Identifying Variables Lecture Bwl

7/30/2019 Identifying Variables Lecture Bwl

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Identifying variables

Bony Wiem LestariEpidemiology and BiostatisticsDepartment


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Why it is important to assess our

variables of interest?

Research design issue Measurement issue Statistical testing


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Research Design

Independent variable/ risk factor(s)/predictor(s)/ exposure(s)

Dependent variable/ disease(s)/

outcome(s) Choosing appropriate research design


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Epidemiologic Study DesignThe plan of an empirical investigation to

assess an E D relationship.Exposure Alcohol

consumption Raw hamburger Smoking

Outcome Breast Cancer E. Coli Lung Cancer


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Type of Epidemiologic Studies

The investigatorthroughrandomizationallocatessubjects to

differentcategories ofexposure.

Investigatorobserves theexposure andoutcome statusof each


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Observational Studies

Descriptive Studies Analytic Studies


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Descriptive StudiesTo organize and summarize data accordingto time, place, and person. Why? Describe natural history of disease Extent of public health problem Identify populations at greatest risk Allocation of health care resources Suggest hypothesis about causation


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Analytic StudiesUsed to quantify the association between anexposure (E) and a health outcome (D), and

to test hypotheses about causalrelationships. Provides a control group (baseline)

Test hypotheses about determinants Causation


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Measurement issue

How to operationalise the way ameasurement is carried out? When should measurements be taken? How many measurements should be taken on

each variable and how should severalmeasurements be combined?

Standardized informationCost and time efficient


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Measurement issue

Variables Definition: something that is likely to vary;

something that can be changed, such as a

characteristic or value. In clinical research: a quantity whose

value may vary from patient to patient


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Measurement issue

Independent variable: the variable that iscontrolled and manipulated by theexperimenter to see how it affects thedependent variable.

Dependent variable: the variable that ismeasured by the experimenter; what isactually being measured in the

experiment? e.g: impact ofsleep deprivation on testperformance


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Measurement issue

Confounding variable: the variable thatmay have an impact on the relationshipbetween the independent and dependent

variables e.g: age, gender and education level


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Operationally Defining aVariable

Before conducting a study, it is essentialto create firm operational definitions forboth the independent variable and

dependent variable. An operational definition describes how

the variables are measured and defined

within the study.


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e.g: impact ofsleep deprivation on testperformance sleep deprivation = IV test performance = DV (scores) Hypothesis: students who are sleep

deprived will score significantly lower on a

test


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sleep deprivation refers to thoseparticipants who have had less than fivehours of sleep the night before the test.

test performance : a students score on achapter exam


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Statistical testing

Choice of methods is largely determinedby the type and character of variables

Qualitative : variable for which the

numerical value is not meaningful, alsocalled categorical variable. e.g: gender,race, social status.

Quantitative: the value of variable shouldbe interpreted as a number, also callednumerical variable. e.g: counts, age, bloodpressure


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Types of variables

CATEGORICAL (or qualitative)1. Nominal: no natural ordering of categoriesE.g: sex, smoker/non-smoker (dichotomous)blood group, married/single/divorced/widowed

(polytomous)2. Ordinal: natural orderingE.g: result of treatment (worsened/nochange/improved)level of education: primary/secondary/highstage of breast cancer: I, II, III, IV


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Types of variables

NUMERICAL (or quantitative)1.Discrete: a variable which can take ononly a countable number of values

e.g: no. of persons in a household, no. of whiteblood cells in blood sample2. Continuous: a variable that can get anyvalue along some line intervale.g: height, age, blood pressure, BMI


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Some well known statistical testsNormal Distribution DichotomousSamples distrib. free data2 T-test Mann-Whitney -square>2 ANOVA Kruskal-Wallis -square>2 ordered Linear Spearmans rho Logisticregres. regression1 (paired) Paired t-test Wilcoxon (signed rank) McNemar>=2 (survi-AFT models Kaplan-Meier curves/val data)Logrank-tests/Cox-regres.

Analysis of count data: Poisson regression


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Thank you

Identifying Variables Lecture Bwl

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