SAMPLING IN

RESEARCH

Ayushma Badal Maya Prakash Pant Sabina Suwal

OVERVIEW OF PRESENTATION IntroductionSampling ProcessBasis For SamplingTypes of Sampling

Probability Based Sampling Non-Probability Sampling

Conclusion

INTRODUCTION

“Technique of selecting a representative part of a population for the purpose of

determining parameters or characteristics of the whole

population.”

Basic Terminologies: Sampling Unit

(Elementary Sampling Unit) Sampling Frame

SAMPLING PROCESSo Define the populationo Specify the sampling frameo Selection of sampling unito Selection of sampling methodo Determine the sampling sizeo Specify the sampling plano Select the sample

BASIS FOR SAMPLING1. Reliability

At least four related factors determine how reliable a measure is:

- Precision- Sensitivity- Resolution- Consistency

2. Validity

To decide whether a measure is valid at least two separate points must be considered :

- Accuracy- Specificity

.

Types of Sampling in Quantitative Researches

Probability Based

Sampling

Non-Probability Sampling

PROBABILITY BASED SAMPLES

Random SamplesStratified SamplesCluster SamplesSystematic SamplesAreaMulti-stage

RANDOM SAMPLESUnrestricted :Equal and independent

chance of selecting chance of being selected.

Restricted : Elements are chosen using a specific methodology as in probability sampling or complex probability sampling.

Advantages of random sampling:Easy to conductHigh probability of achieving a

representative samplesMeets the assumption of many statistical

procedureDisadvantages of random sampling:

Identification of all members of population can be difficult

Contacting all members of samples can be difficult

STRATIFIED SAMPLESStratification: process of splitting

population into strata. In representation of sampling units two

approaches are possible : proportionate and disproportionate.

Extensively used in continuous research activities.

Advantages of stratified samplingMore accurate samplesCan be used for both proportionate and

disproportionate samplesDisadvantages of stratified sampling

Identification of all the member of population is difficult

Difficult to make the sub group

CLUSTER SAMPLINGOne samples the sub-groups.Each sub-group should be the

microcosm of the total population.This sampling technique is used when

“Natural” but relatively homogenous grouping are evident in statistical population.

Advantages of cluster samplingVery useful when populations are large

and spread over a large geographical region

Economically efficient

Disadvantages of cluster samplingStatistically less efficient i.e. standard

error of the estimate is likely to be largeRepresentation is likely to become an

issue

SYSTEMATIC SAMPLINGSelection of the elements from an

ordered sampling framework.Determining sampling interval (k) and

then select a random starting point where after every (K)^th item is selected systematically.

Advantages of systematic samplingVery convenient

Disadvantages of systematic samplingBiases could be possible if there are

any hidden patterns or periodicities in the data

MULTISTAGE SAMPLINGA complex form of cluster sampling One or more clusters are chosen at

random and everyone within the chosen cluster is sampled.

Advantages of multistage samplingNormally more accurate than the

cluster sampling for same sized population

Less time consuming in compare to cluster sampling

Disadvantages of multistage samplingNot as accurate as simple random

sampling if the sample is the same sizeMore testing is difficult to do

NON PROBABILITY SAMPLINGNot determined by chances.Focuses on easily available units of

studies.For quick and cheap studies.May or may not represent

population.

TYPES OF NON PROBABILITY SAMPLING

1. Convenience sampling

2. Judgmental sampling

3. Snowball sampling

4. Quota sampling

1. CONVENIENT SAMPLINGElements in a fraction of the population

can be reached conveniently.Sample are drawn randomly.Also known as Accidental, men-in-the-

street, haphazard samplingSaves time and money.Easy but not systematic

2. JUDGMENTAL SAMPLINGFocus more in judgments and

personal opinionPurposive ; not randomExpert’s experience and appropriate

strategySample is drawn upon the good

judgment of the researcher.

3. SNOWBALL SAMPLINGSample characteristics is rare.Respondents are difficult to identify

and are best located through referral networks

An initial groups helps further for finding the respondents and creating networks.

Also known as chain referral sampling/ network sampling.

4. QUOTA SAMPLING  Population is divided under no. of

segments and quota are formed randomly from each segment.

Non random sample selection from segments .

Non probability version of stratified sampling.

Useful when time is limited.

CONCLUSIONDifferent sampling method have their

respective advantages and disadvantages. So according to nature and need of research appropriate method should be used.

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