factors (Confounding Variable)

A B

C

Dr. RS Mehta, MSND

SN

Independent variable

Dependent variable

Confounding variable

1 Coffee drinking

Coronary heart disease

Cigarette smokinga. It is known that coffee consumption is associated

with cigarette smoking; people who drink coffee are 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.

2 High blood pressure

Coronary heart disease

Increasing ageIncreasing age may be associated with high blood pressure as well as to coronary heart disease.

Dr. RS Mehta, MSND

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,

Inter-relationship between smoking (factor), mining (confounding factor) and lung cancer (problem) in a cohort study

Dr. RS Mehta, MSND

Dr. RS Mehta, MSND

Total cholesterol

Obesity

Myocardial infarction

cause Effect

Confounding variable

Dr. RS Mehta, MSND

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

The control of confoundinga. At the research designing stage: 1. Randomization 2. Restriction 3. Matchingb. At the data analysis stage: 1. Stratification 2. Statistical modeling

Dr. RS Mehta, MSND

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

2. Restriction: - Can be used to limit the study to people who

have particular characteristics. for example- In a study on the effects of coffee on coronary

heart disease, participation in the study could be restricted to nonsmokers.

Coffee drinking Coronary heart disease

Cigarette smokingDr. RS Mehta, MSND

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 example- In 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

B.1. Stratification: - For control of confounding in the analytical phase (in

large studies) - Measurement of the strength of association in well-

defined and homogenous categories (strata) of the confounding variable.

For example-a. 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

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