Sampling
Simple random sampling is ideal for?
A  Vaccinated people  
B  Heterogenous population  
C  Homogenous population  
D 
All of the above 
Simple random sampling is ideal for?
A  Vaccinated people  
B  Heterogenous population  
C  Homogenous population  
D 
All of the above 
Homogenous population REF: Park 20^{th} edition page 752
Indirect repeat from December 2008
SAMPLING METHODS:

Simple random sampling 
Systematic sampling 
Stratified sampling 
Cluster sampling (AKA? Block sampling) 
Population 
Homogenous 
Homogenous 
Heterogenous 
Heterogenous 
Use of random number table 
Required 
Not required 
Required 
Required 
Method 
Each item in the 
Every nth element 
Divide population 
Divide population into 

population has the 
from the list is 
into homogenous 
groups called clusters 

same probability of 
selected as the 
subgroups. Random 
(heterogenous subgroups 

being selected as 
sample 
or systematic samples 
matching the population). 

part of the sample 

are then taken from 
A random sample is then 

as any other item. 

each subgroup. 
taken from within one or more selected clusters. 
Advantages 
Simple, Easy to analyse 
more precise than simple random 
More precision because 
Reduced field costs, Applicable where no 


sampling as more 
heterogeneous 
complete list of units is 


evenly spread over population 
population is split into homogeneous strata 
available 
Disadvan tages 
if population heterogeneous estimates have 
if list has periodic arrangement, sample collected may 
Complicated, Problems if strata not clearly defined 
Complicated, Clusters may not be representative of whole 

large variance 
not be an accurate representation of entire population 

population 
A  Simple random sampling  
B  Stratified random sampling  
C  Systematic random sampling  
D  All if the above 
A  Simple random sampling  
B  Stratified random sampling  
C  Systematic random sampling  
D  All if the above 
Stratified random sampling
Random/probability sample : sample in which researcher can specify the population known factor of any one element (same)in the population being included.(being picking link). So every person has an equal chance of selection in random sampling.
– Probability samples used in inferential statistics
– Non probabilities samples used in only descriptive statistics Type of probability sample (Random sample)
1. Simple random sample – All person have equal right to be selected. Simplest type of random probability.
2. Stratified random sample – in it the population is 1st divided into relatively internally homogenous group of strata from which random sampling are drawn.
Example:Village Population
3. Systematic sample – it involve selecting element in a systematic way.
Example: Every 10th baby born in area. Every 3rd person admitted to hospital.
True about cluster sampling are all except
A 
sample size is same as that of simple random sampling 

B 
it is a two stage sampling 

C 
it is cheaper than other methods of sampling 

D 
it has the disadvantage of higher sampling error 
True about cluster sampling are all except
A 
sample size is same as that of simple random sampling 

B 
it is a two stage sampling 

C 
it is cheaper than other methods of sampling 

D 
it has the disadvantage of higher sampling error 
Sample size is same as that of simple random sampling [Ref: B K Mahajan 6/e p100; Park 20^{th}/e p 752,753 (19/e p702)] Repeat from Nov 08
Sampling
 When a large proportion of individuals or items or units have to be studied, we take a sample. It is easier and more economical to study the sample than the whole population or universe.
 A sample is a subset of the population the part that is actually being observed or studied. It is important to ensure that the group of people or items include in the sample are representative of the whole population to be studied. Various sampling methods are used to select the sample groups.
Sanzpling methods
 Simple random sampling:
 This is done by assigning a number to each unit in the population to be sampled and then using lottery method or a table of random numbers to select units to be included in the sample.
 The principle here is that every unit of the population has an equal chance of being selected.
 This method is applicable when the population is small, homogenous and readily available such as patients coming to hospital or lying in wards.
 Systematic sampling:
 In this method the first unit of the sample is selected at random and the subsequent units are selected in a systematic way i.e every fifth or tenth unit at regular intervals.
 For example in a malaria survey to take a 5 percent sample, all the houses are numbered first. Then the first house is selected at random between 1 and 5. then every 5^{(h }house is selected from that point on.
 This method is popularly used in those cases when a complete list of population from which samples is to be drawn, is available. It is more often applied to field studies when the population is large, scattered and not homogenous.
 Stratified sampling:
 This method is used when the population is heterogenous with regards to characteristic under study. The population under study is first divided into homogenous groups or classes called strata and then the sample is drawn from each stratum at random in proportion to its size.
 This method of sampling gives representation to all strata of society or population such as selecting sample from defined areas, classes, religions, ages or sexes.
 For example a population to be sampled consists of people of different religions like Hindus, Muslims, Sikhs, Christians etc. In stratified sampling the population is first divided into religious groups and then samples are drawn from each group in proportion to its size.
 This method is particularly useful where one is interested in analyzing the data by certain characteristic of the population, viz Hindus, Muslims, Christians, agegroups etc.
 Cluster sampling:
 A cluster is a randomly selected group. This method is used when units of population are natural groups or clusters such as villages, wards, blocks, slums, schools etc.
 In this technique clusters are chosen at random from the entire population, and then these clusters are sampled.
 Each cluster should be a small scale representation of the total population. In singlestage cluster sampling, all the elements from each of the selected clusters are used. In twostage cluster sampling, a random sampling technique is applied to the elements from each of the selected clusters.
 The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so analysis is done on a population of clusters (at least in the first stage). In stratified sampling, the analysis is done on elements within strata. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are studied. The main objective of cluster sampling is to reduce costs by increasing sampling efficiency. This contrasts with stratified sampling where the main objective is to increase precision.
 Cluster sampling is most often used to evaluate vaccination coverage in Expanded Programme of Immunization (EPI) and Universal Immunization Programme (U1P), where only 210 children, taking 7 from each of the 30 clusters are examined.
 Cluster sampling gives a high standard error but the data collection in this method is simpler and involves less time and cost than in other sampling techniques.
 Multistage sampling:
 In this method sampling procedure is carried out in several stages using random sampling techniques.
 Multistage sampling is used when the population units arranged in hierarchal groups. For examples farmers in India may be grouped into various states, then into districts, then into talukas, further into villages within the talukas and then farmers within the villages.
 Multistage sampling of this population of farmers would consist of first randomly selecting districts within each state, then selecting a subset of talukas within the selected districts, then selecting a subset of villages and finally selecting a sample of farmers within the villages.
 Multiphase sampling:
 In this method, part of information is collected from the whole sample and part from the subsample. For example in a tuberculosis survey, in the first phase, physical examination or Mantoux test may be done in all the cases; in the second phase Xray of the chest may be done in Mantoux positive cases and in those with clinical symptoms: finally the sputum may be examined in Xray positive cases. Thus number of samples in the second and third phases would become successively smaller and smaller. Survey by multiphase sampling would involve less cost and labour and would be more purposeful.
True about cluster sampling are all except
A 
sample size is same as that of simple random sampling 

B 
it is a two stage sampling 

C 
it is cheaper than other methods of sampling 

D 
it has the disadvantage of higher sampling error 
True about cluster sampling are all except
A 
sample size is same as that of simple random sampling 

B 
it is a two stage sampling 

C 
it is cheaper than other methods of sampling 

D 
it has the disadvantage of higher sampling error 
Sample size is same as that of simple random sampling I Rer B K Mahajan 6/e p100; Park 20^{ih}/e p 752,753 (19/e p702)] Repeat from May 11
Children surveyed in cluster sampling for coverage of national immunization programme is:
A  30 cluster of 5 children  
B  30 cluster of 7 children  
C  30 cluster of 10 children  
D  30 cluster of 15 children 
Children surveyed in cluster sampling for coverage of national immunization programme is:
A  30 cluster of 5 children  
B  30 cluster of 7 children  
C  30 cluster of 10 children  
D  30 cluster of 15 children 
30 cluster of 7 children
Evaluation of an adult demonstrates chronic headaches accompanied by chronic mild nuchal rigidity. Cerebrospinal fluid sampling demonstrates a chronic inflammatory infiltrate with lymphocytes, plasma cells, macrophages, and fibroblasts. Which of the following is the most likely etiologic agent?
A 
Herpes virus 

B 
Mumps virus 

C 
Mycobacterium tuberculosis 

D 
Neisseria meningitidis 
Evaluation of an adult demonstrates chronic headaches accompanied by chronic mild nuchal rigidity. Cerebrospinal fluid sampling demonstrates a chronic inflammatory infiltrate with lymphocytes, plasma cells, macrophages, and fibroblasts. Which of the following is the most likely etiologic agent?
A 
Herpes virus 

B 
Mumps virus 

C 
Mycobacterium tuberculosis 

D 
Neisseria meningitidis 
The granulomas that are characteristic findings in other tissues may or may not be present in the meningeal tissue, and are usually not recognizable in CSF. Tubercular meningitis is now uncommon in this country.
Which of the following is true regarding sampling?
A 
In simple random sampling the population is divided into strata 

B 
Snowball sampling is used for hidden population 

C 
More sample in systemic random sampling 

D 
Cluster sampling is less cost effective 
Which of the following is true regarding sampling?
A 
In simple random sampling the population is divided into strata 

B 
Snowball sampling is used for hidden population 

C 
More sample in systemic random sampling 

D 
Cluster sampling is less cost effective 
Cluster sampling is cost effective.
Ref: Park, 20th Edition, Page 752
The method in which the sample is taken from each predefined strata of society is called?
A 
Simple random sampling 

B 
Systemic sampling 

C 
Stratified sampling 

D 
Multistaged sampling 
The method in which the sample is taken from each predefined strata of society is called?
A 
Simple random sampling 

B 
Systemic sampling 

C 
Stratified sampling 

D 
Multistaged sampling 
In simple random sampling the samples are taken completely at random from a given population.
In systemic sampling samples are taken according to a predetermined periodicity.
In multistaged sampling, the researcher randomly selects elements from each cluster instead of using all the elements contained in the selected clusters.
Systematic observation and recording of activities of individuals carried out at predetermined or random intervals is known as:
A 
Input output analysis 

B 
Work sampling 

C 
Network analysis 

D 
None of the above 
Systematic observation and recording of activities of individuals carried out at predetermined or random intervals is known as:
A 
Input output analysis 

B 
Work sampling 

C 
Network analysis 

D 
None of the above 
A village is divided into five relevant subgroups for the purpose of a survey. Individuals from each subgroup are then selected randomly. This type of sampling is termed as:
A 
Simple Random sampling 

B 
Stratified sampling 

C 
Cluster Sampling 

D 
Systematic Sampling 
A village is divided into five relevant subgroups for the purpose of a survey. Individuals from each subgroup are then selected randomly. This type of sampling is termed as:
A 
Simple Random sampling 

B 
Stratified sampling 

C 
Cluster Sampling 

D 
Systematic Sampling 
Stratified samples are used when fixed numbers are needed from particular sections or strata of the population in order to achieve balance across certain important factors.
The scenario as seen in this question, shows a study in which a random sample of equal number of individuals are drawn from each of five relevant subgroups of a village, to provide a set of estimates with equal precision for each subgroup.
Ref: Preventive and Social Medicine, By K.Park, 19th edition, Page 702; Oxford Handbook of Medical Statistics, By Janet L. Peacock, Philip J. Peacock, Oxford University Press 2011, Page 55.
A region is divided into 50 villages for the purpose of a survey. 10 villages are then selected randomly for the purpose of a study. This type of sampling is termed as:
A 
Simple Random sampling 

B 
Stratified sampling 

C 
Cluster Sampling 

D 
Systematic Sampling 
A region is divided into 50 villages for the purpose of a survey. 10 villages are then selected randomly for the purpose of a study. This type of sampling is termed as:
A 
Simple Random sampling 

B 
Stratified sampling 

C 
Cluster Sampling 

D 
Systematic Sampling 
Cluster samples may be chosen where individuals fall naturally into groups or clusters.
In the above scenario, a sample of the clusters is chosen at random, and then a random sample of units is chosen from within this selection of clusters.
The villages are naturally formed groups or clusters in the region.
Ref: Encyclopaedic Companion to Medical Statistics, Editors Brian S. Everitt and Christopher R. Palmer, Second Edition; Oxford Handbook of Medical Statistics, By Janet L. Peacock, Philip J. Peacock, Oxford University Press 2011, Page 55.
All of the following holds true about cluster sampling, except:
A 
Is a Rapid and simple method 

B 
Samples are similar to those in Simple Random Sampling 

C 
It is a type of probability sample 

D 
The sample size may vary according to study design 
All of the following holds true about cluster sampling, except:
A 
Is a Rapid and simple method 

B 
Samples are similar to those in Simple Random Sampling 

C 
It is a type of probability sample 

D 
The sample size may vary according to study design 
Cluster sampling is entirely different from Random sampling.
The following are differences between random sampling and cluster sampling.
Ref: Park’s Texbook of Preventive and Social Medicine 19th Edition; Pages 702; High Yield Biostatistics by Anthony N. Glaser 2nd Edition, Pages 2 3; Methods in Biostatistics By Mahajan6th Edition, Pages 96 – 100; Sampling of Populations: Methods and Applications By Paul S. Levy, Stanley Lemeshow 4th Edition; Pages 269 – 272; 223 – 229.
Which of the following statements about ‘Simple Random Sampling’ is true?
A 
Sampling is based on similar characteristics 

B 
Suitable for large heterogeneous population 

C 
Complete list of items within the sampling frame is not required 

D 
Every element in the population has an equal probability of being included 
Which of the following statements about ‘Simple Random Sampling’ is true?
A 
Sampling is based on similar characteristics 

B 
Suitable for large heterogeneous population 

C 
Complete list of items within the sampling frame is not required 

D 
Every element in the population has an equal probability of being included 
Simple random sampling is done by assigning a number to each of the units in the sampling frame.
A table of random numbers is used. With this procedure, each unit has an equal chance of being drawn in the sample.
It provides the greatest number of possible samples.
Sampling error is classified as:
A 
Alpha error 

B 
Beta error 

C 
Gamma error 

D 
Alpha error and Beta error 
Sampling error is classified as:
A 
Alpha error 

B 
Beta error 

C 
Gamma error 

D 
Alpha error and Beta error 
Sampling error is a type of variation between one sample to another.
Due chance and concern either incorrect acceptance or rejection of null hypothesis.
Sampling errors arises because it is based on a part and not on the whole.
There are two types of sampling errors Type I or alpha error and type II or beta error.
The question is not specific, as it does not mention sampling error type I or type II we choose both alpha and delta.
Ref: Biostatistics By I.Saha and B. Paul, Page 74 ; Park’s Textbook of Preventive and Social Medicine By K.Park, 18th Edition, Page 648
A sample is a subset of the population, selected so as to be representative of the larger population. All of the following are TRUE about cluster sampling, EXCEPT:
A 
Sample size is same as that of simple random sampling 

B 
It is a two stage sampling 

C 
It is cheaper than other methods of sampling 

D 
It has the disadvantage of higher sampling error 
A sample is a subset of the population, selected so as to be representative of the larger population. All of the following are TRUE about cluster sampling, EXCEPT:
A 
Sample size is same as that of simple random sampling 

B 
It is a two stage sampling 

C 
It is cheaper than other methods of sampling 

D 
It has the disadvantage of higher sampling error 
A cluster random sample results from a twostage process in which the population is divided into clusters and a subset of the clusters is randomly selected.
Cluster sampling is somewhat less efficient than the other sampling methods because it requires a larger sample size.
For Randomized Control Trial (RCT) to assess dating in adolescent, a study was done by selecting random schools, then random classes, then random sections and then random students. This is an example of:
A 
Stratified sampling 

B 
Simple random sampling 

C 
Cluster sampling 

D 
Multistage sampling 
For Randomized Control Trial (RCT) to assess dating in adolescent, a study was done by selecting random schools, then random classes, then random sections and then random students. This is an example of:
A 
Stratified sampling 

B 
Simple random sampling 

C 
Cluster sampling 

D 
Multistage sampling 
Multistage sampling technique is carried out in several stages by using random sampling technique. Multistage sampling is used when the population units are arranged in hierarchal groups (sampling school, the section and student).
Sampling method: in statistics, the sampling methods used are of two types – Random,/ probability and non random / non probability sampling.
II. Non – Probability sampling: The difference between non probability and probability sampling is that non probability sampling does not involve random selection and sampling does. Does that mean that non probability samples aren’t representative of the population not necessarily. But it does mean that non probability samples cannot depend upon the rationale of probability theory. At least with a probabilistic sample, we know the odds or probability that we have represented the population most sampling methods are purposive in nature because we usually approach the sampling problem with a specific plan in mind. The most important distinctions among these types of sampling methods are the ones between the different types of purposive sampling approaches.
a. Convenience Sampling: One of the most common methods of sampling goes under the various titles listed here. I would include in this category the traditional “man on the street” (of course, now it’s probably the “person on the street”) interviews conducted frequently by television news programs to get a quick (although non representative) reading of public opinion. I would also argue that the typical use of college students in much psychological research is primarily a matter of convenience. b. Quota Sampling: In quota sampling, you select people nonrandomly according to some fixed quota. There are two types of quota sampling; proportional and non proportional. In proportional quota sampling you want to represent the major characteristics of the population by sampling a proportional amount of each. For instance, if you know the population has 40% women and 60% men, and that you want a total sample size of 100, you will continue sampling until you get those percentages and then you will stop. So if you’ve already got the 40 women for your sample, but not the sixty men, you will continue to sample men but even if legitimate women respondents come along, you will not sample them because you have already “met your quota” c. Snowball Sampling: In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Although this method would hardly lead to representative samples, there are times when it may be the best method available. Snowball sampling is especially useful; when you are trying to reach populations that are inaccessible or hard to find. For instance, if you are studying the homeless, you are not likely to be able to find good list of homeless people within a specific geographical area. However, if you go to that area and identify one or two, you may find that they know very well who the other homeless people in their vicinity are and how you can find them d. Judgment Sampling: The basis of selection of sample is based on the judgment or discretion of the investigator.

Ref: Basic Statistics and Epidemiology A practical guide by Antony Stewart p7.
Estimation of percentage of children immunized in community as per WHO is to be done by:
A 
Multistage random sampling 

B 
Cluster random 

C 
Systematic random sampling 

D 
All of the above 
Estimation of percentage of children immunized in community as per WHO is to be done by:
A 
Multistage random sampling 

B 
Cluster random 

C 
Systematic random sampling 

D 
All of the above 
Which of the following is true regarding sampling?
A 
In simple random sampling the population is divided into strata 

B 
Snowball sampling is used for hidden population 

C 
Snowball sampling is used for hidden population More sample in systemic random sampling 

D 
Cluster sampling is less cost effective 
Which of the following is true regarding sampling?
A 
In simple random sampling the population is divided into strata 

B 
Snowball sampling is used for hidden population 

C 
Snowball sampling is used for hidden population More sample in systemic random sampling 

D 
Cluster sampling is less cost effective 
Cluster sampling is cost effective.
Ref: Park, 20th Edition, Page 752
In the WHO recommended EPI Cluster sampling for assessing primary immunization coverage, the age group of children to be surveyed is
A 
012 months 

B 
612 months 

C 
912 months 

D 
1223 months 
In the WHO recommended EPI Cluster sampling for assessing primary immunization coverage, the age group of children to be surveyed is
A 
012 months 

B 
612 months 

C 
912 months 

D 
1223 months 
Ans. is ‘d’ i.e., 1223 months
o In the Expanded programme on Immunization (EPI) cluster technique, a simplified cluster sampling method is used.
It is based on ranodm selection of 210 children who are 1223 months of age.
These patients are selected in 30 clusters of 7 children each to estimate immunization coverage levels.
If sample size is bigger in random sampling , which of the following is TRUE –
A 
It approaches maximum samples 

B 
Reduces non sampling errors 

C 
Increases the precision of the result 

D 
Decreases standard error 
If sample size is bigger in random sampling , which of the following is TRUE –
A 
It approaches maximum samples 

B 
Reduces non sampling errors 

C 
Increases the precision of the result 

D 
Decreases standard error 
Ans. is ‘d’ i.e., Decreased standard error
Also Know:
o Greater the standard deviation (a), greater will be the standard error (SE), especially in small samples o SE can be minimized by reducing SD: By taking a large sample
o SE is measure of variability of sample summaries: SE_{.} is the SD of sample means
 Uses of standard error of mean (SE_{.}) in large samples:
i) To work out limits of desired confidence within which population mean would lie.
ii) To determine if sample is drawn from a known population or not
iii) To find SE of difference between 2 means (to know if difference is real and statistically significant)
iv) To calculate sample size (within desired confidence limits)
All of the following are random sampling methods except
A 
Simple random 

B 
Cluster sampling 

C 
Stratified random 

D 
Quota sampling 
All of the following are random sampling methods except
A 
Simple random 

B 
Cluster sampling 

C 
Stratified random 

D 
Quota sampling 
Ans. is ‘d’ i.e., Quota sampling
Sampling
o A sample is subject chosen from a population for investigation. Why do we require sample?
 When a large population of individuals have to be studied, it is easier and more economic to study the sample than whole population.
 So we require sampling.
Methods of sampling
 It is important to ensure that the group of the people included in the sample are representative of the whole population to be studied.
o To ensure this, there are various type of sampling methods, which may be broadly divided into two groups : â€‘
1. Random sampling (Probability sampling)
A random sample is one chosen by a method involving an unpredictable component.
It is also called probability sample because each item has a known probability of being in the sample.
Types of random sampling are : â€‘
i) Simple random sampling
ii) Systematic random sampling
iii) Stratified random sampling
iv) Cluster random sampling
v) Multiphase random sampling
vi) Multistage random sampling
First three, i.e. simple random sampling, systemic random sampling and stratified random sampling are used most commonly for community health survey.
2. Nonrandom sampling (nonprobability sampling)
Nonrandom sampling technique is used when it cannot be ensured that each item has an equal chance of being selected, or when selection is based on expert knowledge of the population.
The purpose of this method is to make an explicit choice based on your own judgement about exactly whom to include in your sample.
In simple words, in nonrandom sampling not everyone has the equal probability of being a sample —> nonprobability sampling.
Types of nonrandom sampling are.
i) Quota sampling
ii) Volunteer sampling
iii) Convenience sampling
Snow ball sampling
In random sampling chance of being picked up is â€‘
A 
Same and known 

B 
Not same and not known 

C 
Same and not known 

D 
Not same but known 
In random sampling chance of being picked up is â€‘
A 
Same and known 

B 
Not same and not known 

C 
Same and not known 

D 
Not same but known 
Ans. is ‘a’ i.e., Same & known
Simple random sampling
 Simple random sampling, also, known as ‘unrestricted random sampling’; is applicable for small, homogenous, readily available population and is used in clinical trials.
 In simple random sampling each individual is chosen randomly and entirely by chance.
 So, each individual has the same probability of being chosen at any stage during the sampling process. For example
o Let us assume you had a school with 1000 students, divided equally into boys and girls, and you wanted to select 100 of them for further study.
o You might put all their names in a bucket and then pull 100 names out.
o Not only does each person have an equal chance of being selected, we can also easily calculate the probaility of a given person being chosen, since we know the sample size (n) and population (N) and it becomes a simple matter of division —> n/N or 100/1000 = 0.10 (10%).
o This means that every student in the school has a 10% or 1 in 10 chance of being selected using this method. Methods of simple random sampling.
 Lottery method
 Random number table
 Computer sofware —> Computer technique.
Which of the following statements about “Simple Random Sampling” is true –
A 
Every element in the population has an equal probability of being included 

B 
Sampling is based on similar characteristics 

C 
Suitable for large heterogenous population 

D 
Complete list of items within the sampling frame is not required 
Which of the following statements about “Simple Random Sampling” is true –
A 
Every element in the population has an equal probability of being included 

B 
Sampling is based on similar characteristics 

C 
Suitable for large heterogenous population 

D 
Complete list of items within the sampling frame is not required 
Ans. is ‘a’ i.e., Every element in the population has an equal probability of being included
Features of Simple Random sampling
o This is the most basic (simplest) kind of probability sample.
o Elements are selected at random from the entire sampling frame without any stratification or segregation of the sampling frame into subroups or strata with similar characteristics.
o Requires a complete list of items within the sampling frame (All elements must be identified and listed and each element of the population must be assigned a number).
o Suitable for small homogenous population (Large heterogenous populations will require stratification). o Every element in the population has an equal probability of being included
For a survey a village is divided into 5 lanes then each lane is sampled randomly is an example is â€‘
A 
Simple random sampling 

B 
Stratified random sampling 

C 
Systemic random sampling 

D 
All of the above 
For a survey a village is divided into 5 lanes then each lane is sampled randomly is an example is â€‘
A 
Simple random sampling 

B 
Stratified random sampling 

C 
Systemic random sampling 

D 
All of the above 
Ans. is ‘c’ i.e., Stratified random sampling
Simple random sampling has been explained (See above explanation).
Systematic random sampling
 In order to do systematic random sampling, the individuals in a population are arranged in a certain way (for example, alphabetically).
o A random starting point is selected and then every n^{th }(for example 10th or 15th) individual is selected for the sample.
o That is, after arranging the individuals in certain pattern (e.g., alpabetically) a starting point is chosen at random, and choices thereafter at regular intervals.
 For example, suppose you want to sample 8 houses from a street of 120 houses.
 120/8 = 15, So every 15th house is chosen after a random starting point between 1 and 15.
 If the random starting point is 11, then the houses selected are 4 11, 26, 41, 56, 71, 86, 101, and 116. o In contrast to simple random sampling, some houses have a larger selection probabily e.g., in this question 11, 26, 41, 56, 71, 86, 100 and 116.
 While the remaining number can not be selected.
Stratified random sampling
 When subpopulations vary considerably, it is advantageous to sample each subpopulation (stratum) independently. o Stratification is the process of grouping members of the population into relative hemogenous subgroups before sampling.
o The strata should be mutually exclusive, every element in the population must be assigned to only one stratum. o Then systematic random sampling method is applied within each stratum.
Population Stratification 4 Systematic random sampling 4 Sample.
o This often improves the representativeness of the sample by reducing sampling error.
o For example, suppose in a population of 1000, sample of 100 is to be drawn for Hb estimation, first convert the population into homogenous striata (e.g., 700 males and 300 females), then draw 70 males and 30 females by doing systematic random sampling.
Now see the question
o First the village has been stratified into five lanes.
o Then random sampling is done on each lane.
o It is stratified random sampling.
Multistage random sampling:
o Is done in successive stages; each successive sampling unit is nested in the previous sampling unit o Advantage: Introduces flexibility in sampling.
o For example, in large country surveys, states are chosen, then districts, then villages, then every 10th person in village as final sampling unit.
Multiphase random sampling:
Is done in successive phase; part of information is obtained from whole sample and part from the subsample. o For example, in a TB survey, Mantourx test done in first phase, then Xray done in all Mantoux positive, then sputum examined in all those with positive Xary findings.
All of the following are true about cluster sampling except –
A 
Samples are similar to those in simple Random sampling 

B 
Is a Rapid and simple method 

C 
The sample size may vary according to study design 

D 
It is a type of probability sample 
All of the following are true about cluster sampling except –
A 
Samples are similar to those in simple Random sampling 

B 
Is a Rapid and simple method 

C 
The sample size may vary according to study design 

D 
It is a type of probability sample 
Ans. is ‘a’ i.e., Samples are similar to those in simple Random Sampling
People are separated into groups, from each group people are selected randomly. What type of sampling is this –
A 
Simple random 

B 
Stratified random 

C 
Systemic random 

D 
Cluster 
People are separated into groups, from each group people are selected randomly. What type of sampling is this –
A 
Simple random 

B 
Stratified random 

C 
Systemic random 

D 
Cluster 
Ans. is ‘b’ i.e., Stratified random
o Separation of people in groups followed by random sampling from those groups is stratified random sampling.
A population is divided into relevant subgroups and random sample selection is done from each of the subgroups. This is which type of sampling method?
A 
Systematic random sampling 

B 
Stratified random sampling 

C 
Simple random sampling 

D 
Cluster sampling 
A population is divided into relevant subgroups and random sample selection is done from each of the subgroups. This is which type of sampling method?
A 
Systematic random sampling 

B 
Stratified random sampling 

C 
Simple random sampling 

D 
Cluster sampling 
Ans. is ‘b’ i.e., Stratified random Sampaling
o Population in the question has been stratified into subgroups, following which random sampling has been done —> It is stratified random sampling.
A region is divided into 50 villages for the purpose of a survey. 10 villages are then selected randomly for the purpose of a study. This type of sampling is termed as –
A 
Simple Random sampling 

B 
Stratified sampling 

C 
Cluster Sampling 

D 
Systematic Sampling 
A region is divided into 50 villages for the purpose of a survey. 10 villages are then selected randomly for the purpose of a study. This type of sampling is termed as –
A 
Simple Random sampling 

B 
Stratified sampling 

C 
Cluster Sampling 

D 
Systematic Sampling 
Ans. is ‘c’ i.e. Cluster Sampling
o This question is slightly different from previous one.
o Here the target population (i.e. entire region) is divided into naturally occuring separate subpopulations or clusters (50 villages) and this division is not based on any particular population characteristic (like religion or age group etc). This type of sampling is representative of cluster sampling.
oWhile, in previous question, a local population was divided into relevant subgroups which is based on particular population characteristics.
Stratified Sampling
Population is divided into separate subgroups or strata based on one or more characteristics such as Religion (Hindus, Muslims, Christians etc) or age groups etc. The strata are mutually exclusive and collectively exhaustive such that every member of the population is represented and assigned to one and only one stratum.
The elements within a strata should be as homogeneous as possible (eg all hindus) but different strata should be as hetergenous as possible (eg. hindus vs. muslims vs. christians)
o Sampling involves randon selection of elements from each strata (every strata) by simple random sampling or systematic random sampling.
Stratified sampling ensures that every strata of the population is represented in the survey.
Cluster Sampling
 oPopulation is divided into separate subpopulations or clusters (naturally occuring), not based one one or more characterisitcs such as village with a region. o The subpopulations/clusters are mutually exclusive and collectively exhaustive such that every member of population is represented and assigned to only one cluster.
 o The elements within a cluster should be as heterogenous as possible (A cluster is a small scale representation of the population and is not stratified based on characteristics), but different clusters should be as homogeneous as possible (Each cluster should have a similar mix of populations)
o Sampling involves random selection of clusters (eg selecting 5 village from a set of 50 villages in a region) Elements from each/ every cluster are not selected.
Elements from selected clusters are than further selected either in total or by random sampling.
 oCluster sampling allows large populations to be studied rapidly in a sample and economical manner.
All are true about cluster sampling except ?
A 
Sample size is same as that of simple random sampling 

B 
It is a two stage sampling 

C 
It is cheapter than other methods of sampling 

D 
It has a disadvantage of higher sampling error 
All are true about cluster sampling except ?
A 
Sample size is same as that of simple random sampling 

B 
It is a two stage sampling 

C 
It is cheapter than other methods of sampling 

D 
It has a disadvantage of higher sampling error 
Ans. is ‘a’ i.e., Sample size is same as that of simple random sampling
o Cluster sampling is an example of “twostage sampling” or “mulistage sampling” –
i) In first stage a sample or areas is chosen
In the second stage a sample of respondent within those areas is selected.
o Cluster sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the withincluster variation. It has a disadvantage of higher sampling error, which is difficult to measure.
Important facts of cluster random sampling (CRS)
o Cluster sampling is used in India to evaluate immunization coverage.
o WHO used 30 x 7 technique (total = 210 children) for cluster sampling in which there are 30 clusters, each containing 7 children who are 12 – 23 months old and are completely immunized for primary immunization (till measles vaccine at 9 month)
o Clusters are heterogenous within themselves but homogeneous with respect to each other.
o Main objective of cluster sampling is to reduce costs by increasing sampling efficiency; in contrast to stratified sampling where; the main objective is to increase precision.
o Cluster sampling is quiet accurate with low error rate of only ± 5%.
o Cluster sampling can also calculate sampling interval.
o The main limitation of cluster sampling is that cluster cannot be compared with each other.
Sampling method used in assessing immunization status of children under immunization programme is
A 
Systemic sampleing 

B 
Stratified sampling 

C 
Group sampling 

D 
Cluster sampling 
Sampling method used in assessing immunization status of children under immunization programme is
A 
Systemic sampleing 

B 
Stratified sampling 

C 
Group sampling 

D 
Cluster sampling 
Ans. is ‘d’ i.e., Cluster sampling
o In the Expanded programme on Immunization (EPI) cluster technique, a simplified cluster sampling method is used.
It is based on ranodm selection of 210 children who are 1223 months of age.
These patients are selected in 30 clusters of 7 children each to estimate immunization coverage levels.
Type of sampling, if random sample is taken from a characteristic population, eg. Hindus, Muslims, Christians etc
A 
Simple random 

B 
Systemic random 

C 
Stratified random 

D 
Cluster 
Type of sampling, if random sample is taken from a characteristic population, eg. Hindus, Muslims, Christians etc
A 
Simple random 

B 
Systemic random 

C 
Stratified random 

D 
Cluster 
Ans. is ‘c’ i.e., Stratified random
“Stratified random sampling is particularly useful where one is interested in analysing the data by a certain characteristic of the population, viz Hindus, Muslims, Christians, age group etc, – as we know these groups are not equally distributed in the population.” ………… Park
Which of the following is/are nonrandom sampling methods –
A 
Quota sampling 

B 
Stratified random sampling 

C 
Convenience Sampling 

D 
a and c both are correct 
Which of the following is/are nonrandom sampling methods –
A 
Quota sampling 

B 
Stratified random sampling 

C 
Convenience Sampling 

D 
a and c both are correct 
Ans. is ‘a’ i.e., Quota sampling & ‘c’ Convenience Sampling
NonRandom (Nonprobability) Sampling
Convenience Sampling
 Participants are selected at the convenience of the researcher, i.e., the sample comprises the subjects who are simply available in a convenient way to the researcher. For example, standing at a railway station and selecting the passangers for the sample.
 There is no randomization and likelihood of bias is high.
Quota Sampling
 It is a two stage sampling, in first stage population is segmented into quotas and in second stage sample elements are selected by convenience sampling.
 A quota sample sets up a quota, or number, of specific type of people. For example we might decide to include in our survey 3 Hindus, 6 Muslims, 5 punjabis, 8 Gujratis —> i.e., we set up a fix quota (number or percentage) for specific type of subjects. Then the sample elements are selected by convenience sampling.
Snowball Sampling
 Data is collected from a small group of people with special characteristics, who are then asked to identify other people like them. Data is collected from these referrals, who are arised to identify other people like them. This process continues until a target sampe is achieved.
 This is a technique for developing a research sample where existing study subjects recruit future subjects from among their acquaintances; thus the sample group appears to grow like a rolling snowball.
 Is often used in hidden populations which are difficult for researchers to access, e.g., drug users or commercial sex workers.
Volunteer sampling
 A voluntter sampling (voluntary sampling) is one in which the people self select themselves into the survey. These participants have a strong interest in the main topic of the survey.
 For example, a news show asks viewers to participate in an online poll. This would be voluntary sample as the sample is chosen by the viewers, not by the survey administrator.
True statements with regard to samplingâ€‘
A 
Snowball sampling is used for hidden population 

B 
More sample in systemic random sampling 

C 
In stratified random sampling population is divided into strata 

D 
a and c 
True statements with regard to samplingâ€‘
A 
Snowball sampling is used for hidden population 

B 
More sample in systemic random sampling 

C 
In stratified random sampling population is divided into strata 

D 
a and c 
Ans. is ‘a’ i.e., Snowball sampling is used for hidden population; ‘c’ i.e., In stratified random sampling population is divided into strata
Snow ball sampling (chain sampling, chainreferral sampling)
o It is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group appears to grow like a rolling snowball.
o As the sample builds up, enough data is gathered to be useful for research. This sampling technique is often used in hiden population which are difficult for researchers to access; example populations would be drug users or sex workers.
o As sample members are not selected from a sampling frame, snowball samples are subject to numerous biases. For example, people who have many friends are more likely to be recruited into the sample.
o Snowball sampling has a lot of advantages as opposed to other sampling methods. It is possible for the surveyors to include people in the survey that they would not have known. It is also very good for locating people of a specific population if they are difficult to locate.
About other options
o Cluster sampling is cost efficient (economic and feasibility).
“Simple Random Sampling :With this procedure, the sample is drawn in such a way that each unit has an equal chance of being drawn in the sample. This technique provides the greatest number ofpossibe samples”. – Park 20th p. 752 “Systematic random sample : This is done by picking every 5th or 10th unit at regular intervals. By this method, each unit in the sampling frame would have the same chance of being selected, but the number ofpossible samples is greatly reduced”. – Park 20th/e 752.
“Stratified Random sampling : This method is followed when the population is not homogenous. The population under study is first divied into homogenous group or classes called strata and the sample is drawn from each stratum at random in proportion to its size. This technique gives more representative sample than simple random sampling in a given large population”.
Sampling error is classified as –
A 
Alpha error 

B 
Beta error 

C 
Gamma error 

D 
Delta error 
Sampling error is classified as –
A 
Alpha error 

B 
Beta error 

C 
Gamma error 

D 
Delta error 
Ans. is ‘a’ i.e., Alpha error
Sampling error
 Sampling error or estimation error is the error caused by observing a sample instead of the whole population. o This is because, the results obtained from one sample differ to some extent from the result of another sample. Type I Error : Alpha level
It is a sampling error
If a study finds a difference in treatment when there is no difference actually, then a type I error is present.
Under these circumstances results are falsely positive (false +ve)
This is an error at alpha level.
Type II Error : Beta level
It determines the power of a study
If a study fails to find a difference in treatment when actually there is a difference a type II error is said to have occurred.
Under these circumstances results are False negative
This is an error at 13 level.
‘Design Effect’ is associated with which of the following sampling techniques –
A 
Stratified sampling 

B 
Systemic sampling 

C 
Cluster sampling 

D 
Simple Random Sampling 
‘Design Effect’ is associated with which of the following sampling techniques –
A 
Stratified sampling 

B 
Systemic sampling 

C 
Cluster sampling 

D 
Simple Random Sampling 
Ans. is ‘c’ i.e., Cluster sampling
o The Design Effect (difp refers to the difference in precision of the estimates produced by a complex design (like a cluster sample) relative to a simple random sample (SRS).
o The Design Effect (diff) is the ratio of the variance of a statistic calculated from a cluster sample (or any complex sample) to that of the same statistic calculated from a simple random sample of the same size.
o Design effect may be seen in all complex designs including stratified samples, but is most prominent with cluster samples
Children surveyed in cluster sampling for coverage of national immunization programme in?
A 
30 cluster of 5 children 

B 
20 cluster of 5 children 

C 
30 cluster of 10 children 

D 
30 cluster of 7 children 
Children surveyed in cluster sampling for coverage of national immunization programme in?
A 
30 cluster of 5 children 

B 
20 cluster of 5 children 

C 
30 cluster of 10 children 

D 
30 cluster of 7 children 
Ans. is ‘d’ i.e., 30 cluster of 7 children
o WHO used 30 x 7 technique (total = 210 children) for cluster sampling in which there are 30 clusters, each containing 7 children who are 12 — 23 months old and are completely immunized for primary immunization (till measles vaccine at 9 month)
Simple random sampling is ideal for?
A 
Vaccinated people 

B 
Heterogenous population 

C 
Homogenous population 

D 
All of the above 
Simple random sampling is ideal for?
A 
Vaccinated people 

B 
Heterogenous population 

C 
Homogenous population 

D 
All of the above 
Ans. is ‘c’ i.e., Homogenous population
Simple Random sampling is suitable for small homogenous population.
In a community of 3000 people, 80 % are Hindus 10 % Muslims, 5 `)/0 Sikh, 4 % Christians and 1 % Jains To select a sample of 300 people to analyses food habits, ideal sample would be –
A 
Simple random 

B 
Stratified random 

C 
Systematic random 

D 
Inverse sampling 
In a community of 3000 people, 80 % are Hindus 10 % Muslims, 5 `)/0 Sikh, 4 % Christians and 1 % Jains To select a sample of 300 people to analyses food habits, ideal sample would be –
A 
Simple random 

B 
Stratified random 

C 
Systematic random 

D 
Inverse sampling 
Ans. is ‘b’ i.e., Stratified random
Simple random sampling
A 
Provides least number of possible samples 

B 
Haphazard collection of certain number for a sample 

C 
Picking every 5th or 10th at regular intervals 

D 
Sample represent, a corresponding strata of universe 
Simple random sampling
A 
Provides least number of possible samples 

B 
Haphazard collection of certain number for a sample 

C 
Picking every 5th or 10th at regular intervals 

D 
Sample represent, a corresponding strata of universe 
Ans. is ‘b’ i.e. Haphazard collection of certain number for a sample
What is the method of sampling in which the units are picked up at regular intervals from the universeâ€‘
A 
Simple random sampling 

B 
Systematic random sampling 

C 
Stratified random sampling 

D 
Snowball sampling 
What is the method of sampling in which the units are picked up at regular intervals from the universeâ€‘
A 
Simple random sampling 

B 
Systematic random sampling 

C 
Stratified random sampling 

D 
Snowball sampling 
Ans. is ‘b’ i.e., Systematic random sampling
People are arranged alphabetically by their names and then every 3^{rd }person is chosen for study. The type of sampling is â€‘
A 
Stratified random 

B 
Systematic random 

C 
Simple random 

D 
None of the above 
People are arranged alphabetically by their names and then every 3^{rd }person is chosen for study. The type of sampling is â€‘
A 
Stratified random 

B 
Systematic random 

C 
Simple random 

D 
None of the above 
Ans. is ‘b’ i.e., Systematic random
Simple random sampling
 Simple random sampling, also, known as ‘unrestricted random sampling’; is applicable for small, homogenous, readily available population and is used in clinical trials.
 In simple random sampling each individual is chosen randomly and entirely by chance.
 So, each individual has the same probability of being chosen at any stage during the sampling process. For example
 Let us assume you had a school with 1000 students, divided equally into boys and girls, and you wanted to select 100 of them for further study.
 You might put all their names in a bucket and then pull 100 names out.
 Not only does each person have an equal chance of being selected, we can also easily calculate the probaility of
Identify the type of Sampling as shown in the image:
A 
Systematic Random Sampling 

B 
Simple Random Sampling 

C 
Quota Sampling 

D 
Cluster Random Sampling 
Identify the type of Sampling as shown in the image:
A 
Systematic Random Sampling 

B 
Simple Random Sampling 

C 
Quota Sampling 

D 
Cluster Random Sampling 
Ans:A.)Systematic Random Sampling
Sampling
 When a large population of individuals have to be studied, it is easier and more economic to study the sample than whole population.
 So we require sampling.
 It is important to ensure that the group of the people included in the sample are representative of the whole population to be studied.
Methods of sampling
1.Random sampling (Probability sampling)
 A random sample is one chosen by a method involving an unpredictable component.
 It is also called probability sample because each item has a known probability of being in the sample.
 Types of random sampling are : â€‘
 i) Simple random sampling
 A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.
 ii) Systematic random sampling
 It consists of the following steps
 Defining the population (p);
 Deciding on the sample size (s);
 Listing the population and assigning numbers to cases (sampling frame);
 Calculating the ‘sampling interval’ (p/s = K); Total Population size/desired sample size = Sampling interval (K).
 The first unit is selected randomly and then every Kth unit is selected.
 iii) Stratified random sampling
 Separation of people in groups followed by random sampling from those groups is stratified random sampling.
 Stratified random sampling is particularly useful where one is interested in analysing the data by a certain characteristic of the population, viz Hindus, Muslims, Christians, age group etc,
 iv) Cluster random sampling
 Here the target population (i.e. entire region) is divided into naturally occuring separate subpopulations or clusters (50 villages) and this division is not based on any particular population characteristic (like religion or age group etc).
 v) Multiphase random sampling
 vi) Multistage random sampling
 i) Simple random sampling
 First three, i.e. simple random sampling, systemic random sampling and stratified random sampling are used most commonly for community health survey.
2. Nonrandom sampling (nonprobability sampling)
 Nonrandom sampling technique is used when it cannot be ensured that each item has an equal chance of being selected, or when selection is based on expert knowledge of the population.
 Types of nonrandom sampling are.
 i) Quota sampling
 It is a two stage sampling, in first stage population is segmented into quotas and in second stage sample elements are selected by convenience sampling.
 ii) Volunteer sampling
 A volunteer sampling (voluntary sampling) is one in which the people self select themselves into the survey.
 iii) Convenience sampling
 Participants are selected at the convenience of the researcher, i.e., the sample comprises the subjects who are simply available in a convenient way to the researcher.
 iv)Snow ball sampling
 Data is collected from a small group of people with special characteristics, who are then asked to identify other people like them.
 i) Quota sampling
. In a sampling technique, every 10th unit of population is chosen. What is this type of sampling technique?
A 
Systematic random sampling 

B 
Systematic sampling 

C 
Simple random sampling 

D 
Cluster sampling 
. In a sampling technique, every 10th unit of population is chosen. What is this type of sampling technique?
A 
Systematic random sampling 

B 
Systematic sampling 

C 
Simple random sampling 

D 
Cluster sampling 
Ans. a. Systematic random sampling
Which of the following displacement is not seen in Colle’s fracture?
A 
Radial tilt 

B 
Volar tilt 

C 
Dorsal displacement 

D 
Supination 
Which of the following displacement is not seen in Colle’s fracture?
A 
Radial tilt 

B 
Volar tilt 

C 
Dorsal displacement 

D 
Supination 
Ans. b. Volar tilt
Type of sampling, if random sample is taken from a characteristic population, eg. Hindus, Muslims, Christians etcâ€‘
A 
Simple random 

B 
Systemic random 

C 
Stratified random 

D 
Cluster 
Type of sampling, if random sample is taken from a characteristic population, eg. Hindus, Muslims, Christians etcâ€‘
A 
Simple random 

B 
Systemic random 

C 
Stratified random 

D 
Cluster 
Ans. is ‘c’ i.e., Stratified random
 Stratified random sampling is particularly useful where one is interested in analysing the data by a certain characteristic of the population, viz Hindus, Muslims, Christians, age group etc, – as we know these groups are not equally distributed in the population.” …………… Park
Simple random sampling
 Simple random sampling, also, known as ‘unrestricted random sampling’; is applicable for small, homogenous, readily available population and is used in clinical trials.
 In simple random sampling each individual is chosen randomly and entirely by chance.
 So, each individual has the same probability of being chosen at any stage during the sampling process.
For example : â€‘
 Let us assume you had a school with 1000 students, divided equally into boys and girls, and you wanted to select 100 of them for further study.
 You might put all their names in a bucket and then pull 100 names out.
 Not only does each person have an equal chance of being selected, we can also easily calculate the probaility of a given person being chosen, since we know the sample size (n) and population (N) and it becomes a simple matter of division → n/N or 100/1000 = 0.10 (10%).
 This means that every student in the school has a 10% or 1 in 10 chance of being selected using this method.
Systematic random sampling
 In order to do systematic random sampling, the individuals in a population are arranged in a certain way (for example, alphabetically).
 A random starting point is selected and then every n^{th }(for example 10th or 15th) individual is selected for the sample.
 That is, after arranging the individuals in certain pattern (e.g., alpabetically) a starting point is chosen at random, and choices thereafter at regular intervals.
 For example, suppose you want to sample 8 houses from a street of 120 houses.
 120/8 = 15, So every 15th house is chosen after a random starting point between 1 and 15.
 If the random starting point is 11, then the houses selected are→ 11, 26, 41, 56, 71, 86, 101, and 116.
 In contrast to simple random sampling, some houses have a larger selection probabily e.g., in this question 11, 26, 41, 56, 71, 86, 100 and 116.
 While the remaining number can not be selected.
Stratified random sampling
 When subpopulations vary considerably, it is advantageous to sample each subpopulation (stratum) independently.
 Stratification is the process of grouping members of the population into relative hemogenous subgroups before sampling.
 The strata should be mutually exclusive, every element in the population must be assigned to only one stratum.
 Then systematic random sampling method is applied within each stratum.
 Population → Stratification → Systematic random sampling → Sample.
 This often improves the representativeness of the sample by reducing sampling error.
 For example, suppose in a population of 1000, sample of 100 is to be drawn for Hb estimation, first convert the population into homogenous striata (e.g., 700 males and 300 females), then draw 70 males and 30 females by doing systematic random sampling.