The document discusses research methodology and design. It defines key concepts related to research design including dependent and independent variables, extraneous variables, control, experimental and control groups, and treatments. It also discusses research process, sampling techniques including probability and non-probability sampling, and determining sample size for quantitative and experimental studies using statistical formulas.

1. RESEARCH METHODOLOGY
PART II
DR. ANWAR HASAN SIDDIQUI,
senior resident,
dep't of physiology,
jnmc, amu, aligarh

2. Research Process
I. Define Research
Problem
Review concepts
and theories
III. Formulate
hypotheses
IV. Design
research(including
sample design)
V. Collect data
(Execution)
Review previous
research finding
VI. Analyse data
(Test hypotheses)
VII. Interpret
and report
II. Review the literature

3. Research Design
“A research design is the arrangement of conditions for
collection and analysis of data in a manner that aims to
combine relevance to the research purpose with economy in
procedure.”
Research Methods in Social Sciences, 1962, p. 50
• It constitutes he blueprint for the
collection, measurement and analysis of
data.
• An outline of what the researcher will
do from writing the hypothesis and its
operational implications to the final
analysis of data.

4. Research Design
What will be
the sample
design?
What periods
of time will
the study
include?
What
techniques of
data
collection will
be used?
How will the
data be
analysed?
What is the
study about?
Why is the
study being
made?
Where will the
study be
carried out?
Where can the
required data
be found?

5. Research Design
Important concepts relating to research design:
1. Dependent and independent variables:
• A concept which can take on different quantitative values is called a
variable. As such the concepts like weight, height are all examples of
variables.
• Phenomena which can take on quantitatively different values even in
decimal points are called ‘continuous variables’.
• If it can only be expressed in integer values, they are non-continuous
variables or in statistical language ‘discrete variables’.
• If one variable depends upon or is a consequence of the other
variable, it is termed as a dependent variable, and the variable that is
antecedent to the dependent variable is termed as an independent
variable.
• For instance, if we say that height depends upon age, then height is
a dependent variable and age is an independent variable.

6. Research Design
2. Extraneous variable:
• Independent variables that are not related to the
purpose of the study, but may affect the dependent
variable are termed as extraneous variables or
confounding variables.
• Whatever effect is noticed on dependent variable as a
result of extraneous variable(s) is technically described
as an ‘experimental error’.
• A study must always be so designed that the effect
upon the dependent variable is attributed entirely to the
independent variable(s), and not to some extraneous
variable or variables.

7. Research Design
3. Control:
• One important characteristic of a good research design is to
minimise the influence or effect of extraneous variable(s).
• The technical term ‘control’ is used when we design the study
minimising the effects of extraneous independent variables.
• In experimental researches, the term ‘control’ is used to refer
to restrain experimental conditions.
4. Experimental and control groups:
• In an experimental hypothesis-testing research when a group
is exposed to usual conditions, it is termed a ‘control group’,
but when the group is exposed to some novel or special
condition, it is termed an ‘experimental group’
5. Treatments:
• The different conditions under which experimental and control
groups are put are usually referred to as ‘treatments’.

8. Statistics in Research
• Mean:
– Mean, also known as arithmetic average, is the most
common measure of central tendency
– Defined as the value which we get by dividing the total of
the values of various given items in a series by the total
number of items.
– where X = The symbol we use for mean (pronounced as X
bar)
∑ = Symbol for summation
Xi = Value of the ith item X, i = 1, 2, …, n
n = total number of items

9. Statistics in Research
• Median:
– Median is the value of the middle item of series when
it is arranged in ascending or descending order of
magnitude. It divides the series into two halves; in one
half all items are less than median, whereas in the
other half all items have values higher than median.
– If the values of the items arranged in the ascending
order are: 60, 74, 80, 90, 95, 100,110 then the value of
the 4th item viz., 90 is the value of median.

10. Statistics in Research
• Mode:
– Mode is the most commonly or frequently occurring value
in a series.
– The mode in a distribution is that item around which there
is maximum concentration.
– In general, mode is the size of the item which has the
maximum frequency.
– Mode is particularly useful in the study of popular sizes.
– For example, a manufacturer of shoes is usually interested
in finding out the size most in demand so that he may
manufacture a larger quantity of that size.

11. Statistics in Research
• Standard deviation:
– is most widely used measure of dispersion of a series
– Commonly denoted by the symbol ‘ σ ’ (pronounced
as sigma).
– Standard deviation is defined as the square-root of
the average of squares of deviations, when such
deviations for the values of individual items in a series
are obtained from the arithmetic average. It is worked
out as under:

12. Statistics in Research
• Example to calculate SD.
– The pulse rate of 10 student in a class are as follows
80,90,96,80,94,72,84,92,82,90.calculate SD?
Mean = X = 860/10 =86
∑(Xi – X) = 520
S.D= √
S.D= √(520/10)= 7.21
Xi Xi - X (Xi – X)2
80 80-86= -6 36
90 90-86= 4 16
96 96-86= 10 100
80 80-86= -6 36
94 94-86= 8 64
72 72-86=-14 196
84 84-86= -2 4
92 92-86=6 36
82 82-86=-4 16
90 90-86= 4 16
Total = 860 520

13. Sampling
• Population (Universe)- An aggregate of units of
observation either animate or inanimate about which
certain information is required.
• Eg. When recording the pulse rate of boys in the college , all
boys in the college constitute the population or universe.
• Sample – It’s a portion or part of the universe
selected for the study in such a manner that the
inference drawn can be applied to the whole
universe.

14. Sampling Techniques
• The methods of sampling can be divided on the
basis of the element of probability associated
with the sampling technique.
• Probability means chances available to members
of the population for getting selected in the
sample. Accordingly, the methods of sampling
are classified into two broad types:
 Probability Sampling
 Non Probability Sampling

15. Sampling Techniques
Simple Random Sampling Accidental Sampling
Systematic Sampling Convenience Sampling
Stratified Sampling Judgment Sampling
Cluster Sampling Purposive Sampling
Quota Sampling

16. Sampling Techniques
• Non Probability Methods
– The probability of any particular member being chosen
for the sample is unknown.
– In case of non-probability sampling, units in the
population do not have an equal chance or opportunity of
being selected in the sample. The non-probability method
believes in selecting the sample by choice and not by
chance.
– This is an unscientific and less accurate method of
sampling, hence it is only occasionally used in research
activities

17. Sampling Techniques
• Probability Sampling Method
– Probability Sampling is also known as Random
Sampling
– Probability means chance
– Therefore element of the population has known
chance or opportunity of being selected in the sample
– It is the only systematic and objective method of
sampling that provides equal chance to every element
of the population in getting selected in the sample
– The results of probability sampling more accurate and
reliable
– It helps in the formulation of a true representative
sample by eliminating human biases

18. Sampling Techniques
• Simple Random Sampling:
– This sampling procedure gives every unit in the universe
an equal chance or opportunity of being selected.
– This method of sampling can be applied when the
parameter to be estimated is homogeneously
distributed in the population
– A crude method of which is by drawing a lot.
– A good method of simple random sampling involves the
use of published tables called tables of Random
Numbers.
– Now a days computer generated random number can
also be used for the selection

19. Sampling Techniques
• Example : To select a random sample of 25 student
from a class of 75 students.
– In this case all the 75 student in the class are arranged in
some order say alphabetical order of their names or by
their roll numbers.
– From the random number table any arbitrary row or
column is selected and 25 numbers ranging from 1-75
are chosen.
– The students corresponding to the chosen number
constitute a sample.

20. Sampling Techniques
A random number table

21. Sampling Techniques
• Systematic sampling
– In this type of sampling the first unit of the sample is
selected at random and the subsequent unit are selected
in a systematic way.
Example:
A sample of 50 students are required from 600 students of a
school.
• 1st population is divided by the required sample
• 600/50= 12
• Now a random number between 1-12 is obtained (suppose
4)
• Then our first sample will be student number 4
• Rest will be obtained by adding 12 to each number
• 4, 4+12(16), 16+12(28), 40+12 (52) and so on……

22. Sampling Techniques
• Systematic sampling is useful for studying hospital cases.
• If it is proposed to study a sample of 20 cases of a disease
and if the mean annual admission for that disease are 100
then every fifth case who seeks admission to the hospital is
included in the sample
• It is called as quasi-random sampling.
• It is called quasi because it is in between probability and
non-probability sampling.

23. Sampling Techniques
• Stratified Sampling:
– If a population from which a sample is to be drawn does
not constitute a homogeneous group, stratified sampling
technique is generally applied.
– Under stratified sampling the population is divided into
several sub-populations that are individually more
homogeneous than the total population (the different sub-
populations are called ‘strata’)
– We select items from each stratum to constitute a sample.
– Example: If it is known that the prevalence of a certain
disease is different in different age group then to estimate
the prevalence rate of the disease stratified sample is taken
from each of the age group of the population

24. Sampling Techniques
• Cluster Sampling:
– It is a sampling technique where the entire population is
divided into groups, or clusters, and a random sample
of these clusters are selected.
– All observations in the selected clusters are included in
the sample
– Example: Suppose researcher wants to study the
learning habits of the college students from Mumbai.
He may select the sample as:
• First prepare a list of all colleges in Mumbai city
• Then, select a sample of colleges on random basis. Suppose
there are 200 colleges in Mumbai, then he may select 20
colleges by random method.
• 3)From the 20 sampled colleges, prepare a list of all students.
From these lists select the required number of say 1000
students on random basis

25. Determination of Sample size
• When conducting investigation to obtain information
on quantitative data, the sample size is calculated by
the formula:
n= (tα
2 × σ2)/e2
where n =desired sample size
σ = standard deviation of the obserbvation
e= permissable error in the estimation of mean
tα = is the value of ‘t’ statistics at α level of significance

26. Determination of Sample size
• A ‘t’ table Example: In a community survey to estimate the
haemoglobin level of antenatal mothers, it is
assumed from pilot studies, that the mean Hb%
level is about 12 gm% with a standard deviation
of 1.5 gm% then the sample size required to
estimate the Hb.level with a permissible error of
0.5gm% is???
Answer:
Standard Deviation σ = 1.5 gm
Permissible error e= o.5gm
tα is taken as 1.96 as it is conventional to
use 5% significance level
n= (tα
2 × σ2)/e2 ={(1.96)2 × (1.5)2}/(0.5)2
= 36

27. Determination of Sample size
• Sample size in inferential or experimental study is
given by:
Where
N= number of patients required in each group
K = constant which is a function of α and β (see Table)
µ1 = mean of first population
µ2= mean of second population

28. Determination of Sample size
• A clinical trial tests the preventive effect upon neonatal hypocalcemia of
giving Supplement A to pregnant women. Women are randomised and
given either placebo or Supplement A.
– Measure: serum calcium level of baby one week postnatally
– Analysis: Comparisons of difference between two groups of babies
using an independent samples t-test at 5% significance (α = 0.05)
– Serum calcium in babies of untreated women 9.0 mg/100 ml, standard
deviation (s) 1.8mg/100ml
– Study should detect clinically relevant increase in serum calcium of 0.5
mg/100ml, 80 per cent of the time ( β= 0.2)
• Answer:
• In summary: m = Mean serum calcium level = 9.0 mg/100ml
s Standard Deviation = 1.8mg/100ml
d = difference in means m1 - m2 = 0.5mg/100ml
a = 0.05
b = 0.2
K= 7.9

29. Determination of Sample size
• The number of patients required in each group is
given by
• N= 2 × 7.9 × (1.8/0.5)2 = 205

30. To be continued……………………