Sample Size, Types, sampling Error etc

1. Mr. Sunil Kumar
Pre PhD Student
Roll No. 6421420
IHTM, M.D. University Sunil Kumar

2. SAMPLING
Target Population or Universe
The population to which the investigator wants to
generalize his results
Sampling Unit:
smallest unit from which sample can be selected
Sampling frame
The sampling frame is the list from which the potential
respondents are drawn
 Telephone directory
 List of five star Hotel
 List of student
Sampling scheme
Method of selecting sampling units from sampling frame
Sample: all selected respondent are sample

3. SAMPLE
TARGET POPULATION
SAMPLE UNIT
SAMPLE
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• A population can be defined as including all people or items
with the characteristic one wishes to understand.
• Because there is very rarely enough time or money to gather
information from everyone or everything in a population, the
goal becomes finding a representative sample (or subset) of
that population.

4. SAMPLING BREAKDOWN
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All university in India
All university Haryana
List of Haryana university
Three university in haryana

5. Why Sample?
Get information about large populations
 Lower cost
 More accuracy of results
 High speed of data collection
 Availability of Population elements.
 Less field time
 When it‟s impossible to study the whole population
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6. SAMPLING……
To whom do you want to generalize your results?
All Five Star Hotel
All Travel Agency
All Hotel Customer
Women aged 15-45 years
Other
Sample size : Minimum size is 30 no.
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7. SAMPLING…….
 3 factors that influence sample representative-ness
Sampling procedure
Sample size
Participation (response)
 When might you sample the entire population?
When your population is very small
When you have extensive resources
When you don‟t expect a very high response
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8. The sample must be:
1. representative of the population;
2. appropriately sized (the larger the better);
3. unbiased;
4. random (selections occur by chance);
What is Good Sample?
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Merits of Sampling
Size of population
Fund required for the study
Facilities
Time

9. THE RESPRESENATION BASIS
 PROBABILITY SAMPLING
 NON PROBABILITY SAMPLING
ELEMENT SELECTION TECHNIQE
 RESTRICTED SAMPLING
 UN RESTRICTED SAMPLING
TYPES OF SAMPLE BASED ON TWO FACTORS:
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10. •Probability sample – a method of sampling that uses of
random selection so that all units/ cases in the population
have an equal probability of being chosen.
• Non-probability sample – does not involve random
selection and methods are not based on the rationale of
probability theory.
Types of Sampling
Sampling
Techniques
Probability
Non-
ProbabilitySunil Kumar

11.  Probability (Random) Samples
 Simple random sample
 Systematic random sample
 Stratified random sample
 Cluster sample
Probability
Sampling
Simple
Random
Sampling
Systematic
Sampling
Stratified
Random
Sampling
Proportionate
Dis Proportionate
Cluster
Sampling
One-
Stage
Two
Stage
Multi-
Stage
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12. Non-Probability Samples
 Convenience samples (ease of access)
sample is selected from elements of a population that
are easily accessible
 Purposive sample (Judgmental Sampling)
You chose who you think should be in the study
 Quota Sampling
 Snowball Sampling (friend of friend….etc.)
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13. Sunil Kumar

14. SIMPLE RANDOM SAMPLING
• Applicable when population is small, homogeneous & readily
available
• All subsets of the frame are given an equal probability. Each
element of the frame thus has an equal probability of
selection. A table of random number or lottery system is used
to determine which units are to be selected.
Advantage
 Easy method to use
 No need of prior information of population
 Equal and independent chance of selection to every element
Disadvantages
 If sampling frame large, this method impracticable.
 Does not represent proportionate reprenationSunil Kumar

15. Simple random sampling
Every subset of a specified size n from the population
has an equal chance of being selected
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16. Suitability
• This method is suitable for small homogeneous
• Randomly selecting units from a sampling frame.
„Random‟ means mathematically each unit from the
sampling frame has an equal probability of being
included in the sample.
• Stages in random sampling:
Define
population
Develop
sampling
frame
Assign each
unit a
number
Randomly
select the
required
amount of
random
numbers
Systematically
select random
numbers until it
meets the
sample size
requirements
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17. REPLACEMENT OF SELECTED UNITS
 Sampling schemes may be without replacement or with
replacement
 For example, if we catch fish, measure them, and
immediately return them to the water before continuing
with the sample, this is a with replacement design,
because we might end up catching and measuring the
same fish more than once. However, if we do not return
the fish to the water (e.g. if we eat the fish), this becomes
a without replacement design.
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18. • Similar to simple random sample. No table of random
numbers – select directly from sampling frame. Ratio
between sample size and population size
Systematic Sampling
Define
population
Develop
sampling
frame
Decide the
sample size
Work out
what fraction
of the frame
the sample
size represents
Select
according to
fraction (100
sample from
1,000 frame then
10% so every
10th unit)
First unit
select by
random
numbers
then every
nth unit
selected
(e.g. every
10th)
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19. ADVANTAGES:
 Sample easy to select
 Suitable sampling frame can be identified easily
 Sample evenly spread over entire reference population
 Cost effective
DISADVANTAGES:
 Sample may be biased if hidden periodicity in population
coincides with that of selection.
 Each element does not get equal chance
 Ignorance of all element between two n element
Systematic Sampling
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20. Systematic sampling
Every member ( for example: every 20th person) is
selected from a list of all population members.
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21.  The population is divided into two or more groups
called strata, according to some criterion, such as
geographic location, grade level, age, or income, and
subsamples are randomly selected from each strata.
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22. Stratified random sampling can be classified in to
a. Proportionate stratified sampling
It involves drawing a sample from each stratum in
proportion to the letter‟s share in total population
b. Disproportionate stratified sampling
proportionate representation is not given to strata
it necessery involves giving over representation to
some strata and under representation to other.
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23. STRATIFIED SAMPLING……
Advantage :
 Enhancement of representativeness to each sample
 Higher statistical efficiency
 Easy to carry out
Disadvantage:
 Classification error
 Time consuming and expensive
 Prior knowledge of composition and of
distribution of population
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24.  Cluster sampling is an example of 'two-stage sampling' .
 First stage a sample of areas is chosen;
 Second stage a sample of respondents within those areas is
selected.
 Population divided into clusters of homogeneous units,
usually based on geographical contiguity.
 Sampling units are groups rather than individuals.
 A sample of such clusters is then selected.
 All units from the selected clusters are studied.
 The population is divided into subgroups (clusters) like
families. A simple random sample is taken of the subgroups
and then all members of the cluster selected are surveyed
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25. Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
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26. CLUSTER SAMPLING…….
Advantages :
 Cuts down on the cost of preparing a sampling
frame. This can reduce travel and other
administrative costs.
Disadvantages: sampling error is higher for a simple
random sample of same size. Often used to
evaluate vaccination coverage in EPI
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27. • Cluster sampling: selecting a sample based on specific, naturally occurring
groups (clusters) within a population.
- Example: randomly selecting 20 hospitals from a list of all
hospitals in England.
Multi-stage sampling: cluster sampling repeated at a number of levels.-
Example: randomly selecting hospitals by county and then a sample of
patients from each selected hospital.
Complex form of cluster sampling in which two or more levels of units are
embedded one in the other.
First stage, random number of districts chosen in all
states.
Followed by random number of talukas, villages.
Then third stage units will be houses.
All ultimate units (houses, for instance) selected at last step are surveyed.
Cluster/ multi-stage random sample
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28. Difference Between Strata and Clusters
 Although strata and clusters are both non-
overlapping subsets of the population, they differ in
several ways.
 All strata are represented in the sample; but only a
subset of clusters are in the sample.
 With stratified sampling, the best survey results
occur when elements within strata are internally
homogeneous. However, with cluster sampling, the
best results occur when elements within clusters are
internally heterogeneous
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29. Non Probability
CONVENIENCE SAMPLING
 Sometimes known as grab or opportunity sampling or accidental or
haphazard sampling.
 Selection of whichever individuals are easiest to reach
 It is done at the “convenience” of the researcher
 For example, if the interviewer was to conduct a survey at a
shopping center early in the morning on a given day, the
people that he/she could interview would be limited to those
given there at that given time, which would not represent the
views of other members of society in such an area, if the
survey was to be conducted at different times of day and
several times per week.
 This type of sampling is most useful for pilot testing.
 In social science research, snowball sampling is a similar
technique, where existing study subjects are used to recruit more
subjects into the sample.
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30. Advantage: A sample selected for ease of access,
immediately known population group and good response
rate.
Disadvantage: cannot generalise findings (do not know what
population group the sample is representative of) so cannot
move beyond describing the sample.
•Problems of reliability
•Do respondents represent the
target population
•Results are not generalizable
Convenience Sampling
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Use results that are easy to get

31. Judgmental sampling or Purposive sampling
 - The researcher chooses the sample based on who
they think would be appropriate for the study. This is
used primarily when there is a limited number of
people that have expertise in the area being
researched
 Selected based on an experienced individual‟s belief
 Advantages
 Based on the experienced person‟s judgment
 Disadvantages
 Cannot measure the respresentativeness of the
sample
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32. QUOTA SAMPLING
 The population is first segmented into mutually exclusive sub-
groups, just as in stratified sampling.
 Then judgment used to select subjects or units from each segment
based on a specified proportion.
 For example, an interviewer may be told to sample 200 females
and 300 males between the age of 45 and 60.
 It is this second step which makes the technique one of non-
probability sampling.
 In quota sampling the selection of the sample is non-random.
 For example interviewers might be tempted to interview those who
look most helpful. The problem is that these samples may be
biased because not everyone gets a chance of selection. This
random element is its greatest weakness and quota versus
probability has been a matter of controversy for many years
Sunil Kumar

33.  Quota sampling
 Based on prespecified quotas regarding demographics,
attitudes, behaviors, etc
 Advantages
 Contains specific subgroups in the proportions desired
 May reduce bias
 easy to manage, quick
 Disadvantages
 Dependent on subjective decisions
 Not possible to generalize
 only reflects population in terms of the quota, possibility of
bias in selection, no standard error
Types of Non probability Sampling Designs
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34. Sunil Kumar
 Snowball Sampling
 Useful when a population is hidden or difficult to gain access to. The
contact with an initial group is used to make contact with others.
 Respondents identify additional people to included in the study
 The defined target market is small and unique
 Compiling a list of sampling units is very difficult
 Advantages
 Identifying small, hard-to reach uniquely defined target population
 Useful in qualitative research
 access to difficult to reach populations (other methods may not
yield any results).
 Disadvantages
 Bias can be present
 Limited generalizability
 not representative of the population and will result in a biased
sample as it is self-selecting.

35. Potential Sources of Error in Research Designs
Surrogate Information Error
Measurement Error
Population Definition Error
Sampling Frame Error
Data Analysis Error
Respondent Selection Error
Questioning Error
Recording Error
Cheating Error
Inability Error
Unwillingness Error
Total Error
Non-sampling
Error
Random
Sampling Error
Non-response
Error
Response
Error
Interviewer
Error
Respondent
Error
Researcher
Error
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36. Errors in Hospitality Research
 The total error is the variation between the true mean value in the
population of the variable of interest and the observed mean value
obtained in the marketing research project.
 Random sampling error is the variation between the true mean value
for the population and the true mean value for the original sample.
 Non-sampling errors can be attributed to sources other than
sampling, and they may be random or nonrandom: including errors in
problem definition, approach, scales, questionnaire design,
interviewing methods, and data preparation and analysis. Non-
sampling errors consist of non-response errors and response errors.
 Non-response error arises when some of the respondents included in
the sample do not respond.
 Response error arises when respondents give inaccurate answers or
their answers are misrecorded or misanalyzed
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37. • The larger the sample size the more likely error in the
sample will decrease.
•But, beyond a certain point increasing sample size does
not provide large reductions in sampling error.
•Accuracy is a reflection of the sampling error and
confidence level of the data.
Sampling Error and
Confidence
Sunil Kumar

38. Errors in Sampling
 Non-Observation Errors
 Sampling error: naturally occurs
 Coverage error: people sampled do not match the
population of interest
 Underrepresentation
 Non-response: won’t or can’t participate
Sunil Kumar

39. Errors of Observation
 Interview error- interaction between
interviewer and person being surveyed
 Respondent error: respondents have difficult
time answering the question
 Measurement error: inaccurate responses
when person doesn’t understand question or
poorly worded question
 Errors in data collection
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40. Sunil Kumar
DESINGED BY
Sunil Kumar
Research Scholar/ Food Production Faculty
Institute of Hotel and Tourism Management,
MAHARSHI DAYANAND UNIVERSITY, ROHTAK
Haryana- 124001 INDIA Ph. No. 09996000499
email: skihm86@yahoo.com , balhara86@gmail.com
linkedin:- in.linkedin.com/in/ihmsunilkumar
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webpage: chefsunilkumar.tripod.com