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Course: PEBL 101: RESEARCH METHODOLOGY & DESIGN FOR BUSINESS > Section: UNIT 4a: SAMPLING (An Introduction)

Unit 4a Learning Outcomes

Indicative Content

Introduction, Sampling methods, Probability sampling designs, Non-probability sampling designs

Intended Learning Outcomes for Unit 4a:

By the end of unit 4a, you will be able to;

1. explain key principles of sampling in business research
2. apply suitable probability sampling methods to select a sample
3. apply suitable non-probability sampling methods to select a sample

Unit 4a E-tivities

Outcome(s) addressed: Unit 4a addresses Module Learning Outcomes 2 & 3.

Purpose: Sometime the population is too large that it is practically impossible or even dangerous to collect data from every element therein. This part of Unit 4 will enable you learn and share knowledge and experience on sampling in business research and the different methods that can be used to select samples.

Over to you: In this Unit, you will describe sampling and the different types of sampling methods. The unit introduces you to concepts vital for completing Unit 4b, Units 5 & 6. There you go!

Preparatory research: In order to learn about what sampling is and application of the different sampling methods;

1. Read the minilecture on introduction to sampling, its benefits, limitations and the process here.
2. Read minilecture on probability sampling methods here.
3. Read minilecture on non-probability sampling methods here.
4. Read about sampling on pages 263 - 286 of Research Methods for Business by Sekarani (2003) available here.

E-tivity 4.1: As an individual, search the internet for the key sampling principles and methods. Post to your personal portfolio by 6^th March February 2019

E-tivity 4.2: Visit your university library, pick any research report and write a summary of 100 – 200 words about the following by 8^th March 2019;

(a) Sampling technique used to draw the sample.

(b) Whether the sample was a representative of the population from which it was drawn. Why or why not?

(c) Whether the author could have used a different sampling procedure. If so, which one?

E-tivity 4.3: In the group discussion forum, create another new discussion topic in your group forum titled ‘E-tivity 4.3’. Under it, identify sampling techniques that are suitable for the concept paper that you are developing. Group members should discuss and agree on the most suitable technique(s). Select a weaver for this section who should summarise the final solution for E-tivity 4.3 and post it under the discussion topic titled ‘Concept Paper’ that you created under E-tivity 2.1 by 10^th March 2019.

Link to other Units: Unit4a links to Units 1, 2 and 8.

Anticipated time required for Unit 4a activities: 5 hours

Deadlines for Unit 4a activities:

E-tivity 4.1 - Posting sampling principles and methods: by 6^th March 2019

E-tivity 4.2 - Summarising sampling aspects of a research report: 8^th March 2019.

E-tivity 4.3 - Including suitable sampling method in Concept Paper: 10^th March 2019.

Introduction to Sampling

Introduction
In most cases, you may want to collect data about a given population but its size may be too large especially give the time constraint. It may therefore be necessary to collect from a few so that those few represent the rest.

ACTIVITY: Visit 20 shops in any urban trading center of your choice and ask all shopkeepers what strategies they use to attract customers.

On a piece of paper, note down the following;

a) the responses from the shopkeepers
b) your experiences and,
c) challenges you met.

Having gone through the activity of collecting data from shopkeepers, we can now consider what sampling is in more detail.

What is Sampling?

It is the process of selecting a sufficient number of elements from the population (sample) so that by studying the sample, and understanding the properties or characteristics of the sample subjects, it would be possible to generalize the properties or characteristics to the population elements.

Key terms used in sampling

Population:

Refers to the entire group of people, events or things of interest that the researcher wishes to investigate. It is commonly referred to as the survey population.

An element:

It is a single member of the population. If 1,000 blue-collar workers in a particular organization happen to be the population of interest to a researcher, each single blue-collar worker therein is an element.

Census

It is a count of all the elements in the population.

Sample

It is a sub-set of the population. It comprises some members selected from the population. In other words, some but not all elements of the population would form the sample. If 200 members, are drawn from a population or 1,000 blue-collar workers, these 200 members form the sample for the study.

A sample is thus a subgroup or a subset of the population. By studying the sample, the researcher would be able to draw conclusions that would be generalisable to the population of interest.

Sampling frame:

It is a listing of all the elements in the population from which the sample is drawn. It is the physical material from which samples are chosen. It can be a map, telephone directory, a list of debtors, a list of business establishments, census records, etc. The payroll of an organization would serve as the population frame if its members are to be studied. Similarly the university registry containing a listing of all students, academic staff, administrators, and support staff in the university during a particular academic year or semester could serve as the population frame for a study of students in a class.

Before any frame is used for drawing a sample, it must be examined for its coverage. A good frame must cover the population of interest completely. If it is not so, the units which are outside the frame will not get a chance of being included in the sample and this will introduce bias in the sample.

Subject

It is a single member of the sample, just as an element is a single member of the population. If 200 members from the total population of 1,000 blue-collar workers formed the sample for the study, then each blue-collar worker in the sample is a subject.

Sampling Unit

It is the basic unit containing the elements of the population to be sampled. It may be the element itself or the subject on which measurements are taken or a unit in which the element is contained. For example, if one wanted a sample of males over thirteen years of age, it might be possible to sample them directly. In this case, the sampling unit would be identical with the element. However, it might be easier to select households as the sampling unit and interview all males over thirteen years of age in each household. Here, the sampling unit and the population element are not the same.

The sampling unit selected is often dependent upon the sampling frame. If a relatively complete and accurate listing of elements is available, one may well want to sample them directly. If no such listing is available, one may need to sample bigger units as the sampling unit.

Sampling

It is the process of selecting a sufficient number of elements from the population so that by studying the sample, and understanding the properties or characteristics of the sample subjects, it would be possible to generalize the properties or characteristics to the population elements. The characteristics of the population such as μ (the population mean), σ (the population standard deviation) and σ2 (the population variance) are referred to as parameters of the population.

The central tendencies, the dispersions and other statistics in the sample of interest to the research are treated as approximations of the central tendencies, dispersions and other parameters of the population.

Reasons for sampling

1. Resources and in particular time and money are often not adequate to cover the entire population. Sampling saves time and money. Sampling is also justified in situations where the results of the inquiry are required immediately. Time saved enables the researcher;

a) To carry out more checks and counter checks on the interest of accuracy

b) to collect more elaborate information

c) to given more time to analysis and interpretation of results

2. Sometimes it is not possible to inquire from the entire population e.g. when a research is on safety of a product that involves the justification of the product.

3. Sampling achieves better response rates than an attempt at complete coverage

4. The use of sampling permits employment of highly qualified personnel which translates into greater degree of accuracy

5. A well designed and scientifically drawn sample coupled with a well conducted survey will produce results sufficiently close to the population characteristics with no statistically significant with differences.

6. The data can be collected and summarised more quickly by studying a sample than by studying the entire universe. This may be a vital consideration when the information is urgently needed or when complete enumeration may take so much time that the findings are of no use by the time they become available.

Disadvantages / limitations of sampling

Disadvantages / limitations of sampling

Sources:

https://accountlearning.com/advantages-and-disadvantages-of-sampling/

https://www.mathstopia.net/sampling/probability-sampling-advantages-disadvantages

1. Chances of bias

Bias arises when the method of selection of sample employed is faulty. Relative small samples properly selected may be much more reliable than large samples poorly selected.

2. Difficulties in selecting a truly representative sample

Difficulties in selecting a truly representative sample produces reliable and accurate results only when they are representative of the whole group. Selection of a truly representative sample is difficult when the phenomena under study are of a complex nature. Selecting good samples is difficult.

3. Inadequate knowledge in sampling techniques

Use of sampling method requires adequate subject specific knowledge in sampling techniques. Sampling involves statistical analysis and calculation of probable error. When the researcher lacks specialized knowledge in sampling, he may commit serious mistakes. Consequently, the results of the study will be misleading.

4. Changeability of units

When the units of the population are not homogeneous, the sampling technique will be unscientific. In sampling, though the number of cases is small, it is not always easy to stick to the selected cases. The units of sample may be widely dispersed.

Some of the cases of sample may not cooperate with the researcher and some others may be inaccessible. Because of these problems, all the cases may not be taken up. The selected cases may have to be replaced by other cases. Changeability of units stands in the way of results of the study.

5. Impossibility of sampling

Deriving a representative sample is difficult, when the universe is too small or too heterogeneous. In this case, census study is the only alternative. Moreover, in studies requiring a very high standard of accuracy, the sampling method may be unsuitable. There will be chances of errors even if samples are drawn most carefully.

6. Chances of selecting specific class of samples only

If a surveyor is appointed to survey about any data relating to family members, there is likely chances that s/he will develop a trend of starting to number from the eldest member to the youngest and numbers will be only increasing or decreasing only. In this case, only oldest or the latest generations will be taken as samples.

7. Redundant and monotonous work

As the surveyor is asked to do a repetitive job to assign the numbers and to take the information, there is likely chances that the surveyor suffers from monotony and the effectiveness of the system will be reduced.

Steps in the Sampling Process

1. Define the population

2. Specify the sampling frame

3. Specify the sampling unit

4. Specify the sampling method

5. Determine the sample size

6. Specify the sampling plan ie the operational procedure for selection of the sampling units. How exactly are the respondents going to be selected practically?

7. Select the sample

Review questions

1. What is sampling?

2. What is a survey population?

3. What is an element?

4. What is a census?

5. What is a sample?

6. What is a sampling frame?

7. What is a subject?

8. Why is sampling done?

9. What are the disadvantages or limitations of sampling?

SAMPLING TECHNIQUES/ DESIGNS

There are two major types of sampling designs:

A) probability/ random sampling techniques and

B) nonprobability/ non-random sampling techniques

A. PROBABILITY/ RANDOM SAMPLING TECHNIQUES

This is used when the elements in the population have some known chance or probability of being selected as sample subjects. Probability sampling techniques are used when the representativeness of the sample is of importance in the interest of wider generalizability.

Probability sampling can either be

a) unrestricted (or simple random sampling) or

b) restricted (or complex probability sampling) in nature.

Unrestricted or Simple random sampling

This is where all members/ elements have an equal chance of being selected. It may be done with or without replacement. Random sampling with replacement is a situation where population members once selected into the sample are put back into the pool such that there is chance that they may be reselected. This is also referred to as unrestricted sampling.

Random sampling without replacement is whereby once members are selected they cannot be selected again.

There are two basic procedures that are used to select a random sample:-

a) Assign different numbers to members of the population ranging from 1 to N, then put these numbers into a container or urn, mix the numbers thoroughly and pick n items where N is the population size and n is the sample size. This is sometimes referred to as the urn method.

b) Assign numbers to members of the population and use computer generated random numbers or random number tables to pick n items.

The random number method has an advantage over the URN method in that although both procedures have no human judgment in their randomness of choice, the URN method is dependent on the numbers in the container being so thoroughly mixed then the population can be regarded as randomly arranged.

When we draw the elements from the population, it is likely that the distribution patterns of the characteristics we are interested in investigating in the population are also similarly distributed in the subjects we draw for our sample.

Merits/ Advantages

i. Simple random sampling has the least bias and offers the most generalisability.

ii. The method requires minimum knowledge about the population in advance which is needed in the case of purposive sampling.

iii. The method yields good results if the universe being sampled is homogeneous.

iv. Sampling errors can be easily computed and the accuracy of the estimate easily assessed.

Demerits/ Disadvantages

i. However this sampling process could become cumbersome and expensive.

ii. An entirely updated listing of the population may not always be available.

iii. This method does not make use of the knowledge about the population which research may have.

iv. The size of the sample required to ensure statistical reliability is usually large if the universe is heterogeneous.

v. From the point of view of field survey, it has been claimed that cases selected by random sampling tend to be too widely dispersed geographically and that the time and cost of collecting data become too large.

vi. The use of simple random sampling necessitates a complete catalogued universe from which to draw the sample. But it is often difficult for the investigator to have up-to-date lists of all the elements of the population to be sampled.

Restricted or Complex Probability Sampling

This offers viable and sometimes more efficient alternatives to the unrestricted design. Efficiency is improved in that more information can be obtained for a given size using some of the complex probability sampling procedures than simple random sampling design.

These techniques include the following

1) Systematic/ quasi-random sampling

2) Stratified random sampling

3) Cluster sampling

4) Multi stage sampling

5) Multi- Phase Sampling / Double sampling

6) Area sampling

7) Sequential Sampling or Sampling in Instalments

1 Systematic/ Quasi-Random Sampling

This involves selecting n items following a certain interval given by the sampling fraction (k). The following is the procedure.

a)Determine both the population size (N) and sample size (n) and arrange the population on a list.

b)Calculate the sampling fraction (k)

Therefore here you select every 10^th person

c)Choose a starting point randomly ie using simple random sampling and the starting point should be a number from 1 to k.

d)Starting with the chosen number pick every K^th item on your list.

Example

If we want to determine the accuracy of voters given 100 vouchers and say

N=100

n=25

K=4

Then select a no among 1, 2, 3 and 4 to get the starting point e.g. if 2 is selected then every fourth element from 2 is selected thus;

Merits/ Advantages

i.The procedure is easy and convenient to apply

ii.It enables producing a more evenly spread sample than simple random sampling.

Demerits/ Disadvantages

i)The method falls short of strict random selection unless the list is arranged in a random order. In practice lists are arranged in alphabetical order or in order of similarity rather than randomly eg alphabetical order.

ii)The method does not give equal chances of selection. Once K has been selected, the choice of the starting point predetermines all the other members of the sample.

iii)The method limits the number of possible samples and choice of the actual sample will not be as vast and as spread as in simple random sampling.

iv)In systematic sampling selection of one member is dependent on the selection of the previous one whereas in simple random sampling, particularly where the population is large, selection is virtually independent. If by coincidence the list from which the sample is selected is periodically arranged and K coincides with the periodic interval of the list or a multiple of it, the sample is likely to be unrepresentative and the precision of the data is likely to be extremely low. However such coincidences of periodicity and the sampling interval are rare, easy to detect and can therefore be avoided.

NB: Systematic sampling is referred to a quasi random and approximate to simple random sampling if:-

·The sampling frame is arranged more less at random

·The order of arrangement is unrelated to the issue under investigation

2. Stratified Random Sampling

It is a process of sample selection which involves

a)Dividing the population into non-over lapping groups called strata

b)Selecting a sample from each stratum using a random technique

Conditions That Should Be Satisfied In Order To Use Stratified Random Sampling

i.The strata must be mutually exclusive i.e. no member of the population should belong to more than one stratum.

ii.There should be maximum homogeneity within each strata and maximum heterogeneity between strata viz the variable interest. Eg students from different universities.

iii.Sampling within each stratum is done using a random based technique i.e. either systematic or simple random sampling. Each stratum must contribute to the final sample

Stratified random sampling is a form of restricted random sampling. A sample as whole is not a random one because not all possible samples of size n have an equal opportunity of being the sample selected

The stratification factor

This is the criterion upon which the division of the population is based. For good results the, stratification factor should be related to the subject of the survey. Where the survey requires more than one subjected, its best to make a stratification factor the subject within the greatest variability. Selection of elements into the sample from each stratum may be uniform or proportionate or disproportionate.

Whether we use proportionate or disproportionate, fraction should be determined by the relative importance of the determined by the relative importance of the different strata to the survey. Differences in strata may be based on size or on variation i.e. the larger stratum the more it should contribute to the sample and similarity the more it should contribute to the sample.

For example

A researcher wants to study the effect of taxes on the profitability of Small Businesses and wishes to study eighty small businesses., The sample will be selected as follows using stratified random sampling;

The type of activity can be the stratification factor (the basis of dividing the groups or distinguishing them). The strata (categories) can be as follows;

a) Metal fabrication

b) Food vending

c) Hair dressing

d) Carpentry

Note that each business above is different from the other (Heterogeneity between strata) but all businesses in each group/strata deals in similar items with others within that group (homogeneity within)

The researcher will therefore use either simple random sampling or systematic random sampling to select elements from each group. The number of elements selected using disproportionate sampling can be as follows;

Merits/Advantages

i.It increases the precision of sample results increases since there is greater representation of different categories of respondents.

ii.Since the process ensures that each stratum is represented in the sample, this improves generalisability.

iii.A stratified random sample can be kept small in size without losing its accuracy.

iv.Characteristics of each stratum can be estimated and hence comparison can be made.

Demerits/Disadvantages

i.The method does not strictly conform to the random selection criteria .

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

iii.It is very costly to prepare stratified lists of all members.

iv.There is always a possibility of faulty classification and hence increase in variability.

3. Cluster Sampling

This is a sampling method where blocks of the population are chosen to constitute a sample. It consists of first selecting, at random, groups/ blocks of units called clusters from the universe then all the units in the clusters chosen make up the sample and are all interviewed. It is commonly used where;

a) There is no satisfactory sampling frame that covers the entire population

b) Where the population is sparsely spread such that it could be inconvenient and costly to cover the far and wide elements that will be chosen by other probability sampling methods.

To use cluster sampling we need to note the following

i. The aggregation of units to be called a cluster depends on circumstances. A cluster should be composed of units that constitute the unit of inquiry. This is why it is common to use geographical or administrative areas as cluster because they contain households that are often units of inquiry

ii. Cluster needs not to be natural aggregates e.g. administrative block/political constituencies, schools etc. However for practical reasons researchers frequently take advantage of already existing groupings

iii. It is possible to have several levels of clustering in one sampling designs,

iv. In cluster sampling, the sampling frame is the list of clusters and not the individual elements within the cluster

Conditions for cluster sampling

i. It should be possible to separate the population into discernable groups/cluster.

ii. Clusters must contain maximum heterogeneity within each cluster and maximum homogeneity between the clusters. This condition is important or crucial to cluster sampling. It implies that if one inquires from one or a few cluster, the entire variation in the population is computed. Eg Behaviour of residents of flats.

iii. Once the sample clusters have been selected they must be covered by the investigator 100% i.e. every member of the cluster must be inquired from.

iv. Choice of the sample cluster must be through a random method of selection i.e. simple random sampling or systematic sampling

Merits/ Advantages

i. A cluster study is considerably less expensive and less time consuming because field work in this type of study gets localised. The investigator can obtain information about all elements of the cluster at one place.

ii. It does not require a complete frame of the whole population. It requires lists of members of the selected clusters only.

iii. The same sample of clusters can be used again and again for drawing random samples of elements from them in subsequent researchers.

Demerit/ Disadvantage

A cluster is seldom a correct representative of the parent population. This is because the requirement of heterogeneity of the elements within a cluster is rarely fulfilled. The tendency is for the ‘birds of the same feather’ to cluster together. Therefore, estimates about the parent population based on cluster sampling are often inaccurate.

4. Multi- Stage Sampling

Under this method, a sample is selected in stages. The population is divided into a number of large sampling units, each of which in turn is divided into smaller units, and so on. A random sample is taken of the large units at the first stage and from those selected a further random sample, i.e, the second stage is collected of the smaller units. In many practical situations samples are selected using several stages of sampling e.g. if a researcher is interested in the level of technology used by Small and Medium Enterprises (SMEs) in Kampala district at village level. The following stages are followed.

i. Divide the district into divisions (counties)

ii. Select a sample of divisions / counties using either simple or systematic sampling

iii. Stratify the division/ counties into sub-counties.

iv. Then select a sample of sub-counties using either simple or systematic sampling

v. Stratify the sub-counties according to parishes

vi. Select a sample of parishes using simple random sampling or systematic sampling.

vii. Stratify the parishes into villages

viii. Select a sample of villages using simple random or systematic smpling

ix. From the villages selected, study the SMEs.

Merits/ Advantages

i. Sampling lists, identification an numbering are required only by units selected in the sample at each stage and not for all the units of the population.

ii. If sampling units at each stage are geographically defined, such as district, village, etc, this method cuts down field costs.

Demerit/ Disadvantage

Errors increase as the number of sampling units selected decreases.

5. Multi- Phase Sample / Double sampling

With this sampling method, sample data is collected from a given sample but additional questions are asked of a sub-set of the original sample either at the same time or at later time.

This plan is resorted to when further information is needed from a subset of the group which had already provided some information or from which information had been collected. A sampling design where a sample is used to collect some preliminary information of interest and later a subsample of this primary sample is used to examine the matter in more detail is called double sampling or multi phase sampling.

For example, a structured interview might indicate that a sub group of the respondents has more insight into the problems of the organisation. These respondents might be interviewed again with additional questions.

Merit/ Advantage of Multi-phase sampling

Allows for more inquiry

Demerit/ disadvantage of Multi-phase sampling

The researcher may be tempted to use a non-random method at one stage.

6. Area Sampling (Geographical Cluster Sampling)

This sampling design constitutes geographic clusters: that is when the research pertains to populations within identifiable geographic areas such as counties, city blocks, or particular boundaries within a locality, area sampling may be done. Thus, area sampling is a form cluster within as area. It is a form of sampling in which a region is subdivided and some of the divisions are then selected at random for a complete survey. Each of the chosen sub-areas is then fully inspected and enumerated, and may form a frame for further sampling if desired.

A map is used to select the area and can work as a sampling frame.

For example, establishing the needs of consumers before opening a 24-hour convenience store in a particular part of the town would require area sampling. A map of the area can be obtained and the villages used to select the location of the potential customers.

Advantages/ Merits

i. It is less expensive than most other probability sampling designs

ii. It is not dependent on a population frame. A map showing the blocks of the city would be adequate information to allow a researcher to take a sample of the blocks and obtain data from the residents therein.

Disadvantage/ Demerit

i. Sometimes a map is difficult to come by.

ii. Higher sampling error, which can expressed in the so-called "design effect", the ratio between the number of subjects in the area studied and the number of subjects in an equally reliable, randomly sampled unclustered area.

7. Sequential Sampling or Sampling in Instalments

Under this method, the researcher takes a very small sample in the beginning and tries to draw inference on the basis of that sample. If no clear-cut inference is possible, he/she adds some more units to the sample (often one at a time) and weighs the enlarged sample evidence to see if the inference can be drawn now.

This is continued until a point is reached at which a clear-cut inference can be drawn within some previously chosen degree of confidence. In this way, the researcher works on the basis of a pre-determined sample-plan which tells how long he/she should go on increasing the sample.

If the sequence is one sample at a time, and then the sampling process is usually called item-by-item sequential sampling. However, if one can also select sample sizes greater than one, the process is referred to as group sequential sampling.

This method has become very popular in drawing samples of destructive items like explosives which are irretrievably lost or destroyed once they are tested for their quality. This method can also be used in trying to establish causes of an activity with negative effects among a group of people.

Advantages/ Merits

1. The researcher can obtain a limitless number of subjects before finally making inference. The researcher has a limitless option when it comes to sample size and sampling schedule. The sample size can be relatively small or excessively large depending on the decision making of the researcher.
2. Selection of elements into the sample is also completely dependent to the researcher since a second group of samples can only be obtained after conducting the experiment on the initial group of samples or inquiring from the original group.
3. It enables the researcher to fine-tune his/her research methods and results analysis techniques. Due to the repetitive nature of this sampling method, minor changes and adjustments can be done during the initial parts of the study to correct the research method.
4. There is very little effort on the part of the researcher when performing this sampling technique. It is not expensive, not time consuming and not workforce extensive.

Disadvantages/ Merits

1. This sampling method is hardly representative of the entire population. Its only hope of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a big fraction of the entire population.
2. The sampling technique is also hardly randomized. Not all members of the population have an equal chance of being selected.

Review questions

1. Discuss the probabilistic sampling techniques

2. Discuss the non-probabilistic sampling techniques

3. Explain simple random sampling

4. Explain systematic random sampling

B. NON-RANDOM METHODS OF SAMPLING/NON PROBABILITY SAMPLING TECHNIQUES

In many research situations, it may not be possible to assign a calculable probability of selection to each member of the target population. Therefore probability-sampling design cannot be used and the researcher has to use subjective judgment. Some of the situations where these methods may be applied include

i) While carrying out exploratory studies, non-probabilistic methods are more practical.

ii) Where the nature of the research question dictates the use of non-probabilistic methods e.g. one may need to focus on the few elements in the population selected purposely with some prior knowledge of their title.

iii) Where resources are limited. Limited resources including time and urgency in which research results are required may dictate the use of non-probabilistic research methods.

Different Techniques

These include the following

1) Quota sampling

2) Purposive sampling

3) Convenience (Accidental or haphazard)sampling

4) Snow ball (Chain referral) sampling

5) Self selected (Voluntary) sampling

1. Quota Sampling

This is a form of stratified sampling in which the selection of the actual sample is non-random. Quota sampling attempts to cover the entire population by dividing it into categories and allocating a quota to each category e.g. a population may be divided by gender i.e. category of men and women. The allocation of quotas should be determined by the relative predominance of particular category in the target population. The predominance should be related to the variable under investigation.

Quota sampling can be considered as a form of proportionate stratified sampling in which a predetermined proportion of people are sampled from different groups, but on a convenience basis. For instance, it may be suspected that the attitudes of blue-collar workers in an organisation are quite different from those of white-collar workers. If there are 60 percent blue-collar workers and 40 percent white-collar workers in this organisation, and if a total of 30 people are to be interviewed to find the answer to the research question, then a quota of 18 blue-collar workers and 12 white-collar workers will form the sample. 18 conveniently available blue-collar and 12 conveniently available white-collar workers will be sampled according to this quota.

The choice of what is included in the sample is a subjective decision or judgment of the research staff. For this reason, quota samples are basically stratified samples from which subjects are selected non-randomly.

This non-random element is the greatest weakness of quota sampling. It introduces bias and renders impractical the determination of sample representations and the precision of the research results.

Merits/ Advantages of quota sampling

i. It provides quick results. It is most suited in a situation where the field work has to be done quickly perhaps in order to reduce memory errors. Thus, where people are to be questioned about the previous day’s television program or where their immediate reaction to new budget proposals is to be obtained, quota sampling may be the only possibility.

ii. It is less costly. There is no need to travel all over the place the track down pre-selected respondents.

iii. It elicits a higher response rate.

iv. It does not require a sampling frame since respondents are selected conveniently. This makes it administratively easy. The labour of random sample selection is avoided and so are the headaches of non-contacts and call-backs.

Demerits/ Disadvantages of quota sampling.

i. It is fraught with interviewer bias hence the findings lack generalisability

It requires more supervision and control.

2. Purposive Sampling (Judgment Sampling)

In this sampling technique elements in the sample are selected from the population because they conform to certain characteristics that the investigator or the researcher is interested in. In other words the researcher deliberately picks elements of the population that are responsible or relevant to the purpose of the investigation.

Merits/ Advantages

i. The technique is useful and fairly representative of small samples if care is taken to avoid personal basis.

ii. It is also useful in multiphase sampling where data collected from initial samples provides direction of further inquiry.

iii. It is very simple to draw a sample when using this method. People often use it in exploratory investigations which precede major surveys.

iv. It is less costly and involves less field work since those units can be selected which are close to each other.

v. It is more representative of ‘typical’ conditions than a random sample if the size of the sample is small.

Demerits/ Disadvantages

i. Lack of representativeness hence affecting the ability to apply findings to the whole population.

ii. It is not always reliable. The human mind has difficulty in recognising typical items. The difficulty tends to distort purposive sampling.

i. It requires from the researcher considerable knowledge about the population which he/she usually does not possess.

ii. It is not possible to determine the size or likelihood of sampling errors because purposive sampling does not meet the basic requirements of randomness.

3. Convenience (Accidental or haphazard) Sampling

Under this technique the researcher chooses elements that are:-

a)Easy to identify and locate.

b)Easy and convenient to collect data from.

The researcher interviews informally met or readily available persons on the road or shop, office or anywhere. Sampling inaccuracy may be very high but time and money are saved.

Sample selection continues until the required sample size has been obtained.Where human beings are the population the tendency is to choose close friends or those that do not appear to be difficult to interview. The result is that the sample is composed of elements of similar behaviour.

Consequently when convenience sampling is used the representativeness of the sample to the population of interest is always suspect and any generalization of findings will be flawed. However the technique may be used where the population is regarded as fairly homogenous and where there is need for immediacy/ urgency of research findings. The technique may also be used in pilot studies that are prelude for more comprehensive studies.

Advantages/ Merits

i. Easy to organize. There are no formal procedures required to choose the respondents and approach them.

ii. Time saving. The responses can be got quickly since the researcher only approaches those willing to give answers.

Disadvantages/ Demerits

i. The sample is not an accurate representation of the population since the researcher only approaches those who are easy to come by and are willing to participate.

ii. Results have to be extrapolated in order to fine tune them. The researcher has to attempt to imagine what the general population would behave like

iii. It is a completely unstructured approach. There is no specific procedure to carry it out so it is difficult to have a filed sampling frame.

iv. There is no guarantee that the behavior of these people represents the behavior of other groups.

4. Snow-Ball (Chain referral) Sampling

The term snowball comes from the analogy of a snowball, which begins small but becomes bigger and bigger as it rolls down hill. Sampling is done in stages. In the first stage the researcher chooses one or a few respondents whom he/she believes have attributes, benefiting his/her research. He/she interviews them and asks them for other people having the same or even better characteristics he/she is looking for. At the second stage, he/she interviews respondents whom the previous respondents suggested to him/her. These also suggest other interviewees for the third stage. The chain goes on until a sample that is enough is obtained.

This is commonly used in cases where the identification of the target population is difficult and trust from the respondents is needed in some cases e.g. homosexuals. It involves making contact with one or two elements of the target population and using those to identify other cases of interest.

NB: If in a certain subsequent group or in certain groups at certain stages, respondents are chosen using a Random Sampling technique, Snowball sampling is probabilistic; Other wise, it is fully non-probabilistic.

Merits/ Advantages of Snowball sampling

a.Helps to reach respondent who are difficult to identify.

b.It provides certainty that the individuals selected belong to the survey population.

Demerits/Disadvantages of this technique

i.The difficulty lies in making contact with the initial element.

ii.Second problem is lack of representativeness.

iii.The identified elements will bring in new cases that they are familiar with and probably have similar characteristics.

iv.The researcher never gets to know the full size of the population or the extent of variation in the population.

5. Self-Selected/ Voluntary Sampling

In this technique the researcher allows elements usually individuals to express their interest in taking part in the survey. The researcher publicizes the need for participants and collects information from those who respond.

The technique is common in the media, they call for opinions on topical issues but only those who have access to that particular media and have an interest in the subject will respond.

Results from such surveys are not always representative and should not be generalized to make conclusions about the population as whole.

Advantages

 i. Inexpensive way of ensuring sufficient numbers of a study

 ii. Easy to organise so no strict formal procedures are involve. The research just advertised the need for participants and waits for those willing to come.

Disadvantages

 i. Can be highly unrepresentative.

1. There is no guarantee that the behavior of these people interviewed represents behavior of other groups.
2. The researcher never gets to know the entire population. It is therefore difficult to generalise the finding to the entire population.

Considerations when selecting a sampling method

There are several considerations when selecting a method for selecting a sample and they include;

1. The need to avoid bias in the selection of the sample

Bias is introduced in a simple if members selected from the population favour certain events. Bias is introduced under the following circumstances

a) If a non-random method is used in selecting members of the sample. Non-random methods introduce a human element in the selection.

b) If the sampling frame employed is inadequate, incomplete or inaccurate

c) If members selected into the sample decline to be interviewed or cannot be traced

Bias in a survey may originate from the following

i. Faulty measurement

ii. Poor questionnaire design and data collection

iii. Inappropriate estimation procedures

To reduce the bias, we have to endeavour to use a random sampling technique.

A random sampling technique is one in which all members of the population have a calculable non-zero probability of being selected into the sample and all possible samples of size n have an equal chance of being the actual sample

1. Trying to achieve maximum precision in the results for a given cost outlay.

2. The relevant target population of focus to the study.

3. The parameters or variables being investigated.

4. The sample size needed.

5. The time that can be spent in collecting the data from the sample.