GIÁO TRÌNH MARKETING NGHIÊN CỨU - PHẦN 12 pot

Basic Concepts in Sampling• Sampling error: any error in a survey that occurs because a sample is used • A sample frame: a master list of the entire population • Sample frame error: the

Determining How to

Select a Sample

Basic Concepts in Sampling

• Population: the entire group under study as defined by research

objectives

– Researchers define populations in specific terms such as “heads of

households located in areas

served by the company who are

responsible for making the pest

control decision.”

Basic Concepts in Sampling

• Sample: a subset of the population that should represent the entire

Basic Concepts in Sampling

• Sampling error: any error in a survey that occurs because a sample is used

• A sample frame: a master list of the entire population

• Sample frame error: the degree to

which the sample frame fails to

account for all of the population…a telephone book listing does not

contain unlisted numbers

Reasons for Taking a Sample

• Practical considerations such as

cost and population size

• Inability of researcher to analyze

huge amounts of data generated

by census

• Samples can produce precise

results

Two Basic Sampling Methods

• Probability samples: ones in which members of the population have a known chance (probability) of

being selected into the sample

• Non-probability samples:

instances in which the chances

(probability) of selecting members from the population into the

sample are unknown

Probability Sampling Methods

• Simple random sampling

• Systematic sampling

• Cluster sampling

• Stratified sampling

Probability Sampling Methods

Probability Sampling:

Simple Random Sampling

• Simple random sampling: the

probability of being selected into the sample is “known” and equal for all members of the population

– E.g., Blind Draw Method

– Random Numbers Method (see

MRI 12.1, p 335)

• Cumbersome to provide unique

designations to every population

member

Probability Sampling

Systematic Sampling

• Systematic sampling: way to

select a random sample from a

directory or list that is much more efficient than simple random

sampling

– Skip interval=population list

size/sample size

Probability Sampling

Systematic Sampling

– Advantages:

• Approximate known and equal

chance of selection…it is a probability sample plan

• Efficiency…do not need to designate every population member

• Less expensive…faster than SRS

– Disadvantage:

• Small loss in sampling precision

Probability Sampling

Cluster Sampling

• Cluster sampling: method in

which the population is

divided into groups, any of

which can be considered a

representative sample

– Area sampling

results

Cluster Sampling

• In cluster sampling the population

is divided into subgroups, called

“clusters.”

• Each cluster should represent the

population.

• Area sampling is a form of cluster

sampling – the geographic area is

Cluster Sampling

• One cluster may be selected to

represent the entire area with the one-step area sample.

• Several clusters may be selected

using the two-step area sample.

A Two-Step Cluster Sample

• A two-step cluster sample

(sampling several clusters) is

preferable to a one-step (selecting only one cluster) sample unless

the clusters are homogeneous.

Stratified Sampling

• When the researcher knows the

answers to the research question are likely to vary by subgroups…

Stratified Sampling

– Research Question: “To what extent

do you value your college degree?” Answers are on a five point scale: 1=

“Not valued at all” and 5= “Very

highly valued”

• We would expect the answers to vary depending on classification Freshmen

Stratified Sampling

– Research Question: “To what extent

do you value your college degree?”

• We would also expect there to be more agreement (less variance) as

classification goes up That is, seniors should pretty much agree that there is value Freshmen will have less

agreement.

Stratified Sampling

Probability Sampling

Stratified Sampling

• Stratified sampling: method in

which the population is separated into different strata and a sample

is taken from each stratum

– Proportionate stratified sample

– Disproportionate stratified sample

Probability Sampling

Stratified Sampling

– Advantage:

• More accurate overall sample of

skewed population…see next slide for WHY

– Disadvantage:

• More complex sampling plan

requiring different sample size for

Stratified Sampling

• Why is stratified sampling more

accurate when there are skewed

populations?

– The less variance in a group, the less

sample size it takes to produce a

precise answer.

– Why? If 99% of the population (low

variance) agreed on the choice of Brand

A, it would be easy to make a precise

estimate that the population preferred Brand A even with a small sample size.

Stratified Sampling

– But, if 33% chose Brand A, and

23% chose B, and so on (high

variance) it would be difficult to

make a precise estimate of the

population’s preferred brand…it

would take a larger sample size…

Stratified Sampling

– Stratified sampling allows the researcher

to allocate more sample size to strata

with less variance and less sample size

to strata with less variance Thus, for the same sample size, more precision is

achieved.

– This is normally accomplished by

disproportionate sampling Seniors

would be sampled LESS than their

proportionate share of the population

Stratified Sampling

• Note that we would expect this question to be

answered differently depending on student

Nonprobability Sampling

• With nonprobability sampling

methods selection is not based on fairness, equity, or equal chance.

– Convenience sampling

– Judgment sampling

– Referral sampling

– Quota sampling

Nonprobability Sampling

Nonprobability Sampling

• May not be representative but they

are still used very often Why?

– Decision makers want fast,

relatively inexpensive answers… nonprobability samples are faster and less costly than probability

samples

Nonprobability Sampling

• May not be representative but they

are still used very often Why?

– Decision makers can make a

decision based upon what 100 or

200 or 300 people say…they don’t feel they need a probability

sample

Nonprobability Sampling

• Convenience samples: samples

drawn at the convenience of the

interviewer

– Error occurs in the form of

members of the population who are infrequent or nonusers of that

location

Nonprobability Sampling

• Judgment samples: samples that require a judgment or an

“educated guess” as to who

should represent the population

– Subjectivity enters in here, and

certain members will have a

smaller chance of selection than

Nonprobability Sampling

samples): samples which require respondents to provide the names

of additional respondents

– Members of the population who are less known, disliked, or whose

opinions conflict with the

respondent have a low probability

of being selected

Nonprobability Sampling

• Quota samples: samples that use

a specific quota of certain types of individuals to be interviewed

– Often used to ensure that

convenience samples will have

desired proportion of different

respondent classes

Online Sampling Techniques

• Random online intercept

sampling: relies on a random

selection of Web site visitors

• Invitation online sampling: is when potential respondents are alerted that they may fill out a

questionnaire that is hosted at a

specific Web site

Online Sampling Techniques

• Online panel sampling: refers to

consumer or other respondent

panels that are set up by

marketing research companies for the explicit purpose of conducting surveys with representative

samples

Developing a Sample Plan

• Sample plan: definite sequence of steps that the researcher goes

through in order to draw and

ultimately arrive at the final sample

Developing a Sample Plan

1 Define the relevant population.

2 Obtain a listing of the population.

– The incidence rate is the

percentage of people on a list who qualify as members of the population

Developing a Sample Plan

4 Draw the sample.

– Substitution methods:

• Drop-down substitution

• Oversampling

• Resampling

Developing a Sample Plan

5 Validate the sample.

– Sample validation is a process in

which the researcher inspects some characteristic(s) of the sample to judge how well it represents the population

6 Resample, if necessary.