mba 687 assignment

8/22/2019 Mba 687 Assignment

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AN

ASSIGNMENTON

PROBABILITY SAMPLING

MARKETING RESEARCH(MBA 687)

PREPARED BY

MADUAKOR CHIDINMA IFEOMAADP11/12/H/0845

SUBMITTED TO

DR J O ADETAYODEPT OF MANAGEMENT & ACCOUNTING,

OBAFEMI AWOLOWO UNIVERSITY,ILE-IFE, OSUN STATE.


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JULY, 2013

\

SAMPLING TECHNIQUES

Research studies are distinct events that involve a particular group of

participants. However, researchers usually intend on answering a general

question about a larger population of individuals rather than a small select

group. Therefore, the main aim of psychological research is to be able to make

valid generalizations and extend their results beyond those who participate. For

this reason, the selection of participants is a very crucial issue when planning

research. Obviously, researchers cannot collect data from every single

individual from their population of interest, since this would be extremely

expensive and take a very long time! So instead they use a small group of

individualscalled a sample. The sample is chosen from the population and is

used to represent the population. Researchers use sampling techniques to

select the participants for their sample these techniques help to minimise

cost whilst maximizing generalizability. I am going to be discussing the Non-

Probability sampling techniques and methods, and considering the issue of

sampling bias and the problems associated in research.


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There are a variety of different sampling methods available to researchers to

select individuals for a study. Sampling method fall into two categories:

PROBABILITY SAMPLING

Historical background

Probability-based sampling is a development of the last 60 to 70 years. Around

the turn of thecentury Kiar, in Norway, was an advocate for sampling. In the

early work, purposive methods (ienon-probability sampling) predominated, but

in 1934 Neyman published a paper which laid the basis ofsampling theory,

and explained the advantages of random sampling over purposive selection. (He

used a number of examples in his paper, particularly an unsuccessful

purposive sub-sample drawn from the 1921 Italian Census by the Italian

census bureau.) Over the next 20 or so years, the theory of probability-based

sample design was further developed, and the major statistical offices were all

won over to probability-based design. The first generation of sampling

textbooks appeared around 1950.

Probability Sampling includes some form of random selection in choosing the

elements. Greater confidence can be placed in the representativeness of

probability samples. This type of sampling involves a selection process in which

each element in the population has an equal and independent chance of being

selected.

Four main methods include:

Simple Sampling


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Sratified Sampling Cluster Sampling and Systematic Sampling

Non-probability sampling:

The elements that make up the sample are selected by non random methods.

This type of sampling is less likely than probability sampling to produce

representative samples. Even though this is true, researchers can and do

use non-probability samples.

The three main methods are:

Convenience Quota Purposive

.

NON PROBABILITY SAMPLING

The difference between non-probability and probability sampling is that non-

probability sampling does not involve random selection and probability

sampling does. Does that mean that non-probability samples aren't

representative of the population? Not necessarily. But it does mean that non-

probability samples cannot depend upon the rationale of probability theory. At

least with a probabilistic sample, we know the odds or probability that we have

represented the population well. We are able to estimate confidence intervals


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for the statistic. With non-probability samples, we may or may not represent

the population well, and it will often be hard for us to know how well we've

done so. In general, researchers prefer probabilistic or random sampling

methods over non-probabilistic ones, and consider them to be more accurate

and rigorous. However, in applied social research there may be circumstances

where it is not feasible, practical or theoretically sensible to do random

sampling. Here, we consider a wide range of non-probabilistic alternatives.

.

We can divide non-probability sampling methods into two broad types:accidental or purposive. Most sampling methods are purposive in nature

because we usually approach the sampling problem with a specific plan in

mind. The most important distinctions among these types of sampling methods

are the ones between the different f the population are chosen based on their

relative ease of access. To sample friends, co-workers, or shoppers at a single

mall, are all examples of convenience sampling. Such samples are biased

because researchers may unconsciously approach some kinds of respondents

and avoid others (Lucas 2012), and respondents who volunteer for a study may

differ in unknown but important ways from others (Wiederman 1999).

Snowball sampling -The first respondent refers a friend. The friend alsorefersa friend, and so on. Such samples are biased because they give people with

more social connections an unknown but higher chance of selection (Berg

2006).
http://en.wikipedia.org/wiki/Convenience_samplinghttp://en.wikipedia.org/wiki/Snowball_samplinghttp://en.wikipedia.org/wiki/Snowball_samplinghttp://en.wikipedia.org/wiki/Convenience_sampling


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Judgmental sampling or Purposive sampling - The researcher chooses thesample based on who they think would be appropriate for the study. This is

used primarily when there is a limited number of people that have expertise in

the area being researched.

Deviant Case - Get cases that substantially differ from the dominant pattern (aspecial type of purposive sample).

Case study - The research is limited to one group, often with a similarcharacteristic or of small size.

ad hoc quotas - A quota is established (say 65% women) and researchers arefree to choose any respondent they wish as long as the quota is met.

Even studies intended to be probability studies sometimes end up being non-

probability studies due to unintentional or unavoidable characteristics of the

sampling method. In public opinion polling by private companies (or other

organizations unable to require response), the sample can be self-selected

rather than random. This often introduces an important type of error: self-

selection bias. This error sometimes makes it unlikely that the sample will

accurately represent the broader population. Volunteering for the sample may

be determined by characteristics such as submissiveness or availability. The

samples in such surveys should be treated as non-probability samples of the

population, and the validity of the estimates of parameters based on them

unknown.

REASONS FOR USING NON-PROBABILITY SAMPLING
http://en.wikipedia.org/wiki/Self-selection_biashttp://en.wikipedia.org/wiki/Self-selection_biashttp://en.wikipedia.org/wiki/Self-selection_biashttp://en.wikipedia.org/wiki/Self-selection_bias


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No sampling frame is available. This is often true for the product dimension but

less frequently so for the outlet dimension, for which business registers or

telephone directories do provide frames, at least in some countries, notably in

Western Europe, North America and Oceania. There is also the possibility of

constructing tailor-made frames in a limited number of cities or locations,

which are sampled as clusters in a first stage. For products, it may be noted

that the product assortment exhibited in an outlet provides a natural sampling

frame, once the outlet is sampled as a kind of cluster, as in the BLS sampling

procedure presented above. So the absence of sampling frames is not a good

enough excuse for not applying probability sampling. Bias resulting from non-

probability sampling is negligible. There is some empirical evidence to support

this assertion for highly aggregated indexes. Dalen (1998b) and De Haan,

Opperdoes and Schut (1999) both simulated cut-offsam pling of products

within item groups. Dalen looked at about 100 groups of items sold in

supermarkets and noted large biases for the subindices of many item groups,

which however almost cancelled out after aggregation. De Haan, Opperdoes

and Schut used scanner data and looked at three categories (coffee, babies

napkins and toilet paper) and, although the bias for any one of these was large,

the mean square error (defined as the variance plus the squared bias) was

often smaller than that for pps sampling. Biases were in both directions and so

could be interpreted to support Dale ns findings. The large biases for item

groups could, however, still be disturbing. Both Dalen and De Haan,


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Opperdoes and Schut report biases for single-item groups of many index

points.

We need to ensure that samples can be monitored for some time. If weare unlucky with our probability sample, we may end up with a product

that disappears immediately after its inclusion in the sample. We are

then faced with a replacement problem, with its own bias risks. Against

this, it may happen that short-lived products have a different price

movement from the price movement of long-lived ones and constitute a

significant part of the market, so leaving them out will create bias.

A probability sample with respect to the base period is not a proper probability

sample with respect to the current period. It is certainly true that the bias

protection offered by probability sampling is to a large extent destroyed by the

need for non-probabilistic replacements later on.

Price collection must take place where there are price collectors. Thisargument applies to geographical sampling only. It is, of course, cheaper

to collect prices near the homes of the price collectors, and it would be

difficult and expensive to recruit and dismiss price collectors each time a

new sample is drawn. This problem can be reduced by having good

coverage of the country in terms of price collectors. One way to achieve

this is to have a professional and geographically distributed interviewer

organization within the national statistical agency, which works on many

surveys at the same time. Another way of reducing the problem is to have


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a firststage sample of regions or cities or locations which changes only

very slowly.

The sample size is too small. Stratification is sometimes made so finethat there is room for only a very small sample in the final stratum. A

random selection of 15 units may sometimes result in a final sample

that is felt to be skewed or otherwise to have poor representativity

properties. Unless the index for this small stratum is to be publicly

presented, however, the problem is also small. The skewness of small

low-level samples will even out at higher levels. The argument that

sample size is too small has a greater validity when it relates to first-

stage clusters (geographical areas) that apply to most subsequent

sampling levels simultaneously.

Sampling decisions have to be taken at a low level in the organization.Unless price collectors are well versed in statistics, it may be difficult for

them to perform probability sampling on site. Such sampling would be

necessary if the product specification that has been provide centrally

covers more than one product (price) in an outlet. Nevertheless, in the

United States (U.S. BLS, 1997) field representatives do exactly this. In

Sweden, where central product sampling (for daily necessities) is carried

to the point of specifying well-defined varieties and package sizes, no

sampling in the outlets is needed. In countries where neither of these

possibilities is at hand, full probability sampling for products would be

more difficult.


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In some situations, there are thus valid reasons for using non-probabilitytechniques. We discuss two such techniques below.

Cut-off sampling

Cut-off sampling refers to the practice of choosing the n largest sampling units

with certainty and giving the rest a zero chance of inclusion. In this context,

the term largeness relates to some measure of size that is highly correlated

with the target variable. The word cut-off refers to the borderline value

between the included and the excluded units.

In general, sampling theory tells us that cut-off sampling does not produce

unbiased estimators since the small units may display price movements which

systematically differ from those of the larger units. Stratification by size or pps

sampling also has the advantage of including the largest units with certainty

while still giving all units a non-zero probability of inclusion. If the error

criterion is not minimal bias but minimal mean square error (=variance+

squared bias) then, since any estimator from cut-off sampling has zero

variance, cut-off sampling might be a good choice where the variance reduction

more than offsets the introduction of a small bias. De Haan, Opperdoes and

Schut (1999) demonstrate that this may indeed be the case for some item

groups.

Often, in a multi-stage sampling design there is room for only a very small

number of units at a certain stage. Measurement difficulties that are

sometimes associated with small units may then be a reason, in addition

to large variances, for limiting price collection to the largest units.


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Note that a hybrid design can also be applied in which there is a certainty

stratum part, some probability sampling strata and a low cut-off point below

which no sample at all is drawn. In practice, this design is very often used

where the below cut-offsection of the universe is considered insignificant and

perhaps difficult to measure.

A particular CPI practice that is akin to cut-off sampling is for the price

collector to select the most sold product in an outlet, within a centrally defined

specification. In this case, the sample size is one (in each outlet) and the cut-

offrule is judgemental rather than exact, since exact size measures are only

rarely available. In all cases of size-dependent sampling in an outlet, it is

crucial to take a long-term view of size, so that temporarily large sales during a

short period of reduced prices are not taken as a size measure. Such products

will tend to increase in price in the immediate future much more than the

product group which they represent and thus create a serious overestimating

bias.


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CONVENIENCE OR ACCIDENTAL SAMPLING

Definition

Distinctive Features One of the most common methods of sampling goes under

the various titles listed here. I would include in this category the traditional

"man on the street" (probably the "person on the street") interviews conducted

frequently by television news programs to get a quick (although non

representative) reading of public opinion. I would also argue that the typical

use of college students in much psychological research is primarily a matter of

convenience. In clinical practice we might use clients who are available to us as

our sample. In many research contexts, we sample simply by asking for

volunteers. Clearly, the problem with all of these types of samples is that we

have no evidence that they are representative of the populations we're

interested in generalizing and in many cases we would clearly suspect that they

are not.

Distinctive Features


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Convenience samples are also known as accidental or opportunity samples.

The problem with all of these types of samples is that there is no evidence that

they are representative of the populations to which the researchers wish to

generalize. This approach is often used when the researcher must make use of

available respondents or where no sampling frame exists. A good example is

provided by my own experience of conducting evaluative research designed to

explore the efficacy of community treatment programmes for sex offenders.

Here an accidental sample of those men sentenced to treatment was used by

necessity (Davidson, 2001). TV, radio station, newspaper and magazine polls

and questionnaires are also an example of this type of sampling, where

respondents may be asked to phone in or fill in a questionnaire.


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PURPOSIVE SAMPLING

Definition

A form of non-probability sampling in which decisions concerning the

individuals to be included in the sample are taken by the researcher, based

upon a variety of criteria which may include specialist knowledge of the

research issue, or capacity and willingness to participate in the research.


Some types of research design necessitate researchers taking a decision about

the individual participants who would be most likely to contribute appropriate

data, both in terms of relevance and depth. For example, in life history

research, some potential participants may be willing to be interviewed, but may

not be able to provide sufficiently rich data. Researchers may have to select a

purposive sample based on the participants oral skills, ability to describe and

reflect upon aspects of their lives, and experience of the specific focus of the

research.

A case study design is another type of research that often requires a purposive

sample. Imagine that a research team wishes to explore the types of academic


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support provided for students in a single high school. In selecting that school

the researchers may need to take a variety of factors into account. They may

want a school in which academic support is sufficiently innovative to make the

final research report of wide interest in the profession. They will require a

school in which the management are supportive of the research, and in that

the teachers and students show a willingness to participate. The researchers

may want a school that is exceptional in terms of overall academic

performance, or that has an average level of attainment. Finally, they may

prefer a school that is reasonably accessible for members of the research team.

When all relevant factors have been considered, the research team will select

the case study school, which will constitute the purposive sample. If it is

appropriate, a purposive sample may be combined with a probability sample.

Once the high school has been selected, a random sample of teachers and

students could be selected from whom to collect data.

Evaluation

The advantage of purposive sampling is that the researcher can identify

participants who are likely to provide data that are detailed and relevant to the

research question. However, in disseminating the findings, the researcher

should make fully transparent the criteria upon which the sampling process

was based.

The principal disadvantage of purposive sampling rests on the subjectivity of

the researcher's decision making. This is a source of potential bias, and a


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significant threat to the validity of the research conclusions. These effects may

be reduced by trying to ensure that there is an internal consistency between

the aims and epistemological basis of the research, and the criteria used for

selecting the purposive sample.

QUOTA SAMPLING

Definition

A non-probability method of selecting respondents for surveys. The interviewer

begins with a matrix of the target population that is to be represented and

potential respondents are selected according to that matrix. Quota sampling is

also known as a purposive sample or a non-probability sample, whereby the

objective is to select typical, or representative, subjects and the skill and

judgment of selectors is deliberately utilized (Abrahamson, 1983).


Quota sampling allows the researcher to control variables without having a

sampling frame. This method is often used for market research because it does

not require a list of potential respondents. The interviewer finds respondents,

usually in public areas, who fit into the predetermined categories until the

quotas are filled. To that end, quota sampling is a convenient and inexpensive

method of research. If the interviewee is unavailable or refuses to participate


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they can easily be replaced with another potential respondent who meets the

same criteria.

Evaluation

Statisticians criticize quota sampling for its methodological weakness. Although

the interviewer randomly chooses respondents he or she comes across on the

street, quota sampling cannot be considered a genuinely random method of

sampling because not every member of the population has an equal chance of

survey selection (for example, those who are at work or at home). Therefore, the

principles of statistical inference cannot be invoked.

There are a number of factors that can result in research bias. First,

interviewers may misjudge a potential respondent's characteristic, such as

their age. Secondly, the interviewer runs the risk of subconsciously making a

subjective judgment before approaching a potential respondent. As a result, the

interviewer may not approach those deemed unfriendly and runs the risk of

distorting the findings. This is also known as systematic bias (Abrahamson,

1983). Finally, quota sampling can never be truly representative because

certain factors may prevent certain groups of people from being chosen for the

research. For example, as noted above, market research conducted during the

day may over-represent housewives shopping in the city centre and under-

represent office workers.

Despite these limitations, quota sampling continues to be used because there

are circumstances when random or stratified random sampling is not possible.


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SNOWBALL SAMPLING

Definition

A form of non-probability sampling in which the researcher begins by

identifying an individual perceived to be an appropriate respondent. This

respondent is then asked to identify another potential respondent. The process

is repeated until the researcher has collected sufficient data. Sometimes called

chain letter sampling.


Snowball sampling can be a useful technique in research concerned with

behavior that is socially unacceptable or involves criminal activity. The nature

of such activities may make it a virtually impossible task to identify all

members of the research population; even identifying a few members can be

very difficult. In the case of research on, say, shoplifting or car theft, the

identification of a single willing respondent may be difficult. The first stage in

the process usually involves a purposive sampling decision to identify one

respondent who is willing to provide data. Once the data collection has been

completed, the researcher asks the respondent to nominate another person

who may be willing to provide the type of data required. The process continues


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until either the researcher fails to make any new contacts, or the new data do

not appear to add anything substantial to existing understanding.

Snowball sampling can also be a relevant technique for groups of people who

may feel lacking in confidence to participate in a research project. Such people

could include the homeless, alcoholics, or those who have suffered illness or an

assault. In such cases, they may have more confidence and be more likely to

participate if they are approached by a person with similar experiences.

Evaluation

The advantage of snowball sampling is that it enables the researcher to identify

potential participants when it would otherwise be extremely difficult to do so. It

is also a sampling strategy that demonstrates sensitivity to potential

participants, in that they are identified by people with a similar experience.

The disadvantage of the approach is that it is dependent upon each participant

sufficiently understanding the nature of the research in order to be able to

identify another suitable participant. The next nominated participant may have

a limited or biased understanding of the research issue. In addition, the

members of the snowball sample may have certain features in common which

are uncharacteristic of the research population as a whole. The very fact that

they are all acquainted with each other is a source of potential bias.


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REFERENCES

1. Berg, Sven. (2006). "Snowball SamplingI," pp. 78177821 inEncyclopedia of Statistical Sciences, edited by Samuel Kotz, Campbell

Read, N. Balakrishnan, and Brani Vidakovic. Hoboken, NJ: John Wiley

and Sons, Inc.

2. Deming WE (1960), "Sample Design in Business Research", John Wileyand Sons, New York.

3. Denzin, N. K., & Lincoln, Y. S. (2000). Handbook of qualitative research.London: Sage Publications.

4.Neyman J, "On the two different aspects of the representative method:

the method of stratified sampling and the method of purposive selection",

Journal of the Royal Statistical Society, Vol. 97, pp 558-606.

5. Polit, D. F. & Beck, C. T. (2003). In Nursing Research: Principles andMethods. 7th ed.) (413-444). Philadelphia: Lippincott Williams & Wilkins.

6. Smith TFM (1983), "On the validity of Inferences from Non-randomSamples", Journal of the Royal Statistical Society, Vol. 146, pp 394-403.

7. Stephan FP and McCarthy PJ (1958), "Sampling Opinions", John Wileyand Sons, New York.

8. William M.K. Trochim,(2006) Research Methods Knowledge Base


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