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SAMPLE DESIGN

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POPULATION AND UNIVERSE Population or Universe is any complete

group of people, companies, hospitals, stores, college students or like that who share some set of characteristics.

Population and universe can also be distinguished as: If complete set of element is finite it is

known as Population. If complete set of element is infinite it is

known as Universe.

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ELEMENTS All those primary units which constitute

universe or population, consisting some common set of characteristics are know as elements.

In a survey when elements are human being we call them respondents.

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SAMPLING It is a process of using small portion of

the population or universe to make conclusions about the whole population.

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PopulationPopulation

SampleSample

A subset or part of population capable of representing almost in same ratio the characteristics which are present in the population or universe

SAMPLE

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CENSUS An investigation of all the individual

elements making up a population. It should be noticed that universe

cannot be studied through census method.

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CENSUS VS SAMPLE ENUMERATION A complete enumeration of all items in

the ‘population’ is known as a census inquiry.

A Sample survey is a sub group of population.

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WHEN A CENSUS IS APPROPRIATE If the size of population is small Researcher is interested in gathering the

information from every individual.

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WHEN SAMPLE IS APPROPRIATE When the size of population is large. When time and cost are the main

consideration in research If population is homogenous Sampling reduces the labour

requirements and gathers vital information.

Reduces non sampling errors

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ESSENTIALS OF A SAMPLEA sample must have following things

which are very essential for drawing valid conclusions:

It should be representative It should be independent It should be homogenous It should be adequate

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OBJECTIVES OF SAMPLING To obtain reliable information about the

population. To arrive at the characteristics of the

parent population. To test the reliability of difference

between the sample estimates and population parameters.

To test the validity

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THE SAMPLING PROCESS

Define the Targetpopulation

Define the Targetpopulation

Select a Sampling Frame

Select a Sampling Frame

Determine if a probability or non-probability sampling

method will be chosen

Determine if a probability or non-probability sampling

method will be chosen

Plan procedure for selecting sampling units

Plan procedure for selecting sampling units

Determine sample sizeDetermine sample size

Select actual sampling unitsSelect actual sampling units

Conduct fieldworkConduct fieldwork11

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DEFINING THE TARGET POPULATION The specific, complete group relevant to

the research project. Who has the information/data you

need? How do you define your target

population?Geography DemographicsUseAwareness

Reason: To define a proper source from which the data are to be collected.

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DEFINING THE TARGET POPULATION The target group should be clearly

delineated. Thus, population is defined as: Elements Sampling unit Extent Time

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THE SAMPLING FRAME A sampling frame is the list of elements

from which the sample may be drawn. Sampling frame is also known as

working population. Examples of sampling frames are a

student telephone directory, the list of companies on the stock exchange, the yellow pages (for businesses).

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THE SAMPLING FRAME Generally, it is not feasible to compile a

list that includes the entire population, leading to sampling frame error.

Sampling frame error - Error that occurs when certain sample elements are not listed or available and are not represented in the sampling frame

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CLASSIFICATION OF SAMPLING METHODS

Sampling Technique

Probability sampling

Simple Random Sampling

Complex Random Sampling

Non-Probability sampling

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PROBABILITY AND NON-PROBABILITY SAMPLING A Probability Sampling is one in

which every unit in the population has an equal chance or a non zero probability of being selected in the sample.

A Non-Probability Sampling is one in which units of the sample are chosen on the basis of personal judgment or convenience

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SIMPLE RANDOM SAMPLING Purest form of probability sampling It is a process in which every item of the

population has an equal probability of being chosen.

Applicable when population is small, homogeneous & readily available.

A table of random number or lottery system is used to determine which units are to be selected.

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METHOD OF SELECTING RANDOM SAMPLE It involves writing the name of each

element of a finite population on a slip of paper and putting them into a box or a bag.

After this, mix them thoroughly and then the required number of slips for the sample shall be picked one after the other without replacement.

While doing this, it has to be ensured that in successive drawings each of the remaining elements of the population has the same chance of being selected

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COMPLEX RANDOM SAMPLING Also known as mixed sampling design. Under restricted sampling techniques,

the probability sampling may result in complex random sampling designs.

such designs may represent a combination of probability and non-probability sampling procedures in selecting a sample.

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Complex Random Sampling

Systematic

sampling

Stratified

Sampling

Cluster Sampli

ng

Area samplin

g

Multi stage

Sampling

Sequential

Sampling

Sampling with

probability

proportional to

size

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SYSTEMATIC SAMPLING A sampling procedure in which an initial

starting point is selected by a random process and then every nth number on the list is selected.

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ADVANTAGES AND DISADVANTAGES This method is an improvement over a

simple random sample. Easier and less costlier method Can be conveniently used even in case

of large populations.

Problem of Systematic Sampling is Periodicity.

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STRATIFIED SAMPLING A procedure in which simple random

subsamples are drawn from within different strata that are more or less equal on some characteristics.

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STRATIFIED SAMPLINGReasons for stratified sampling: If population does not constitute a

homogeneous group. To have more efficient sampling Reducing random sampling error Assuring that sample would correctly

reflect the population

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STRATIFIED SAMPLING Under stratified sampling the population is

divided into several sub-populations that are individually more homogeneous than the total population.

Then items are selected from each stratum to constitute a sample.

Since each stratum is more homogeneous than the total population, research is able to get more precise estimates for each stratum and by estimating more accurately each of the component parts.

Stratified sampling results yield more reliable and detailed information.

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CLUSTER SAMPLINGReason: When the total area of research interest is

largeProcess: Firstly, population is divided into a number

of smaller non-overlapping areas, which are clusters of homogeneous units

Secondly, few clusters are selected by using a simple random sampling method.

Finally, all the units in the selected clusters are studied.

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CLUSTER SAMPLING Advantages

Low cost/high frequency of use Requires list of all clusters, but only of

individuals within chosen clusters Can estimate characteristics of both

cluster and population Disadvantages

Larger error for comparable size than other probability methods

Multistage very expensive and validity depends on other methods used

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MULTISTAGE SAMPLING Sampling that involves using a combination of

two or more probability sampling techniques. Complex form of cluster sampling in which

two or more levels of units are embedded one in the other.

Process: 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.

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SAMPLING WITH PROBABILITY PROPORTIONAL TO SIZE In case the cluster sampling units do not

have the same number or approximately the same number of elements, it is considered appropriate to use a random selection process where the probability of each cluster being included in the sample is proportional to the size of the cluster.

The actual numbers selected in this way do not refer to individual elements, but indicate which clusters and how many from the cluster are to be selected by simple random sampling or by systematic sampling.

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SEQUENTIAL SAMPLING A complex sample design The ultimate size of the sample is not

fixed in advance When the number of samples is more

than two but it is neither certain nor decided in advance, this type of system is often referred to as sequential sampling.

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NON PROBABILITY SAMPLING TECHNIQUES

Non Probability Sampling

Convenience Sampling

Judgement Sampling

Quota Sampling

Snowball Sampling

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TYPES OF NON PROBABILITY SAMPLING TECHNIQUES Convenience Sampling: The sampling procedure of obtaining

those people or units that are most conveniently available.

Judgement Sampling: A technique in which an experienced

individual selects the sample based on personal judgement about some appropriate characteristic of the sample member.

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TYPES OF NON PROBABILITY SAMPLING TECHNIQUES Quota Sampling: This procedure ensures that various sub

groups of a population will be represented on pertinent characteristics to the exact extent that the investigator desires.

Snowball Sampling: A sampling procedure in which initial

respondents are selected by probability methods and additional respondents are obtained from information provided by the initial respondents.

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SAMPLING ERROR In a sample survey, since only a small

portion of the population is studied and its results are bounded to differ from census results and thus having a certain amount of error.

In Statistics, the word error is used to denote the difference between the true value and the estimated or approximated value.

Sampling error is the gap between the sample mean and population mean.

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NON SAMPLING ERROR Sampling Frame Error: A sampling frame

is a specific list of population units, from which the sample for a study being chosen.

Non Response Error: This occurs because the planned sample and final sample vary significantly.

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GRAPHICAL DEPICTION OF SAMPLING ERRORS

Total Population

Sampling Frame Error

Random Sampling Error

Sampling FramePlanned Sample

Non-Response Error

Respondents(actual sample)

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HOW TO REDUCE ERRORS Errors in sampling can be reduced if the

size of sample is increased. Avoid leading questions Pre-test the questionnaire Train the interviewer to establish good

rapport with the respondents.

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