Research methodology lectures new prof.. mbassa

RESEARCH
METHODOLOGY

GK Mbassa

INTRODUCTION TO
RESEARCH
 Objective
 Introduces researchers and
students to scientific research
methods, enable them prepare
research proposals in:

 Veterinary Sciences
 Animal health
 Animal production
 Biotechnology
 Medicine
 Biomedical and Laboratory sciences
 Agriculture
 Wildlife
 Others

CONTENTS
 1. Introduction; Research planning
and process
 2. Types of research
 3. Problem identification process
 4. Literature review on subject

 Factor-Outcome relationship
5.
 6. Measurements

 7. Research designs

 8. Data collection

 9. Data processing, analysis, and

management

 10. Data presentation
 11. Research project description
 12. Report writing
 13. Research ethics

Introduction
 Research is a systematic for
search or inquiry for information
(new information)
 Research purpose is to explore,
describe, explain and control

Stages in Research
 Planning stage
 Data collection (gathering the

information)
 Data analysis (processing data
to yield knowledge)

 Interpreting the data
(extracting the
knowledge and
information)
 Results utilization phase

Planning stage

 (a) Building the concept
 (b) Problem search

 (c) Research justification

 (d) General objectives

 (e) Specific objectives
 (f) Assumptions

 (g) Limitations

 (h) Hypotheses themes,

arguments
 (i) Operational concepts

 (j) Planning of research and
purpose;
 (k) Literature review

 (l) Proposal write up

 Data collection (gathering
the information)
 (a) Population source of data

 (b) Logistics of data collection

 (c) Collection of samples from
the population

 Data analysis (processing
data to yield knowledge)
 Facilities for data analysis

 Laboratory procedures

 Treated samples

 Control samples

 Recording of results

 Statistical procedures

 Interpreting the data
(extracting the knowledge
and information)
 Data grouping and splicing

 Tables and figures

 Means and trends;

 Equivocal and unequivocal

conclusions

 Results utilization
 Identify beneficiaries of results

(solved problem, generated
technology)
 Professional Research Report

 Scientific briefs

 Communications of knowledge

and technology

 Seminars and workshops
 Policy changes

 Further research or activities

 Publication of data

 Patent technology

 Apply/ Sell technology

 Sales of research products and

technology

Research Planning phase

Building the concept, Problem search,
General and Specific objectives Hypotheses
Research justification

Investigation phase

Data collection (gathering information) Logistics of data collection Collection of
from source population samples

Interpreting the data (results)

Data grouping and splicing, tables, figures,
Equivalent and unequivocal conclusions
means and trends

Utilizing the Result
Communications, Briefs, Reports, Seminars, Further research or activities, Publications,
workshops, Beneficiaries, Policy changes Patents, Applications, Product sales

Variables in Research
 Variables are factors, parameters,
attributes or qualities of the cases that
are being measured or recorded,
examples being sex, age, height,
weight, colour, number etc and are
either independent or dependent.

 Variables vary in their
scores on the different
attributes, observations,
records or population
numbers.

 Independent variables, also
called predictor or explanatory
variables are the factors that
cause the variation in the
dependent variable
 Dependent variable is the
outcome resulting from the
independent variable

TYPES OF RESEARCH
 Several categories
 On basis of numerical principles

1. Qualitative
2. Quantitative
3. Both qualitative and
quantitative

 Qualitative; describes/analyzes culture
and behaviour of humans, animals,
plants, materials, cells, flowers, fruits,
organic or inorganic matter, for example
staining characteristics, organoleptic
tests of foods such as taste, smell,
colour, consistence, interactive, opinions,
feelings. In social sciences qualitative
research is called naturalistic inquiry or
field studies.

Qualitative research
 Ethnographies (observations of groups);

 Phenomenologies, studying subjects

over a period of time;
 Case studies to investigate subject over
time

 Quantitative Research
 Uses numeric data to verify, confirm,
prove causation, correlation,
corroboration or substantiation
 Establishes cause-effect relationship,
focusing on measurements, assigning of
numerical events according to rules

 Application of quantitative research
 Requirement for statistical tests
 Quantify extent of cause of effect
 Frequencies needed to explain meanings,
collects numerical data to explain
phenomena;
 Discovery of unexpected and in-depth
investigation

 In finding effect, control of one or more
factors required. Rigorous and rigid
methodologies and all procedures are
specified before data collection and
consistently followed
 Data analysis is statistical (deductive)
 Scenario is artificial, as in a laboratory.

 Qualitative and quantitative research
complement each other
 Combined in biological systems to
maximize strengths and minimize
limitations of each.

 Other Research types criteria
 Basic sciences: —study of macro and
micro morphology & physiology
 Applied sciences; —apply knowledge to
develop drugs, vaccines, seeds,
molecules, genes and more;
 Observational (descriptive)- observe
activities in natural or artificial systems
and reports the findings by description

 Analytical: — test causal association
between factor and outcome
 Evaluative studies:—find out whether
some factor introduced in a population
has imparted any influence (retrospective
studies)
 Innovative: —new inventions;
technology, materials and testing
alternative technologies
 Experimental (research designed to test
hypothesis)

 Surveys; prevalence of
diseases, distribution of a
factor. A survey to screen and
detect existence of a factor in a
population, and determine (or
not), the magnitude of the
factor

RESEARCH PROBLEM
IDENTIFICATION
 Purpose of research; solve problem
 Identification is essential
 Develop ideas from your subject
 Purpose new knowledge solve problem
 Know client of the research results

 Characteristics of a good
research question
 Very clear;

 Specific;

 Elements, variables & factors

affecting variables definable and
measurable;
 Investigations on the question
achievable within given time

 Clear relationship between
research question and
hypothesis;
 Research question fitted into
hypothesis and statistical tests;
 Research question methods and

assumptions well definable

 Source of data well definable; primary,
secondary, routine or published data
 Factors affecting research process
defined; logistics and finances
 Research on problem and question
acceptable scientifically, socially,
politically and budgetary
 Research question has not been
previously answered

 A researchable problem is found
through:
 A directly observed problem or
puzzle in science, social,
economic, cultural,
development, political or other
systems, animals, plants,
materials, foods, diseases,
machines, education, health,
weather and many others;

 A thorough review of literature
on the subject and on closely
related subjects to find out
what exists, what has already
been done and what the exact
problem is and how it can be
solved in terms of experimental
design, materials and methods

 Improvement of existing
systems, machines, life of
organisms, foods (palatability,
nutritive value, contents,
effects), forests, domestic
animals, wildlife, cells, drugs,
vaccines and other biological
products

 To evaluate effectiveness of
systems, materials, vaccines,
drugs, shelf life of goods,
animal/human nutrition,
knowledge delivery in teaching,
teaching aids, and others

 Once defined, decide type, kind
of data to be collected and
experimental designs.
 Research is preceded by

preparation of comprehensive
research proposal to guide the
research process.

 Research proposal is a step by
step manual of all activities
 It follows critical path of events

achievable in sequences
 Sound and effective research

proposal is completed after
thorough literature review of
subject

 When elements, variables and
factors are known, plan and
type of research are decided
based on nature of problem or
question to be answered

LITERATURE REVIEW ON
SUBJECT OF INVESTIGATION
 Literature means published knowledge
stored in any retrieval system, books,
journals, periodicals, newsletters,
microfilms, films, music, video and
others.

 Literature review means
reading, extensively with a
purpose of updating knowledge
on specific subject and keep list
of titles of published material

 Bibliography; collection of titles
of published papers and books
on a subject indicating source of
paper, no abstracts
 Example all publications on

mitochondria make bibliography
of the mitochondria

 Literature review is analysis of
current state of knowledge
 Helps research proposal to state

clearly what will be known after
research, that is not known now
 Summarizes current state of
knowledge, giving up-to-date
bibliography

 Purposes (aims) of reviewing
literature

Reveal
Determine Gain
investigations
relevant information
related to the
literature to on subject to
proposed
study current level
research

Show how Determine valid
other approach to Form research
researchers did the problem, in historical &
on similar associational
Reveal other
problems, perspective and
sources of
Obtain a in relation to
data,
method or earlier attempts
technique of Obtain new in solving
dealing with ideas and similar problem
the problem approaches

 Places of literature storage
 Library of Universities and Institutions;
 Computers on world wide web
(internet);
 National libraries and bibliographic
centers;
 National Bureau of Statistics;
 National Archives;
 Ministries;
 United Nations Organization offices

 Two categories of sources
of knowledge
 Published
 Non published

 Published
 Books

 Journals

 Periodical publications

 Annual subject reviews

 Proceedings of conferences,

symposia and professional
society meetings

 Non-published
 Ph.D. theses

 MSc. Dissertations

 Various reports

 Office documents

 Project reports

 Special collections and even
Minutes of meetings

Review summarizes literature precisely
and succinctly
Gathers specific knowledge to which the
research will add

Reflect on review of related literature,
what others have written in relation to
what is planned

Review literature from a comprehensive
perspective, like an inverted pyramid,
broad end first

Constantly explain relatedness of
proposed research to one in literature
Define gap where new research will fill

Rewrite content of literature sources
in own words and style, not copying
texts of other authors

Read source, understand it, list points
you remember, re-check points and
join them into proper sentences

 Outcomes of literature review;-
 Framed hypothesis within current
literature
 Defined scope of research and
objectives
 Defined type or category of research
within current literature
 Defined variables in the research within
current literature

 Clear research plan
 Identified source of data (Research
sites)
 Identified action plan, logistics to the
research
 Ascertained objectives and purpose of
research
 Sequencing the activities
 Clear implementation strategy,
variables, parameters, factors

 Conclude literature review by
giving specific objectives of
what the research is going to
achieve

 All the published materials must
be indicated at two places in the
researchers project plan / or
text
 In the text

 In the list of references

Citing literature in the text
 Literature is cited in text to indicate
source of scientific findings being
quoted from retrievable sources
 1. Normally journals
 2. Sometimes books
 3. May quote a review paper

 References are required for
 1. 0riginal findings

 2. Knowledge established by

previous researchers
 3. Other information needing
to indicate source

a) Articles with single author

 (1) Subject comes before quotation
 (i) Name of author placed at end of subject
of the sentence
 (ii) Author's name followed by coma (,),
the year of publication
 (iii) Author's name & year of publication
placed in brackets (parentheses)

 Examples
 Biotechnology is a primary tool of control
of foot and mouth diseases in cattle
(Thompsen, 1995).
 African goats produce more milk than
Toggenburg breeds (Smith, 1989)
 Lymphocytes cultured in vitro in RPMI
1640 medium proliferate within minutes
(Belinger, 2000)
 Total serum protein in African buffaloes
lies within 100 -140 g/l limits (Mbassa,
1990)

 (2) Author before subject
 (i) Name of author placed at
beginning or middle of subject of
sentence
 (ii) Author's name not followed by

comma but year of publication
 (iii) Only year is in parentheses

Examples
 Thompsen (2006) observed that
the native dense core protein of
Babesia bovis has several
epitopes, thus it is a candidate
vaccine
 Smith (2007) concludes that
African goat milk contains an
antiallergic factor

 According to Belinger (2006)
lymphocytes cultured in vitro in
RPMI1640 medium proliferate
within minutes
 Mbassa (2006) observed the
total serum protein in African
buffaloes to be 100-140 g/l

b) Articles with two authors
 (i) Names of authors in order
they appear in article placed at
end of subject
 (ii) Name of last author followed
by coma (,) and year
 (iii) Names of authors and year
in parentheses

Examples
The butter-fat content of zebu
cattle milk is 5.2 % (Mtenga and
Aboud, 2004)
Endemic stability in East Coast
fever is well established in Lake
Victoria Region (Mpangala and
Mollel, 2005)

 Pars intermedia is not found in
the pituitary gland of the greater
flamingo, Phoenicopterus rubber
rouseus (Mhowa and Dominico,
2007)

 Authors before subject of
sentence
 (i) Names of authors as in article
placed before subject
 (ii) Name of last author not
followed by coma, but year of
publication
 (iii) Only year of publication in
parentheses

 Examples
 Mtenga and Aboud (2004) found the
butter-fat content of zebu cattle milk to be
5.2 %
 Mpangala and Mollel (2005) noted that
endemic stability in East Coast fever is well
established in Lake Victoria Region
 Mhowa and Dominico (2007) did not find
any pars intermedia in the pituitary gland
of the greater flamingo, Phoenicopterus
rubber rouseus

(c) Articles with more than
two authors

 Mention first author,
followed by words
 "et al.,‖

 in italics

 (i) Subject before authors
 Name of 1st author followed by "et
al.,― placed after subject
 "et al ‖ in italics, then full stop (.),
coma (,) and year
 Name of author, "et al.,‖ and year
all placed in parentheses

 Examples
 Biotechnlogy is the primary tool in the control of
foot and mouth diseases in cattle (Thompsen et
al., 2006)
 African goats produce more milk than
Toggenburg breeds (Smith et al., 2007)
 Lymphocytes cultured in vitro in RPMI 1640
medium proliferate within minutes (Belinger et
al., 2006)
 Total serum proteins in African buffaloes lie
within 100 -140 g/l limit (Mbassa et al., 2006)

(ii) Subject after authors
 Name of 1st author followed by
"et al.‖ in italics placed after
subject
 "et al.― not followed by coma (,)
but year
 Only year in parentheses

Examples
1. Thompsen et al. (2005) observed that foot
and mouth diseases is more severe in cattle
than in goats
2. Smith et al. (2003) concludes that African
goats produce more butterfat in milk than
Toggenburg
3. According to Belinger et al. (2006)
lymphocytes cultured in vitro, in RPMI 1640
medium proliferate within minutes
4. Mbassa et al. (2007) observed the total
serum protein in African buffaloes to lie
within 100 -140 g/l limits

Compiling list of references
 Gives detail of
 1. Names of all authors quoted

 2. Only relevant references

 3. References not cited, not to
appear
 4. Names and, initials of all
authors said as "et al " in the text
must appear in reference list

 Information for each author
quoted
 (i) Surname followed by coma (,)
 (ii) Initials (eg. M. M.)
 (iii) Year article published (e.g. 2006)
 (iv) Full stop (.)
 (v) Title of article (e.g. " Peripolar
cells form the majority of granulated
cells in kidneys of antelopes and
goats‖
 full stop.

 (v) Full title (or std abbreviation of journal
where article was published (e.g.
Veterinary Parasitology" or Vet. Parasitol.,
Acta Anatomica or Acta Anat., Internat. J.
Biotechnol.)
 (vi) Volume of journal, colon (e.g. 51:)
 (vii) First page of article, dash (e.g.3894-
 (viii) Last page of article (e.g. 3902)
 (ix) full stop.

 Unless specially required, issue
numbers in same volume of
journal are not shown

 Articles published in journals
 Single author articles example

 DeVos, A. J. 1978. Immunogenicity
and pathogenicity of Babesia bovis in
Bos indicus cattle. Onderstepoort J.
Vet. Res. 45:119-123.

Two authors articles, Example
 Fu-Chu, He and Ghu-tse Wu

1993. Molecular evolution of
Cytokines and receptors. Exp.
Hematol. 21:521-524.

 Articles with more than two
authors
 All authors, with initials must be

provided
 Word ―et al ‖ not allowed to

appear in list of references

Examples
Brown, W. C., V. Shkap, Damine Zhu, T. C.
McGuire, W. T, T. F. McElwain and G. H.
Palmer 1998. CD4+ T lymphocyte and
immuno-globulin 2 responses in calves
immunized with Anaplasma marginale outer
membranes and protected against
homologous challenge. Infect. Immun.
66:5406-5413

 Luziga, C., Yamamoto Y., Horii Y., Mbassa G.
and Mamba K. 2006. Phagocytotic removal
of apoptotic endocrine cells by
folliculostellate cells and its functional
implications in clusterin accumulation in
pituitary colloids in helmeted guinea fowl
(Numida meleagris). Acta Histochemica
108:69-80.

Quoting in a book with single
author
 (i) Name of author with initials, year of
publication
 (ii) Title of book
 (iii) Pages (e.g. 601-623).
 (iv) Publisher (e.g. Lea and Febiger)
 (v) Place of publication (e.g. Dar es
Salaam, Philadelphia, Toronto)

 Example
 Klaus, G. G. B. 1987. Lymphocytes:
A practical approach. 261. IRL
Press, Oxford England

Quoting from book of two
authors
 Example
 Losos, G. J. and Brown J. M. 1996.
Infectious tropical diseases of domestic
animals. Langman Scientific and
Technical. 742-795, Washington D.C.

Quoting from a book with many
contributors

 Example
 Keller, G. , K. Roger Tsang and I. Kakoma.
1988. Advances in the in vitro cultivation of
Babesia species. In: Babesiosis of domestic
animals and man. Miodrag Ristic Eds, CRC
Press Inc. Boca Raton Florida, 71-79.

Quoting chapter in a book
(i) Name(s) of author(s) with initials, year of
publication
(ii) Chapter title
(iii) Followed by In: title of book followed by
Name(s) of author(s) of book with
initials, Pages, Publisher, Place of
publication.

 Examples
 Habour, C. and A. Fletcher 1991.
Hybridomas: Production and selection.
In: Mammalian Cell Biotechnology: a
practical approach, by M. Buttler,
Oxford University Press, Oxford,
109-138

 (ii) Mathew, P., S. Michael and Juma K.
2002. Modeling of changes in belief. In:
The psycho-technological development for
inductive changes in pastoralism. First
Edition, by Williams P. K and Manus S. P.
2002. Oxford University Press, Nairobi
Kenya. pp 234-313

 (iii) Hames, B. D. 1996. One dimensional
polyacrilamide gel electrophoresis. In:
Gel electrophoresis of proteins, Edited by
B. D. Holmes and D. Rickwood. IRS
Press Oxford. 1-147.

Quoting from book with collection
of articles (Multi-authored book)

 (i) Name of author(s) of article in the
book
 (ii) Title of article, year of publication
 (iii) Followed by In:
 (iv) Name(s) of book authors (s) with
initials, then in parentheses (Editors)

 (v) Title of book
 (vi) Publisher's name(s)

 (vii) Place of publication

 (viii) Pages read (first - last)

 OR
 (i) Name of author(s) of article in book
 (ii) title of article
 (ii) Year published
 (iii) Followed by In:
 (iv) Title of book, followed by Edited
by then Name(s) of Editor(s) with
initials, and pages read first - last

 Brown, L. R. and C. Flavin 1999. A new
economy for a new century. In: L. R.
Brown, C. Flavin and H. French (Editors),
State of the World; A World-watch
Institute Report on Progress Towards a
Sustainable Society, 3-21. W.W. Norton
and Company, New York

 Alternative quote from a book with many
articles (multi-authored book)
 (i) Name of author(s) of article in the book
 (ii) year
 (iii) title of paper, followed by, In.. title of
book, then Edited by, name(s) of author(s)
with initials, publisher, place of publication
and pages being referred to first – last

 Example
 Brown, L. R. and C. Flavin 1999. A new
economy for a new century. In: State of
the World; A World-watch Institute Report
on Progress Towards a Sustainable Society,
Edited by L. R. Brown, C. Flavin and H.
French, 3-21. W.W. Norton and Company,
New York

Quoting from conference
proceedings, symposia
 (i) Name(s) of author(s) with initials
 (ii) Year
 (iii) Title of article
 (iv) Followed by words; Proceedings
of (names of conference) held on
(date & year) in (city and country)

 (v) Name of Editor(s) with initials
 (vi) Publishers
 (vii) Place of publication
 (viii) Volume of the proceeding
 (ix) Pages being read.

 Examples
 Mbassa G. K., Young S., Kauzeni P. and
Nkangaga J. J. 2003. Quantitative impact
analysis of wildlife conservation strategies:
a study of impact of community-based
conservation in Gonabis wildlife
management area and rare antelopes in
Game Reserves. Proceedings of the Fourth
Scientific conference of the Tanzania
Wildlife Research Institute Held at Impala
Hotel, Arusha Tanzania Dec 3 to 6, 2003:
50-65;

Quoting from non regular
publications
 Workshops, Meetings, Other sources
 (i) Name(s) of author(s) if
possible (ii) Year of publication
(iii) Publisher or custodian of work

 Examples
 Mgongo F.O.K., Mellau, L.S.B., Mbassa,
G.K., Silayo R.S., Kimbita, E.N.,
Hayghaimo, A.A., Mlangwa J.E.D., Mbiha
E.R., and Gwakisa P.S. 2007. Improving
cattle selection and reproduction in the
traditional pastoral sector with efficient
control of tick borne diseases. Published by
PANTIL-SUA Project Programme for
Agricultural and Natural Resources
Transformation for Improved Livelihoods.
27 pp

RESEARCH/ EXPERIMENTAL
DESIGNS

 An experiment is planned inquiry
to get new facts, confirm
hypotheses,
 A trial to test validity of prior set
hypothesis

Factors in selecting
experimental designs
 1. Solve problem or answer an un-
resolved question
 2. Contribute new information,

improve understanding of matter
 3. Develop/Test technology

 4. Searching the unknown

 In basic research new scientific
information is generated
 E.g. genome nucleotide sequence of
animal/plant, gene e.g. fibronectin in
liver cell, gene for resistance against
disease in cattle, beans etc
 Applied research generates, tests and
validates technology

 Experimental design selected to be optimal
to answer research problem

Availability of
Research
New material adequate
type
samples

Number of
Statistical
factors being
methods
analysed

Research types
Innovative, survey, Quantitative,
analytical, qualitative
experimental
Living or
non living
In vivo or in
Prospective, matter, vitro macro or
retrospective social, microscopic
species

Categories of experiments

Preliminary Critical Innovative

• Large • Compare • Generate
number of responses new
treatments of variable products,
to gather to different test them
information, treatments, for their
with or adequate uses,
without units to compare
replication detect with those
differences in use

Planning the experiment
 State research problem
 State problem in hypothesis
form
 List objectives

 Select experimental design
 Describe materials and

methods in detail, including
statistical analysis
 Filter results from inputs

State research problem
What question to be answered, knew knowledge to be found

State research problem in hypothesis form
List objectives, select experiment design

Describe experiment, materials
Detail methods
State statistical
analysis of results

Filter results from inputs

2. State
1. State problem objectives and
& hypothesis methods

3. Perform
experiment

Collect results

Qualities of well- designed experiments

Equality in
sample Uniform Clear
sizes and variables Simplicity
treatment
member Proper in design
properties Appropriat plan for Provide
to e interpretat necessary
eliminate statistical ion of data
systematic design results
errors

Experimental design to envisage

Proper
results Biological materials vary
interpretation, greatly, due to inherent
variability (individual,
Estimate error intrinsic) and lack of
Error control uniformity in dispensing
treatments in the experiment

Strategies to control errors
 Replication in space (location)
 Increase sample sizes
 Replication in time
 Randomization
 Inclusion of controls
 Blocking certain natural variations
 Refinement of methods and chemicals
 Minimize mechanical errors

Control of errors

 Replication
in space
(location),
same time

 Replication in
experimental
units at the
same time

 Replication in time, repeat experiment
several times

 Randomization,
select samples by
random principles

 Inclusion of
controls,
omitting certain
factors so that
only a single
factor is allowed
to act on
selected groups
of treatments

 Blocking certain natural variations, all
samples uniform in size, age, material,
time of treatment and other factors

 Refine methods
and chemicals,
use very refined
materials to
eliminate blocking
of active
ingredients by
impurities
H O
H OH

H H
H O

 Minimize mechanical errors, investigator
and experiment dispenser to be perfect

Common experimental
designs
 1. Single factor experimental design
 Single factor varies, others constant
 Treatments; different levels of same
factor, e.g. testing suitable dosage for a
growth factor, therapeutic drug, animal
feed, mitogenic factor, mutagenic factor

without
factors etc

with different
combinations

Experiment
set,
samples
with single
factor

 Drug dosage studies in guinea pigs

Results Results Results Results

Report

Report

Publish Patent

Sell Disseminate
technology technology

2. Experimental design for two
or more factors

Effects of
many different A factor is a
These are factors are treatment,
factorial investigated consisting of
experiments simultaneously, all possible
economic & combinations
time serving

Magnitudes of effects are measured
to determine contribution of each
factor to effect
Example two or more
milk replacers
Factorial design
differing in
reduces costs in
composition
animals, chemicals,
investigated in same
feed, space, time,
group of animals at
labour, drug.
the same time in the
same experiment

Uniform animals Uniform
(homogenous treatments

Statistical
Random
methods T test,
selection at all
X square, GLM,
levels
ANOVA

(b) Completely randomized block design
animals in blocks can be replaced by farm plots,
cells, etc
Animal Animal Animal
Animal 1 Animal 2 Animal 3 10 11 12



Animal Animal Animal Animal Animal Animal
19 20 21 28 29 30

22 23 24 31 32 33

25 26 27 34 35 36

Uniform study subjects
(homogenous) Uniform treatments

Random selection Measure effect of more than
at all levels one factor in an experiment

Variables include metabolic
product, growth level, disease
Economic and
occurrence, immunity level time serving

Animals grouped as natural as possible
& as homogenous as possible to
eliminate errors (homogenous units
called blocks or herds, pens etc)

Then apply
Statistical methods are
randomised
ANOVA, GLM, T test, X
treatments for the
square
blocks

 Analysis is performed by model;
 Yijk = +Ai + Bj + (AB)ij + eijk
 Where Yijk = observation on kth animal
receiving ith level of first factor A, =
general mean common to all observations,
Ai = an effect of ith level factor, first factor,
Bj = an effect of jth level of second factor B,
(AB)ij = an interaction effect due to
combination between ith level of factor A
and jth level of factor B and eijk = a random
effect specific to each animal

3. Latin square experimental
design when:-

Number of Same sample
units very few Time sizes of units
consuming at different
Limited measurements periods in
facilities sequence

One/more animal per treatment
Statistics; sum of squares

Too few samples reduces power
of experiment

Carry over effects on using
same animals causes
confounding

4. Quasi-experiments

Design has some but not all of
characteristics of true experiment

No random assignment of subjects to
control and experimental conditions

Natural experiments, nature assigns
subjects to conditions, e.g. trends in
rainfall, wind, hurricane, crimes etc.

Characteristics
Matching instead of
randomization, similar
locations, not control
but as comparison, also
called nonequivalent
group design

Time series analysis
(longitudinal study over
time), impact analysis

Unit of • not humans, contextual concepts; sociology,
quality of life, anomalies, organization,
analysis disorganization, morale, climate, atmosphere

• Sufficient number of events to control threats
Validity to validity and reliability, independent variable
is time

• Quasi-experiments are creative on causes of
events, no control of independent variable
Control • Baseline and natural interventions (legislation,
relocation)

 In quasi-experiments use trend, not cause,
major ones are syndromes or cycles, minor
ones are normal or abnormal events
 Quasi-experiment research designs to
involve many different, but interrelated
variables, causal relationships can be
modeled to identify spurious (false),
intervening and suppressing variables

Statistical methods in data
analysis
 Statistics is an applied
mathematical science that provides
an objective basis for analysing
problems where the cause and
effect of observations are not
apparent

 There are many statistical methods in
biological research
 For living organisms it is biometry
 Social sciences (non-parametric statistics)
 Living things include their cellular parts,
organelles, molecules in cells (amino acids,
vitamins, proteins, minerals, carbohydrates,
lipids, water, nucleic acids and their
combinations).

Statistical methods require
 1. Quantitative measurements of causal
and effect factors, called variables
(variates)
 2. Populations

 3. Samples

 4. Measures of central tendency

 5. Measures of dispersion and

hypotheses testing

 Variables include plant number, height,
pods, weight, milk quantity, protein
content, number of genes, number of
genetic recombinations on chromosomes,
many others
 Qualitative and quantitative measurements
constitute data
 Qualitative variables are non-numeric for
example colour, taste, smell, molecular
reaction in a cell, stain uptake and others

 Data may be continuous, taking
any value on a continuous scale
(for example weight, height,
milk yield)
 Or discontinuous (discrete)

appearing only as integral values
eg, animal (cannot have half
animal).

Populations
 A population (a universe) means all

individuals of a particular
experimental set; animals, plants,
cells, molecules and others) and
includes all possible values of
measurements, ranges from small
to infinite

Samples
 A sample is a selection from where
observations, information or variables
are recorded
 The results of measurements in a
sample can be extrapolated to the
population
 To be truly representative of the
population, it must have been
obtained by random selection or by
purposive sampling

 In a random sample each member of
the population has equal and
independent chances of being included.
 The sample constitutes an experimental
unit
 Treatment in experiment is a procedure
applied on a member of the sample,
e.g. drug, milk, growth factor

Measures of central tendency (location)
 Average (arithmetic mean)

 Statistical mean (central location or

ordinary value)
 Median (central number when

observations are arranged in ascending
or descending order)
 Mode (most frequently occurring

observation value)

 Measures of dispersion
(Variability), first, second, third and
fourth moments about the mean
 First moment gives mean deviation

about the mean
 Second moment about the mean
gives variance and standard
deviation its square root

 Third & fourth moments about
the mean measure degree of
skewness and curtosis of
frequency distribution about the
mean
 Range is the difference between

the highest and lowest values

 Coefficient of variation (CV) or
coefficient of variability (CV) measures
precision of measurements
 CV (%) = (std x 100) divided by the
mean
 Small CV indicates greater precision
than in large CV, thus results are more
reliable
 Large CV indicate increased
experimental errors

Hypothesis testing is based on
statistical decision
 A hypothesis is logical assumption

about characteristics of a
population.
 The researcher guesses about the
results of experiment, the guess is
the hypothesis, which is tested

 Examples
 a new gene may treat a disease
 a new drug is more effective than
standard drug
 a formulated material treats this
disease
 a new compound causes higher plant
growth or animal growth

 a new breed of plant or animal
yields more product
 a new breed of animal is resistant

to disease
 a gene knockout results in abolition

of disease, fertility, transcription,
mutation, and many other
examples of hypothesis

Statistical tests include
 T-test; 2 means tested, two way test

 Analysis of variance (ANOVA), where more
than two means are being tested, sample
sizes in groups uniform
 General linear models (GLM), more than two
means tested, samples sizes in groups
different

 Chi (X) square, tests if one
method is better than the
other one or two way
 Many others (parametric, non-

parametric)

ANOVA partitions total variation into
different sources, for example
 among experimental units treated

differently
 among experimental units treated

similarly
 due to non-experimental variables

 various interactions

 ANOVA works if the sample sizes in
different treatments are equal.
 If the sample sizes are different

another similar but stringent test
similar to T –test is used, called
General linear models (GLM)

Other statistical analytical
methods
 Regression

 Correlation

 Wilcoxon

 Shapiro-Wilk statistic

 Duncan’s multiple range

RESEARCH PROPOSAL
A research proposal is an idea
intended or advised on a researchable
subject, put forward or suggested
systematically and complete

Research Pro-posals are suggestions,
intentions, plans, schemes or requests

Systematic planned design of protocol
to conduct research in specific subject

• Provides objectivity & critical
Objective insight of planned research

• Advanced manual to be
Manual followed in research process

• Defines information being
Search sort

• Sets out problem to be researched
What • What information being sort

• Why this is important to be known
Why

• How it is to be extracted from
How population

Where • When and where will the research
and be done
when

Addresses Proposes data
subject from necessary for
known towards Relates to solving
unknown, problem
superficial to
collateral or indicated, how
detailed, related to collect data,
general to studies of treat, process
specific other interpret
States researchers Determines
objectives to needs to
achieve success

Research is to
Indicates how provide new
results be knowledge & Research
presented, technology proposal is
used, published
and
Good research divided into
requires early three or
disseminated; thinking on
Gives total subject & more
budget for collection of component
completion of adequate s
research recent
literature

Components of Research Proposal

Introduction Purpose and
Title (background objectives,
Researchers information, Hypothesis,
problem conceptual or
Summary or theoretical
statement, framework
Concept
justification) Literature review
Goal (aim) of subject

Research Budget Logic
methodology framework
materials Results of goals,
methods disseminati purpose,
data analysis on outputs,
work plans Literature objectives
Expected cited & activities
outputs (References) Appendices

Title

Short Leads to
Clear understand
Describes concepts,
Reflects content of methods &
content of research output of
research proposed
research

Researchers

Their
Their
affiliations
Researchers qualifications
and
involved and
telephone
named addresses
numbers
mentioned
given

Summary/concept

Why should it
No more than Where and
be done
250 words when
(problem)
on; research is to
How it will be be done
What is to be
done (what
done Who are the
samples)
beneficiaries

Selecting topic or subject of research

Identify
Identify interests keywords on
or puzzles topic Formulate topic
by searching for
Identify puzzling Express puzzle in articles to
points; scientific, specific
identify
social, economic, keywords
researchable
health, political, Define topic by problem
cultural analyzing
keywords

Qualities of a good research topic

Researchable, Provocative,
instruments are open to varied
easily Contributes views and
formulated, new knowledge interpretations;
population, Findings Clear and
samples, publishable focused, not
objectives, vague or
measurable ambiguous

Problems in topic selection

Vague topic
not possible Poor timing
Too wide for in-depth
subject, no Limited
study accessibility
limit of scope
Too complex to materials
study subject

Selection of Title

Discusses topical
Title is heading, issues in science,
label or tag, business, life and Formulated
describes study, living or others, after
mini-abstract, analyzing factors identification
portrays enhancing or of research
summary of key hindering topic (subject)
idea(s) success of
generations

Steps in title selection
Identify title keywords
Reflect on key issues
Identify independent and
dependent variables, link
them in title
Evaluate title; clear, specific,
independent & dependent
variables identified
Control length to 12-15
words only

Qualities of good and effective
research proposal title

Brief and specific
Salient & have strong Focused
impact

Easier to see
Summary of
independent In line with
what study is
& dependent objectives
about
variables

Portray Reflect relationship
aims of between independent
study & dependent variables

Show researchable subject with
measurable results

Unambiguous, not to cause
various interpretations of the
study

Challenges in title selection

Lack of
Not consistency,
Too long and objectives not
specific, too wordy apparent or
varied different from
interpretati Difficult to
problem
understand statement & or
ons
methodology

Introduction
Opens study
Discusses background to research
States & defines problem
Aims & objectives stated & how work
will progress given

Establishes existence of problem &
need/justifies investigation

 Sub-divisions of introduction

 .

Background Significance
Aims &
knowledge of study,
objectives,
limitations,
Statement hypothesis,
conceptual
of problem research
& theoretic
Justification questions
frameworks

 Background information

Scientific,
Knowledge on establish a
Determines
the problem in cause and
user/client or
context of effect
beneficiary
current relationship
literature & Explains the
Prerequisite
status matter, what is
knowledge
known about
Gives setting before
the subject
of study problem is
stated

Background information reveals

Current view
Problem or
What has of research
opportunity
brought about problem;
exists &
need for Familiarity
needs being
study; with subject
addressed;
Challenges with clear
Opportunities
faced due to linkage of
for
the issue flow of
improvement
knowledge

Qualities of good background
information
Uses simple,
Brief, specific, straightforward
Gives a glimpse
summary fluent language;
of the research
literature review; Informative,
problem;
Generates persuasive,
Gives idea on
concerns on states urgency
how the
problem & of addressing
proposal is
opportunity of problem so
structured
solution resources be
allocated

How to write background information

Reflection Analyze subject & title

Identify variables

Independent Dependent

Literature search, logical & balanced
statement shows vision of research
Material compilation in Formulation, use materials
library to write background

Challenges in writing background
information (BI)

Confusion Lack of clarity,
between BI & Confusion of BI jargon, slang,
literature review with justification trendy words,
(LR). LR studies of study, but BI abbreviations,
related areas, BI is to give brief colloquial,
is short, briefly overview of redundant
on why to study problem phrases,
& opportunities Bad quotations confusing
after language

Qualities of good backgrounds

Brief, specific Researchers
overview of show
Previous
problem familiarity with
studies
current events
Simple justifying study
and
straightforward are cited
information on
language problem

Statement of problem

Identified problem
One way; use
is reason for
logical approach When solved
research
to establish separately,
Problem
identification may causation sub-problem
involve exploratory Problems have s resolve
research logical
(diagnostic survey) sub-components main
to collect
(sub-problems) problem
background data

Sub-problems are
Research problem
researchable units,
must be specific to
making
make methods
interpretation of
specific &
data more
appropriate, set
apparent, adding
precise limits of
up to totality of
problem area
problem

Qualities of sound & good statement
of problem

Clear & Indicates Problem
concise urgency of researchable
Has an research & through
impact on that research collection
the whole is definitely and analysis
topic needed of data

Steps to write research problem

Reflection— Present Reflect
start with research topic,
idea, what ideas or independent
kind of puzzles, &
question is then assess dependent
to be selected variables of
answered topic & title investigation

Formulation;
when problem Justification -
Identify explain
uncertainties is identified
formulate it repercussions
State why is a to follow if
clearly.
problem, how problem is not
Indicate how it
will addressed, use
came out, statement to
communities personal
be better off show that
observation or research has to
after research previous be performed
research

Challenges in research problem
formulation
Clarity
• Research problem is lack of clarity

• lacks unity and relationship to objectives,
independent and dependent variables &
Unity literature review

• Lacks urgency, no urgency for investigation, no evidence that if not
Urgen addressed, repercussions are serious for country, people, lost
cy opportunities

• Statements lack objectivity, reinforce emotions over topic, problem
Emotio not easily investigated by collection and analysis of data.
n

Goals (Aims) & Objectives

Objectives are Goals or aims
aims the In any research are intentions,
research there are goals or what
envisages to indicators of research strives
achieve, intention & to achieve, long
eventually the direction of term objectives
purpose of study e.g. national
study development

Aim is general statement

• Reflects intention or purpose of
research, stated in terms not easily
measurable

Aims assist in formulation of
objectives
• Pinpoints purpose of study, reveals
whether research is urgent or not

Quality aims & goals of research
proposal

State
accomplishment
Pragmatic;
of group not Broad enough to
State purpose of individuals lead to specific
study, not objectives
Stated in general
referring to
terms providing Clearly stated &
specific
direction for are reflective
achievements
research
development

Formulation of study aims, goals &
purpose

Reflection – Formulation Analysis –
think, decide – write analyze aims
on what to purpose of to confirm
accomplish study (what they address
by end of to research
the study, accomplish problem &
analyze title within time) questions

Challenges in formulation of
aims

Lack of
Over-
Lack of cohesion - no
ambitious
clarity, clear link
aims – not
purpose of between title,
achievable by
study is not purpose,
resources &
articulated objectives or
time available
problem

Objectives
 Objectives; intentions or purposes stated
in specific measurable terms
 Results are evaluated via objectives
 Specific objectives constitute means by
which aim/ goal is achieved
 Specify what to be done
 Are operational, stating specific tasks with
measurable results to be carried out

Objectives are vital because they
guide
Variables,
Literature Evaluation;
Methods, review;
instruments, Break aim into
study area; Precision on achievable &
what to measurable
Data collected, accomplish; pieces,
analysis &
Study into Consistent
report
defined parts focus activities
in sequence

Purpose is
What to be achieved by the research, has many
objectives

Desired condition after research
Problem reformulated positively

Objectively state target, quantity, quality, of
research output
specified time & location

Assumptions, limitations

Practical &
theoretical
Factors Factors
limitations
facilitate preventing
make results
completion research to
valid and
of research be done
applicable or
inapplicable

Qualities of suitable objectives

Methodology (materials &
methods)

Describes
systematically in
detail materials, Methodology is
tools, experimental to be clear on
designs, methods, experimental
logistics how to
research to cover
procedure on…
objectives

Selected Explain how data
What data materials for will be analyzed;
required; testing;
Trimmed, spliced
What standards Study area; to show trends
data to meet; Procedures for or associations;
How to collect, measuring Communication,
process, analyze variables; dissemination &
data Units of utilization of
measurements results

Matrix of research work plan

Goal Outcome Month to
Objective/
Output Activities to observed
(Aim) observe outcome
1 1 Jun 2009
1 1 2 2 Dec 2009
Mar 2010
3 3

1 1 Jun 2010
Sep 2010
1 2 2 2 Dec 2010
3 3

Logic framework matrix or logframe

States Rows
conditions represent
Effective tool necessary for
for planning research to different levels
of project
and succeed goal,
evaluation of objectives,
Summarized
research in four column outputs,
table activities

Columns indicate how the achieved
objectives are measured, and
assumptions for achieving results
In vertical logic are
narrative summary Inputs include
at goal, purpose, personnel,
objectives or physical and
outputs, and inputs financial
in terms of resources
activities levels

Outputs are measures
of what comes after
inputs energy (inputs
& activities cause
outcomes [Inputs are
similar to independent
variables])

outputs are
outcomes caused
by activities
[outputs are similar
to dependent
variables]

The goal is ultimate objective, not
the immediate objective

The immediate objective is the
purpose, which is the main output

• Relationship between goal and
Goal
and purpose is less direct and causal
purpose

• Many exogenous factors influence
Factors the goal

• Achieving the purpose is necessary
Contribu
tion to but not sufficient to achieve goal
goal

In horizontal logic are
evidence of
verifiable achievement & how that
indicators & of research evidence is
means of project (in found &
verification deed every measured
project),

Indicators & their Assumptions are
means of verification factors which are not
show criteria for controlled by the
attaining objectives research but
of nature, quantity, influence it (external
quality and time factors)

Logic framework
Objectively
Narrative Means of Assumptions
verifiable
summary verification (Risks)
indicators

Sources of Assumptions
Measure information affecting
Goal of goal Methods Purpose,
used Goal

End of information affecting the
Purpose project Methods
Output,
Purpose
status used linkage

Objectively
verifiable
indicators

Nature & level of Initial
resources, Sources of
Inputs necessary costs,
planned starting information
assumptions
about the
date project

Quantitative Sources of
magnitudes Assumptions
information affecting
Output 1 of outputs
Methods to Input/Output
and planned linkage
use
dates

Quantitative
magnitudes of information, affecting
Activities
outputs and Methods to use activities
planned dates

Objectively
verifiable
indicators

Nature & level of Initial
resources, Sources of
Inputs necessary costs,
planned starting information
assumptions
about the
date project

Quantitative Sources of
magnitudes Assumptions
information affecting
Output 2 of outputs
Methods to Input/Output
and planned linkage
use
dates

Quantitative
magnitudes of information, affecting
Activities
outputs and Methods to use activities
planned dates

Monitoring & evaluation plan

Goal Month to
Objective/ Output
Activities observe
(Aim) Output quantity
output
1 1 Jun 2009
1 1 2 2 Dec 2009
Mar 2010
3 3

1 1 Jun 2010
Sep 2010
1 2 2 2 Dec 2010
3 3

Literature cited
(References)

List all literature
cited in the text

DATA COLLECTION

 Samples may be whole mammals,
birds, reptiles, amphibians, fish,
higher or lower plants, heminthic
worms; nematodes, cestodes and
trematodes,

 fungi, bacteria and virus or their
parts, cells, tissues and organs,
secretions such as milk, sweat,
tears, hormones, antibodies,
vitamins, proteins, urine, bile acid,
bile, cell, embryo, saliva, semen or
other

 They may be macromolecules such
as nucleic acids, proteins, fats,
carbohydrates or product, metallic
or non metallic, feeds, pathologic
tissues, blood, plasma, serum,
dyes or various compounds, simple
and complex

DATA ANALYSIS AND
PRESENTATION
 Biological samples collected
from plants or animals are
processed in the laboratory and
analyzed to yield results by
standard biological methods

 Samples may be whole mammals,
birds, reptiles, amphibians, fish, higher
or lower plants, heminthic worms;
nematodes, cestodes and trematodes,
fungi, bacteria and virus or their parts,
cells, tissues and organs, secretions
such as milk, sweat, tears, hormones,
antibodies, vitamins, proteins, urine,
bile acid, bile, cell, embryo, saliva,
semen or other

 Each biological product is analyzed
by a standard biological procedure
in various instruments such as
microscopes, celloscopes,
spectrophotometers, scans, dyes,
chromatograph, and many others

 The results of field data,
observations, morphology,
measurements, questionnaires,
innovation data; new material,
drug, vaccine, machine, bacteria,
feed to be found and tested are
described

 Impact data for a process going
on, qualitative and quantitative
data information obtained is also
presented in detail.
 Data is first entered in electronic

form and storage (Data Base or
Spreadsheet), then cleaned,
statistical methods for analysis
chosen and executed

Presentation of Results
 Descriptive texts
 Tables

 Figures (graphs, pictures,

diagrams, charts, histograms, box
plots or other drawings)

 Describes in detail salient features
of results obtained referring to
tables and figures which explicitly
or brightly show the characteristic
or trend being reported
 Tables classify data to facilitate

comparisons and reveal
relationships or trends of the data
being presented

 Tables are self-explanatory, giving
more illustrative information in
support of the description in the
text.
 There are many varieties of

organizations of research results in
tables, depending on the type of
experiment, its design and
treatment structure

 Tables are numbered in the proper
sequence relating to the flow of ideas
and/or the sequence of events in the
text but typed on separate pages
 In the text, the first letter of the word
table(s) is capitalized e.g. Table 1,
Tables 4 and 5, even when it appears
in the middle of the sentence

 Tables may be arranged in different
ways, but the format that
demonstrates results most
effectively is selected
 Many types of data have decimal

points. It is important to reduce
decimal places to the precision
required in the parameter being
determined

 Figures in form of pictures, diagrams,
charts are commonly used in biological
presentations, the most commonly used
are line graphs, bar or pie charts in two or
dimensions with X, Y or Z axes
 Line graphs; suitable for presenting a
relationship between continuous
(quantitative) variables e.g milk or rice
grain yield response to varying rates of
fertilizer levels

 Bar charts are used for discrete
(discontinuous) data e.g.
frequency distribution
 Pie charts are used to present

the relative magnitude of the
components of a whole unit

 In degree of accuracy in presenting
data, graphs can be ranked in
descending order of line graphs,
bar and lastly pie charts

 In bar charts, Y axis begins at the
zero so that both the relative and
absolute bar heights reflect
accurately magnitude of treatment
means and their differences,
truncating it exaggerate the
differences

 A line graph cannot be valid unless
it is based on a minimum number
of three treatments i.e three data
points)
 Tables are more accurate in data

presentation

DATA INTERPRETATION
 1. Analysis of causation of effect
by a factor
 2. Interpretation in multi-causal

factors and multi-effects
 3. Statistical association

 4. Application of statistics in

interpreting data

1. Analysis of causation of
effect by a factor
A Variable is a property, a factor, or a
characteristic of a system, an individual,
a group, or a system that changes or
causes a change in quality or quantity of
another property or characteristic of any
system

 Putative causes of an outcome (or
effect) such as transcription level,
translation level, death of a cell,
disease in an animal, milk produced,
protein produced, etc

 Putative causes are exposure or
risk factors (as independent
predictor or variable, or
explanatory variables) producing
the outcome of interest (the
effect)

 An Outcome
or Effect is a
measurable
outcome of
action of a
cause

 (e.g. disease, gene transcript,
response, productivity, cell secretion,
absorption, reproduction, .growth or
others). An effect is a measurable
response and is a dependent variable

 A determinant is any factor
that when altered produces a
change in the frequency or
magnitude or characteristics of
a dependent variable

 Factors are such as age,
breed or sex in animals and
many others in non animal
biological or non biological
systems

 Association of causal and
outcome variables may be
true associations or spurious
associations (chance, bias,
confounding)

 Many determinants are external to
the system (animal, cell, culture,
tissue others)
 Internal factors relate to the
intrinsic elements of the system (such
as the pathogenesis of a disease in an
animal).
 Sufficient cause is amount of factor
that reaches a threshold to cause an
outcome

 Purpose of data analysis in many
experiments and research is to
determine whether suspected
factor (agent, independent
variable) is the cause of a
specific outcome or response
(dependent variable).

Early (Henle-Koch) Guidelines
for determining causal-
effect relationship
 Independent variable casual (agent)
must be present in every case of that
outcome (dependent variable);
 Independent variable must not be
present in other systems (where the
outcome of interest is not there);

 Independent variable must be
isolated from dependent variable
(tissues, cultures);
 Independent variable must be
capable of inducing the response or
the outcome of interest under
controlled experimental conditions

 Henle-Koch guidelines are not
sufficient because outcome of interest
may not be caused by a single agent,
i.e. dependent variable may be
affected by many independent
variables (multi-causal effects)

 Causal agent (independent variable)
may also cause many effects (many
outcomes) or many dependent
variables. There are;
 (1) Multiple aetiological factors/
agents of effects (e.g. disease,
genes, secretions, hormones) (multi-
causality in addition to mono-
causality for one causal factor)

 (2) Multiple effects of single causes
 (3) Causes may also be affected by
other factors (quantitative causal
factors or determinants)

2. Interpretation in multi-
causal factors and multi-
effects
 Guidelines for multi-causal agents
and multi-effect outcomes & many
outcomes are analyzed by;

 Methods of agreement
 Methods of difference
 Methods of concomitant
variation
 Methods of analogy
 Methods of residue

Method of agreement
 An outcome (an effect) occurs
under a variety of circumstances
but there is a common factor. This
factor is the cause of the outcome

Method of difference
 If circumstances where an effect
(dependent variable) occurs are similar to
those circumstances where causal factor (
independent variable) does not occur,
except where there is one factor
difference, this factor or its absence is the
cause of outcome (effect of interest).
 Method of difference is basis for keeping
all factors constant except for one factor
in experimental research design

Methods of concomitant
variation
 If independent factor (Causal factor) and
effect (outcome, response, dependent
variable) have a dose dependant
relationship factor may be cause of
response
 Independent factor (variable) or agent
whose strength or frequency varies
directly with occurrence of outcome
convinces a causal- effect relationship

Method of analogy
 If the distribution of an
outcome is sufficiently similar to
another factor, it may be that
there is a causal-effect
relationship

Method of residue
 If the factor only explains X%
of the outcome other factors
must be identified to explain
the remainder or (100-X%)

to increase the experimental
precision

 Control variables that are not of
interest
 Increase the sample size
 Repeat experiment in another
location
 Sampling without bias

Guidelines to concept on inferences
in causation (developing causal
inferences)

 Incidence of outcome must be higher
in the material animal, cell or other
system which has been exposed to
the putative causal agent than in non
exposed systems

 Exposure should be more common in
cases where outcome occurs/has
occurred than in those where outcome
(effect) has not occurred
 Exposure to putative cause must precede
outcome
 There should be a spectrum of
measurable responses on dependent
variable

 Elimination of putative cause results
in elimination of the outcome
 Preventing or modifying the
independent variable decreases or
eliminates the outcome or effect
 The outcome must be reproducible
experimentally

 The sequence for the researcher
in assessing causation is;
 to demonstrate that association

exists
 To assess likelihood that causal

association exists
 To elaborate nature of causal

association

3. Statistical association
 For factor to be causally associated
with outcome, rate of outcome in
exposed members must be different
from not exposed members.
 To evaluate probability that sampling
error may have accounted for
observations a statistical test is
required

A 2x2 table displaying the relationship
between two dichotomous variables, one the
factor, the other for the outcome

Member Outcome Outcome Total
status present absent
Exposed a b a+b

Not c d c+d
exposed
Total a+c a+b+c+d

 Proportional or rate of interest
 Exposed to factor in population p(F+)
 Outcome (effect) is positive in population
(E+)
 Affected and exposed to factor p(F+ and
D+)
 Affected in exposed members p(D+/F+)
 Affected in non exposed members p(D+/F-)
 Exposed to factor in effected members
p(F+/D+)
 Exposed to factor in non affected members
p(F+/D-)

 Working out rates from statistics
(representing parameters)
 F+ = (a + b)/n
 D+ = (a + c)/n
 F+ and D+ = (a/n)
 D+/F+ = a/(a + b)
 D+/F- = c/(c+d)
 F+/D+ = a/(a+c)
 F+/D- = b/(b+d)
 n=a+b+c+d

 A statistical test would be the chi-
square X2

 X2 = [Іaxd)-(bxc)l- O.5n] x n
(a+b) x (c+d) x (a+c) x
(b+d)

 In a 2x2 table all statistics have one
degree of freedom, the critical value
for significance at 5% level is 3.84

4. Application of statistics in
interpreting data
 Statistical difference is a
function of;
 The magnitude of difference

 The variability of difference

 The sample size

 Strength of association between causal
factor and effect (outcome, response) is
called Relative Risk, Risk ratio, incidence
ratio, prevalence ratio.
 Relative Risk is calculated as ratio
between rate of response in members
exposed to causal factor and rate of
response in members not exposed to
causal factor.
 If there is no association between cause
and outcome RR is 1.

 The greater the departure of RR from
1 larger or smaller, the stronger the
association
 Attributable fraction (AF) is the
proportion of response (outcome) in
the factor exposed group that is due
to the factor

 Measures of association for
independent proportions in 2x2
tables.

 RR = [a/(a+b)]/[c/(c+d)]

 Odds ratio = ad/bc

 Effect of factor in exposed members here
is some effect in factor negative
group/members, not all outcome in factor
exposed members is due the factor
 In calculating attributable rate assumption
is that other factors which lead to some
outcome in factor negative group operate
with same frequency and intensity in factor
positive group
 This absolute difference is called
Attributable Rate (AR)

 AR is calculated by subtracting the rate of
response (outcome) in the un-exposed
group from the rate in the exposed group
 AR is the rate of outcome response in the
group due to the exposure
 The larger the AR the greater the effect of
the factor in the exposed group
 AR = [a/(a+b)]-[c/(c+d)]
 AF = AR/[a/(a+b)] or = (RR-I)/RR

Causal Inferences in
Observational Studies
 In interpreting ARs and AFs assumption is
that there is a cause and effect
relationship
 However statistical associations do not in
themselves represent a causal association
 There are important precautions needed
to eliminate this problem

Solutions for statistical
deficiencies
 Select sampling method better for
measuring associations, such as cohorts
 Refine independent and dependent
variables by using cause specific rather
than crude factors, this strengthens
associations
 Seek other variables that produce or
explain associations or lack of
associations (eliminate confounding)

Confounding variables
 A confounding variable is one
associated with the independent
variable and the dependent variable
 Confounding variables may be
determinants of an outcome
 For example a disease e.g. mastitis may
be associated with age, castration is
associated with age, therefore the
effect of age is to be taken into account

Control of confounding
variables
 Exclusion (restricted sampling), select
units with only one level of confounding
or without confounding
 Matching, Equalize the frequency of
confounding variable in two groups
being compared, e.g. cohorts
 Analysis, stratify data and display in a
series of 2x2 tables, one table for each
level of confounding

 A method that summarizes
associations in multiple tables is called
Mantel-Haenszel technique, here the
Odds ratio (OR) is the measure of
association.
 OR = (Σad/n)/( Σbc/n)
 These methods are not mutually exclusive,
all may be applied at the same time

Criteria of judging causal
inference
1. Time sequence, the factor to cause an
outcome must precede the outcome,
well designed cohort studies give best
experiments
2. Strength of association, is measured
RR or Odds ratio. The greater the .
departure from 1, the strong the
association

1. Dose-Response relationship, there exists
an association if increasing amount of
causal factor produces higher and higher
level of outcome
2. Coherence, an association is more likely
to be causal if it is sensible
3. Consistence, an association is likely to
exist if it is supported by similar findings
under different conditions

 4. Specificity of association, a single
causal factor (if crude) may produce a
number of effects (outcomes). Refining
the causal variable produces a better
outcome thus strengthens the
association

Elaborating Causal
Mechanisms
 If an association of two
variables is causal the nature of
the association may be
determined by
 Indirect and direct causes the causal
association is direct when there is no
intervening variable between the factor
and the outcome.

 Both the independent and the dependent
variables are measured at same level of
organization (individual, cell, tree, fruit,
group of trees, farm). All other causes are
indirect.
 Necessary and sufficient causes; another
dimension of classifying determinants. A
necessary cause is one without which the
outcome cannot occur. Sufficient cause is
one that always produces the outcome

 Path model causation; Path model
provide other ways for conceptualizing,
analyzing and demonstrating the causal
effects of multiple factors. Variables are
ordered and causal effects flow along
arrows and paths
 Statistical methods are applied to
estimate the relative magnitude path
coefficients of each path

 Displaying effects of multiple factors;
Produce ven diagram, important when the
risk values increase steadily in number of
putative causal factors. Calculate RR or OR
of outcome for each combination of
independent variables relative to the lower
risks (group member outcome).

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