SAMPLING TECHNIQUES: SYSTEMATIC AND PURPOSIVE SAMPLING

Presented by:

• Neeraj SiNgh• Siddhi Sood• VarSha KapSe

INDEX

1. SAMPLING TECHNIQUES (by Siddhi Sood)

2. SYSTEMATIC SAMPLING (by Varsha Kapse)

3. PURPOSIVE SAMPLING

(by Neeraj Singh)

Sampling is a concept that is more often used in researches. Lets know something essential herein about RESEARCH.

PURPOSE OF THE RESEARCH

The primary purpose of a research is to discoverprinciples that have universal application.

BASIC PROBLEM IN RESEARCH

Most of the educational phenomena consist of a large number of units.

Impractical to test or observe each unit of the population under controlled conditions for arriving at generalizations.

Such a study is expensive in terms of time, money, effort, and manpower.

PRIMARY SOLUTION

Process of Sampling makes it possible to draw valid inferences.

It includes careful observation of variables within a relatively small proportion of the population.

The process of sampling involves the use of SAMPLE.

WHAT IS A SAMPLE?

A sample is a small proportion of a population selected for observation and analysis.

By observing the characteristics of the sample, one can make inferences about the characteristics othe population from which it is drawn.

For example, if all public schools teachers represent a Population, the teachers of public schools at Indore, may be taken as Sample.

PRINCIPAL ADVANTAGES OF SAMPLING

Sampling-

Reduces expenditure cost. Saves time and energy. Permits measurement of greater scope. Produces greater precision and accuracy.

Applying a process effectively always involve some techniques, and so does sampling.

SAMPLING TECHNIQUES

Samples cannot be drawn haphazardly.

Its adequacy depends upon our knowledge of the population and also on the methods or techniques used in drawing the sample.

Before moving on to various techniques of sampling, we should be aware of some components of sampling.

KEY TERMS IN SAMPLING

SAMPLING ELEMENT is the entity from the population about which information is collected.

SAMPLING UNIT can be a single member (element) or collection of members subject to data analysis in the sample.

SAMPLING FRAME is the complete list of all units/elements from which sample is drawn. It is also known as working population.

…cont.

TARGET POPULATION is the one to which the researcher would like to generalize his results on.

SAMPLING TRAIT acts as a base of taking out the sample out of the population. It may be quantitative or qualitative.

SAMPLING FRACTION is the proportion of the total population to be included in the sample.

…cont.

Formula: Size of sample or nTotal population N

SAMPLING ESTIMATE is an estimate from a sample value of what the value would be in the total population from which the sample is drawn.

SAMPLING ERROR is the difference between total population value and the sampling value.

…cont.

It is the degree to which the ‘sample characteristics’ approximate the ‘characteristics of the total population’.

Smaller the sample, greater will be the sampling error.

NOTE:• Parameter is the summary description of a variable for a

population.

• Statistic is a measured value based upon sample data.

Parameter20 years

Statistic21 years

Statistic24 years

Statistic22 years

Error= 1 year

Error= 4 years

Error= 2 years

SAMPLE 1

SAMPLE 2

SAMPLE 3

SAMPLING ERROR

TYPES OF SAMPLING

There are basically two types of sampling:

SAMPLING

Probability Sampling Non-probability sampling

PROBABILITY SAMPLING

It is one in which every unit of the population has an equal probability of being selected for the sample.

It offers a high degree of representativeness.

Today, it remains the primary method for selecting large, representative samples for social science and business researches.

CONDITIONS TO BE SATISFIED

According to Black and Champion (1976):

1. complete list of the subjects to be studied is available;

2. size of the universe must be known;

3. desired sample size must be specified, and

4. each element must have an equal chance of being selected.

DISADVANTAGE

This method is expensive, time consuming, and relatively complicated since it requires a large sample size and the units selected are usually widely scattered.

NON-PROBABILITY SAMPLING

This sampling makes no claim for representativeness.

Every unit does not get the chance of being selected

Researcher decides which sample should be chosen.

WHEN TO USE NON- PROBABILIYTY SAMPLING

In many research situations, especially where there is no list of persons to be studied, probability sampling is inappropriate to use.

In such researches, non-probability sampling is the most appropriate one.

It is usually used for qualitative exploratory analysis.

DISADVANTAGES

The informants do not represent the population.

Informants’ observation and opinions may be misleading.

Unwillingness to help and cooperate.

Selecting informants who are convenient for study

Personal leanings of the researchers of being prejudiced against certain types of persos or groups, say bias in choosing a caste, community, social strata, etc.

FORMS OF PROBABILITY AND NON-PROBABILITY SAMPLING

Probability Sampling

1. Simple random

2. Stratified random

3. Systematic (or interval)

4. Cluster

5. Multi-stage

6. Multi-phase

Non-probability Sampling

Convenience Purposive Quota Snowball Volunteer

DEFINITION

According to Joan Joseph Castillo (2009),

“ Systematic sampling is a random sampling technique which is frequently chosen by the researchers for its simplicity and periodic quality.”

i.e., the researcher first randomly picks the first item or subject from the population and then he will select each ‘kth’ item or subject from the list.

Here, k = Population size / sample size

USE

It is an improved version of simple random sampling and used in a research, were the information about the population is known.

This method is used when group is to be arranged in a sequence.

It is used where the ordering of the population is essentially random or contains at most a mild stratification.

…cont.

Used for sub-sampling clustered units.

Used for sampling populations with variations of a continuous type, provided that an estimate of the sampling error is not regularly required.

ADVANTAGES

It is a simple method of sampling. Systematic samples are convenient to draw

and execute. It ensures that the population will be evenly

sampled and so reliable conclusions can be drawn.

This method is comparatively economic in terms of time, labor and money.

It systematically eliminates the clustered selection of the subjects.

DISADVANTAGES

Poor precision when unsuspected periodicity is present.

No trustworthy method for estimating from the sample data, is known.

Possibility of illusionary conclusions if there is a circular change within the intervals of population.

Rare possibility of using this method in economic and social surveys.

Ignores all persons between two kth nos.

Possibility of over or under representation of several groups is greater.

LIMITATIONS

Information about every unit and population should be known.

No complete assurance for the representative sample.

Risk in extracting conclusions on the basis of obtained results from samples.

Since, each element has no chance of being selected, it is not probability random sampling (Black and Champion, 1976)

Thus, this technique cannot be free from errors.

MEANING AND OBJECTIVE

Purposive sampling is a sampling method in which elements are chosen from among the whole population based on purpose of the study.

The main objective of purposive sampling is that the researcher, with his good decision and appropriate policy, chooses those elements which are meant for fulfilling the research objective.

DEFINITION

In the words of J.P. Guilford:

“ A purposive sample is the one arbitrarily selected because there is good evidence that it is very representative of the total population.”

According to Jahoda and Cook:

“The basic assumption behind purposive sampling is the good judgment and appropriate strategy, one can hand-pick the class to be included in the sample and thus develops sample that are satisfactory in relation to one’s need.

CHARACTERISTICS

The researcher is well aware of the attributes and characteristics of the population units.

The sample is selected keeping the problem of study in mind for its purposeful solution.

The results obtained are policy affected and bit biased.

ADVANTAGES

Moderately economic in terms of cost and time because of small sample size.

The selected units from the population are closely related to the problem of study

Pre-knowledge of the population units makes the selected sample more representative.

Guarantees meeting specific objectives. Useful for certain types of forecasting.

DISADVANTAGES

This method is useful only when the researcher has the complete information and knowledge about the population.

More biased.

Impurities of the sample cannot be judged.

Understanding the whole group is not that easy.

Statistically , the obtained results through this techniques are less reliable.