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MARKETING RESEARCH

Chapter 12

Determining the Sample Plan

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Samples

To obtain information from

every individual within a marketis either impossible or

impractical or too costly;

therefore use a sample

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Sampling The population is the entire group

under study as specified by the

objectives of the research project

A census is the complete population

A sample is a subset of the population

that should represent the entire group

A sample unit is the basic level of

investigation

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Sampling Error

The difference between the sample

result and the result of a census Occurs whenever a sample of the

population is used

Caused by two factors:1. Method of sample selection

2. Size of sample

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Method of Sample Selection

Probability samples - members of

the population have a known chanceof being selected

Nonprobability samples - the chance

of members from the populationbeing selected is unknown

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Probability Sampling Methods

Four types:

1. Simple random sampling2. Systematic sampling

3. Cluster sampling4. Stratified sampling

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Simple Random Sampling

The probability of being selected

into the sample is equal for allmembers of the population

For example:

Blind Draw method

Table of random numbers

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Simple Random SamplingAdvantages:

As every member of the population has a

known and equal chance of being selected intothe sample, the sample will be a valid

representation of the population.

Disadvantages:

Requires a complete listing of the population

Unique designations must be provided for each

population member which is tedious

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Simple Random Sampling

Main uses:

Small and stable populationsRandom digit dialling

Computerised lists

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Systematic Sampling A skip interval is calculated and

names are selected based on this

Advantages: More efficient (faster and less expensive) than

simple random sampling

Disadvantages: Requires a complete listing of the population

Less representative than simple random

sampling

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Cluster Sampling The population is divided into groups

For example:

Area sampling1. One-step approach - Select just one area

randomly, perform a census of its members,

and then generalise results to the entirepopulation

2. Two-step approach - Randomly select a sample

of all areas, than apply a probability method to

sample individuals within the chosen areas

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Stratified Sampling Used when one expects different responses across

subgroups within a population

Identify the subgroups or strata contained within

the population and apply a probability method to

sample individuals within the subgroups

By allocating sample size based on variability in

the subgroups, a more efficient sample design isachieved

The diversity of the various subgroups are

preserved

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Non-probability Sampling Methods

Every member of the population does not

have a chance of being included in thesample

Four types:

1. Convenience sampling2. Judgement sampling

3. Referral sampling

4. Quota sampling

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Convenience Sampling The sampling procedure of obtaining the people or

units that are most conveniently available.

Also known as haphazard or accidental sampling

A high-traffic location is used to gather

potential respondents

Disadvantages:

Certain members of the population areautomatically eliminated from the sampling

process

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Judgement Sampling Members of the population are selected

according to the researchers judgement

about some appropriate characteristic

required of the sample member

Also known as purposive sampling

Subjectivity results in certain members of

the population having a smaller chance of

selection than others

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Referral (or Snowball) Sampling Respondents are asked to provide the

names of additional individuals who

might qualify for the sampleDisadvantage:

Members of the population who are

less known, disliked, or whose opinionsconflict with the respondent have a low

probability of being selected

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Quota Sampling A specific quota is given for the

inclusion of various types of

individuals with demographic orproduct usage characterisitcs in the

sample

Ensures that the various subgroups ina population are represented on

pertinent sample characteristics

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Developing a Sample Plan

Seven steps that the researcher

goes through in order to drawand arrive at the final sample

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Step 1: Define the Relevant

Population

The researcher needs to specify

the sample unit in the form of aprecise description of the type of

person to be surveyed

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Step 2: Obtain a Listing of the

Population

Obtain a suitable list to serve as a sampling

frame

Lists may be directories or databases

available in-house, to the public or for a

price by an external provider

Most lists suffer from sample frame error

Aim for lists with high incidence rates

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Step 3: Design the Sample Plan

(Size & Method)

Varies according to the

objectives of the survey and itsconstraints

Trade-off between the desire for

statistical precision and the needfor efficiency and economy

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Step 4: Access the Population

Establish guidelines as to how

much effort will be applied tocontact potential respondents

For example: Number of

telephone rings, the number ofcall-back attempts

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Step 5: Draw the SampleTwo phase process:1. Select sample unit

2. Gain information from that unit

Because some respondents refuse or dont have

the time to answer questions, it is necessary to

have a substitution provision in a sample plan

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Substitution Methods1. Drop-down substitution

Drop down to the next name on the list, and so on,

until successful. Then resume skip interval, using

the original name as your beginning point

2. Oversampling

Eg. Require a minimum of 100 respondents, feel

10% response rate, survey 1000 potentialrespondents

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Substitution Methods3. Resampling

Response rate may turn out to be much

lower than anticipated, and moreprospective respondents must be drawn

from the sample frame

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Step 6: Validate the Sample Ensure the sample is representative of

the population of interest

Compare the samples demographicprofile with the populations known

profile

Assures the decisions you may base onthe samples results will be relevant to

the population

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Step 7: Resample, if necessary

If the sample does not adequately

represent the population,resample

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MARKETING RESEARCH

Chapter 13

Determining the Size of a Sample

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Sample Size

Sample size does not affect sample

representativeness

Sample representativeness is dependent on

the sample plan (the way the sample is

selected)

Sample size affects the accuracy of results

(i.e., how accurate the samples findings

are relative to the true values in the

population)

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Methods of Determining Sample Size

1. Arbitrary Approach rule of thumb

Easy to remember and to apply

Not economical when the population under studyis large

2. Conventional Approach

Can result in a sample that may be too small ortoo large

Ignores the special circumstances of the survey at

hand

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Methods of Determining Sample Size

3. Cost Basis Approach (applicable to

nonprobability sampling)

4. Statistical Analysis Approach

5. Confidence Interval Approach

(applicable to probability samplingonly)

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MARKETING RESEARCH

Chapter 14

Data Collection in the Field,Nonresponse Error, and

Questionnaire Screening

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Nonsampling Errors

All errors in a survey except

those due to the sample plan andthe sample size

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Types of Nonsampling Errors

in Field Data Collection

Two sources:

1. Fieldworker errors

2. Respondent errors

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Fieldworker Errors

1. Intentional

Cheating (often related to compensation

system used)

Leading the respondent (through wording,

voice inflection or body language)

2. Unintentional

Interviewer characteristics (accent, gender,appearance)

Misunderstandings

Fatigue

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Respondent Errors

1. Intentional

Falsehoods

Nonresponse

2. Unintentional

Misunderstandings

Guessing

Attention loss

Distractions

Fatigue

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Control of Intentional

Fieldwork Error

1. Supervision

2. Validation

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Control of Unintentional

Fieldwork Error

1. Selection and training of

fieldworkers2. Orientation and role playing

sessions

3. Frequent breaks and/or

alternate surveys

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Control of Intentional

Respondent Error (Falsehoods)

1. Assuring anonymity and

confidentiality

2. Offering of incentives

3. Validation checks4. Third-person technique

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Control of Intentional

Respondent Error(Nonresponse)

1. Assuring anonymity andconfidentiality

2. Offering of incentives

3. Third-person technique

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Control of Unintentional

Respondent Error1. Well-drafted questionnaire

instructions and examples

2. Direct questions

3. Provide no opinion, do not

recall or unsure response options3. Reversal of scale endpoints

4. Use of prompters

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Nonresponse Error Occurs whenever a questionnaire is notcompleted by the respondent

Three types:1. Refusal to participate in the survey

2. Break-off during the interview

3. Item omission, refusal to answer a specific

question

Non-response error is calculated as 100

minus the response rate

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Response Rate

Defined as:

Response rate =Number of completed interviews

Number of eligible units in sample

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Reducing Nonresponse Error

1. Advance notification

2. Tangible incentives3. Follow-ups

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Adjusting Results Due to

Nonresponse Error

Weighted averagesOversampling

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Preliminary Questionnaire Screening

Unsystematic and systematic checks

of completed questionnaires

Types of response problems: Incomplete questionnaire

Nonresponse to specific questions

Yes or no response patterns Middle-of-the-road patterns

Unreliable responses