SAMPLING

IntroductionSampling is a complex and technical topic to which entire have been devoted. At the same

time, the basic features of sampling are familiar to us all. In the course of our daily activities, we gather knowledge, make decision and make decisions and make predictions through sampling.

Researchers too, generally derive knowledge from samples. For example, in testing the efficacy of a medication for asthma patients, a researcher must reach a conclusion without administering the drug to every asthmatic patient. However, researchers cannot afford to draw conclusion about the effectiveness of interventions or the validity of relationships based on a sample of only three or four subjects.

Sampling is an important step in the research process for quantitative studies. It is the selection of some part of an aggregate or totally of population on the basis of which a judgment or inference about the aggregate or totality is made.

Definitions:-

Sampling is a process of selecting representative units of a population for study in a research.

-B. T. Basavanthappa.

Sampling refers to the process of selecting a position of the population to represents the entire population.

-Polit.

Sampling is the process of selecting a representative segment of the population under study

Statistical method of obtaining representative data or observations from a group. (Clot, batch, population or universe).

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Terminology

Some of the main terms used in sampling process are

Population

Population is the aggregate of all the units in which a researcher is interested. In other words, population is the set of people or entities to which the result of a research are to be generalized.

For example, a researcher needs to study the problems faced by post graduate nurse of India; in this the population will be all the post graduate nurses who are Indian citizens.

Target population

A target population consists of the total number of people or objects which are meeting the designates set of criteria. In other words, it is aggregate of all the cases with a certain phenomenon.

For example, a researcher is interested in identifying the complication of DM type II among people who have migrated to Ludhiana. Here, the target population is all the migrants at Ludhiana suffering with DM- type II.

Accessible population

It is the aggregate of cases that conform to designated criteria and are subject for a study.

For example, ‘a researcher is conducting a study on the registered nurses(RN) working in Dayanand medical college and hospital (DMCH), Ludhuana. Here, the population for this study is all the RNS who meet the designated criteria and who are available for the research study.

Sampling

Sampling is the process of selecting a representative segment of the population under study.

Sample

Sample may be defined as representative unit of a target population, which is to be worked upon by researcher during their study.

Elements

The individual entities that compromise the samples and population are known as elements and an element is the most basic unit about which information is collected. An element is also known as subject in research.

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Sampling frame

It is a list of all the elements or subjects in the population from which the sample is drawn. Sample frame could be prepared by the researcher or an existing frame may be used.

Population

 

Sample frame

 

Available units

 

Sample

 

 

 

 

For example, a research may prepare a list of the all the household of a locality which have pregnant women or may used a register of pregnant women for antenatal care available with the local Anganwadi worker.

Sampling error

There may be fluctuations in the values of the statistics of characteristics from one sample to another, or even those drawn the same population.

Sampling bias

Distortion that arises when a sample is not representative of the population from it was drawn.

Sampling plan

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The formal plan specifying a sampling method, a sample size, and the procedure of the selecting the subjects.

Sample planning

A sampling plan is a detailed outline of which measurements will be taken at what times, on which material, in what manner, and by whom?

Assignment

Having drawn the sample, these may be assigned to different groups.

A common grouping is an experimental group which receive the treatment under study and a control group that gives a standard against which experimental results can be compared. To sustain internal validity, this is usually random assignment. Non-random assignment is for example where two school classes are selected as coherent groups and one chosen as the control.

Sampling distribution

If the sample is described as a histogram (a bar chart showing numbers in different measurement ranges) it will have a particular shape. Multiple samples should have similar shapes, although random variation means each may be slightly different. The larger the sample size, the more similar sample distributions will be.

Generalizing

After sampling you then generalize in order to make conclusions about the rest of the population.

Validity

Validity is about truth and accuracy. A valid sample is representative of the population and will allow you to generalize to valid conclusions. This aligns with external validity.

A valid sample is both big enough and is selected without bias so it is representative of the population.

Strata

Strata (singular: stratum) are sub-groups within a population or sample frame. These can be random groups, but often are natural groupings, such as men and women or age-range groups. Stratification helps reduce error.

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Oversampling

Oversampling occurs when you study the same person twice. For example if you selected people by their telephone number and someone had two phone numbers, then you could end up calling them twice. This can cause bias.

Purposes of sampling

Economical

It is not possible and economical for researcher to study an entire population. The researcher can save lots of time, money and resources to study a phenomenon.

Improved quality data

When a researcher is handling the information from only a part of the population under study, it is easier to maintain the quality of the research work, which would not be possible in case the entire population is involved.

Quick study results

Studying an entire population itself will take a lot of time and generating research results of a large mass will be almost possible as most research studies have time limits. It is possible to generate study results faster, which is one of the important objectives of every researcher.

Precision and accuracy

of data conducting a study on an entire population provides researchers with voluminous data, and maintaining precision of that data becomes a cumbersome task, while carrying a study on a part of the population helps the researcher to generate more precise data; where formulation of the interpretations of the data becomes much.

Need for Sampling

Saves time and energy

Enable more accurate measurements

Only way when population is infinite

Only choice when test involves destruction of item under the study

Enables to estimate sampling errors – more information on population

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Characteristic of good sampling

There are various qualities and characteristic features that make a sample good. To generalize the finding for an entire population, a good sample for the research study must have following characteristics.

Representative

a representative sample is one that the key characteristic of which are closely related to those of the population. Representativeness of the sample makes it possible to generalize the findings for the population.

Free from bias and errors

a good sample is one which is free from deliberate selection of the subjects for study. Sample should be free from simple random sampling errors or sampling bias.

No substitution and in completeness

A Sample is the said to be good if once a subject is selected for the study, it is neither replaced nor it is incomplete in any aspects of researcher’s interest.

Appropriate sample size:

Generally it is believed that in quantitative studies the larger the sample size better is the probability of the goodness of the sample.

Low Sampling Error

Every time you poll a sample of a population (as opposed to asking everyone), you're going to get some statistics that are a little different from the "true" statistics. This is called sampling error, and is often expressed as percentage points. For example, a poll might be plus or minus "ten points." In other words, if a pollster finds that 55 percent of people will vote for a certain candidate, plus or minus ten points, they are really saying that somewhere between 45 and 65 percent will vote for that candidate. A good sample will have a low sampling error (a point or two).

High Confidence Level

Population, the more the data resembles a bell curve. Confidence levels are expressed as a percentage, such as a "90 percent confidence level." The higher the confidence level, the more sure a researcher is that his data looks like a bell curve: a 99 percent confidence level is desirable and likely to have better results than a 90 percent (or lower) confidence level.

Degree of VariabilityThe degree of variability refers to how diverse a population is. For example, a poll of all political parties about health care is likely to result a more widespread variation in responses than a simple poll of a single party. The higher the stated proportion, the greater

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the level of variability, with .5 being the highest (and possibly, least desirable) value. For smaller samples, you would want to see a low degree of variability

Factors influencing sampling process

The factors which may affect the sampling process are-

Nature of the researcher

Inexperienced investigator

If the investigator lacks adequate knowledge and experience about the conditions of the researcher process, the sample selection be adversely affected.

Lack of the interest

Lack of the self motivation and appreciation for carrying out task or establishing research methodology on the part of the research also affect the drawing of the sample.

Lack of honest

Lack of the honesty will affect sampling process in research. Research should be honestly involved in each step of the research process.

Intensive work load

Lack of adequate resources and ability to carry out the research process result in inadequate selection and application of all resource process including the sampling process.

Inadequate supervision

There should be adequate supervision of the research activity to ensure the appropriate implementation of the research process including the sampling process.

Nature of the sample-

Inappropriate sampling a sampling technique, the whole sampling process may get affected.

Sample size:

Very large samples become heterogeneous and do not exhibit characteristics of whole population in general; if the sample is too small, a researcher may not be able to generalize the study findings to the whole population.

Definitive sampling frame

Defective sampling frames leads to faculty sampling process. Researcher should have adequate knowledge about population under study to have an appropriate sampling frame.

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Circumstances

Lack of time

Adequate time should be available with the researcher to have adequate planning and implementation of the sampling process.

Large Geographic area

A large Geographic needs lots of time and resource to accomplish the sampling process. In addition, large Geographical areas can also lead to mental and physical exhaustion and thus the sampling process can get adversely affected.

Large of cooperation

During sample process, researcher needs cooperation from competent authorities as well as from competent authorities as well as from the subjects.

In the absences of cooperation of the requisite authorities and study subjects, the sampling process may be affected.

Natural calamities

Sometimes the sampling process is affected by natural calamities such as floods and other natural distress, death , or other environmental constrains.

Types of sampling techniques.

Sampling is the process of selecting a representative part of the population. There are several methods or techniques of sampling; basically sampling techniques are classified into two broad categories, i.e., probability and non probability sampling techniques.

Types of sampling techniques Probability sampling Non probability sampling

Simple random Purposive SamplingStratified random convenient samplingSystematic random Consecutive samplingCluster/multistage sampling Quota samplingSequential sampling Snow ball samplingProbability Proportional to Size Sampling

PROBABILITY SAMPLING

It is based on the theory of probability. It involves random selection of the elements/members of the population.

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Probability sampling includes techniques that select samples based on the concept of random selection

It is a scheme in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined

Probability sampling is a technique where in the samples are gathered in a process that gives all

NON-PROBABILITY SAMPLING

Non-probability sampling techniques are not based on random selection

It is any sampling method where some elements of the population have no chance of selection (these are sometimes referred to as 'out of coverage'), or where the probability of selection can't be accurately determined.

Simple Random Sampling

This is the most pure and basic probability sampling design. In this type of sampling design, every member of the population has the equal chance of being selected as subject. Each choice of sampling unit is independent of all other choices.

There is a need of two essential prerequisites to implement the simple random technique.

1. The population must be homogeneous and2. Researcher must have list of members/elements of the accessible population

The first step of the simple random sampling technique is to identify the accessible population and prepare a list of all element / members of the population.

The list of the subject in population is called as sample frame and a sample drawn from sampling frame by using following methods:

The lottery method:

There are many methods to proceed with simple random sampling. The most primitive and mechanical would be the lottery method. Each member of the population is assigned a unique number. Each number is placed in a bowl and mixed thoroughly.

The blind- folded researcher then picks numbered tags. All the individuals bearing the numbers picked by the researcher are the subjects for the population.

The use of table random numbers

This is the most commonly and accurately used method in simple random sampling . Random table present several numbers in rows and columns. Researcher initially prepares a numbered list of elements /members of population, and then with blind folded chooses a number from the random table.

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The same procedure is continued until the desired number of the subjects is achieved. If repeatedly similar numbers are considered until desired numbers of subjects are achieved.

10 09 73 25 33 76 52 1

37 54 20 48 05 64 89 47

8 42 26 89 53 19 64 50

9 1 90 25 29 9 37 67

12 80 79 99 70 80 15 73

66 6 57 47 17 34 7 27

31 6 1 8 5 45 57 18

85 26 97 76 2 2 5 16

63 57 33 21 35 5 32 54

73 79 64 57 53 3 52 96

The use of computer

For population with a small number of members, it is advisable to use the first method, but if the population has many members, a computer aide random selection preferred.

This sampling technique gives each element an equal and independent chance of being selected. An equal chance means equal probability of section e.g., in a population of 300 each element theoretically has 11300th chance of being selected .

Equal probability selection method is described as epsin sampling. An independent chance means that the draw of one element will not affect the chances of other elements being selected.

Merits-

One of the best things about simple random sampling is the ease of assembling the sample. It is also considered a fair way of selecting a sample from a given population. Since every

member is given equal opportunity of being selected.

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The simple random sampling requires minimum knowledge about the population in advance.

This is one of the most unbiased probability methods of sampling. This is method sampling which is free from sampling errors. Sample errors can be easily computed and the accuracy of estimate easily assessed.

Demits-

One of the most obvious limitations of simple random sampling method is the requirement of and an up-to-date list of all the members of the population

This method does not make use of knowledge about population that researcher already have.

Lots of procedures need to be done before sampling is accomplished. The cases selected by random sampling tend to be widely dispersed geographically and the

time and cost of collecting data becomes too large.

Stratified random sampling

This method is used for heterogeneous population. Stratified sampling is a probability sampling technique where the researcher divides the entire population into different homogenous subgroups or strata and then randomly selects the final subjects proportionally from the different strata.

The strata are divided according to selected traits of population such as age, gender, religion, socioeconomic status, diagnosis etc.

According to the weight age of the sample and proportion; stratified random sampling is further divided into two categories:

1. Proportionate stratified random sampling2. Disproportionate stratified random sampling.

Proportionate stratified random sampling

In this the sample chosen from each stratum is in proportion to the size of total population. The sample size of each stratum in this technique is proportionate to the population size of the stratum when vied against the entire population. This means that each stratum has the same sampling fraction.

The important thing in this technique is to use the sample fraction for each stratum regard less of the differences in proportion size of strata.

For example, researcher has three strata with 100, 200, 300 population size respectively and the researcher choose a sampling fraction of ½. Then the researcher must randomly sample 50,100 and150 subjects from each stratum respectively.

Disproportionate stratified random sampling

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In this sub type, the sample chosen from each stratum are not in proportion to size of total population in that stratum. The only difference between proportionate and disproportionate sampling is their sampling fraction.

The precision of this design is highly dependent on the sampling fraction allocation of the researcher. If the researcher commits mistake in allotting sampling fractions, a fraction may be over represented or underrepresented.

For example if the researcher wants to study biophysical profiles of nursing students, in a CON, which contains 100 students of from Himachal, 200 students from Haryana and 300 students from Punjab. The researcher choose different sampling fraction and then randomly select sample of 50 subjects from each strata.

Merits

It ensures representation of all groups in a population Researcher also employs stratified random sampling when they want to observe

existing relationship between 2 or more subgroups. Therefore comparisons is possible With stratified sampling the researcher representatively sample even the smallest and

most in accessible subgroups in the population. This allows the researcher to sample the rare extremes of a given population.

With stratified sampling technique, there is a higher statistical precision compared to simple random sampling. This is because of variability within the subgroups is lower compared to the variations when dealing with entire population.

Because this technique has high statistical precision, it also means that it requires a simple sample size which can save lot of time money and effort of the researcher.

Demerits

Proportionate stratification requires accurate information on the proportion of population in each stratum.

Large population must be available from which to select subjects. There is always possibility for faulty classification and increase in variability.

Systematic random sampling

Systematic random sampling can linked to an arithmetic progression, where in the difference between any two consecutive members is the same it involves the selection of every k th list of group such as every tenth person or a patient list or every 100 th person from a phone directory.

Systematic sampling is sometimes used to sample every K th person entering the a book store etc. Systematic sampling can be applied so that an essentially random sample is drawn. If we had a list of subjects or sample frame, then, the desired sample can be sample size is established at some (n) and the size of population must be known or estimated (N).

K=N/n (or)

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Number of subjects target population (N)

Size of the sample (n)

For example a researcher wants to choose about 100 subjects from a total target population of 500 people, 500/100=5. Therefore 5th person will be selected.

In this method list of subjects is prepared for the target population (sample frame) and then the first subject is randomly selected; later every Kth subject is selected from the sampling frame.

Merits

This technique is convenient and simple to carry out. Distribution of sample is spread evenly over the entire given population. Less cumbersome, time consuming, and is cheaper than simple random sampling. Statistically more efficient and provides a better representative sample when population

elements are randomly distributed.

Demerits

If first subject is not randomly selected,, then it becomes a non random sampling technique. This may result in biased sample. If sample frame has nonrandomly distributed subjects, this sampling technique may not be

appropriate to select a representative sample.

Cluster or Multistage sampling

Cluster random sampling is done when simple random sampling is almost impossible because of size of the population.

Cluster sampling means random selection of sampling unit consisting of population elements. Then from each selected sampling unit, a sample population element is drawn by either simple random selection or stratified random sampling. This method is used in cases where the population elements are scattered over a wide area, and it is not possible to list all the elements.

Geographical units are the most commonly used. For example, a researcher wants to survey academic performance of high school students in India.

He can divide the entire population of India into different clusters(cities) Then the researcher selects the number of clusters depending on the research based on simple

random sampling. Then from the selected clusters the researcher can either include all the high school students as

subjects or he can select a number of subjects from each cluster through or systematic random sampling.

TYPES OF CLUSTER SAMPLES

One stage cluster sample

Two stage cluster sampling

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One stage cluster sample

It occurs when the researcher includes all the high school students from all the selected clusters as sample

Two stage cluster sampling

From the cluster sample selected, the researcher selects few number of students from each cluster by using simple or systematic random sampling technique.

Merits This sampling technique enables the investigator to use existing divisions such as

districts, villages, towns etc. Can be used when there is no exhaustive list of all elements. Reduces the cost and workload. Some clusters can also used again for sampling

Demerits

Less precise than other random sampling techniques. If clusters chosen are biased in anyway, inferences drawn about population will not be

accurate. Possibility of sampling bias and errors.

Sequential sampling

This sampling technique is slightly different from other methods. Here the sample size is not fixed. The investigator initially selects a small sample and tries out to take inferences; if not able to draw results, he or she then adds more subjects until clear cut inference can be drawn.

Merits

Facilitates to conduct study on a best possible smallest representative sample Helps ultimately in finding the inferences of the study

Demerits

A phenomena cannot be studied at one point of time Requires repeated entries into the field to collect the sample

Probability proportionate to size sampling

In this procedure if cluster has large a population as other, it is given twice the chance of being selected. A sampling technique, commonly used in multi-stage cluster sampling, in which the probability that a particular sampling unit will be selected in the sample is proportional to itssize.

The selection procedure is

1. Draw a list of clusters with their size measures 2. Cumulate the size measures in sequences

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3. Divide the list into a certain appropriate equal strata with reference to cumulated measures example if the cumulative total is 600 the list may be divided into 3 equal zones 1-200,201-400and 401-600.

4. Select the required equal number of sample in each zone , applying preferable systematic selection with a random start &

5. Draw a same fixed number of population elements from each selected cluster at random

Advantages

- Pps lead to greater precision than simple random sample of clusters.- Equal sized samples from each selected primary cluster is convinent - Pps cannot be used if the sizes of the primary clusters rae not known.

Non probability sampling :

Every subject does not have equal chance to be selected necause elements are chosen by choice not by chance through non random sampling methods.

It is believed that non random `methods of sampling are more likely to produce a biared sample than random methods. In aprobability sampling certain elements have more probability to be the part of sampling while other may have no chance of being included in the sample. This restricts the generalization that can be made abord the study findings.

Features of the non probability sampling:

Non probability sampling is a technique where in the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.

Most researchers are bound by time money and work force and because of these limitations it is almost impossible to randomly sample the entire population and it is often necessary to employ another sampling technique the non probability sampling technique.

In contrast with probability sampling , non probability sample is not a product of a randomized selection process subjects in a non probability sample are usually selected on basis of their accessibility or by the purposive personal judgment of the researcher.

Uses of non probability sampling:

Non probability sampling is used in following situations.

This type of sampling can be used when demonstrating that a particular trait exists in the population.

It can also be used when the researcher aims to do a qualitative, pilot or exploratory study It can be used when the research is not possible when the population is almost limitless

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