Cheat Sheat

define decision problem *specify research question

*establish research objective *benefits of expected

information = report. Types of data: primary, secondary

(internal & external), customer knowledge. QUALITATIVE

RESEARCH = primary exploratory research, subjective in

nature. Advantages = cheaper, no better way to

understand in-depth motivations & feelings, can improve

efficiency of quantitative research. Limitations = many

success and failures based on small differences, not

necessarily representative of population of interest,

dominant individual can skew results. QUANTITATIVE

RESEARCH = used to quantify the problem by way of

numerical data/data that can be transformed into useable


Basic (pure) research- attempts to expand the limits of

knowledge Applied Research- conducted when decision

must be made about a specific real-life problem

Exploratory research techniques- pilot studies, focus

group, secondary data (Qualitative)

Descriptive research- survey/questionnaire (Quantitative)

Causal Research- cause and effect among variables – test

marketing Relationships – Independent variable: stands

alone and doesn’t change by other variables you are trying

to measure (Persons age no other factor


– Objectives focus on ‘exploring’ and ‘having a

closer look’, Gaining background information, Defining

terms, Initial stage of research process, Not intended to

provide conclusive evidence, Purpose: to narrow the

scope of the research topics

– Can employ techniques from:

– Secondary data analysis o Pilot studies

– Case studies

– Focus groups

DESCRIPTIVE RESEARCH: Partially defined problem

Objectives focus on describing and measuring marketing

phenomena at a particular point in time

Already know what it is to be measured- just don’t know

how it is going to be measured

Purpose: to describe characteristics of a population (who,

what, when, where, how)

Accuracy is important

Surveys most common method (in person, telephone,


CAUSAL RESEARCH: Sharply defined problem such as

eating can change this) Dependent variable: depends on

other factors and is liable to change


Nominal: Operation – Counting I Descriptive Stats:

Frequency, Percentage, Mode

Ordinal: Rank ordering, Median, Range, Percentile ranking

Interval: Order and relative magnitude, Mean Standard

deviation, Variance

Ratio: Operations on actual quantities, geometric mean,

coefficient of variation

Traditional Tests

– Adv: Conducted in actual distribution channels. Can

determine both customer acceptance and traded


– Disadv: Cost, time and exposure to competition

Controlled Test Markets

– Adv: Distribution is assured, Cost are lower, Competitive

monitoring is difficult

– Disadv: Limited number of markets, trade support is


Stimulated test markets

– Adv: Cost and time saving,predict trial and purchase


– Disadv: isolation from real world, Broad based customer

reaction is difficult to measure

Experimental Design

A group of subjects is administered a treatment and then

measured (or observed). No attempt is made to randomly

assign subjects to the groups, nor does the design provide for

any additional groups as comparisons. GP – T – O

The one-shot design may be useful as an inexpensive

measure of a new treatment of the group in question.

One-group, Pre-post

One group is given a pre-treatment measurement or

observation, the experimental treatment, and a post-treatment

measurement or observation. The post-treatment measures

are compared with their pre-treatment measures.

Static group

Two intact groups are used, but only one of them is given the

experimental treatment. At the end of the treatment, both

groups are observed or measured to see if there is a

difference between them as a result of the treatment.


GP —– O

Whether the groups were comparable or not is crucial in

determining the extent of information yielded by this design

Post test-only control group

Similar to static group but attempt to insure similarity of the

groups before treatment. The design works toward a

guarantee of comparability between groups assigning subjects

to groups at random.



Pretest-Post test Control Group

Adds a pre-test to the previous design as a check on the

degree of comparability of the control and experimental groups

before the treatment is given.



This yields information on pre-treatment behaviour and a

comparison of post-treatment behaviour between groups.

Avoids most threats to internal validity. Groups are comparable

because they are randomised.

Solomon Four Group

Attempts to control for the possible “sensitising” effect of the

pre-test or measurement by adding two groups who have not

been a part of the pre-test or pre-measurement process. R–





Frequently used in behaviour, educational and medical studies

where the testing process allows the subject to “learn”

Factorial design

Assign variations of the treatment. E.g. we may wish to try

kinds of treatments varied in two ways (called a 2×2 factorial

design) Some factorial designs include both assignment of

subjects (blocking) and several types of experimental

treatment in the same experiment.


A1 B1


A1 B2


A2 B1


A2 B2

Time series Design

This design, or variations of it, is used to assess the effects of

a treatment with the same group or the same individual over a

period of me. A measure, or observation is made more than

once to assess the effects of the treatment.

GP–T–O–T–O–T–O or GP–O–O–O–T–O–O–O

There is no randomisation oftest units to treatments. The

timing of treatment presentation, as well as which test units

are exposed to the treatment, may not be within the

researcher’s control. e.g. ad spending on sales Exampleplain

packaging law of cigarette sales

Simple Random Sampling (SRS)

– Researchers use a table of random numbers, random

number generator or some random selection procedure

that ensures that each sampling unit make the target


Systematic Random Sampling

– More efficient than SRS. If we think target population has a

non-normal ( or skewed) distribution for one or more of its

distinguishing characteristics (e.g. age, income, product

ownership), researchers must identify sub-populations,

referred to as strata. After the strata are segmented a

simple random sample is drawn for each stratum.

Stratified Random Sampling

– When the defined target population is believed to have a

non-normal (skewed) distribution for one or more of its

distinguishing characteristics (age, sex, income, etc),

researchers must identify sub-populations referred to as


After ‘strata’ are segmented, a simple random sample is

drawn for each stratum

Cluster Sampling

Requires the defined target population to be segmented into

geographic areas, each of which are considered to be very

similar to the others

Researchers randomly select a few areas, then conduct a

census of what the elements in each are


Convenience sampling

Samples drawn at the convenience of the researcher/

interviewer, often as the study is being conducted

Has potential for a lot of bias, but does it really make that

much of a difference?

Judgment sampling

Participants selected according to the researcher’s or some

other experienced individual’s belief that they will meet the

requirements of the study

Whole population is not of interest Looking for a specific

target market

Quota sampling

– Selection of prospective participants according to the prespecified

quotas regarding demographic characteristics

o Demographics (age, race, sex, income) o Specific attitudes

(satisfied/dissatisfied, liking/disliking, great/marginal/no

quality) o Specific behaviours (regular/occasional/rare, user/

non-user, heavy/light)

– Underlying purpose: to provide assurance that pre-specified

subgroups of the defined target population are represented

on pertinent sampling factors that are determined by the

researcher or client, Cross-section

Snowball sampling

– Identifying and qualifying a set of initial

prospective respondents who can help the researcher identify

additional people to be included in the study

– After interviewing one person, the interviewer would solicit

that person’s help to identify other people with similar

characteristics, opinions or feelings


Goal: to obtain high-quality data

Construct development

o Construct abstractness o Construct dimensionality o

Construct development

– Precisely identify and define what is to be measured

– Hypothetical variable comprised of responses or behaviours

that are thought to be related

Construct abstractness- concrete vs subjective properties; the

more subjective, the more abstract Construct dimensionalityidentifiable

and measurable components that constitute the

domain of observables

Construct validity- process of establishing that the construct is

valid, by testing for content, convergent, discriminant and

nomological validity

Construct operationalisation- process of explaining a

construct’s meaning in measurement terms by specifying the

activities to measure it


Concept- generalised idea

Conceptual- verbal explanation of the meaning of a concept (what

it is and what it is not)

Operational- gives meaning to a concept by specifying the

activities or operations necessary to measure it


3 criteria for good measurement:

Reliability – degree to which measures are free from random error

Split-half method – to determine constancy by checking one half of

a set of results against the


• Random sampling error: e.g. caused by choosing only 50

respondents who enrolled into MKTG202 to represent the

population of all the MQ students.

Unavoidable but can be estimated (calculating confidence

intervals) or reduced (increasing sample size).

• Systematic error (non-sampling error) Systematic error results

from some imperfect aspect of the research design or from a

mistake in the execution of the research. Can be managed

(research execution)

• Sample Bias (respondent error) Persistent tendency to deviate

in one direction from the true value of the population parameter.

• Systematic error: respondent error sample bias/error resulting

from some respondent action or inaction.

• Non-response error: The statistical differences between a

survey that includes only those who responded and a perfect

survey that would also include those who failed to respond. –

caused by those who are not involved in the research, not

knowing what is unknown. Non-respondent: a person who is not

contacted or who refuses to cooperate in the research.

No contact: a person who cannot be reached on the 1st or 2nd

contact e.g. target population members not online. Refusalperson

unwilling to participate in research. Self-selection biaspeople

who feel strongly about a subject are more likely to

respond to survey question than people who feel indifferent

about it e.g. those volunteers to do the research.

• Response bias: respondents tend to answer questions with a

certain bias that consciously or unconsciously misrepresents the

truth. Deliberate falsification: occasionally people deliberately

give false answers to appear intelligent, conceal personal

information etc. Common when interviewing children and

politicians. Unconscious misrepresentations: response bias

arising from question format or content, even when respondent

is trying to be truthful. Acquiescence bias: some individuals tend

to agree with all questions e.g. caused by an obedient

respondent who chooses “yes”/” agree” to all questions

Extremity bias- some individuals tend to use extremes when

responding to questions. A respondent ticks the lowest or

highest marks for all questions, difference between eastern and


• Auspices bias- respondents are influenced by the organisation

conducting the study e.g. questions about general shopping

experiences asked within a Woolworths. Social desirability biasrespondents’

desire, either conscious or unconscious to gain

prestige or appear in a different social role e.g. questions related

personal or moral behaviour.

• Surveys may be classified according to several criteria. A

structured question is a question that imposes a limit on the

number of allowable responses, where as an unstructured

question is a question that does not restrict the respondents’

answers. They are open-ended and allows the respondent

considerable freedom in answering. The research must also

decide whether to use undisguised questions, which are

straightforward and assumes that the respondent is willing to

answer, or disguised questions, which are indirect and assumes

that the purpose of the study must be hidden from the

respondent. However, these classifications have two limitations.

• First the degree of structure and the degree of disguise vary;

they are not clear-cut categories. Second, most surveys are

hybrids, asking both structured and  unstructured questions.