Study designs in research methodology

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STUDY DESIGNS IN RESEARCH METHODOLOGY

Dr SAKSHI KAUR CHHABRA

2nd YR PG STUDENT

DEPARTMENT OF PUBLIC HEALTH DENTISTRY

PACIFIC DENTAL COLLEGE AND HOSPITAL,DEBARI

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CONTENTS[1] INTRODUCTION

[2] DEFINITION

[3] NEED FOR THE STUDY DESIGN

[4] FEATURES OF A GOOD DESIGN

[5] IMPORTANT CONCEPTS RELATING RESEARCH

[6] TYPES OF STUDIES

[7] EXPERIMENTAL DESIGN

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CONTENTS[8] ERRORS IN INTERFERENCE

[9] EXPERIMENTAL VERSUS OBSERVATIONAL STRATEGIES

[10] DESCRIPTIVE DESIGNS

[11] ANALYTICAL DESIGNS

[12] CONCLUSION

[13] REFERENCES

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INTRODUCTION The formidable problem that follows the task of defining the

research problem is the preparation of the design of the research project, popularly known as the “research design”. Decisions regarding what, where, when, how much, by what means concerning an inquiry or a research study constitute a research design.

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DEFINITION“ A research design is the arrangement of conditions for collection

and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.” In fact, the research design is the conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data. As such the design includes an outline of what the researcher will do from writing the hypothesis and its operational implications to the final analysis of data.

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NEED FOR THE STUDY DESIGNS

It facilitates the smooth sailing of the various research operations.

Making research as efficient as possible yielding maximal information with minimal expenditure of effort, time and money.

We need a research design or a plan in advance of data collection and analysis for our research project.

Research design stands for advance planning of the methods to be adopted for collecting the relevant data and the techniques to be used in their analysis, keeping in view the objective of the research and the availability of staff, time and money

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FEATURES OF A GOOD RESEARCH DESIGN

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A research design appropriate for a particular research problem, usually involves the consideration of the following factors:

IMPORTANT CONCEPTS RELATING RESEARCH DESIGN

Before describing the different research designs, it will be appropriate to explain the various concepts relating to designs so that these may be better and easily understood.

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1. Dependent and independent variables:

A concept which can take on different quantitative values is called a variable. As such the concepts like weight, height, income are all examples of variables.

CONTINUOUS

VARIABLE

Phenomena which can take on quantitatively different values even in decimal points .

AGE IS AN EXAMPLE

DISCRETE VARIABLES

But all variables are not continuous. If they can only be expressed in integer values, they are non-continuous variables or in statistical language

INDEPENDENT

VARIABLE

If one variable depends upon or is a consequence of the other variable, it is termed as a dependent variable, and the variable that is antecedent to the dependent variable

EXAMPLE

For instance, if we say that height depends upon age, then height is a dependent variable and age is an independent variable.

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2. Extraneous variable:

Independent variables that are not related to the purpose of the study, but may affect the dependent variable

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2. Extraneous variable:

Suppose the researcher wants to test the hypothesis that there is a relationship between children’s gains in social studies achievement and their self-concepts. In this case self-concept is an independent variable and social studies achievement is a dependent variable. Intelligence may as well affect the social studies achievement, but since it is not related to the purpose of the study undertaken by the researcher, it will be termed as an extraneous variable

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The variable which is being manipulated by the researcher is called the independent variable and the dependent variable is the change in behaviour measured by the researcher.

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[3] CONTROL

The technical term ‘control’ is used when we design the study minimising the effects of extraneous independent variables. In experimental researches, the term ‘control’ is used to refer to restrain experimental conditions.

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[4] CONFOUNDED RELATIONSHIP

When the dependent variable is not free from the influence of extraneous variable(s), the relationship between the dependent and independent variables is said to be confounded by an extraneous variable(s).

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[5] RESEARCH HYPOTHESIS

When a prediction or a hypothesised relationship is to be tested by scientific methods, it is termed as research hypothesis.

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[6] Experimental and non-experimental hypothesis-testing research:

When the purpose of research is to test a research hypothesis, it is termed as hypothesis-testing research.

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Research in which the independent variable is manipulated is termed ‘experimental hypothesis-testing research’ and a research in which an independent variable is not manipulated is called ‘non-experimental hypothesis-testing research’.

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[7] EXPERIMENTAL AND CONTROL GROUPS

when a group is exposed to usual conditions, it is termed a ‘control group’, but when the group is exposed to some novel or special condition, it is termed an ‘experimental group’.

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In the above illustration, the Group A can be called a control group and the Group B an experimental group. If both groups A and B are exposed to special studies programmes, then both groups would be termed ‘experimental groups.’ It is possible to design studies which include only experimental groups or studies which include both experimental and control groups.

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[8] TREATMENTS

The different conditions under which experimental and control groups are put are usually referred to as ‘treatments’.

In the illustration taken above, the two treatments are the usual studies programme and the special studies programme. Similarly, if we want to determine through an experiment the comparative impact of three varieties of fertilizers on the yield of wheat, in that case the three varieties of fertilizers will be treated as three treatments.

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[9] EXPERIMENT

The process of examining the truth of a statistical hypothesis, relating to some research problem, is known as an experiment.

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For example, we can conduct an experiment to examine the usefulness of a certain newly developed drug. Experiments can be of two types viz., absolute experiment and comparative experiment. If we want to determine the impact of a fertilizer on the yield of a crop, it is a case of absolute experiment; but if we want to determine the impact of one fertilizer as compared to the impact of some other fertilizer, our experiment then will be termed as a comparative experiment. Often, we undertake comparative experiments when we talk of designs of experiments.

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[10] EXPERIMENTAL UNIT

The pre-determined plots or the blocks, where different treatments are used, are known as experimental units. Such experimental units must be selected (defined) very carefully.

CLASSIFICATION OF EPIDEMIOLOGY STUDIES

Epidemiological studies can be classified as observational and experimental studies with further subdivisions :

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[A] DESCRIPTIVE STUDIES

[B] ANALYTICAL STUDIES


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[B] ANALYTICAL STUDIES :-{1} ECOLOGICAL OR

CORRELATIONAL {2} CROSS – SECTIONAL OR

PREVALENCE {3} CASE – CONTROL 0R CASE

– REFERENCE {4} COHORT OR FOLLOW - UP


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[2] EXPERIMENTAL STUDIES INTERVENTION STUDIES

{A} RANDOMIZED CONTROLLED TRIALS OR CLINICAL TRIALS

{B} FIELD TRIALS

{C} COMMUNITY TRIALS OR COMMUNITY INTERVENTION

STUDIES

EXPERIMENTAL DESIGNS

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INTRODUCTION

An experiment is the best epidemiological study design to prove causation. It can be viewed as the final or definitive step in the research process, a mechanism for confirming or rejecting the validity of ideas, assumptions, postulates and hypotheses about the behaviour of objects, or effects upon them which result from interventions under defined sets of conditions.

In health research, we are often interested in comparative experiment, where one or more groups with specific interventions is compared with a group unexposed to interventions (placebo in clinical trials) or exposed to the best treatment currently available.The effect of the new interventions on one or more outcome variables is compared between the groups by the use of statistical procedures, and the significance of observed differences assessed for concordance with the null hypotheses.

Two types of comparative experiments, the randomized clinical trial (RCT) and the community intervention trial (CIT)

PURPOSE OF EXPERIMENT The design of experiments serves the purpose of ensuring valid

data relevant to the hypothesis under test as economically (maximum statistical power with minimum cost and inconvenience) as possible.

A population survey tells us about the characteristics observed and the association between these characteristics in the population.

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BASIC PRINCIPLES OF EXPERIMENTAL DESIGNS

Professor Fisher has enumerated three principles of experimental designs:

(1) the Principle of Replication;

(2) the Principle of Randomization

(3) Principle of Local Control

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[1] PRINCIPLE OF REPLICATION

According to the Principle of Replication, the experiment should be repeated more than once. Thus, each treatment is applied in many experimental units instead of one. By doing so the statistical accuracy of the experiments is increased

suppose we are to examine the effect of

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two varieties of rice

Other variet

y1

variety

We can then compare the yield of the two parts and draw conclusion on that basis.

For this purpose we may divide the field into two parts and grow one variety in one part and the other variety in the other part.

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But if we are to apply the principle of replication

then we first divide the field into several parts

grow one variety in half of these parts and the other variety in the

remaining parts.

We can then collect the data of yield of the two varieties and draw

conclusion by comparing the same

The result so obtained will be more reliable in comparison to the conclusion we draw without applying the principle of replication

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[2] PRINCIPLE OF RANDOMIZATION

The Principle of Randomization provides protection, when we conduct an experiment, against the effect of extraneous factors by randomization. In other words, this principle indicates that we should design or plan the experiment in such a way that the variations caused by extraneous factors can all be combined under the general heading of “chance.”

Suppose , if we grow one variety of rice

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two varieties of rice

Other variet

y1

variety

then it is just possible that the soil fertility may be different in the first half in comparison to the other half. If this is so, our results would not be realistic

For this purpose we may divide the field into two parts and grow one variety in one part and the other variety in the other part.

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But if we are to apply the principle of replication

we may assign the variety of rice to be grown in different parts of the field

on the basis of some random sampling technique i.e., we may apply randomization principle

and protect ourselves against the effects of the extraneous factors

As such, through the application of the principle of randomization, we can have a better estimate of the experimental error.

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[2] PRINCIPLE OF LOCAL CONTROL

The Principle of Local Control is another important principle of experimental designs. Under it the extraneous factor, the known source of variability, is made to vary deliberately over as wide a range as necessary and this needs to be done in such a way that the variability it causes can be measured and hence eliminated from the experimental error

Suppose , if we perform a two-way analysis of variance,

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experimental errorthe extraneous

factor (soil fertility in our case

In brief, through the principle of local control we can eliminate the variability due to extraneous factors from the experimental error.

in which the total variability of the data is divided into three components attributed to treatments

varieties of rice in our case

IMPORTANT EXPERIMENTAL DESIGNS

Experimental design refers to the framework or structure of an experiment and as such there are several experimental designs.

We can classify experimental designs into two broad categories :-

Informal experimental designs:

Before-and-after without control design. After-only with control design. Before-and-after with control design

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Formal experimental designs:

Completely randomized design (C.R. Design).Randomized block design (R.B. Design).Latin square design (L.S. Design).Factorial designs.

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INFORMAL EXPERIMENTAL DESIGNS

[A] Before-and-after without control design

A single test group or area is selected, and the dependent variable is measured

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The treatment is then introduced and then the dependent variable is measured again .

The effect of the treatment the level of the phenomenon after the treatment - the level

of the phenomenon before the treatment .• The design can be represented thus:

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• The design can be represented thus:

2. After-only with control design

Two groups or areas (test area and control area) are selected and the treatment is introduced into the test area only

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3. Before-and-after with control design

In this design two areas are selected and the dependent variable is measured in both the areas for an identical time-period before the treatment. The treatment is then introduced into the test area only, and the dependent variable is measured in both for an identical time-period after the introduction of the treatment

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FORMAL EXPERIMENTAL DESIGNS

[A]Completely randomized design (C.R. design)

Involves only two principles i.e the principle of replication and the principle of randomization of experimental designs.

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We can present a brief description of the two forms of such a design as :-

[1] Two-group simple randomized designfirst of all the population is defined and then from the population a sample is selected randomly . After being selected randomly from the population, be randomly assigned to the experimental and control groups (Such random assignment of items to two groups is technically described as principle of randomization).

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(2) Random replications design

The limitation of the two-group randomized design is

usually eliminated within the random replications design.

In the illustration just cited above, the teacher differences

on the dependent variable were ignored, i.e., the

extraneous variable was not controlled. But in a random

replications design, the effect of such differences are

minimised (or reduced) by providing a number of

repetitions for each treatment. Each repetition is

technically called a ‘replication’.

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there are two populations in the replication design .sample is taken randomly from the population available to conduct experiments . and the eight individuals so selected should be randomly assigned to the eight groups. Similarly ,the sample is taken randomly from the population available for study and is randomly assigned to, say, four experimental and four control groups.

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[B] Randomized block design (R.B. design) In the R.B. design the principle of local control can be

applied along with the other two principles of experimental

designs. In the R.B. design, subjects are first divided into

groups, known as blocks. In general, blocks are the levels at

which we hold the extraneous factor fixed, so that its

contribution to the total variability of data can be

measured. The main feature of the R.B. design is that in

this each treatment appears the same number of times in

each block. The R.B. design is analysed by the two-way

analysis of variance (two-way ANOVA)* technique.

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Suppose four different forms of a standardised test in statistics were given to each of five students (selected one from each of the five I.Q. blocks) and following are the scores which they obtained.

If each student separately randomized the order in which he or she took the four tests (by using random numbers or some similar device), we refer to the design of this experiment as a R.B. design. The purpose of this randomization is to take care of such possible extraneous factors (say as fatigue) or perhaps the experience gained from repeatedly taking the test.

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[C] Latin square design (L.S. design) It is an experimental design very frequently used in

agricultural research.

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[D] FACTORIAL DESIGNS Factorial designs are used in experiments where the effects

of varying more than one factor are to be determined. They

are specially important in several economic and social

phenomena where usually a large number of factors affect

a particular problem.

Factorial designs can be of two types:

(i) simple factorial designs

(ii) complex factorial designs

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(i) Simple factorial designs In case of simple factorial designs, we consider the effects

of varying two factors on the dependent variable, but when

an experiment is done with more than two factors, we use

complex factorial designs.

Simple factorial design is also termed as a ‘two-factor-

factorial design’, whereas complex factorial design is

known as ‘multifactor-factorial design.’

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Simple factorial design may either be a 2 × 2 simple factorial

design, or it may be, say, 3 × 4 or 5 × 3 or the like type of

simple factorial design. We illustrate some simple factorial

designs as under :-

• Illustration 1: (2 × 2 simple factorial design). A 2 × 2 simple factorial design can graphically be depicted as follows:

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• Illustration 2: (4 × 3 simple factorial design).• The 4 × 3 simple factorial design will usually include four

treatments of the experimental variable and three levels of the control variable. Graphically it may take the following form:

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(2) Complex factorial designs Experiments with more than two factors at a time involve

the use of complex factorial designs. A design which

considers three or more independent variables

simultaneously is called a complex factorial design.

In case of three factors with one experimental variable

having two treatments and two control variables, each one

of which having two levels, the design used will be termed 2

× 2 × 2 complex factorial design which will contain a total

of eight cells as shown in :-

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ERRORS IN INTERFERENCE

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In order to develop a good research strategy, we need to understand the nature of these ‘errors’ or ‘variations’ and the methods available to measure the errors.

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Two common sources of error that need to be controlled result

from problems with ‘reliability’ and ‘validity’. Our inference

should have high reliability and high validity .

The reliability and validity of inferences depend on the reliability

and validity of the measurements as well as the reliability and

validity of the samples chosen .

The reliability of a sample is achieved by selecting a large sample,

and the validity is achieved by ensuring the sample selection is

unbiased.

In statistical terms, reliability is measured using ‘random error’

and validity by ‘bias’.

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ReliabilityReliability of

measurements

If repeated measurements of a characteristic in the same individual under identical conditions produce similar results, we would say that the measurement is reliable.

If independent, repeated observations are taken and the probability distribution is identified, the standard deviation of the observations provide a measure of reliability.

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Reliability of study

A result is said to be reliable if the same result is obtained when the study is repeated under the same conditions. The natural variability in observations among individuals in the population is commonly known as random error.

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VALIDITY

A measurement is said to be valid if it measures what it is supposed to. If a measurement is not valid, we say it is ‘biased’. Bias is a systematic error (as opposed to a random error) that skews the observation to one side of the truth. Thus, if we use a scale that is not calibrated to zero, the weights we obtain using this scale will be biased.

EXPERIMENTAL V/S OBSERVATIONAL STRATEGIES Advantages of the experimental approach include the following: [1]The ability to manipulate or assign independent variables . [2] The ability to randomize subjects to experimental and control groups . [3]The ability to control confounding and eliminate sources of spurious association . [4]The ability to replicate findings. Experiments are often more replicable than observational studies

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Experiments also have the following limitations:-

Lack of reality.

Difficulties in extrapolation

Ethical problems

Difficulties in manipulating the independent variable

Non-representativeness of samples. 68

DESCRIPTIVE STUDIES Definition

When an epidemiological study is not structured formally as an analytical or experimental study, i.e. when it is not aimed specifically to test a hypothesis, it is called a descriptive study, and belongs to the observational category of studies.

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CONDUCT OF DESCRIPTIVE STUDIES

Descriptive studies entail the collection, analysis and interpretation of data.

Both qualitative and quantitative techniques may be used,

including questionnaires, interviews, observations of participants, and service statistics, as well as documents describing communities, groups, situations, programmes and other individual or ecological units.

The distinctive feature of this approach is that its primary concern is with description rather than with the testing of hypotheses or proving causality

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KINDS OF DESCRIPTIVE STUDIES

1. [1] CASE SERIES

2. [2] COMMUNITY DIAGNOSIS AND NEEDS ASSESSMENT

3. [3] EPIDEMIOLOGICAL DESCRIPTION OF DISEASE 4. OCCURRENCE

5. [4] DESCRIPTIVE CROSS-SECTIONAL STUDIES OR 6. COMMUNITY (POPULATION) SURVEYS

7. [5] ECOLOGICAL DESCRIPTIVE STUDIES 71

[1] CASE SERIES This kind of study is based on reports of a series of cases of a

specific condition, or a series of treated cases, with no specifically allocated control group. These represent the numerator of disease occurrence, and should not be used to estimate risks.

In an attempt to make such series more impressive, clinicians may calculate proportional distribution, which consists simply of percentages of the total number of cases that belong to a specific category of age, sex, ethnic group or other characteristic.

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[2] COMMUNITY DIAGNOSIS AND NEEDS ASSESSMENT

This kind of study entails collection of data on existing health problems, programmes, achievements, constraints, social stratification, leadership patterns, focal points of resistance or high prevalence, or groups at highest risk. Its purpose is to identify existing needs and to provide baseline data for the design of further studies or action.

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[3] EPIDEMIOLOGICAL DESCRIPTION OF DISEASE

OCCURRENCE

This common use of the descriptive approach entails the collection of data on the occurrence and distribution of disease in populations according to specific characteristics of individuals (e.g. age, sex, education, smoking habits, religion, occupation, social class, marital status, health status, personality), place (rural/urban, local, subnational, national, international) and time (epidemic, seasonal, cyclic, secular).

A description may also be given of familial characteristics such as

birth order, parity, family size, maternal age, birth interval or family type.

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[4] DESCRIPTIVE CROSS SECTIONAL STUDIES OR COMMUNITY SURVEYS

Cross-sectional studies entail the collection of data on, as the term implies, a cross-section of the population, which may comprise the whole population or a proportion (sample) of it.

Many cross-sectional studies do not aim at testing a hypothesis about an association, and are thus descriptive.

They provide a prevalence rate at a particular point in time (point prevalence) or over a period of time (period prevalence). The study population at risk is the denominator for these prevalence rates.

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[4] DESCRIPTIVE CROSS SECTIONAL STUDIES OR CUOMMUNITY SURVEYS

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[5] ECOLOGICAL DESCRIPTIVE STUDIES

When the unit of observation is an aggregate (e.g. family, clan or school) or an ecological unit (a village, town or country) the study becomes an ecological descriptive study.

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ANALYTICAL STRATEGIES IN EPIDEMIOLOGY

In this type of study, hypothesis testing is the primary tool of

inference. The basic approach in analytical studies is to develop a

specific, testable hypothesis, and to design the study to control any

extraneous variables that could potentially confound the observed

relationship between the studied factor and the disease.

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[A] CASE-CONTROL STUDIES

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SIMPLEST

MOST COMMONLY USED

It is designed primarily to establish the causes of diseases by

investigating associations between exposure to a risk factor and

the occurrence of disease.

The design is relatively simple, except that it is backward-looking

(retrospective) based on the exposure histories of cases and

controls.

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Data are analysed to determine whether exposure was different

for cases and for controls. The risk factor is something that

happened or began in the past, presumably before disease onset,

e.g. smoking, or a previous infection or medication. Information

about the exposure is obtained by taking a history and/or from

records.

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SELECTION OF CASES

The selection of cases should be such that the study results are reliable and valid. For these reasons, the following guidelines should be used when selecting cases in a case-control study:-

[1] The criteria for inclusion in the study (what

constitutes a case) and criteria for exclusion from the

study must be clearly specified; this will improve the

validity of the results

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SELECTION OF CASES

[2] The sources of cases may be:

• all cases admitted to or discharged from a hospital, clinic, or private practice within a specified period.

• all cases reported or diagnosed during a survey or surveillance programme within a specified period

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SELECTION OF CONTROLSThis is the most important aspect of the case-control study, as biases in the selection of controls may invalidate the study results, and bias in the selection of controls is often the greatest cause for concern when analysing data from case-control studies.

[A] The sources of comparison groups may be:

• a probability sample of a defined population, if the cases are drawn from that defined population;

• a sample of patients admitted to, or attending the same institution as the cases;

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SELECTION OF CONTROLS

[B] The selection of controls may involve matching on

other risk factors:

• Matching means that controls are selected such that

cases and controls have the same (or very similar)

characteristics other than the disease and the risk factor

being investigated.

MULTIFACTORIAL CASE CONTROL STUDIES

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The common form of case-control study addresses one main

factor or attribute at a time. It is possible, however, to

investigate several exposure factors in the same study.

ADVANTAGES OF CASE CONTROL STUDIES

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The following are examples of the advantages of case-control studies:

feasible when the disease being studied occurs only rarely,

e.g. cancer of a specific organ;

relatively efficient, requiring a smaller sample than a cohort study;

little problem with attrition, as when follow-up requires periodic

investigations and some subjects refuse to continue to cooperate;

sometimes they are the earliest practical observational strategy for

determining an association (e.g. use of diethylstilbesterol and clear-

cell adenocarcinoma of the vagina in daughters).

DISADVANTAGES OF CASE-CONTROL STUDIES

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The following are some of the problems associated with case control studies:

the absence of epidemiological denominators (population at risk) makes the calculation of incidence rates, and hence of attributable risks, impossible;

measurement bias may exist, including selective recall and misclassification (putting cases in the control group, or vice versa); there is also the possibility of the Hawthorne effect: with repeated interviews, respondents may be influenced by being under study;

case-control studies are incapable of disclosing other conditions related to the risk factor: for example, in a study of the side-effects of oral contraceptives, one has to know their side-effects before a case-control design can be set up.

[B] PROSPECTIVE COHORT STUDY

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The common strategy of cohort studies is to start with a reference population , some of whom have certain characteristics or attributes relevant to the study (exposed group), with others who do not have those characteristics (unexposed group). Both groups should, at the outset of the study, be free from the condition or conditions under consideration. Both groups are then observed over a specified period to find out the risk each group has of developing the condition of interest.

DESIGN FEATURES

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[1] SELECTION OF COHORT

• a community cohort of specific age and sex;• an exposure cohort, e.g. radiologists, smokers, users

of oral contraceptives;• a birth cohort, e.g. school entrants;• an occupational cohort, e.g. miners, military

personnel;• a marriage cohort;• a diagnosed or treated cohort, e.g. cases treated with

radiotherapy, surgery, hormonal treatment.

DESIGN FEATURES

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[2] METHOD OF COLLECTION OF

DATASeveral methods are used to obtain the above data, which should be on a longitudinal basis. These methods:- • interview surveys with follow-up

procedures;• medical records monitored over time;• medical examinations and laboratory

testing;• record linkage of sets with exposure

data and sets with outcome data, e.g. work history data in underground mines with mortality data from national mortality files

ADVANTAGES OF COHORT STUDIES

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The following are some of the advantages of a cohort study compared with a case-control study:

Because of the presence of a defined population at risk, cohort studies allow the possibility of measuring directly the relative risk of developing the condition for those who have the characteristic, compared to those who do not, on the basis of incidence measures calculated for each of the groups separately.

In a cohort study, it is known that the characteristic precedes the development of the disease, since all the subjects are free of disease at the beginning of the study; this allows for a conclusion of cause-effect relationship (a necessary, but not sufficient, condition).

ADVANTAGES OF COHORT STUDIES

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Cohort studies are capable of identifying other diseases that may be related to the same risk factor.

Unlike case-control studies, cohort studies provide the possibility of estimating attributable risks, thus indicating the absolute magnitude of disease attributable to the risk factor

DISADVANTAGES OF COHORT STUDIES

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The following are some of the disadvantages of cohort studies:

These studies are long-term and are thus not always feasible; they are relatively inefficient for studying rare conditions.

They are very costly in time, personnel, space and patient follow-up.

Sample sizes required for cohort studies are extremely large, especially for infrequent conditions; it is usually difficult to find and manage samples of this size.

[C] HISTORICAL OR RETROSPECTIVE

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A historical cohort study depends upon the availability of data or records that allow reconstruction of the exposure of cohorts to a suspected risk factor and follow-up of their mortality or morbidity over time.

[C] HISTORICAL OR RETROSPECTIVE

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Historical cohort studies have, however, the following disadvantages: • All of the relevant variables may not be available in the original records. • It may be difficult to ascertain that the study population was free from the condition at the start of the comparison. This problem does not exist if we are concerned with deaths as indicators of disease.

[D] PROGNOSTIC COHORT STUDY

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Prognostic cohort studies are a special type of cohort study used to identify factors that might influence the prognosis after a diagnosis or treatment.

These follow-up studies have the following features: • The cohort consists of cases diagnosed at a fixed time, or cases treated at a fixed time by a medical or surgical treatment, rehabilitation procedure

• By definition, such cases are not free of a specified disease, as in the case of a conventional cohort study (but are free of the ‘outcome of interest’).

[E] ANLYTICAL CROSS – SECTIONAL

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In an analytical cross-sectional study, the investigator measures exposure and disease simultaneously in a representative sample of the population. By taking a representative sample, it is possible to generalize the results obtained in the sample for the population as a whole

ADVANTAGES

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• Cross-sectional studies have the great advantage over case-control studies of starting with a reference population from which the cases and controls are drawn.

• They can be short-term, and therefore less costly than prospective studies.

• They are the starting point in prospective cohort studies for screening out already existing conditions.

• They provide a wealth of data that can be of great use in health systems research.

DISADVANTAGES

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• They provide no direct estimate of risk.

• They are prone to bias from selective survival.

• Since exposure and disease are measured at the same point in time, it is not possible to establish temporality (i.e. whether the exposure or presence of a characteristic preceded the development of the disease or condition).

[F] ECOLOGICAL STUDIES

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In ecological studies, the unit of observation is an aggregate, a geographical administrative locality, a cluster of houses, a town, a whole country, etc.

They may take any of the following forms: • descriptive • case-control • cross-sectional • cohort • experimental.

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CONCLUSION• There are several research designs and the researcher must

decide in advance of collection and analysis of data as to which

design would prove to be more appropriate for his research

project.

• He must give due weight to various points such as the type of

universe and its nature, the objective of his study, the resource

list or the sampling frame, desired standard of accuracy and the

like when taking a decision in respect of the design for his

research project

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REFERENCES

[1] C.R. COTHARI,RESEARCH AND RESEARCH METHODOLOGY ,METHODS AND TECHNIQUES,2ND EDITION,NEW AGE INTERNATIONAL PUBLISHERS,NEW DELHI-2004.

[2] HEALTH RESEARCH METHODOLOGY A Guide for Training in Research Methods,2ND EDITION, WORLD HEALTH ORGANIZATION Regional Office for the Western Pacific Manila, 2001

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Education

Study designs in research methodology