This document defines and discusses different types of research methodology. It begins by defining research and explaining that research aims to systematically generate knowledge and answer questions. It then discusses the different drivers of research, including curiosity, addressing needs, profit motives, and available opportunities. The document also categorizes research as empirical vs theoretical, basic vs applied, and distinguishes research in biomedical sciences, population sciences, and health policy sciences. It further discusses quantitative and qualitative research approaches.

1. Research Methodology
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2. INTRODUCTION TO RESEARCH
Definition:
– It is the systematic collection, analysis and interpretation
of data to generate knowledge and answer a certain
question or solve a problem.
– a scientific inquiry aimed at learning new facts, testing
ideas, etc.
– quest for knowledge through diligent search or
investigation or experimentation aimed at the discovery
and interpretation of new knowledge.
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3. Definition. Cont…
– Research is an approach in obtaining a solution for a
specific problem.
– Research is necessary to generate new knowledge and
technologies to deal with major unresolved health
problems.
– Research is essential for guiding action.
– Research is necessary to identify
• priority problems
• design and evaluate policies and programs
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4. Why Research?
• There are several reasons why research is undertaken:
– address gaps in knowledge
– expand knowledge
– improve practice through new ideas, new insights into methods
– make more informed choices/decisions based on available
information
– create data-base for policy-making as research provides an
understanding of
– the factors affecting desired outcomes
– helps to build skills – organizational, analytical, writing,
presentation, time management, etc.
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5. Scientific Research
• a systematic body of procedures and techniques applied in
carrying out investigation or experimentation targeted at
obtaining new knowledge.
• Science is aimed at understanding the world around us
– Scientists first, observe and describe objects and events
appearing around us
– Second, they discover regularities and order of events
– Third, they seek to formalize and generalize into theories or
laws
• It is worth to note that non scientific research also pursue the
same goals.
• All of us observe and describe the world around us, we seek to
find regularities and seek to formalize theories.
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6. What is health research?
• Health research is the application of principles of research on
health. It has been broadly defined as the generation of new
knowledge using the scientific method to identify and deal
with health problems (Commission on Health Research for
Development, 1991)
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7. What drives health research?
• Health research may be:
– curiosity-driven,
– needs-driven,
– profit-driven or
– opportunity-driven.
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8. Curiosity-driven research
• Scientists like to pursue research out of curiosity, in their own
lines of interest, according to traditions of academic freedom.
• But hunting for discovery is not a straightforward undertaking.
• many important discoveries in science were not found
because they were actively sought; they were found because
it was possible to find them.
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9. Curiosity-driven research….
• Science is unpredictable.
– In fact, chance plays an important role in scientific
discovery.
• Many of the drugs we use today have been discovered in
research programmes designed for other purposes;
– Minoxidil (the drug for male baldness) was originally
developed and tested for the treatment of hypertension.
– Sildenafil (Viagra), used for the treatment of erectile
dysfunction, was discovered in a cardiovascular research
programme.
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10. Needs-driven research
• Governments are responsive to the concerns of their
constituencies, and would like to support research that will
promote the health of their populations, or will generate
wealth.
• The relative magnitude of a health problem is determined by
its prevalence and its seriousness.
• A health problem may be prevalent but not serious, and may
be serious but not widely prevalent.
• The burden of disease as a result of any health problem is
commonly expressed as the disability-adjusted life years
(DALYs) lost.
• This measure expresses both time lost through premature
death and time lived with a disability.
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11. Needs-driven research…
• Before a health problem can be put as a priority for research,
other questions need to be asked.;
– Is enough known about the problem now to consider
looking for possible interventions?
– Does the state of the art allow a move forward to develop
new interventions?
– How cost-effective will these interventions be? Can they
be developed soon and for a reasonable outlay?
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12. Profit-driven research
• Private industry is becoming the major actor in health research, in
terms of funding. Being accountable to their shareholders,
companies pursue research for profit.
• The research and development share of sales revenues varies
among pharmaceutical companies, but is estimated on average to
be 13%
– For e.g. Pharmaceutical industry investments in research and
development surpassed public investments in four of the
countries (France, Japan, Switzerland and United Kingdom).
• Only a very small share of the large research investment by industry
is addressed to the health problems of developing countries
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13. Opportunity-driven research
• As far as the individual researcher is concerned, research may
also be opportunity driven.
• It may be driven by
– the opportunity for funding from national or international
sources,
– the opportunity to participate in multi-centre international
research, or
– opportunities to participate in industry-sponsored
research.
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14. Availability of funding
• Research is often driven by the availability of funding, which
may or may not correspond to local priority needs or to the
curiosity of scientists.
• Modern research is becoming more and more expensive, and
external funding is needed to conduct good research.
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15. Health research
• Broadly speaking, the following categories of science are
involved in health research:
• Biomedical sciences: include all biological, medical and
clinical research, and biomedical product development and
evaluation.
• Population sciences: include epidemiology, demography and
the socio-behavioural sciences.
• Health policy sciences: include health policy research, health
systems research and health services research. Economic
analysis studies are now an important subcategory of health
policy research.
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16. Categories of research
• Researches could be categorized as
– Empirical Versus theoretical research based on the
philosophical approach
– Basic Versus applied based on its functions or
– biomedical, health services and behavioural research, the
so-called health research triangle
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17. Empirical and theoretical research
• Health research mainly follows the empirical approach, i.e. it
is based upon observation and experience more than upon
theory and abstraction.
• Epidemiological research, for e.g., depends upon the
systematic collection of observations on the health related
phenomena of interest in defined populations.
• Empirical research in the health sciences can be qualitative or
quantitative in nature.
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18. Empirical research
• Quantification in empirical research is achieved by 3 related
numerical procedures:
(a) measurement of variables;
(b) estimation of population parameters
• (parameters of the probability distribution that captures the
variability of observations in the population); and
(c) statistical testing of hypotheses, or estimating the extent
to which ‘chance’ alone may account for the variation among
the individuals or groups under observation.
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19. Basic and applied
• Basic research is usually considered to involve a search for
knowledge without a defined goal of utility or specific
purpose.
• Applied research is problem-oriented, and is directed towards
the solution of an existing problem.
• there needs to be a healthy balance between the two types of
research
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20. Research triangle depending on area of focus
• Biomedical research deals primarily with basic research involving
processes at the cellular level;
• Health research deals with issues in the environment surrounding
man, which promote changes at the cellular level; and
• Behavioural research deals with the interaction of man and the
environment in a manner reflecting the beliefs, attitudes and
practices of the individual in society.
• Each of the three categories could be empirical or theoretical, basic
or applied
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21. Quantitative versus qualitative research
• Health care workers are often trained to think mechanistically,
and are therefore most familiar with quantitative research.
• However, medicine is not only a mechanistic and quantitative
science.
• Patients are not broken down machines or malfunctioning
biological systems.
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22. Quantitative versus qualitative research…
• Doctors do not treat diseases; doctors treat patients.
• Health is more in the hands of people than in the hands of
health professionals.
• Qualitative research is needed to provide insights into
people’s lifestyle behaviour, their knowledge, their feelings
and attitudes, their opinions and values and their experience
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23. Quantitative versus qualitative research…
• Quantitative research typically answers the questions WHAT,
WHO, WHEN, HOW FREQUENTLY
• While Qualitative research usually deals with responding to
questions of HOW and WHY
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24. Action research
• Action research is a style of research, rather than a specific
methodology.
• In action research, the researchers work with the people and
for the people, rather than undertake research on them. The
focus of action research is on generating solutions to
problems identified by the people who are going to use the
results of research.
• Action research is not synonymous with qualitative research.
But it typically draws on qualitative methods such as
interviews and observations.
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25. Defining research according to utility
Research Domain Focus of the Research
*Utility of
Research
Operational Operational issues of
specific health programs
Local
Implementation Implementation strategies
for specific products or
services
Local/Broad
Health System Issues affecting some or all
of the health system
Broad
*How amenable the research outputs are to adaptation, scaling
up or use or in other contexts or locations
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26. Users of research
• The users of the research outputs fall broadly into 3 groups
with
• Operational research being predominantly, but not
exclusively, of use to health care providers;
• Implementation research predominantly of use to managers
of programmes scaling up an intervention; and
• Health systems research of most use to those who manage or
need to make policy for the health system.
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27. Characteristics of research
– Originates with a question or problem.
– Requires clear articulation of a goal.
– Follows a specific plan or procedure.
– Often divides main problem into sub-problems.
– Guided by specific problem, question, or hypothesis.
– Accepts certain critical assumptions.
– Requires collection and interpretation of data.
– Cyclical in nature.
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28. Steps in undertaking a research
I. Problem identification and definition
II. Reviewing relevant literature
III. Development of proposals
IV. Methodology
Choosing the appropriate study design
Data collection
Data analysis
Interpreting results
Writing a report
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29. Topic selection
Topic Identification and Selection
• The development of a health project (proposal) goes
through a number of stages.
• It should be noted that development of a research
proposal is often a cyclical process.
• If the answer to the research question is obvious, we
are dealing with a management problem that may be
solved without further research.
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40. ANALYSIS AND STATEMENT OF
THE PROBLEM
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41. Analyzing the problem
A systematic analysis of the problem, completed
jointly by the researchers, health workers,
managers, and community representatives is a
very crucial step in designing the research
because it:
• Enables those concerned to bring together their
knowledge of the problem,
• Clarifies the problem and the possible factors that
may be contributing to it,
• Facilitates decisions concerning the focus and scope of
the research.
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42. Formulating the problem statement
• After identifying, selecting and analyzing the
problem, the next major section in a research
proposal is “statement of the problem”
a) Why is it important to state and define the
problem well? Because a clear statement of the
problem:
– Is the foundation for the further development of the
research proposal (research objectives, methodology,
work plan, etc);
– Makes it easier to find information and reports of
similar studies from which your own study design can
benefit;
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43. – Enables the researcher to systematically point out why
the proposed research on the problem should be
undertaken and what you hope to achieve with the study
results.
b) Points that need to be considered for justifying the
selected research problem
– A health problem selected to be studied has to be
justified in terms of its:
• Being a current and existing problem which needs solution
• Being a widely spread problem affecting a target population
• Effects on the health service programmes
• Being a problem which concerns the planners, policy makers and
the communities at large.
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44. c) Information included in the statement of a
problem
• A brief description of socioeconomic and cultural
characteristics and an overview of health status.
• A more detailed description of the nature of the
problem
- basic description of the research problem
- the discrepancy between what is and what should be
- its size, distribution, and severity (who is affected, where,
since when, etc.)
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45. • An analysis of the major factors that may
influence the problem and a convincing argument
that available knowledge is insufficient to answer
a certain question and to update the previous
knowledge.
• A brief description of any solutions that have
been tried in the past, how well they have
worked, and why further research is needed.
• A description of the type of information expected
to result from the project and how this
information will be used to help solve the
problem
• If necessary, a short list of definitions of crucial
concepts used in the statement of the problem.
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46. Literature Review
Use of literature review
• It prevents you from duplicating work that has
been done before.
• It increases your knowledge on the problem you
want to study and this may assist you in refining
your "statement of the problem".
• It gives you confidence why your particular
research project is needed.
• To be familiar with different research methods
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47. Sources of information
– Card catalogues of books in libraries
– Organizations (institutions)
– Published information (books, journals, etc.)
– Unpublished documents (studies in related fields,
reports, etc.)
– Computer based literature searches such as
Medline, cochran
– Opinions, beliefs of key persons
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48. Some examples of resources where information could
be obtained are:
– Clinic and hospital based data from routine activity
statistics
– Local surveys, annual reports
– Scientific conferences
– Statistics issued at region and district levels
– Articles from national and international journals (e.g.,
The Ethiopian Journal of Health Development, The
Ethiopian Medical Journal, The East African Medical
journal, The
– Lancet, etc.)
– Internet
– Documentation, reports, and raw data from the Ministry
of Health, Central Statistical
– Offices, Nongovernmental organizations, etc.
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49. After collecting the required information the
investigator should decide in which order
he/she wants to discuss previous research
findings:
• from global to local
• from focus to broader
• from past to current
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50. NB:
all what is known about the study topic
should be summarized with the relevant
references. This review should answer
– How much is known?
– What is not known?
– What should be done based on what is lacking?
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51. literature review:
• adequate, relevant and critical.
• appropriate referencing procedures should always
be followed in research proposals as well as in
research reports.
• While reviewing a literature give emphasis to both
positive and negative findings and avoid any
distortion of information to suit your own study
objectives.
• Finally, after an exhaustive literature review,
summarize the findings and write a coherent
discussion by indicating the research gap which
supports the undertaking of your study.
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52. OBJECTIVES
• General objectives: aim of the study in general
terms
• should be closely related to the statement of the
problem.
• If we break down this general objective into
smaller and logically connected parts, then we
get specific objectives.
– Example: In a study on missed opportunities for EPI in
Addis Ababa the general objective was: “to assess
missed opportunities for EPI in Addis Ababa”.
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53. Specific objectives: measurable statements
on the specific questions to be answered.
• Unlike the general objectives, the specific
objectives are more specific and are related to
the research problem situation. They indicate
the variable to be examined and measured.
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54. Example: In the study of missed opportunity for
EPI in Addis Ababa the specific objectives could
be:
– To find out the magnitude of missed opportunities
for children who attend OPD, MCH, etc. in Addis
Ababa,
– To examine the reasons for children not being
immunized while attending the OPD, MCH, etc.
services.
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55. Formulation of the research objectives
The formulation of objectives will help us to:
• Focus the study (narrowing it down to essentials)
• Avoid collection of data that are not strictly
necessary for understanding and solving the
identified problem
• Organize the study in clearly defined parts
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56. How should we state our objectives?
We have to make sure that our objectives:
• Cover the different aspects of the problem and its
contributing factors in a coherent way and in a logical
sequence
• Are clearly expressed in measurable terms
• Are realistic considering local conditions
• Meet the purpose of the study
• Use action verbs that are specific enough to be
measured
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57. Action verbs
-to determine
- to compare
- to verify
- to calculate
- to describe
- to find out
- to establish
Avoid vague non-action
verbs such as;
- to appreciate
- to understand
- to study
- to believe
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58. Research objectives can be stated as:
• Questions - the objectives of this study are to
answer the following questions ….
• Positive sentence - the objectives of this study
are to find out, to establish, to determine, …
• Hypothesis - the objective of this study is to
verify the following hypothesis
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59. UNIT II
Study Designs
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60. Descriptive vs. Analytic Epidemiology
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Descriptive
• Used when little is
known about the
disease
• Rely on preexisting
data
• Who, where, when
• Illustrates potential
associations
Analytic
 Used when insight about
various aspects of disease is
available
 Rely on development of new
data
 Why
 Evaluates the causality of
associations
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61. Descriptive Studies
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• Relatively inexpensive and less time-consuming
than analytic studies, they describe,
• Patterns of disease occurrence, in terms of,
– Who gets sick and/or who does not
– Where rates are highest and lowest
– Temporal patterns of disease
• Data provided are useful for,
– Public health administrators (for allocation of resources)
– Epidemiologists (first step in risk factor determination)
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62. Descriptive Epidemiology
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– Case reports
– Case series
– Cross sectional studies
– Correlational studies
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63. Case reports or case series
• Useful for the recognition of new diseases,
• Useful for constructing of the natural history of a
disease,
• Use to formulate a hypothesis and to detect an
epidemic
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64. A. Case report:
• It is the study of health profile of a single
individual using a careful and detailed report by
one or more clinicians.
• It is common form that is published in articles
• It is made using
 Simple history,
 Physical examination and
 Lab. / radiologic investigation.
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65. Cont…
• Report is usually documented if there is unusual
medical occurrence, thus it may be first clue for
identification of a new disease.
• It is useful in constructing a natural history of
individual disease.
It was a single case report that formulated the
hypothesis of oral contraceptive use increases
venous thrombo-embolism.
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66. • Individual case report can be expanded to a case
series, which describes characteristics of a number
of patients (usually 5-12) with a similar disease.
• Similar to case report, it is usually made on cases
having new and/ or unusual disease (giving interest
to clinicians)
• It is often used to detect the emergence of new
disease or an epidemics.
Eg. The first five AIDS cases in USA.
B. Case series
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67. Cont…
Example:
 Five young, previously health homosexual men were
diagnosed as having Pneumocystis carinii pneumonia at
Los Angeles hospital during a six month period from
1980 to 1981.
• This form of pneumonia had been seen almost
exclusively among older men and women whose
immune systems were suppressed.
• This unusual circumstance suggested that these
individuals were actually suffering with a previously
unknown disease, subsequently it was called AIDS.
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68. Cont…
• Both case report and case series are able to formulate
a hypothesis but are not able to test for presence of
valid association.
• Fundamental limitation of case report is presence of a
risk factor that is simply coincidental (by chance)
• It is difficult to test for association because there is no
relevant comparison group
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69. Cross-Sectional /prevalence/ survey study
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• The major type of descriptive study designs.
• It is mainly concerned with the distribution of diseases with
respect to time, place and person.
• By conducting survey, the magnitude of diseases or other health
related condition will be known.
• They are useful for priority setting, resource allocation etc.
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• Information about the status of an individual with
respect to the presence or absence of exposure
and disease is assessed at a point in time.
• The point in time may be as short as few minutes
or as long as two or three months.
• The time frame of "point in time" is based on the
speed of data collection.
Cross-Sectional Studies, cont….
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• Measures disease and exposure simultaneously in a well-
defined population
• Advantages
– They cut across the general population, not simply those
seeking medical care
– Good for identifying prevalence of common outcomes,
such as arthritis, blood pressure or allergies
• Limitations
– Cannot determine whether exposure preceded disease
– It considers prevalent rather than incident cases, results
will be influenced by survival factors
– Remember: P = I x D
Cross-Sectional Studies, cont….
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72. Cross-Sectional Studies, cont….
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 Can be used as a type of analytic study for testing
hypothesis, when;
Current values of exposure variables are unalterable over
time
Represents value present at initiation of disease
E.g. eye colour or blood group
If risk factor is subject to alterations by disease, only
hypothesis formulation can be done
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73. Correlational/ Ecological study
 Uses aggregated data from entire population (as a
whole) to compare disease frequencies.
(i.e. it doesn’t need data from individuals)
 Can be done quickly and inexpensively, often using
already available data.
 The aggregate data could be
 Prevalence of a health event,
 Death rate,
 Incidence of a health related problem
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74. Example
1. Circum-incision and HIV in Ethiopia
– HIV prevalence of districts in Ethiopia
Vs
– Proportion of male circum-incision in the same
districts
2. Fluoride content of water and dental caries
– Proportion of people with dental caries in villages
Vs
– Fluoride content of water in villages
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75. Rationale for ecological studies
1. Low cost and convenient
2. Measurement limitation (conditions that are
difficult to measure at individual level)
(eg environmental contact, dietary exposure,
fluoride content)
3. Other designs may be unable to measure
4. Scientists having interest on ecologic effect
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76. Level of analysis
• Completely ecologic analysis; all variables are
ecologic measures and analysis is in a group.
• Partially ecologic analysis; addition of some
individual variables and ecologic variables
• Measures of analysis in Correlational studies is using
correlation coefficient (r)
• Correlation coefficient (r) is a descriptive measure
between continuous variables that varies between -1
and +1)
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77. Cont….
50
70
90
110
5 6 7 8 9 10
Mean
diastolic
BP
Coffee sold (100gms/person/year)
District
Linear (District)
Fig. Factious data to show correlation between
coffee sold and mean diastolic BP. (positive r ~
0.67)
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78. X
Y
X
r ~ +1
Y
X
r ~ -1
Y
r ~ 0 r ~ 0 X
Y
Correlation coefficient
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79. Limitations
 Unable to link an exposure to occurrence of disease
in a single individual.
 Lack of the ability to control for effect of
confounders.
 Data represent average exposure levels rather than
actual individual values as in ecological “fallacy” or
bias.
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80. Example of ecological fallacy
• Imagine a study of the rate of coronary heart disease in the
capital cities of the world relating the rate to average income.
• Within the cities studied, coronary heart disease is higher in
the richer cities than in the poorer ones.
• We might predict from such a finding that being rich increases
your risk of heart disease.
• In the industrialised world the opposite is the case - within
cities such as London, Washington and Stockholm, poor
people have higher CHD rates than rich ones.
• The ecological fallacy is usually interpreted as a major
weakness of ecological analyses.
• Ecological analyses, however, informs us about forces which
act on whole populations.
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81. Analytic
Observational Interventional
Simply observes
the natural course
of event
Role of the investigator.
assigns study subjects to
exposure & non-exposure
then simply follows to measure
for disease occurrence
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82. Analytical Study…
• Are designed to explain the distribution of event or
disease by testing hypothesis.
• These hypothesis may be derived from descriptive
study, clinical observation, or examination of records.
• The primary goal is to establish a relationship
(association) between a ‘risk factor’ (etiological agent)
& an outcome (disease), i.e. analytical.
Always require two or more comparison group
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83. 1. It focuses on determinants of disease by testing
hypothesis.
Try to answer questions “why” and “how”
What is the source of infection for an outbreak?
What are the risk factors for the disease?
What factors are associated with increased
mortality?
Does smoking cause lung cancer?
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84. 2. To test hypothesis about causal relationship
 Proof and Sufficient evidence
 To search for cause and effect relationship
 Hypothesis is tested using explicit type of
comparison using appropriate comparison group.
3. To quantify the association between exposure and
outcome  Measure of association
• To test whether certain factors are
“associated”
• Is this association statistically significant?
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85. Observational
Cohort
By comparison group
by difference in
disease occurrence
By difference in
exposure status
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86. Case-control
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• people diagnosed as having a disease (cases) are
compared with persons who do not have the
disease (controls) to determine if the two groups
differ in the proportion of persons exposed to a
specific factor or factors.
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87. Definition
• A case-control study is one in which persons with a condition
("cases") and suitable comparison subjects ("controls") are
identified, and then the two groups are
• Compared with respect to prior exposure.
• Subjects are sampled by their outcome status
• Is relatively simple & commonly used analytical strategy
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88. Disease
No disease
Exposure
?
?
Retrospective Nature
Case-Control Study
(Case)
(Control)
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89. Retrospective
Study
Look for past
exposure to factors
In cases & control
Select case &
control
Past Present
Schematic diagram of time factor in case-control study
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90. Design of case control
Exposed
Non-exposed
Exposed
Non-exposed
Cases(People
with disease)
Controls(People
without
disease)
Population
Time
Direction of inquiry
Starting of Observation
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91. Case-Control Study
• PAST PRESENT
• Compares one group among whom a problem is
present with another group where the problem
is absent in order to find out factors contributing
to the problem
• Ex- malnutrition, lung cancer, contracting
cholera, neonatal death
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92. Application of Case-Control studies
• It is good to do for RARE diseases or
outcomes
• Better for diseases with long latency between
exposure and outcome
• It may be possible to explore a wide range of
potential exposures for a single outcome
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93. Advantages Disadvantages
• Feasible in rare disease,
• Quick, inexpensive
• Disease & exposure
measurement can be made at
the same time
• Requiring a smaller sample
• No problem of attrition,
• Is the earliest practical
observational strategy for
determining association.
• can examine multiple
etiologic factors (exposure)
for a single disease.
• the absence of
epidemiological denominators
(population at risk) makes the
calculation of population level
measurement incidence or
prevalence rates, and hence of
attributable risks, impossible;
• Determining temporality is
difficult, i.e. difficult to establish
that "cause“ preceded "effect".
i.e.to determine whether the
attribute led to the disease or
vice versa;
• particularly prone to bias in
particular selection & recall
bias,
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94. Cohort study
• Dictionary definition of “cohort”
– Group of people who have something in common
when they are assembled.
– A group of individuals that are all similar in some
trait and move forward together as a unit.
– Designated group of people who are followed or
traced for a particular period of time.
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95. • Cohort study is The observation of a cohort (or cohorts), over
time, to measure outcome's)
– Longitudinal, follow-up studies
• The 2nd major types of analytic study
• Groups are defined on the basis of exposure to risk factors.
• At the beginning free from the disease
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96. Design of a Cohort Study
NO
Yes
Defined Population
Target Pop:
Population
at Risk
Study Sample
Disease/Outcome
Present?
Representative Sample?
NO
Yes
Exposed
Not Exposed
Time
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97. Types of cohort studies
• Based on temporal r/ship between the initiation of the
study and occurrence of the disease.
• Both classify subjects based on risk.
– Prospective
• characterized by determination of exposure levels
(exposed vs. not exposed) at baseline (present) and
followed for occurrence of disease in future
 Groups move through time as they age
– Retrospective
• Makes use of historical data to determine exposure level at
some baseline in the past and then determine subsequent
disease status in the present
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98. Past Present Future
Cohort Follow-up
Assembled
Cohort Follow-up
Assembled
Prospective =
= Historical (retrospective)
Time and Cohort Studies
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99. Prospective Studies
• Also called
– longitudinal
– concurrent
– incidence studies
• Looking into the future
• Example:
– Framingham Study of coronary heart disease
(CHD)
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100. • Approaches to follow up
–The major challenge
–Characteristics of losses to follow
• Equal distribution by exposure
–Validity of the study questioned (>30-40%)
–Calculate using the most extreme values
related to exposure to disease association.
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101. Measures in Cohort Studies- Relative Risks (RR)
Develops
Disease
Doesn’t
Develop
Disease Totals
Incidence
Rates of
Disease
Exposed a b a + b
a
a + b
Not
Exposed
c d c + d
c
c + d
Relative Risk (RR) = Iexp / Inon-exp = [a/(a+b)] / [c/(c+d)]
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102. • The essential characteristic in the design of
cohort studies is the comparison of outcome in
an exposed group and a nonexposed group (or a
group with a certain characteristic and a group w/o that
characteristic).
 A study population can be chosen by selecting
groups for inclusion in the study on the basis of
whether or not they were exposed
Design of a Cohort
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103. • There are two basic ways to generate cohort
groups.
 Select a cohort (defined population) BEFORE any of
its members become exposed or before the
exposures are identified.
 Select a cohort on the basis of some factor (e.g.,
where they live) and take histories (e.g., blood tests)
on the entire population to separate into exposed
and non-exposed groups.
• Regardless of which selection approach is used,
we are comparing exposed and non-exposed
persons.
Selection of Cohort Groups
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104. Design of a Prospective Cohort
Major problem with a prospective cohort design is that the cohort must be followed up
for a long period of time.
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105. Sampling
• Valid, reliable surveys
• Critical number of subjects
– the more, the better
• Randomize
– random selection
– random assignment
• Rule out bias
– For example, degree of accuracy with which subjects have
been classified with respect to their exposure.
– For example, individuals who are sick may be more likely
to give the kind of responses that they believe the
investigator wants to hear
Garbage in,
garbage out
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106. Data Gathering
• Person - to - person
• Drop off questionnaire
• Mailed to people
• Telephone interview
• Newsletter or magazine
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107. Potential Biases in Cohort
Studies
• Information bias
• Bias in estimation of the outcome
• Bias from non-response
• Bias from losses to follow-up
• Analytic bias
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108. Advantages of
Prospective Cohort Studies
• Captive groups
• Large sample sizes
• Certain diseases or risk factors targeted
• Can be used to prove cause-effect
• Assess magnitude of risk
• Baseline of rates
• Number and proportion of cases that can be
prevented
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109. Advantages of Prospective
Studies (cont’d)
• Completeness and accuracy
• Opportunity to avoid condition being studied
• Quality of data is high
• Considers seasonal and other variations over a long
period
• Tracks effects of aging process
• Particular important when exposure is rare. E.g. OC use
and HIV transmission in Africa?
• Can examine multiple effects with a single exposure
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110. Disadvantages of Prospective
Cohort Studies
• Large study populations required
– not easy to find subjects
• Expensive
• Unpredictable variables
• Results not extrapolated to general population
• Study results are limited
• Time consuming/results are delayed
• Requires rigid design and conditions
• Inefficient for evaluation of rare diseases
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111. Disadvantages of Prospective Studies (cont’d)
• Subjects lost over time (dropouts)
• Logistically demanding
• Maintaining quality, validity, accuracy and
reliability can be a problem
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112. Experimental Study Design
• Experimental studies differ from observational studies
described /reported rather than simply to observe, the
exposure of interest.
• There are many different approaches used in experimental
studies, from very tightly controlled laboratory experiments to
large scale community intervention.
• Experimental studies either focus on assessing change at the
level of the individual or the group.
• The most important aspect of experimental studies, no matter
what study group is used., is to ensure that the allocation of
the study group to the different treatments/ interventions /
exposures under investigation is done randomly.
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113. • Focus primarily on how to measure the effect of an exposure
on an outcome with consideration of the effects of other
factors (potential confounders as well as factors related to the
efficacy of the delivery of the intervention)
• In broad terms there are two major types of experimental study.
1. the individual.
2. the population
• Individual-based experimental studies are sometimes sub-
divided on the basis of the level of the outcome;
– Clinical trials (or therapeutic, secondary, or tertiary
prevention
– Field trials (primary prevention trials)
• Study subjects are healthy individuals
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114. • Experimental studied could include changes in knowledge,
attitudes, or behavior (such as eating patterns).
• The outcome variable may be changed in a continuously
distributed variable such as blood pressure or serum
cholesterol or blood glucose, or changes in incidence or
mortality from specific diseases or risk factors such as obesity,
low birth weight babies, or hypertension (all derived from
continuous variables).
• The outcome may be measured in individuals (clinical trials)
or groups/populations (community intervention trials).
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115. • Irrespective of the disease state or outcome measure being
investigated, all subjects or groups should be measured in the
same way, and allocation to treatment (exposure) groups
should not be influenced by the disease state or level of the
outcome measure of the subjects or groups in the study.
• All eligible subjects or groups should be randomly allocated to
treatments.
• Whatever the type of study, the main objective is to explore
an exposure-outcome cause-effect) relationship free from
bias.
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116. • General considerations in experimental
studies
• There are a number of general principles that are
relevant to all experimental studies.
– selection of the study population;
– allocation of treatment regimes;
– length of observation;
– observer effects;
– participant effects;
– compliance;
– ascertainment of exposure and outcome;
– statistical power;
– analysis and interpretation
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117. Selection of study population
• The issues of internal and external validity aim to design a
study so that it is free from bias and internally valid.
• For short-term, tightly controlled metabolic studies,
compliance and loss to follow-up are less likely to be a
problem.
• In a larger, less tightly controlled intervention trial which
requires a longer follow-up to assess the desired effect, poor
compliance and loss to follow-up may be crucial.
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118. • In clinical trials, volunteers are usually recruited who are not
necessarily representative of the general population; here the
main concern is to demonstrate whether a change in
exposure leads to a change in an outcome (effectiveness).
• In community intervention studies, the aim is to assess
whether the intervention works at a practical level (efficacy),
and some notion of the representation of the study sample is
important in order to be able to generalize the results.
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119. • For clinical trials where a therapeutic agent or procedure
is to be tested, consideration may need to be given as to
admission criteria.
• These criteria may include certain demands for exclusion
and inclusion
• The restriction of subjects to be included in the study may
also relate to the underlying hypothesis being tested; e.g.
the effect of changing the exposure may differ at different
levels of the exposure and the researcher may only be
interested in the effects in those with either a high or low
intake.
• In a clinical trial the investigator may want to specify
suitable clinical indications for treatment.
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120. • In a community trial the selection of towns may be influenced
by the treatment to be tested.
– If the treatment is a general media campaign it will be
necessary for the treatment and comparison communities
to be sufficiently discrete as to minimize exposure of the
control community to the treatment.
– The selection of such towns may also be influenced by
other pragmatic issues, such as ease of access to the town
by the investigators or support from local community
leaders in staging the research.
– Irrespective of these pragmatic issues, the towns should be
randomly allocated to treatment group and monitored at
baseline and followed-up in the same way.
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121. Allocation of treatment regimes
• Random assignment:
– individuals or communities are allocated randomly to each
study group and that allocation of subjects to a group is
independent of the allocation of other subjects.
• In a community trial:
– randomization occurs at the level of the community,
subjects within a community are not randomly assigned to
treatment or control group.
• The random allocation ensures that neither the observers nor
the individual participating in the study can influence, by way
of personal judgment or prejudice, who is allocated to receive
which treatment.
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122. Length of observation
• An experiment should be just long enough to allow the effect
of exposure change to result in the hypothesized change in
outcome.
• In deciding on the length of the study the investigator must
have an idea as to the mechanism of action of the proposed
treatment and thereby some idea as to how long it should
take to affect the various steps in the pathway (whether
related to change in knowledge, attitudes, or behavior).
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123. • The outcome of interest will affect the length of
observation.
– For example, catecholamines or glucose metabolism, the study
may only last a few hours.
– For studies of diet and serum cholesterol or blood pressure the
study may need to last weeks.
– For endpoints such as death the length of observation will need to
be longer, perhaps many years.
– If the treatment (or lack of treatment in the control group)
appears to be resulting in an increased rate of disease, it may also
be advisable to stop the trial.
– clearly defined stopping rules should be incorporated into the
study design.
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124. Observer effects
• It is desirable that both the observer and the participants are
blinded as to the participants treatment group.
• Prior to the commencement of the study, all personnel
involved in the study must be carefully trained to ensure
uniformity in the administration of the protocol
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125. Compliance
• They respect to participant effects relates to compliance.
• Deviation from the protocol needs to be documented in all
subjects, not just those on the treatment.
• It may be that a comparison or control group alters their
behavior so as to make them more like the treatment group in
their exposure status.
• Perhaps more commonly, participants will forget or
deliberately fail to take drugs, or, if they have been placed on
a dietary regime, they may occasionally 'break-out' and
deviate from the protocol.
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126. UNIT III
Sampling Techniques &
sampling Errors
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127. What is Sample?
• A sample is a finite part of a statistical population whose
properties are studied to gain information about the
whole(Webster, 1985).
• Sampling is the act, process, or technique of selecting a
suitable sample, or a representative part of a population for
the purpose of determining parameters or characteristics of
the whole population.
• A population is a group of individuals persons, objects, or
items from which samples are taken for measurement for
example a population of presidents or professors, books or
students.
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128. • Complete information would emerge only if data were
collected from every individual in the population, which are
undoubtedly a monumental if not an impossible task.
• Thus, the limitation of time, resources, and facilities, and
sometimes the destructive nature of the study that leads to
incomplete information for the fact that the data are collected
in the course of conducting the experiment necessitate
sampling.
• Sampling is the taking or measuring of more than one
observation per experimental unit.
• It is not a design but it is an aspect of experimental design.
• Sampling error occurs during sample measurements.
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129. • The main reason for sampling is to save resources (time,
money and efforts).
• The second reason for sampling is that, even though part of
all the information about the population is there, the sample
data can be useful in drawing conclusions about the
population, with appropriate sampling method and sample
size.
• The third reason for sampling applies to the special case
where the act of measuring the variable destroys the
individual, such as in destructive sampling.
– Clearly, testing a whole batch of explosives would be
inappropriate, for example
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130. • In large population, an appropriate sample is the one that
provides a sample value close to the value that would have
been obtained had all entities in the experimental units
(plots) been measured.
• The difference between the sample value and the plot value
constitutes the sampling error.
• A good sampling technique is one that gives a small sampling
error.
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131. • sampling techniques
-Describe how sample is determined.
-Describe methods of sample selection.
-Use diagrams if needed.
• Sampling
– Selection of a number of study units from a defined
study population.
– Reason: the population is too large.
- cost
- time
- quality of data…
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132. Definitions
• Target population (reference population):
– Is that population about which an investigator wishes
to draw a conclusion.
• Study population (population sampled):
– Population from which the sample actually was
drawn and about which a conclusion can be made.
– For Practical reasons the study population is often
more limited than the target population. In some
instances, the target population and the population
sampled are identical.
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133. • Sampling unit:
– The unit of selection in the sampling process. For
example, in a sample of districts, the sampling unit
is a district; in a sample of persons, a person, etc….
• Sampling frame:
– the list of units from which the sample is to be
selected.
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134. Sampling methods
• There are two different approaches to sampling in
survey research:
– Probability sampling approach
– Non-probability sampling approach and
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135. A. Probability sampling
• The probability sampling approach for research methods gives
each element a known non-zero chance of being included in
the sample.
• This method is closer to a true representation of the
population.
• It can be difficult to use due to size of the sample and cost to
obtain, but the generalizations that come from it are more
likely to be closer to the a true representation of the
population.
• Probability sampling includes specific sampling procedures
such as Simple random sampling, Systematic random
sampling, stratified random sampling and Cluster sampling
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136. 1. Simple Random Sampling
• Simple random sampling is defined as one for which each
measurement or count in the population has the same or
known chance (probability) of being selected.
• A sample selected with the probabilities of not getting
representative sample for each measurement or count is said
to be a biased sample
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137.  most basic scheme of random sampling.
 It is the simplest form of probability sampling.
 Implementation of SRS
• Make a numbered list(frame) of all the units in the
population
• Decide on the size of the sample
• Select the required number of sampling units, using a
“lottery” method or ‘a table of random numbers’.
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138. Computer generated random numbers:
832645 573158 467460 838921 171721 152885
708009 285644 727733 343305 539264 907568
305761 995036 740619 054728 746425 713746
536405 504168 750032 367682 626278 855480
217862 782003 409660 155199 129514 484511
844905 296231 103727 053603 562252 219726
670523 707073 049209 830572 337034 716264
334920 023934 808901 740693 170372 095017
885588 384435 129958 303040 264636 858065
458268 058670 888935 064613 661404 411861
277649 076177 482951 876389 898190 927367
977683 759956 553916 983998 331578 981306
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139. 2. Systematic Sampling
• Systematic sampling is perhaps the most widely known
selection procedure.” - Leslie Kish, 1965
• An alternative method for random sampling
• Sometimes called “pseudo-random” selection
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140. • suitable in that it provides a quasi-random sample
• Individuals are chosen at regular intervals e.g. every 5th
• First subject is randomly selected from 1- k an tells
• us where to start selecting individuals from the list.
• Less time consuming and easier to perform than SRS.
• provides a good approximation to SRS
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141. Implementation of systematic
sampling
• In systematic sampling, only the first unit is selected at
random,
• The rest being selected according to a predetermined
pattern.
• to select a systematic sample of n units,
– the first unit is selected with a random start r from
1 to k sample, where k=N/n sample intervals,
– and after the selection of first sample, every kth unit
is included where 1 r  k.
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142. When to use systematic sampling?
• Even preferred over SRS
• When no list of population exists
• When the list is roughly of random order
• Small area/population
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143. 3. Stratified sampling:
– Used if sample includes groups of study units with specific
characteristics (e.g. residents from urban & rural areas)
– Subjects are divided into groups, or strata, according to these
characteristics.
– Random or systematic samples of a predetermined size are
obtained from each group (stratum).
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144. Stratified sampling, cont…..
• In simple random sampling we want that the samples should
be distributed randomly.
In reality the random selection may be like this
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145. Stratified sampling, cont….
• In stratified sampling the population is partitioned into
groups, called strata, and sampling is performed separately
within each stratum.
• The principal objective of stratification is to reduce sampling
errors.
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146. Basic Rules of Stratified Sampling
• stratum variables are mutually exclusive (non-over lapping),
e.g., urban/rural areas, economic categories, geographic
regions, race, sex, etc.
• the population (elements) should be homogenous within-
stratum, and
• the population (elements) should be heterogenous between
the strata
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147. When?
• Population groups may have different values for the
responses of interest.
• If we want to improve our estimation for each group
separately.
• To ensure adequate sample size for each group.
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148. Reasons for stratifying the population:
– Different sampling schemes may be used in different
strata, e.g. Urban & rural
– Conditions may suggest that prevalence rates will vary
between strata: the overall estimate for the whole
population will be more precise if stratification is used.
– Administrative reasons may make it easier to carry out
the survey through an organization with a regional
structure.
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149. 4. Cluster sampling:
– Selection of groups of study units (clusters)
– Clusters are often geographic or organizational units
(e.g. villages, clinics).
– all units in the selected cluster are studied
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150. Cluster Sampling, cont…..
In cluster sampling, cluster, i.e., a group of
population elements, constitutes the
sampling unit, instead of a single element of
the population.
In cluster sampling, clusters are the first
sampling units.
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151. Why cluster?
The main reason for cluster sampling is
“cost efficiency”
(economy and feasibility),
but we compromise with variance estimation efficiency
(larger variance than SRS)
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152. Selection procedure
• Primary sampling units (PSU): clusters
– select the PSU’s by using a specific element sampling
techniques, such as simple random sampling,
systematic sampling or by PPS sampling.
• Secondary sampling units (SSU): households/individual
elements
– select all SSU’s for convenience, or
– select few by using a specific element sampling
techniques (such as simple random sampling, or
systematic sampling).
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153. Example
• Draw 10 clusters with 30 elements, or
• draw 3 clusters with 100 elements.
– the principal reason of conducting cluster sampling is to
reduce costs.
– obviously, the 2nd option is cheaper as we need to go to
only 3 clusters.
– the first option should be implemented (take more clusters
with fewer elements) as a balance between “cost
efficiency” and “variance efficiency.”
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154. 5. Multi-Stage Sampling:
– appropriate when the population is large and widely
scattered.
– The number of stages of sampling is the number of
times a sampling procedure is carried out.
• The primary sampling unit (PSU) is the sampling unit in the
first sampling stage;
• The secondary sampling unit (SSU) is the sampling unit in
the second sampling stage, etc.
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155. E.g.
• After selection of a sample of clusters (e.g. household), further
sampling of individuals may be carried out within each household
selected.
– This constitutes two-stage sampling, with the PSU being
households and the SSU being individuals.
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156. Stratified multistage random sampling
• A combination of stratified random sampling and multistage
random sampling.
• In this method, first multistage random sampling is applied,
followed by application of stratified sampling on the selected
sampling stages.
• Example: EDHS
– 11 geographic/administrative regions (the nine regional
states and two city administrations
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157. • Regions in Ethiopia are divided into zones, and zones, into
administrative units called weredas.
• Each wereda is further subdivided into the lowest
administrative unit, kebele.
• Each kebele was subdivided into census enumeration areas
(EAs), which were convenient for the implementation of the
census.
• Sample was selected using a stratified, two-stage cluster
design, and EAs were the sampling units for the first stage.
• The sample urban areas and rural areas.
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158. Probability Proportional to Size (PPS)
• PPS is very common in large surveys.
• In simplistic sense, the selection probability that a particular
sampling unit will be selected in the sample is proportional to
the size of the variable of interest (e.g., in a population survey,
the population size of the sampling unit).
• PPS sampling provides self-weighted samples
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159. B. Non-probability sampling
 In non-probability sampling, every item has an
unknown chance of being selected.
 There is an assumption that there is an even
distribution of a characteristic of interest within the
population.
 For probability sampling, random is a feature of the
selection process.
 In non-probability sampling, since elements are
chosen arbitrarily, there is no way to estimate the
probability of any one element being included in
the sample.
8/10/2023 Sampling 159

160. 1. Convenience or haphazard sampling
 Convenience sampling is sometimes referred to as
haphazard or accidental sampling.
 It is not normally representative of the target
population because sample units are only selected
if they can be accessed easily and conveniently.
 It can be used when time and resources are too
short, but that advantage is greatly offset by the
presence of bias.
8/10/2023 Sampling 160

161.  Although useful applications of the technique are
limited, it can deliver accurate results when the
population is homogeneous.
 For example, a scientist could use this method to
determine whether a lake is polluted or not.
 Assuming that the lake water is well-mixed, any
sample would yield similar information.
 A scientist could safely draw water anywhere on the
lake without bothering about whether or not the
sample is representative
8/10/2023 Sampling 161

162. 2. Volunteer sampling
 Occurs when people volunteer to be involved in the
study.
 In psychological experiments or pharmaceutical
trials (e.g., drug testing), for example, it would be
difficult and unethical to enlist random participants
from the general public.
 In these instances, the sample is taken from a group
of volunteers.
 Sampling voluntary participants as opposed to the
general population may introduce strong biases.
8/10/2023 Sampling 162

163. 3. Judgment or purposive sampling
 The sampling procedure in which an experienced
researcher selects the sample based on some
appropriate characteristic of sample members… to
serve a purpose.
 The underlying assumption is that the investigator will
select units that are characteristic of the population.
 The critical issue here is objectivity: how much can
judgment be relied upon to arrive at a typical sample?
8/10/2023 Sampling 163

164. 4. Quota sampling
 Is a method that ensures that a certain number of
sample units from different categories with specific
characteristics are represented.
 In this method the investigator interviews as many
people in each category of study unit as he can find
until he has filled his quota.
Eg. A sample of 50 men and 50 women
 Quota sampling is an effective sampling method when
information is urgently required and can be conducted
without sampling frames.
8/10/2023 Sampling 164

165. 5. Snowball sampling
 The sampling procedure in which the initial
respondents are chosen by probability or non-
probability methods, and then additional
respondents are obtained by information provided
by the initial respondents.
 It used in Sampling people who are difficult to
contact or hidden populations. Eg. drug users,
CSWs, homeless or street children, etc.
 It is subjected to numerous biases, because
sampling units not independent
8/10/2023 Sampling 165

166. Sampling error
• Sampling error comprises the differences between the sample
and the population that are due solely to the particular units
that happen to have been selected.
– For example, suppose that a sample of 100 women are
measured and by chance all found to be taller than 170
cm.
– It is very clear even without any statistical prove that this
would be a highly unrepresentative sample leading to
invalid conclusions.
8/10/2023 166
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167. 1. Errors in sampling:
• By taking a sample, results will not be exactly
equal to the correct results for the whole
population.
The two components.
1. Sampling error (Random error) :during sampling
process, can be minimized by increasing the size of
the sample
• Affect precision
2. Non – Sampling error (bias): in the design or
conduct of a research.
• Affect validity
Hirbo S., 2016 167
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168. Chance (random error)
• Chance is the error that occurs just because of bad
luck and may result in untypical choices.
• Unusual units in a population do exist and there is
always a possibility that an abnormally large number
of them will be chosen.
• Chance (sometimes called random error) exists no
matter how carefully the selection procedures are
implemented.
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169. Validity: This refers to the degree of closeness
between a measurement and the true value of
what is being measured.
Reliability (or precision): the degree of closeness
between repeated measurements of the same
value.
Hirbo S., 2016 169
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170. The sources of variation resulting in poor reliability
include:
a) Variation in the characteristic of the subject being
measured. Example: blood pressure
b) The measuring instruments, e.g. questionnaires
c) The persons collecting the information (observer
variation)
Inter-observer variation: differences between
observers in measuring the same observation
Intra-observer variation: differences in measuring the
same observation by the same observer on different
occasions.
Hirbo S., 2016 170
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171. How to minimize Random Error (Chance)
• Obtaining adequate sample size for the study could reduce
the likelihood of chance as a possible explanation.
• statistically significant finding leave little room for chance.
8/10/2023 Hirbo S., 2016 171

172. 2. Non Sampling error (i.e., bias)
– Systematic error that skews the observation to one
side of the truth
– It is possible to eliminate or reduce the non sampling
error (bias) by careful design of the sampling
procedure.
Hirbo S., 2016 172
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173. Sampling bias
• Sampling bias is a tendency to favor the selection of
units that have particular characteristics.
• Sampling bias is usually the result of a poor sampling
plan.
• The most notable is the bias of non response when for
some reason some units have no chance of appearing
in the sample.
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174. Types of bias may be grouped into two broad
categories:
A. Selection bias:
– refers to any error that arises in the process of
identifying the study populations
– occur whenever the identification of individual
subjects for inclusion in the study on the basis of
either exposure (cohort) or disease (case-control)
status depends in some way on the other axis of
interest.
8/10/2023 Hirbo S., 2016 174

175. Example of selection bias
1. Berkson's bias:-
– Case-control studies carried out exclusively in hospital
settings are subject to selection bias attributable to the
fact that risks of hospitalization can combine in patients
who have more than one condition.
2. Ascertainment bias:-
– Differential surveillance or diagnosis of individuals make
those exposed or those diseased systematically more or
less likely to be enrolled in a study.
8/10/2023 Hirbo S., 2016 175

176. Example cont….
3. Non-response bias:-
 Rates of response to surveys and questionnaires in many studies
may also be related to exposure status, so that bias is a reasonable
alternative explanation for an observed association between
exposure and disease.
 It results in significant bias when:
– When non‐respondents constitute a significant proportion of
the sample (about 15% or more)
– When non‐respondents differ significantly from respondents
4. Loss to follow-up:-
This is a major source of bias in cohort studies.
Persons lost to follow-up may differ from with respect to both
exposure and outcome, biasing any observed association.
8/10/2023 Hirbo S., 2016 176

177. Example cont….
5. Volunteer/Compliance bias:-
– In studies comparing disease outcome in persons who
volunteer or comply with medical treatment to those who
do not, better results might be expected among those
persons who volunteer or comply than among those who do
not.
6. Cohort bias:-
– Refers to the biased view of the natural history of disease
presented in survival cohorts, since only the prevalent cases
(those with less lethal disease) are available for study in the
latter part of the period of observation.
8/10/2023 Hirbo S., 2016 177

178. A. Observation or information bias:
– includes any systematic error in the measurement
of information on exposure or outcome.
Examples:
1. Recall bias:-
– May result because affected persons may be more (or less)
likely to recall an exposure than healthy subjects, or
exposed persons more (or less) likely to report disease.
– This source of bias is more problematic in retrospective
cohort or case-control studies
Hirbo S., 2016 178
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179. Example, cont….
2. Interviewer bias:-
– This can occur if the interviewer or examiner is aware of
the disease status (in a case-control study) or the exposure
status (in cohort and experimental studies).
– This kind of bias may affect every kind of epidemiologic
study.
3. Social desirability bias:-
– Occurs because subjects are systematically more likely to
provide a socially acceptable response.
4. Hawthorn effect:-
– Refers to the changes in the dependent variable which
may be due to the process of measurement or observation
itself.
8/10/2023 Hirbo S., 2016 179

180. Example, cont…
5. Placebo effect:-
– In experimental studies which are not placebo-controlled,
observed changes may be ascribed to the positive effect of
the subject's belief that the intervention will be beneficial.
• Healthy worker bias:-
– Refers to the bias in occupational health studies which
tend to underestimate the risk associated with an
occupation due to the fact that employed people tend to
be healthier than the general population.
8/10/2023 Hirbo S., 2016 180

181. How to minimize Bias
• Selection bias is best eliminated by
– randomization
• Information bias can be eliminated by:
– using blinding procedures
• Using standard and comparable exposure and outcome
ascertainment in both groups.
– Choose study design carefully
– Choose "hard" (i.e., objective) rather than subjective
outcomes.
– Use well-defined criteria for identifying a "case" and use
closed ended questions whenever possible
8/10/2023 Hirbo S., 2016 181

182. Refer all
8/10/2023 Hirbo S., 2016 182

183. Sample Size Determination
• ”In planning of a sample survey, a stage is always reached at
which a decision must be made about the size of the sample.
The decision is important. Too large a sample implies a waste
of resources, and too small a sample diminishes the utility of
the results.“
Cochran, 1977
8/10/2023 183
Hirbo S., 2016

184. Sample size estimation: Why?
• Provides validity of the clinical trials/intervention studies – in
fact any research study
• Assures that the intended study will have a desired power for
correctly detecting a (clinically meaningful) difference of the
study entity under study if such a difference truly exists
8/10/2023 184
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185. Sample size estimation objectives
– Measure with a precision:
• Precision analysis
– Assure that the difference is correctly detected
• Power analysis
8/10/2023 185
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186. Be aware of the sampling design
• Sample size estimation depends on the sampling design – as
variance of an estimate depends on the sampling design
• The variance formula we use in statistics is based on “simple
random sampling” (SRS)
• However in practice, SRS strategy is rarely used
8/10/2023 186
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187. Three factors for sample size
estimation in SRS
1. The proportion (or percentage) of the sample that will
chose a given answer to a survey question,
2. The margin of error (is the level of ‘error’ which can be
tolerated),
3. The confidence level
• Note: the calculation of an appropriate sample size
relies on a subjective choice of these factors and most
times crude estimates of others, and may as a result
seem rather artificial. However, it is at worst a well
educated guess, and is considerably more useful than a
completely arbitrary choice
8/10/2023 187
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188. Steps in Estimating Sample Size
1. Identify major study variable
2. Determine type of estimate (%, mean, ratio,...)
3. Indicate expected frequency of factor of interest
4. Decide on desired precision of the estimate
8/10/2023 188
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189. Cont….
5. Decide on acceptable risk that estimate will fall outside
its real population value
6. Adjust for population size
7. Adjust for estimated design effect
8. Adjust for expected response rate
8/10/2023 189
Hirbo S., 2016

190. Why are Sample Size & Power Important?
 Sample size and power are essential for the
evaluation of the role of chance
 If a study has a inadequate sample size, then a
result could not show us a real difference as a
difference
 A true association will be difficult or impossible to
distinguish from a non-true association because of
inadequate power
8/10/2023 190
Hirbo S., 2016

191. SAMPLE SIZE
Depending on:
1) Variability in the target population.
(If unknown, assume maximum variability)
2) Desired precision in the estimate
3) Desired confidence in the estimate
4) Feasibility
8/10/2023 191
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192. aand Confidence Level
 a: The significance level of a test: the probability of
rejecting the null hypothesis when it is true
(or the probability of making a Type I error). It is usually
5% (0.05)
 Confidence level: The probability that an estimate of a
population parameter is within certain specified limits of
the true value;
(commonly denoted by “1- a”, and is usually 95%).
8/10/2023 192
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193. Power and β
Power: The probability of correctly rejecting the
null hypothesis when it is false; commonly denoted
by “1- β”.
 β : The probability of failing to reject the null
hypothesis when it is false
(or the probability of making a Type II error).
8/10/2023 193
Hirbo S., 2016

194. Descriptive study
• The sample size n required to estimate a population proportion
with a given level of precision d is
– d (Precision) refers to width of the interval one is
willing to tolerate and
– Z=1.96 reflects the confidence level.
..1
p)........
p(1
d2
2
Z
n 2
/
1

 a
8/10/2023 194
Hirbo S., 2016

195. The sample size formula for testing two proportions under independence without the assumption
of common variance is then:
Note that Fleiss (1981) suggested more precise formula:
When n1 and n2 is not equal and related by a ratio, say by r, the formula is:
The final formula (using normal approximation with continuity correction [without the
correction, the power is considered low than expected] with proportions) is:
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8/10/2023 195
Hirbo S., 2016

196. Example 1 (Prevalence of diarrheae)
a) p = 0.26 , d= 0.03 , Z = 1.96 ( i.e., for a 95% C.I.)
n = (1.96)2 (.26 × .74) / (.03)2 = 821.25 ≈ 822
– Thus, the study should include at least 822 subjects.
• small population (say N = 3000), the required minimum sample
will be obtained from the above estimate by making some
adjustment.
• nf = n/(1+n/N)
• 821.25 / (1+ (821.25/3000)) = 644.7 ≈ 645 subjects
Hirbo S., 2016 196
8/10/2023

197. Example
• The proportion of nurses leaving the health service is compared
between two regions. In one region 30% of nurses is estimated
to leave the service within 3 years of graduation. In the other
region it is probably 15%.
• The required sample size to show, with 90% likelihood (power),
that the percentage of nurses is different in these two regions
would be:
• (Assume a confidence level of 95% z-stat)
• n = (1.28 + 1.96)2 ((.3 x.7) + (.15x.85)) =158
(.30-.15)2
8/10/2023 Hirbo S., 2016 197
158 nurses in each region

198. The sample size n required to estimate a
population mean
• d- level of precision
• s- refers to standard deviation
8/10/2023 Hirbo S., 2016 198
• The objective in interval estimation is to obtain narrow
intervals with high reliability
• The width of the interval is determined by the
magnitude of the quantity
2
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2
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a



199. Comparison of two means (sample size in each group)
• n= (S1
2 + S2
2) (zα/2 + Z1-β )2...............3
(m1 - m2)2
• m1= mean of group 1
• m2= mean of group 2
8/10/2023 Hirbo S., 2016 199

200. As shown in the graph, the larger the margin of error, the smaller the
sample size and the wider (the less faith) one should have that the
reported results are close to the "true" figures; that is, the figures for
the target population.
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201. Design effect (Cluster sampling)
• The reliability of cluster sampling is predicted on whether
or not the study population from the selected clusters
are representative of the overall target population (and,
of course a function of the response rate within each
cluster)
• Every time you conduct sampling you are committing a
sampling error; in multistage cluster sampling the
sampling error is therefore multiplied
• To adjust the sampling error due to the clustering effect
we need to multiply the sample size by a design effect
8/10/2023 201
Hirbo S., 2016

202. Plan for data collection
Why should you develop a plan for data
collection?
A plan for data collection should be developed so
that:
• you will have a clear overview of what tasks have to
be carried out, who should perform them, and the
duration of these tasks;
• you can organize both human and material resources
for data collection in the most efficient way; and
• you can minimize errors and delays which may result
from lack of planning (for example, the population
not being available or data forms being misplaced).
Hirbo S., 2016 202
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203. Stages in the Data Collection Process
Three main stages can be distinguished:
Stage 1: Permission to proceed
Stage 2: Data collection
Stage 3: Data handling
Hirbo S., 2016 203
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204. Stage 1: permission to proceed
– Consent must be obtained from the relevant
authorities, individuals and the community in
which the project is to be carried out.
– This may involve organizing meetings at national or
provincial level, at district and at village level.
– For clinical studies this may also involve
obtaining written informed consent.
Hirbo S., 2016 204
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205. Stage 2: Data collection
When collecting our data, we have to consider:
• Logistics: who will collect what, when and with
what resources
• Quality control
Hirbo S., 2016 205
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206. Logistics of data collection
WHO will collect WHAT data?
• When allocating tasks for data collection, it is
recommended that you first list them.
• Then you may identify who could best
implement each of the tasks.
• If it is clear beforehand that your research team
will not be able to carry out the entire study by
itself, you might plan to look for research
assistants to assist in relatively simple but time-
consuming tasks.
Hirbo S., 2016 206
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207. HOW LONG will it take to collect the data for each
component of the study?
Step 1: Consider:
– The time required to reach the study area(s);
– The time required to locate the study units (persons,
groups, records);
– If you have to search for specific informants (e.g.,
users or defaulters of a specific service) it might take
more time to locate informants than to interview
them.
Hirbo S., 2016 207
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208. Step 2: Calculate the number of interviews that can
be carried out per person per day
Step 3: Calculate the number of days needed to
carry out the interviews.
For example:
• you need to do 200 interviews,
• your research team of 5 people can do 5 x 4 = 20
interviews per day,
• you will need 200:20 = 10 days for the interviews.
Hirbo S., 2016 208
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209. Step 4: Calculate the time needed for the other
parts of the study, (for example, 10 days)
Step 5: Determine how much time you can
devote to the study.
Hirbo S., 2016 209
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210. WHEN should the data be collected?
The type of data to be collected and the demands
of the project will determine the actual time
needed for the data to be collected. Consideration
should be given to:
• availability of research team members and research
assistants,
• the appropriate season(s) to conduct the field work (if
the problem is season-related or if data collection
would be difficult during certain periods),
• accessibility and availability of the sampled
population, and
• public holidays and vacation periods.
Hirbo S., 2016 210
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211. Ensuring quality
It is extremely important that the data we collect are of good
quality, that is, reliable and valid. Otherwise we will come up
with false or misleading conclusions.
Measures to help ensure good quality of data:
• Prepare a field work manual for the research team as a
whole, including:
— Guidelines on sampling procedures and what to do if
respondents are not available or refuse to co-operate,
— A clear explanation of the purpose and procedures of the study
which should be used to introduce each interview, and
— Instruction sheets on how to ask certain questions and how to
record the answers.
Hirbo S., 2016 211
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212. Select your research assistants, if required, with
care. Choose assistants that are:
— from the same educational level;
— knowledgeable concerning the topic and local
conditions;
— not the object of study themselves; and
— not biased concerning the topic (for example,
health staff are usually not the best possible
interviewers for a study on alternative health
practices).
Hirbo S., 2016 212
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213. Train research assistants carefully in all topics
covered in the field work manual as well as in
interview techniques and make sure that all
members of the research team master interview
techniques such as:
— asking questions in a neutral manner;
— not showing by words or expression what answers
one expects;
— not showing agreement, disagreement or surprise;
and
— recording the answers precisely as they are
provided, without sifting or interpreting them.
Hirbo S., 2016 213
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214. Pre-test research instruments and research
procedures with the whole research team,
including research assistants.
Take care that research assistants are not placed
under too much stress (requiring too many
interviews a day; paying per interview instead of
per day).
Arrange for on-going supervision of research
assistants. If, in case of a larger survey, special
supervisors have to be appointed, guidelines
should be developed for supervisory tasks.
Hirbo S., 2016 214
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215. Devise methods to assure the quality of data
collected by all members of the research team.
For example, quality can be assured by:
– requiring interviewers to check whether the
questionnaire is filled in completely before finishing
each interview;
– asking the supervisor to check at the end of each day
during the data collection period whether the
questionnaires are filled in completely and whether
the recorded information makes sense; and
– having the researchers review the data during the
data analysis stage to check whether data are
complete and consistent.
Hirbo S., 2016 215
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216. Stage 3: DATA HANDLING
• Once the data have been collected and checked
for completeness and accuracy, a clear
procedure should be developed for handling and
storing them.
• Decide if the questionnaires are to be
numbered; identify the person who will be
responsible for storing the data; and how they
are going to be stored.
Hirbo S., 2016 216
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217. Methods of data collection
The most commonly used methods of collecting
information (quantitative data) are the use of
documentary sources, interviews and self-
administered questionnaires.
The choice of methods of data collection is based
on:
– The accuracy of information they will yield
– Practical considerations, such as, the need for
personnel, time, equipment and other facilities, in
relation to what is available.
Hirbo S., 2016 217
8/10/2023

218. The use of documentary sources
Clinical records and other personal records, death
certificates, published mortality statistics, census
publications, etc.
Advantages:
– Documents can provide ready made information
relatively easily
– The best means of studying past events.
Hirbo S., 2016 218
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219. Disadvantages:
– Problems of reliability and validity
– There is a possibility that errors may occur when the
information is extracted from the records.
– Since the records are maintained not for research
purposes, but for clinical, administrative or other
ends, the information required may not be recorded
at all, or only partly recorded.
Hirbo S., 2016 219
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220. Interviews and self-administered questionnaires
– Interviews may be less or more structured.
– a checklist of topics
Self-administered questionnaire
– respondent reads the questions and fills in the
answers by himself
– Can be administered to many persons
simultaneously
• to students of a school
• they can also be sent by post unlike interviews.
Hirbo S., 2016 220
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221. Questionnaire Design
Questions may take two general forms:
– Open ended
– Closed
Methods of collecting qualitative data
– In depth-interviews,
– focus groups and
– observation
Hirbo S., 2016 221
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222. Plan for data processing and analysis
– Data processing and analysis should start in the field
– plan helps the researcher assure that at the end of the
study:
• all the information (s)he needs has indeed been collected, and
in a standardized way;
• (s)he has not collected unnecessary data which will never be
analyzed.
What should the plan include?
• Sorting data,
• Performing quality-control checks,
• Data processing, and
• Data analysis.
Hirbo S., 2016 222
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223. Ethical considerations
– research with human subjects, you are likely to
require ethical approval
– patients’ rights were often ignored
– many individuals were seriously harmed by medical
experimentation
– Tuskegee Syphilis Study in USA (1932-1970s) to study the long-
term effects of untreated syphilis- 400 men out of the 600
participants
– A study to examine the natural progression of cervical carcinoma
in New Zealand (1980s)
– Atrocities committed during World War II in the Nazi Germany
Hirbo S., 2016 223
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224. – Ethical decisions are based on three main
approaches:
• duty,
• rights and
• goal-based
Ethical principles
Autonomy- we ought to respect the right to self-
determination
Non-Maleficence- we ought not to inflict evil or
harm
• This principle states that we may not inflict harm on or
expose people to unnecessary risk as a result of our
research project
Hirbo S., 2016 224
8/10/2023

225. Beneficence – we ought to further others’
legitimate interests
• This is the principle that obliges us to take positive steps to
help others pursue their interests.
Justice- we ought to ensure fair entitlement to
resources
• This principle is concerned with people receiving their due.
• This means people should be treated equally in every way
since not all people are equally competent or equally
healthy.
Hirbo S., 2016 225
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226. 8/10/2023 Hirbo S., 2016 226

227. CHAPTER SEVEN
WORK PLAN AND BUDGET
Work Plan
– A WORK PLAN is a schedule, chart or graph that
summarizes the different components of a research
project and how they will be implemented in a
coherent way within a specific time span.
– It may include:
• The tasks to be performed;
• When and where the tasks will be performed; and
• Who will perform the tasks and the time each person will
spend on them.
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228. – Work plan could be presented in different forms
• work schedule and
• GANTT chart
– depicts graphically the order in which various tasks must be
completed
– the duration of each activity
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229. A work plan can serve as:
• A tool for planning the details of the project activities and drafting a
budget.
• A visual outline or illustration of the sequence of project operations. It
can facilitate presentations and negotiations concerning the project
with government authorities and other funding agencies.
• A management tool for the Team Leader and members of the research
team, showing what tasks and activities are planned, their timing, and
when various staff members will be involved in various tasks.
• A tool for monitoring and evaluation, when the current status of the
project is compared to what had been foreseen in the work plan
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230. Budget
Why do we need to design a budget?
• Identify which locally available resources and which
additional resources may be required.
• Encourage you to consider aspects of the work plan
you have not thought about before and will serve as
a useful reminder of activities planned, as your
research gets underway
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231. Budget justification
– It is not sufficient to present a budget without
explanation.
– The budget justification
– why the various items in the budget are required. clear
explanations concerning why items that may seem
questionable or that are particularly costly are needed
– Discuss how complicated expenses have been
calculated.
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232. THE END
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233. CHAPTER EIGHT
MAJOR COMPONENTS AND OUTLINE OF THE DIFFERENT
PHASES IN A RESEARCH PROCESS
• Summary of the major components of a research
proposal
• health research proposal (protocol) design is
required to include at least the contents given
below:
– Title and cover page
• The cover page should contain the title,
• the names of the authors with their titles and positions, the
institution and
• The month and year of submission of the proposal
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234. – Abstract: Summary of the proposal which should
include (in short):
• Objectives, hypothesis, methods, time schedule and the
total cost.
– Table of contents: A table of contents is essential.
• It provides the reader a quick overview of the major
sections of your research proposal, with page references,
so that (s)he can go through the proposal in a different
order or skip certain sections
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235. I. Introduction
• Statement of the research problem
– Background and definition of the problem of the study
– Why the proposed study is important, i.e., general statement on
rationale behind the research project.
• State of knowledge: knowledge pertinent to subject
under study
– Local data/knowledge
– Literature review
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236. – Significance of the proposed work
• Specific statements on the significance of the results of the
study should be given
• Where to use the results;
• who to make use of the results;
• what for the result would be used;
II) Objective of the study
• General objective: aim of the study in general terms
• Specific objectives: measurable statements on the specific
questions to be Answered
• Hypotheses
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237. III) Materials and methods
– If the investigation deals with human beings, the terms 'study
population' or 'subjects' are preferable to 'materials'.
– Type of study (study design)
– Study population
- Describe the study areas and populations
- Mapping and numbering of the study area
- Appropriateness of the study
- Accessibility (provide background information, travel, time, etc...)
- Cooperation and stability of the population
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238. – Type of data (defining each variable to be collected
and methods for collecting them)
• Operational definitions
• Some elements of the variables to be studied:
– What characteristics will be measured? How will the variables be
defined? What scales of measurement will be used etc.
– Inclusion/ exclusion criteria
– Sampling procedure to be used and sample size and
power calculation.
– Data collection and management
- Data collection and coding forms should be appended to
protocol
- Training and quality control, bias control, data entry and
storage, data clean-up and correction of deficiencies
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239. – Data analysis
• Management of dropouts
• Frequencies, rates, other parameters
• Statistical programs and tests to be used
• Data presentation (dummy tables to be appended)
– Ethical considerations: rights and welfare of the
subjects and method of obtaining their informed
consent
– Pretest or pilot study: (allows us to identify
potential problems in the proposed study)
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240. IV) Work plan (project management)
– Personnel, job descriptions, training
– Schedule (timetable)- provide actual dates for each
activity
- Pilot phase
- Final study
– Onset, data collection, analysis, write-up
– Relevant facilities
– Cooperating organizations
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241. V) Budget (itemize all direct costs in Ethiopian Birr)
– Personnel, material/supplies, travel, analysis,
contingency, etc.
VI) References: List only those cited in text and
number by order they appear in text using
Arabic numerals.
VII) Appendices:
– Data collection and coding forms
– Dummy tables for data presentation
– Letters of support (cooperation)
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242. Writing a research report
• major components report
– Title and cover page
– Abstract (Summary)
• a very brief description of the problem (WHY this study
was needed)
• the main objectives (WHAT has been studied)
• the place of study (WHERE)
• the type of study and methods used (HOW)
• major findings and conclusions, followed by
• the major (or all) recommendations.
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243. – Acknowledgements
– Table of contents
– List of tables, figures
• If you have many tables or figures it is helpful to list these
also, in a ‘table of contents’ type of format with page
numbers.
– List of abbreviations/acronyms (optional)
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244. I) Introduction
• Should include relevant (environmental/
administrative/ economic/ social) background
data about the country,
• the health status of the population, and health
service data
• statement of the problem should follow
• Global literature can be reviewed followed by
relevant literature from individual countries
may follow as a separate literature review
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245. Hirbo S., 2016 245
II) Objectives
– The general and specific objectives
III) Methods
– the study type;
– major study themes or
– the study population(s), sampling method(s) and the
size of the sample(s);
– data-collection techniques used for the different
study populations;
– how the data were collected and by whom;
– procedures used for data analysis, including
statistical tests (if applicable).
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246. IV) Results
– Findings should be presented
– Tables and graphs could be used (should be well titled and
captioned)
– The tables should be well constructed, and without anomalies
such as percentages which do not add up to 100 percent
– Avoid too many decimal places
– Graphs should clarify and not complicate, and care should be
taken that they do not mislead
– If appropriate statistical tests are used, the results should be
included. P-values alone are not very helpful. Confidence
intervals and the type of tests used should be indicated.
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247. V) Discussion
– Interpretation of the findings
– Care should be taken not to introduce new findings
– include findings from other related studies that
support or contradict your own.
– Limitation of the study and generalizability of the
finding should also be mentioned.
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