SAMPLING

Meaning and  Definition of Sampling

Sampling a process of selecting a number of individuals for a study in a way that represents the larger group from which they were selected.

A sample represents the larger group—population

According to Non Lin, "sampling design is a subset of cases from the population chosen to represent it. By using the subset, we can infer the characteristics of the population.

Key terms in Sampling

Sample: A fraction or portion of the population of interest e.g. consumers, brands, companies, products, etc

Population: All members of a defined category of elements such as people, objects or events.

Census: Studying the entire population. A census offers high precision and representativeness but is generally impractical, costly, and time consuming.

Sampling: The process of selecting a sample from the population

Sample Frame: A list of all the elements, units, or members of a population of interest

Sampling Error: Differences in characteristics (e.g. mean, variance, etc) obtained from a sample compared to that of the population of interest. Goal: minimize sample error

Probability Sample: Sampling methods in which each element in the population have a known chance or probability of being selected

Non-probability Sample: Sampling methods that does not use random selection but relies on the judgement of the researcher or the circumstances.

SAMPLING PROCESS

-  Defining the population of concern.

- Specifying a sampling frame, a set of items or events possible to measure.

- Specifying a sampling method for selecting items or events from the frame.

- Determining the sample size.

- Implementing the sampling plan.

- Sampling and data collection

Reasons for Sampling

Why sampling?

- Less costs (cheaper than studying whole population)

- Less errors due to less fatigue (better results)

- Less time (quicker)

- Accurate and Reliable Results

- Greater speed of dta collection.

- Availability of population elements.

- Quality of a study is often better with sampling than with a complete

ADVANTAGES OF SAMPLING 

-  Sample easy to select

 - Suitable sampling frame can be identified easily

 - Sample evenly spread over entire reference population

- It provides more detailed information by inspecting few units.

- Only a solution when pop is infinite & the area of study is wide

 - Sampling saves money, time & labor.

DISADVANTAGES OF SAMPLING

- Chances of bias

- Difficulties in selecting truly a representative sample

- Changeability of sampling units

- Impossibility of sampling.

TYPES OF SAMPLING

Probability Sampling(RANDOM SAMPLING)

Probability sampling utilizes random sampling techniques to create a sample. This group of sampling methods give all the members of a population equal chances of being selected.

                                                      OR

A random sample is obtained by using methods such as random numbers, which can be generated from calculators, computers, or tables.

1) Simple Sampling :- Simple random Every member of a population has an equal chance of being selected.

2) Systematic Random Sampling

In systematic Random Sampling, the first unit is selected with the help of random the help of random numbers and the remaining units are selected automatically according to a predetermined pattern. This method is known as systematic sampling.

3) Stratified Sampling

Stratified sampling, refers to random sampling techniques that clubs items of whole population into different groups called strata, based on their similar characteristics. Then, samples from each stratum are taken, whether proportionately or disproportionately.

4) Cluster Sampling:-  cluster sampling is  sample a naturally occurring group of people

 For example: A group could be a classroom of students

Non Probability Sampling (NON RANDOM SAMPLING)

It is a group of sampling techniques where the samples are collected in a way that does not give all the units in the population equal chances of being selected. Probability sampling does not involve random selection at all.

1) CONVENIENCE SAMPLING

Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected because they happen to be in the right place at the right time.

Example: Interviewer conducts survey at shopping center early in morning on a given day

People he/she could interview limited to those in shopping center at that time on that day

2) Purposive sampling, also known as judgmental, selective or subjective sampling, reflects a group of sampling techniques that rely on the judgment of the researcher; when it comes to selecting the units that are to be studied.

For Example Specific People, Specific cases/organizations, Specific events, Specific pieces of data)

3) Quota Sampling :- Selecting participant in numbers proportionate to their numbers in the larger population, no randomization.

For example;

The number of students from each group that we would include in the sample would be based on the proportion of male and female students amongst the 10,000 university students. (Proportion; 50 male & 50 Female or 40 Female & 60 Male)

4) Snowball Sampling

Snowball ball sampling is known as network or chain referral sampling.  In this sampling technique, first one or two persons in the population are contacted and ask them to identify further persons