SAMPLING AND ITS TYPES

Mr. Sunil KumarPre PhD StudentRoll No. 6421420IHTM, M.D. University

SAMPLINGTarget Population or Universe The population to which the investigator wants to

generalize his resultsSampling Unit: smallest unit from which sample can be selectedSampling 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 schemeMethod of selecting sampling units from sampling frameSample: all selected respondent are sample

SAMPLE

TARGET POPULATION

SAMPLE UNIT

SAMPLE

• 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.

SAMPLING BREAKDOWN

All university in India

All university Haryana

List of Haryana university

Three university in haryana

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

SAMPLING……

To whom do you want to generalize your results?All Five Star HotelAll Travel Agency All Hotel Customer Women aged 15-45 yearsOther

Sample size : Minimum size is 30 no.

SAMPLING…….

3 factors that influence sample representative-nessSampling procedureSample sizeParticipation (response)

When might you sample the entire population?When your population is very smallWhen you have extensive resourcesWhen you don’t expect a very high response

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?

Merits of SamplingSize of populationFund required for the studyFacilitiesTime

THE RESPRESENATION BASIS PROBABILITY SAMPLING NON PROBABILITY SAMPLINGELEMENT SELECTION TECHNIQE RESTRICTED SAMPLING UN RESTRICTED SAMPLING

TYPES OF SAMPLE BASED ON TWO FACTORS:

•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-Probability

Probability (Random) Samples Simple random sample Systematic random sample Stratified random sample Cluster sample

Probability Sampling

Simple Random

Sampling

SystematicSampling

StratifiedRandom Sampling

Proportionate

Dis Proportionate

ClusterSampling

One-Stag

e

Two Stag

e

Multi-

Stage

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.)Non-

Probability

Convenience Sampling

QuotaSampling

Judgment Sampling

Snowball Sampling

Difference between Probability sampling and Non Probability

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 reprenation

Simple random sampling

Every subset of a specified size n from the population has an equal chance of being selected

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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 popula

tion

Develop

sampling

frame

Assign each unit a numbe

r

Randomly

select the

required

amount of

random

numbers

Systematica

lly select rando

m numbers until

it meets

the sample

size requirements

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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• 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 populat

ion

Develop

sampling

frame

Decide the

sample size

Work out

what fractio

n 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)

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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Systematic sampling

Every member ( for example: every 20th person) is selected from a list of all population members.

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Stratified Random Sample 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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Stratified Random Sample

Define populatio

n

Develop sampling

frame accordin

g to character

istics required

Determine the

proportion of each populatio

n variable

of interest

Systematic

sampling methods can then

be followed to select sample

unit

Stratified random sampling can be classified in toa. 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 stratait necessery involves giving over representation to some strata and under representation to other.

STRATIFIED SAMPLING……Advantage : Enhancement of representativeness to each sample Higher statistical efficiency Easy to carry outDisadvantage: Classification error Time consuming and expensive Prior knowledge of composition and of

distribution of population

CLUSTER SAMPLING 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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Cluster sampling

Section 4

Section 5

Section 3

Section 2Section 1

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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• 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

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

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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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

Use results that are easy to get

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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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

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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

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.

Potential Sources of Error in Research Designs

Surrogate Information ErrorMeasurement ErrorPopulation Definition ErrorSampling Frame ErrorData Analysis Error

Respondent Selection ErrorQuestioning ErrorRecording ErrorCheating Error

Inability ErrorUnwillingness Error

Total Error

Non-sampling Error

Random Sampling

Error

Non-response Error

Response Error

Interviewer Error

Respondent Error

Researcher Error

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

• 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

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

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

DESINGED BY

Sunil KumarResearch Scholar/ Food Production FacultyInstitute of Hotel and Tourism Management,MAHARSHI DAYANAND UNIVERSITY, ROHTAKHaryana- 124001 INDIA Ph. No. 09996000499email:  skihm86@yahoo.com , balhara86@gmail.com  linkedin:- in.linkedin.com/in/ihmsunilkumarfacebook: www.facebook.com/ihmsunilkumar webpage: chefsunilkumar.tripod.com