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# Variables for bn 1

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### Transcript of "Variables for bn 1"

1. 1. Variables Dr. RS Mehta, MSND
2. 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. 3. 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
4. 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. 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. 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. 7. Classification of variables in showing relationship • Dependent variable • Independent variable Dr. RS Mehta, MSND
8. 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. 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. 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. 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. 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. 13. Confounder Confounder Family Income Mother’s Education Malnutrition Independent Dependent Dr. RS Mehta, MSND
14. 14. Confounding (from the Latin confundere, to mix together) Dr. RS Mehta, MSND
15. 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. 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. 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. 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. 19. Some common confounders: • • • • • • • • • • Age Sex Religion Educational level Social status Family income Marital status Employment Obesity Smoking…….. Dr. RS Mehta, MSND
20. 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. 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. 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
23. 23. Dr. RS Mehta, MSND
24. 24. cause Effect Myocardial infarction Total cholesterol Obesity Confounding variable Dr. RS Mehta, MSND
25. 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. 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. 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. 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. 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. 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
31. 31. Thanks Dr. RS Mehta, MSND
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