LECTURE 6
Casual Research
Causality when the occurrence of X increases the probability of the occurrence of Y.
Conditions for Causality
Before making causal inferences, or assuming causality, three conditions must be satisfied. These
are:
1. concomitant variation
2. time order of occurrence of variables
3. elimination of other possible causal factors
Concomitant variation is the extent to which causes X and an effect Y, occur together or vary
together in the way predicted by the hypothesis under consideration.
Time Order of Occurrence of Variables
The time of occurrence condition states that the causing event must occur either before or
simultaneously with the effect; it cannot occur afterwards. By definition an effect can not be
produced by an event that occurs after the effect has taken place.
Absence of Other Possible Causal Factor
The absence of other possible causal factors means that the factor or variable being investigated
should be the only possible causal explanation.
Definitions and Concepts
We define basic concepts and illustrate those using examples.
Independent Variables
Independent variables are variables or alternatives that are manipulated i.e. the levels of these
variables are changed by the researcher and whose effects are measured and compared. These
variables also known as treatments may include price levels, package design and advertising
themes.
1|Page
©St. Paul’s University
Test Units
Test units are individuals, organizations or other entities whose response to the independent
variables or treatments is being examined. Test units may include consumers, stores or geographic
areas.
Dependent Variables
Dependent variables are the variables that measure the effect of the independent variables on the
test units. These variables may include sales, profits and market shares.
Extraneous variables
Extraneous variables are all variables other than the independent variables that affect the response
of the test units. These variables can confound the dependent variable measures in a way that
weakens or invalidates the results of the experiment. Extraneous variables include store size, store
location and competitive effort.
Experiment
An experiment is formed when the researcher manipulates one or more independent variables and
measures their effect on one or more dependent variables, while controlling for the effect of
extraneous variables.
Experimental Design
An experimental design is a set of procedures specifying:
1. the test unit and how these units are to be divided into homogenous subsamples
2. what independent variables or treatments are to be manipulated
3. what dependent variables are to be measured
4. how the extraneous variables are to be controlled
2|Page
©St. Paul’s University
Definition of Symbols
To facilitate our discussion of extraneous variables and specific experimental designs, define a set
of symbols that are now commonly used in marketing research.
X= the exposure of a group to an independent variable, treatment or event the effects of which are
to determined.
O= the process of observation or measurement of the dependent variable on the test units or group
of units
R= the random assignment of test units or groups to separate
treatment In addition the following conventions are adopted
·
Movement from left to right indicates movement through time
·
Horizontal alignment of symbols implies that all those symbols refer to one specific group,
treatment or control.
·
Vertical alignment of symbols implies that all those symbols refer to activities or event that
occur simultaneously
For example, the symbols arrangement
X
O1O2
Means that a given group of test units was exposed to the treatment variable (X) and the response
was measured at two different points in time, O1 andO2
Validity in Experimentation
When conducting an experiment, a researcher has two goals,
1. Draw valid conclusions about the effects of independent variables on the study group
2. Make valid generalizations to a larger population of interest. Internal validity the second
external validity the first goal concerns
Internal Validity
3|Page
©St. Paul’s University
Internal validity refers to whether the manipulation of the independent variables or treatments
actually caused the observed effects on the dependent variables. Control of extraneous variables
is a necessary condition for establishing internal validity.
External Validity
External validity refers to whether the cause and effect relationships found in the experiment can
be generalized. It is desirable to have an experimental design that has both internal and external
validity but in applied marketing research often we have to trade one type of validity for
another.
Threats to Internal Validity
Following are threats to internal validity in the form of extraneous variables.
History
History refers to specific events that are external to the experiment but occur at the time as the
experiment. These events may affect the dependent variable.
What if general economic conditions declined during the experiment and the local area was
particularly hard hit by layoffs and plant closing. The longer the time interval between
observations, the greater the possibility that history will confound an experiment of this type.
Maturation
Maturation refers to change in the test units themselves, occur with the passage of time, involving
people. Maturation takes place as people become older, more experienced, tried, bored or
uninterested. Stores change over time in terms of physical layout, décor, traffic and composition.
Testing Effects
Testing effects are caused by the process of experimentation. The main testing effect (MT) occurs
when a prior observation affects a latter observation.
Instrumentation
Instrumentation (I) refers to changes in the measuring instruments are modified during the course.
Instrumentation effects are likely when interviewers make pre and post-treatment measurements.
The effectiveness of interviewers can be different at different times.
4|Page
©St. Paul’s University
Statistical Regression
Statistical regression (SR) effects occur when test units with extreme scores move closer to the
average score during the course of the experiment. People with extreme attitude have more room
for change, so variation is more likely.
Selection Bias
Selection bias (SB) refers to the improper assignment of test units to treatment conditions. If test
units self-select their own groups or are assigned to groups on the basis of the researcher’s
judgment, selection bias is possible. For example, consider a merchandising experiment in which
two different merchandising displays (old and new) are assigned to different department stores.
The stores in the two groups may not be equivalent to begin with. They may vary with respect to a
key characteristic, such as store size. Store size is likely to affect sales regardless of which
merchandising display was assigned to s store.
Mortality
Mortality (MO) refers to the loss of test units while the experiment is in progress. This happens for
many reasons, such as test units refusing to continue in the experiment. Mortality confounds
results because it is difficult to determine if the lost test units would respond in the same manner to
treatment as those that remain.
Controlling Extraneous Variables
Extraneous variables confound the results, they are also called confounding variables. There are
four ways of controlling extraneous variables: randomization, matching, statistical control and
design control.
Randomization
Randomization refers to the random assignment of test units to experimental groups by using
5|Page
©St. Paul’s University
random numbers. Treatment conditions are also randomly assigned to experimental groups.
Randomization may not be effective when the sample size is small.
Matching
Matching involves comparing test units on a set of key background variables before assigning
them to the treatment conditions.
Statistical Control
Statistical control involves measuring the extraneous variables and adjusting for their effects
through statistical analysis (ANCOVA).
Design Control
Design control involves the use of experiments designed to control specific extraneous variables.
Experimental Designs
Various experimental designs are described below.
One-Shot Case Study
Also known as the after only design, the one-shot case study may be symbolically represented as
O
X
1
Static Group Design
O
X
1
O
2
The treatment effect (TE) would be measured as TE=O1-O2.
Pretest-Posttest Control Group Design
6|Page
©St. Paul’s University
In the pretest-posttest control group design, test units are randomly assigned to either the experimental or the
control group, and a pretreatment measure is taken on each group. This design is symbolized as:
O
R
O1
X
2
O
R
O3
4
The treatment effect (TE) is measured as
TE = (O2-O1.) - (O4-O3.)
This design controls for most extraneous variables. Selection bias is eliminated by randomization.
Posttest-Only
Control
Group
Design
Randomized Block Design
A randomized block design is useful when there is only one major external variable, such as sales, store size,
or income of the respondent that might influence the dependent variable.
Latin Square Design
A Latin square design allows the researcher to statistically control two non-interacting external variables as
well as to manipulate the independent variable.
An example of Latin square design
Interest in the store
Store patronage
High
Medium
Low
High
B
A
C
Medium
C
B
A
Low and none
A
C
B
Experimental Settings
7|Page
©St. Paul’s University
There are two types of settings in which experiments conducted.
Laboratory Experiment
It is an artificial setting for experimentation in which the researcher constructs the desired conditions.
Field Experiment
An experimental location set in actual market conditions.
Laboratory experiments have some advantages over field experiments. The laboratory environment offers a
high degree of control because it isolates the experiment in a carefully monitored environment.
Limitations of Experimentation
Time
Experiments can be time consuming.
Cost
Experiments are often expensive.
Administration
Experiments can be difficult to administer. It may be impossible to control for the effects of the
extraneous variables, particularly in a field environment.
Test marketing
Test marketing, also called market testing, is an application of controlled experiment, done in
limited but carefully selected parts of the marketplace called test markets. It involves a replication
of a planned national marketing program in the test markets. Often, the marketing mix variables
(independent variables) are varied in test marketing, and the sales (dependent variable) are
monitored so that an appropriate national marketing strategy can be identified. The two major
objectives of test marketing are:
1. To determine market acceptance of the product
2. To test alternative levels of marketing mix variables.
Test marketing procedures may be classified as standard test markets, controlled and min-market
tests, and simulated test marketing.
Standard Test Market
8|Page
©St. Paul’s University
It is a test market in which the product is soled through regular distribution channels. For example,
no special considerations are given to products simply because they are being test-marketed.
The duration of the test depends on the repurchase cycle for the product, the probability of competitive
response, cost considerations, the initial consumer response, and company philosophy. The test should last
long enough for repurchase activity to be observed. If competitive reaction to the test is anticipated, the
duration should be short. Recent evidence suggests that tests of new brands should run for at least 10 months.
Controlled Test Market
A test-marketing program conducted by an outside research company in field experimentation.
The research company guarantees distribution of the product in retail outlets that represent a
predetermined percentage of the market.
Measuring and Scaling
Measurement in marketing research is determining how much of a property/ characteristic is
possessed by an object. Measurement is to determine the intensity of some characteristic of
interest to the researcher.
Now what are we really measuring? We are not measuring objects but we are measuring some
properties-called attributes or characteristics. Thus we do not measure buyers but their
characteristics like their preferences or perceptions. Objects in marketing research are consumers,
brands, stores, advertisements etc. Properties are characteristics of an object that can be used to
distinguish one object from the other. Properties may be:
a.Objective properties which are physically verifiable characteristics such as age, income,
number of cans purchased, store last visited and so on.
b.Subjective properties which cannot be directly observed and are mental constructs such as
perceptions, attitudes. Subjective properties are observer-able and intangible into a rating scale. There are
several examples of both these properties in marketing e.g. market potential for new product, sales of existing
9|Page
©St. Paul’s University
product, demographic and psychographic characteristics of the buyers, effectiveness of a new
advertising campaign, market share, and the like.
Rating scales
Subjective properties e.g. attitude, beliefs, intentions, preferences etc. are measured
with the help of rating scales usually called scales. In a scale numbers are assigned to
the amount of characteristic or “construct” being measured. The number varies
according to the amount of characteristic available in the object. There are four basic
scales.
1. Nominal scale
A nominal scale is one in which number serve as labels to identify or categorize
objects or events. All numbers are equal with respect to characteristics of objects.
Each number is assigned to only one object and each object has only one number.
The number in a nominal scale does not have any relationship with the amount of
characteristic. For example a unique number is given to each player in a football
team but player having a number 8 does not play better twice than the player having
number 4.
Although nominal scales are used for the lowest form of measurement, yet nominal
scales are frequently used in marketing research. Nominal level identification are
needed in marketing research to identify brands, store types, sales territories
customers, gender, geographic location, race, religion, buyer/non buyer heavy and
light users etc. The numbers assigned to such categories are mutually exclusive.
Alphabets, even symbols, could be used instead of numbers in nominal scales.
Nominal scales simply label objects and do not provide information on greater than
or less than. Usually counting is permissible operation in nominal scale. Thus
statistics like frequency distribution, percentages, mode, chi-square etc. are used
10 | P a g e
©St. Paul’s University
while analyzing data gathered by nominal scale. Average in these scales is
meaningless.
11 | P a g e
©St. Paul’s University