Market and Social Research Part 4

The learning outcomes of this lecture are to be able to:

understand the principles of sampling

Study Skills and Learning Resources - http://www.griot.org

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Sampling

The population = all of the objects of interest e.g. everyone living in London

A census = the study of everyone in the population.

We would gather necessary data from everyone living in London.


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A census is not normally a practicable option:-•Number in population•Logistics•Cost•Time

Sampling


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We select a number of people from the population and study them.

This process of selection is sampling and the people selected are called the sample.

We study the sample to find out about the population.

Sampling


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The sample represents (i.e. stands in place of) the population.

To draw conclusions from the sample we must know how representative it is of the population under consideration.

Therefore we must carefully manage the process.

Sampling


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1. Understand the objective of the study2. Define the people of interest (POPULATION)3. Find suitable source for identifying the population

members4. Decide on the sampling type and approach 5. Decide on the sample size6. Proceed with the fieldwork7. Correct sampling errors ready for reporting

Sampling


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Sampling

Defining the population of interest

Specify the characteristics of the population under investigation from which the sample is to be drawn.

some combination of the following characteristics:

· Demographic (Age, sex, socio-economics)

· Geographic (Town, city, county, country, TV region)

· Product/services use (Non-user, current users, lapsed users)


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Identifying the population members - Sampling Frames


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Good frames are •up to date •complete•Affordable

Poor frames are•old•incomplete•inappropriate


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Common frames include

– the Electoral Register

– the Postcode Address File (PAF)

– Telephone directories

– Subscriber / membership records

– Customer records



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Probability sample

• A member has a positive, calculable probability of being chosen

• Response rate important• Is expensive• Objective

Non-probability sample

• Uses human judgement• Subject to errors that

cannot be measured• Usually cheaper than

random methods• Subjective

Sampling type and approach


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• Simple random sampling

• Stratified random sampling

• Cluster sampling

Sampling type and approach - probability methods


Sampling

Simple random sampling

A procedure in which every possible sample of a certain size within a population has a known and equal probability of being chosen.

the lottery there is a population of 49 numbered balls, from which a simple random sample of six balls is taken.

Things that make it a simple random sample:

every member of the population has an equal chance of being included (6/49) in the sample.


Sampling

Stratified random sampling

• Segments of the population are identified; these segments are called strata.

• The strata might be something like Big companies and Little companies or Men and Women.

• We carry out separate simple random samples for each stratum and then pool the results.

• Strata are chosen so that groups that we think have similar characteristics are in the same stratum. (Similar characteristics with respect to the study aims.)

• Gives better estimates than simple random sample and ensures all parts of the population are represented in the sample.


SamplingCluster sampling

The sampling frame is made up of convenient administrative chunks CLUSTERS. We take a simple random sample within these clusters.

For example, sampling students on this module, there are six tutorial groups; the tutorial groups would be our clusters. We might randomly choose two out of the six tutorial groups.

All or some of the units in the chosen cluster are studied.

We could e.g., apply a questionnaire to the students, during their tutorials. Compare with trying to find one third of the students on the module by simple random sample.

Studying schools we might use local education authorities as our clusters. Study Skills and Learning

Resources - http://www.griot.org

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Sampling type and approach Non-probability methods

• Quota sampling

• Convenience sampling

• Judgement sampling


Sampling

Quota sampling

In this form of sampling, the interviewer instead of a list of names and addresses, receives a quota to fill This will instruct her to conduct a set number of interviews with people in various categories, e.g. six interviews with women aged under 35 in the AB social grades

Collins


Sampling

Creating the cells for quota sampling is straightforward (resembles stratified random sampling)

Step 1 The population of interest is divided into segments (cells) via certain based on control characteristics: e.g., age, sex, level of education.

Step 2 A quota of units to be selected is determined by the judgement of the researcher.

Step 3 Interviewers are instructed to fill quotas assigned to the cells. Unlike probability sampling, here the interviewer is given some freedom in selecting the sample units.


Sampling

Choice of variables in quota sampling

Choice of quota variables is governed by such things as:

• Up-to-dale available data being at hand

• The data being applicable for setting quotas for a wide range of different geographic areas

• Interviewer being able to classify people correctly on the quota variable, e.g. social class

• Relevance, e.g., working and non-working in a convenience foods study


Sampling

Convenience sampling

No sample design. Similar to an interviewer questioning people as they meet them. Choice of the respondent is left entirely to the interviewer .Some methods, allow potential respondents to decide for themselves whether or nor to respond.

E.g.,•test new chocolate recipes on the staff in the chocolate factory;

•heart-disease studies carried out on the nurses at the hospital;

•psychological perception tests done by university research staff on university students.


Sampling

Judgment sampling

Relies on sound judgment or expertise.

Depends on selecting elements believed to be typical or representative of the population

Believed that errors of judgment in the selection will cancel each other out

Tend to be used more often in industrial market research than in consumer marker research.


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Reading:

• McGivern Ch2, Ch8• Bradley Ch 5• Kent Ch 7• WILSON, A.M. (1998) The Use of Mystery Shopping in

the Measurement of service Delivery, Services Industry Journal, 18, pp 148 – 163

• COLLINS, M. (1997), Interviewer variability: a review of the problem, Journal of the Market Research Society,39, 67 – 84

• PUNCH, K. (2003) Survey Research. The Basics. London, Sage


Education

Market and Social Research Part 4