Sampling in Research

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Research Methodology

& Report Writing

By

Rabail Arif Shaikh

Roll No. 43

SAMPLING

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Report

On

“Sampling in Research”

October, 4th 2010

D

EPARTMENT OF

P

UBLIC

A

DMINISTRATION UNIVERSITY OF KARACHI

Submitted By Rabail Arif Shaikh

Submitted to

Ma’am Anila Akhtar

Course and Project Incharge Dept. of Public Administration

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Sampling in Research and Introduction

1 Definition of Sampling

2 Typology 3-6

Importance of Sampling

6 Arguments of Sampling

7 Conclusion 8

References 9

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SAMPLING IN RESEARCH

Scientific research consists of an exploration to seek answers from questions, and used the systematic way to find out the results with the help of evidences. The qualitative type of research is empirical research in which researcher explores relationship using textual, rather than quantitative data. Case study, observation, and ethnography are considered forms of qualitative research. Results are not usually considered generalizable, but are often transferable. This is a subjective form of research that relies on analysis of controlled observations of the researcher.

The strength of qualitative research is its ability to provide complex textual descriptions of how People experience a given research issue. Even if it were possible, it is not necessary to collect data from everyone in a community in order to get valid findings. In qualitative research, only a sample of a population is selected for any given study. The study’s research objectives and the characteristics of the study population determine which and how many people to select.

INTRODUCTION

“According to Webster (1985), to research is to search or investigate exhaustively. It is a careful

or diligent search, studious inquiry or examination especially investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts or practical application of such new or revised theories or laws, it can also be the collection of information about a particular subject.”

Sampling is to pick a sufficient number of people or any element which is considered under observation for any experiment. In this process we pick a sample of elements and each of the members of sample is called subject.

For example: if 200 members are drawn from a population of 1,000 blue-collar workers, these 200 members form the sample for the study. That is, from a study of these 200 members the researcher would draw conclusion about the entire population of 1,000 blue-collar workers. And each blue-collar worker in the sample is a subject.

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Research Methods Sampling in Research

DEFINITION OF SAMPLING

“

Sampling is the act, process, or technique of selecting a suitable sample, or a representative

part of a population for the purpose of determining parameters or characteristics of the whole population.”

&

“Sampling is the process of selecting a sufficient number of elements from the population, so that a study of the sample and an Undertaking of its properties or characteristics would make it

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TYPOLOGY

There are two major types of sampling probability sampling and non-probability sampling. In probability sampling, the elements in the population have some known chance or probability of being selected as sample subjects. In non-probability sampling, the elements do not have a known or predetermined chance of being selected as subject. Probability sampling designs are used when the representativeness of the sample is of importance in the interests of wider generalizability. When time or other factors, rather than generalizability, become critical, non-probability sampling is generally used.

These types further divided into their sub types.

PROBABILITY SAMPLING:

Probability samples are selected in such a way to be representative of the population. They provide the most valid or convincing results, because they reflect the characteristics of the population.

• Simple random sampling • Systematic sampling • Stratified sampling • Cluster sampling • Area sampling • Double sampling

Simple random sampling: In the simple random sampling, every element in the

population has an equal chance of being selected as a subject. It means each member in the population of interest has an equal likelihood of selection.

The statement of an equal chance of selection means that sources such as a telephone book or voter registration lists are not adequate for providing a random sample of a community. In both these cases there will be a number of residents whose names are not listed. Telephone surveys get around this problem by random digit dialing, but that assumes that everyone in the population has a telephone. The key to random selection is that there is no bias involved in the selection of the sample. Any variation between the sample characteristics and the population characteristics is only a matter of chance.

Systematic sampling: when the sample is selected from the population at a regular interval (e.g. every 5th member from a subject pool is selected). This sampling is used when there is a large frame of population. In this type before sampling the population is divided into characteristics important for the research, for example by gender, social

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class, education level, religion, etc. then population is randomly sampled with each of the stratum. For example if 20% of the population is college educated, then 20% of sample is randomly selected from college educated people.

Stratified sampling: When the population is divided into categorical subgroups, it is

good choice when differentiated information is needed regarding various strata within the population, which are known to differ in their limit. It provides more exact results than a simple random sample of same size, so the small sized sample is required which saves money. Stratified sampling designs falls into one of the two categories;

1. Proportionate stratified sampling 2. Disproportionate stratified sampling

Proportionate stratified sampling:

In this type of stratified sampling technique the sample size of each stratum is equal to the population size of the stratum. Each stratum have equal fraction of sample.

Disproportionate stratified sampling:

In this type of design sampling fraction may vary from stratum to stratum.

Cluster sampling

:

In this type of sampling chunks of elements that ideally would have heterogeneity among the members within each group are chosen for study. Most large scale surveys are done by this technique. Cluster sampling is further divided into two types:

1. One-stage sampling: In this type all the members within selected cluster are directly included in the sample

2. Two-stage sampling: In this type a sub-set of elements within selected clusters are randomly picked up for the inclusion in sample.

Area sampling: When the total area under investigation is divided into small sub-areas, this includes geographical clusters. Each of chosen sub-areas then fully inspected and enumerated and may form a frame for further sampling if required.

Double sampling:

A sample is designed initially to get some basic information of interest, and

later a sub-sample of the same primary sample is used to get further information in detail, this sub-sample of the previous sample is used to examine the matter in more detail.

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Non-probability samples are limited with regard to generalization. Because they do not truly represent a population, we cannot make valid inferences about the larger group from which they are drawn. Validity can be increased by approximating random selection as much as possible, and making every attempt to avoid introducing bias into sample selection. There are two types of non-probability sampling:

•

Convenience sampling

•

Purposive sampling

Convenience sampling: This sampling generally assume a homogeneous population, this is the collection of information from people who are conveniently available, and pretty much alike. Use people in the street, people you know, people who work with you, customers and so on. For example you decided to take interview of 15 people next morning, you just wake up and went for your work where you get some people around you get the desire information from them, it can save your time as well as cost. It is the poorest type of sampling in all sampling methods.

Purposive sampling: This type of non-probability sampling is used when you want to target a specific group of people, who fits in a particular profile. Purposive sampling starts with a purpose in mind and the sample is thus chosen to take account of people of interest and keep out those who do not suit the rationale.

Purposive sampling further divides into two major types: 1. Judgment sampling

2. Quota sampling

Judgment sampling: Judgment sampling is the one that is done when there is no time, and you

want a quick sample and you believe that you are able to select a satisfactorily representative sample for the motive.

For example, a researcher may decide to draw the entire sample from one representative town, even though the population includes all towns. When using this method, the researcher must be secure that the chosen sample is truly representative of the entire population.

Quota sampling: This is done when you need a desire sample size and you use all possible efforts to fulfill your demand. It is used when you know that the magnitude of particular sub-groups within a population and you want to ensure each group is proportionately represented. Example: A researcher is interested in the attitudes of members of different religions towards the death penalty. In Iowa a random sample might miss Muslims (because there are not many in that state). To be sure of their inclusion, a researcher could set a quota of 3% Muslim for the sample. However, the sample will no longer be representative of the actual proportions in the population.

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This may limit generalizing to the state population. But the quota will guarantee that the views of Muslims are represented in the survey.

The basic problem with this form of sampling is that even when we know that a quota sample is representative of the particular characteristics for which quotas have been set, we have no way of knowing if sample is representative in terms of any other characteristics. If we set quotas for gender and age, we are likely to attain a sample with good representativeness on age and gender, but one that may not be very representative in terms of income and education or other fact. Quota sampling can be describes in two ways:

1. Proportionate quota sampling 2. Snowball sampling

Proportionate quota sampling: In this type of sampling a predetermined proportion of people are picked up from different groups to form a sample, it is used when you know the distribution of target people across a set of collections.

Snowball sampling:

This sampling is done when the subjects are hard to locate. The process of

snowball sampling is much like asking your subjects to nominate another person with the same characteristic as your next subject. The researcher then observes the chosen subjects and continues in the same way until the obtaining enough number of subjects.

IMPORTANCE OF SAMPLING

Sampling is a very important aspect of any research, because it makes work easy and trouble-free. We obtain sample rather than a complete enumeration of population for many reasons; obviously it is cheaper to observe a part rather than the whole population, if you take under consideration the whole population it will take a huge amount of time. A sample may provide you with needed information quickly. The economic advantage of using a sample in research obviously, taking a sample requires fewer resources than consider the whole population. There are some people that are very difficult to access so we can pick a sample easily to observe their nature. The importance of sampling should not be underestimated, as it determines to whom the results of your research will be applicable. It is important, therefore to give full consideration to the sampling strategy to be used and to select the most appropriate. Before you go to select a sample you should have a better know how about sampling techniques, you should know which type of sampling is suitable in what circumstances. A sample may be more accurate than a whole survey; a messily conducted survey can provide less reliable information than a carefully obtained sample. The reason for using a sample rather than collecting information from the entire population, are self-evident. In research investigations involving several hundreds or even

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thousands of elements, it would be practically impossible to collect information or examine every element.

The goals of sampling are to decrease time and money costs, to increase the amount of data and detail that can be obtained, and to increase accuracy of data collection by preventing errors.

ARGUMENTS ON SAMPLING

As we all know this is 21st century people have no enough time to expand their work if they have a simple alternative available. Sampling is the trend which needs lesser time, fewer resources and gives almost equal or sometime better results than applying the whole survey process. It is true that sampling is more economical than other lengthy procedures; it consumes less money and other resources. . For complete count, we need a big team of supervisors and enumeration who are to be trained and they are to be paid properly for the work they do. Thus the sample study requires less time and less cost. As we talk about the reliability of the sampling technique, if we collect the information about all the units of population, the collected information may be true. But we are never sure about it. We do not know whether the information is true or is completely false. Thus we cannot say anything with confidence about the quality of information. We say that the reliability is not possible.

Unfortunately, all samples deviate from the true nature of the overall population by a certain amount due to chance variations in drawing the sample's few cases from the population's many possible members. This is called sampling error. The sample which has an error can become a disaster to your research as its bases are not perfect. The whole research is based upon the sample you are considering if that has some error or defect in it, so it’s very hard to conclude the desire results. The two basic causes of sampling errors are chance which occurs just because of bad luck and another is sampling bias, sampling bias is a tendency to favors the selection of units that have particular characteristics. If It is an error caused naturally or by a mistake that occurred during research and that cannot be treated its’ your bad luck, bias in sampling is a systematic error in sampling procedures, which leads to a distortion in the results of the study. Once a story was told of a French astronomer who once proposed a new theory based on spectroscopic measurements of light emitted by a particular star. . When his collogues discovered that the measuring instrument had been contaminated by cigarette smoke, they rejected his findings. As somewhere sampling is the better option to get desire information, on the other side it has some serious hazards as well. This can make your research useless for you and waste your applied resources as well

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CONCLUSION

Considering the significance, advantages and disadvantages of sampling in research, we can say that sampling is a technique which is very important for the present era. Because as the world growing with the faster rate people wants to complete their works with a good result and minimum time, sampling is the method which can give you nearly accurate results in a short period of time as compare to the detailed survey work. And it’ll obviously consume lesser cost then the complete counts.

In wrapping up, it can be said that using a sample in research saves mainly on money and time, if an appropriate sampling strategy is used; appropriate sample size selected and necessary precautions taken to reduce on sampling and measurement errors, then a sample should yield convincing and reliable information.

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REFERENCES

1. Uma Sekaran-4th edition Research methods for business-A skill building approach 2. www.foundationsforliteracy.ca/index.php/Glossary 3. www.socialresearchmethod.net 4. Utexas.edu 5. Changingminds.org 6. Oandp.org 7. www.statpac.com/surveys/sampling.htm 8. stattrek.com/Lesson6/STR.aspx