Causal Research MR

By: Prof Neelam Tandon

Concept of CausalityA statement such as "X causes Y " will have thefollowing meaning to an ordinary person and to ascientist. ____________________________________________________

Ordinary Meaning Scientific Meaning____________________________________________________X is the only cause of Y. X is only one of a number of

possible causes of Y.

X must always lead to Y The occurrence of X makes the (X is a deterministic occurrence of Y more probablecause of Y). (X is a probabilistic cause of Y). It is possible to prove We can never prove that X is athat X is a cause of Y. cause of Y. At best, we can

infer that X is a cause of Y.

Conditions for Causality

• Concomitant variation is the extent to which a cause, X, and an effect, Y, occur together or vary together in the way predicted by the hypothesis under consideration.

• The time order of occurrence condition states that the causing event must occur either before or simultaneously with the effect; it cannot occur afterwards.

• The absence of other possible causal factors means that the factor or variable being investigated should be the only possible causal explanation.

Evidence of Concomitant Variation betweenPurchase of Fashion Clothing and Education

High

High Low

363 (73%) 137 (27%)

322 (64%) 178 (36%)

Purchase of Fashion Clothing, Y

500 (100%)

500 (100%)Low

Ed

ucati

on

, X

Purchase of Fashion Clothing ByIncome and Education

Low IncomePurchase

High Low

High

LowEd

ucati

on

200 (100%)

300 (100%)

300

200

122 (61%)

171 (57%)

78 (39%)

129 (43%)

High IncomePurchase

High

High

Low

Low

241 (80%)

151 (76%)

59 (20%)

49 (24%)

Ed

ucati

on

Definitions and Concepts• Independent variables are variables or alternatives that

are manipulated and whose effects are measured and compared, e.g., price levels.

• Test units are individuals, organizations, or other entities whose response to the independent variables or treatments is being examined, e.g., consumers or stores.

• Dependent variables are the variables which measure the effect of the independent variables on the test units, e.g., sales, profits, and market shares.

• Extraneous variables are all variables other than the independent variables that affect the response of the test units, e.g., store size, store location, and competitive effort.

Experimental Design

An experimental design is a set of procedures specifying:

the test units and how these units are to be divided into homogeneous subsamples,

what independent variables or treatments are to be manipulated,

what dependent variables are to be measured; and

how the extraneous variables are to be controlled.

X = the exposure of a group to an independent variable, treatment, or event, the effects of which are to be 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 treatments

Movement from left to right indicates movement through time Horizontal alignment of symbols implies that all those symbols

refer to specific treatment group Vertical alignment of symbols implies that those symbols refer to

activities or events that occur simultaneously

X O1 O2 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 and O2.

R X1 O1 R X2 O2 Means that two groups of test units were randomly assigned to

two different treatment groups (X1 and X2 respectively) at the same time , and the dependent variable was measured in the two groups simultaneously

Validity in Experimentation

• 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 refers to whether the cause-and-effect relationships found in the experiment can be generalized. To what populations, settings, times, independent variables and dependent variables can the results be projected?

• History refers to specific events that are external to the experiment but occur at the same time as the experiment.

• O1 X1 O2

• O1 and O2 are measures of sales of a department store in a specific region and X1 represents a new promotional campaign. The difference (O2 – O1) is the treatment effect. If there is no difference between O2 and O1 doesn’t mean that promotional campaign was ineffective. This may be due to economic factors.

• The longer the time period between the observations, the greater the probability that history will confound an experiment of this type.

• .

Extraneous Variables

Maturation (MA) refers to changes in the test units themselves that occur with the passage of time. Example: Store change over time in terms of physical layout, décor, traffic , and composition.

Testing effects are caused by the process of experimentation. Typically, these are the effects on the experiment of taking a measure on the dependent variable before and after the presentation of the treatment.

The main testing effect (MT) occurs when a prior observation affects a latter observation Example : effect of advertising on attitudes of customers toward a certain brand.

The respondents are given a pretreatment questionnaire measuring background information and attitude toward the brand. After viewing the commercial , the respondents again answer a questionnaire measuring among other things ,attitude toward the brand. It comprises of internal validity of the experiment.

Extraneous Variables• In the interactive testing effect (IT), a prior

measurement affects the test unit's response to the independent variable.

• Example : People may become aware of the brand . They are sensitized to that brand and become more likely to pay attention to the test commercial than people who were not included in the experiment . The measured effects are then not generalizable to the population.

Instrumentation (I) refers to changes in the measuring instrument, in the observers or in the scores themselves.

Example if price change between O1 and O2 this results change in instrumentation because sales will be measured using different unit prices. In this case treatment effect (O2 – O1) could be attributed to a change in instrumentation.

Statistical regression effects (SR) occur when test units with extreme scores move closer to the average score during the course of the experiment. People with extreme attitudes have more room for change. on post treatment measurement their attitude may move towards the average.

Selection bias (SB) refers to the improper assignment of test units to treatment conditions.

Example : different store size for a merchandise experiment. The store size may affect sales regardless of which merchandising display (old or new) was assigned to a store.

Mortality (MO) refers to the loss of test units while the experiment is in progress. Reason may be test units refusing to continue in the experiment.

Controlling Extraneous Variables

• Randomization refers to the random assignment of test units to experimental groups by using random numbers. Treatment conditions are also randomly assigned to experimental groups.

• Matching involves comparing test units on a set of key background variables before assigning them to the treatment conditions.

• Statistical control involves measuring the extraneous variables and adjusting for their effects through statistical analysis.

• Design control involves the use of experiments designed to control specific extraneous variables.

A Classification of Experimental Designs

Pre-experimental

One-Shot Case Study

One Group Pretest-Posttest

Static Group

True Experimental

Pretest-Posttest Control Group

Posttest: Only Control Group

Solomon Four-Group

Quasi Experimental

Time Series

Multiple Time Series

Statistical

Randomized Blocks

Latin Square

Factorial Design

Experimental Designs

A Classification of Experimental Designs

• Pre-experimental designs do not employ randomization procedures to control for extraneous factors: the one-shot case study, the one-group pre test-post test design, and the static-group.

• In true experimental designs, the researcher can randomly assign test units to experimental groups and treatments to experimental groups: the pretest-posttest control group design, the posttest-only control group design, and the Solomon four-group design.

A Classification of Experimental Designs

• Quasi-experimental designs result when the researcher is unable to achieve full manipulation of scheduling or allocation of treatments to test units but can still apply part of the apparatus of true experimentation: time series and multiple time series designs.

• A statistical design is a series of basic experiments that allows for statistical control and analysis of external variables: randomized block design, Latin square design, and factorial designs.

One-Shot Case Study

X 01

• A single group of test units is exposed to a treatment X.

• A single measurement on the dependent variable is taken (01).

• There is no random assignment of test units.

• The one-shot case study is more appropriate for exploratory than for conclusive research.

One-Group Pretest-Posttest Design

01 X 02

• A group of test units is measured twice.

• There is no control group.

• The treatment effect is computed as 02 – 01.

• The validity of this conclusion is questionable since extraneous variables are largely uncontrolled.

Static Group Design

EG: X 01

CG: 02

• A two-group experimental design.

• The experimental group (EG) is exposed to the treatment, and the control group (CG) is not.

• Measurements on both groups are made only after the treatment.

• Test units are not assigned at random.

• The treatment effect would be measured as 01 - 02.

True Experimental Designs: Pretest-Posttest Control Group Design

EG: R 01 X 02

CG: R 03 04

• Test units are randomly assigned to either the experimental or the control group.

• A pretreatment measure is taken on each group. • The treatment effect (TE) is measured as:(02 - 01) - (04 - 03). • Selection bias is eliminated by randomization. • The other extraneous effects are controlled as follows:

02 – 01= TE + H + MA + MT + IT + I + SR + MO

04 – 03= H + MA + MT + I + SR + MO= EV (Extraneous Variables)

• The experimental result is obtained by:

(02 - 01) - (04 - 03) = TE + IT• Interactive testing effect is not controlled.

Posttest-Only Control Group Design

EG : R X 01

CG : R 02

• The treatment effect is obtained by:

TE = 01 - 02

• Except for pre-measurement, the implementation of this design is very similar to that of the pretest-posttest control group design.

Quasi-Experimental Designs: Time Series Design

01 02 03 04 05 X 06 07 08 09 010

• There is no randomization of test 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.

Multiple Time Series Design

EG : 01 02 03 04 05 X 06 07 08 09 010

CG : 01 02 03 04 05 06 07 08 09 010

• If the control group is carefully selected, this design can be an improvement over the simple time series experiment.

• Can test the treatment effect twice: against the pretreatment measurements in the experimental group and against the control group.

Statistical DesignsStatistical designs consist of a series of basic experiments that allow for statistical control and analysis of external variables and offer the following advantages:

– The effects of more than one independent variable can be measured.

– Specific extraneous variables can be statistically controlled.

– Economical designs can be formulated when each test unit is measured more than once.

The most common statistical designs are the randomized block design, the Latin square design, and the factorial design.

Randomized Block Design

• Is useful when there is only one major external variable, such as store size, that might influence the dependent variable.

• The test units are blocked, or grouped, on the basis of the external variable.

• By blocking, the researcher ensures that the various experimental and control groups are matched closely on the external variable.

Randomized Block Design

Treatment Groups Block Store Commercial Commercial Commercial Number Patronage A B C 1 Heavy A B C 2 Medium A B C 3 Low A B C 4 None A B C

Latin Square Design• Allows the researcher to statistically control two noninteracting

external variables as well as to manipulate the independent variable.

• Each external or blocking variable is divided into an equal number of blocks, or levels.

• The independent variable is also divided into the same number of levels.

• A Latin square is conceptualized as a table ,with the rows and columns representing the blocks in the two external variables.

• The levels of the independent variable are assigned to the cells in the table.

• The assignment rule is that each level of the independent variable should appear only once in each row and each column.

Latin Square Design

Interest in the Store Store Patronage High Medium

Low

Heavy B A C Medium C B

A Low and none A C

B

Factorial Design

• Is used to measure the effects of two or more independent variables at various levels.

• A factorial design may also be conceptualized as a table.

• In a two-factor design, each level of one variable represents a row and each level of another variable represents a column.

Factorial Design

Amount of Humor

Amount of Store No Medium High

Information Humor Humor HumorLow A B C

Medium D E F

High G H I

Laboratory Versus Field Experiments

Factor LaboratoryField

Environment ArtificialRealisticControl HighLow Reactive Error

High Low Demand Artifacts High Low Internal Validity HighLowExternal Validity LowHighTime Short LongNumber of Units Small LargeEase of Implementation HighLow Cost

Low High

Limitations of Experimentation• Experiments can be time consuming, particularly if

the researcher is interested in measuring the long-term effects.

• Experiments are often expensive. The requirements of experimental group, control group, and multiple measurements significantly add to the cost of research.

• Experiments can be difficult to administer. It may be impossible to control for the effects of the extraneous variables, particularly in a field environment.

• Competitors may deliberately contaminate the results of a field experiment.

Selecting a Test-Marketing Strategy

Competition

Overall Marketing Strategy

Socio

-Cu

ltu

ral En

vir

on

men

t

Need

for

Secre

cy

New Product DevelopmentResearch on Existing ProductsResearch on other Elements

Simulated Test Marketing

Controlled Test Marketing

Standard Test Marketing

National Introduction

Sto

p a

nd R

eevalu

ate

-ve

-ve

-ve

-ve

Very +veOther Factors

Very +veOther Factors

Very +veOther Factors

Criteria for the Selection of Test Markets

Test Markets should have the following qualities:

1) Be large enough to produce meaningful projections. They should contain at least 2% of the potential actual population.

2) Be representative demographically.

3) Be representative with respect to product consumption behavior.

4) Be representative with respect to media usage.

5) Be representative with respect to competition.

6) Be relatively isolated in terms of media and physical distribution.

7) Have normal historical development in the product class.

8) Have marketing research and auditing services available.

9) Not be over-tested.

Measurement and Scaling:

Fundamentals and Comparative Scaling

Measurement and Scaling

Measurement means assigning numbers or other symbols to characteristics of objects according to certain pre-specified rules.

– One-to-one correspondence between the numbers and the characteristics being measured.

– The rules for assigning numbers should be standardized and applied uniformly.

– Rules must not change over objects or time.

Measurement and Scaling

Scaling involves creating a continuum upon which measured objects are located.

Consider an attitude scale from 1 to 100. Each respondent is assigned a number from 1 to 100, with 1 = Extremely Unfavorable, and 100 = Extremely Favorable. Measurement is the actual assignment of a number from 1 to 100 to each respondent. Scaling is the process of placing the respondents on a continuum with respect to their attitude toward department stores.

Primary Scales of Measurement

7 38

ScaleNominal Numbers

Assigned to Runners

Ordinal Rank Orderof Winners

Interval PerformanceRating on a

0 to 10 Scale

Ratio Time to Finish, in

Seconds

Thirdplace

Secondplace

Firstplace

Finish

Finish

8.2 9.1 9.6

15.2 14.1 13.4

Primary Scales of MeasurementNominal Scale

• The numbers serve only as labels or tags for identifying and classifying objects.

• When used for identification, there is a strict one-to-one correspondence between the numbers and the objects.

• The numbers do not reflect the amount of the characteristic possessed by the objects.

• The only permissible operation on the numbers in a nominal scale is counting.

• Only a limited number of statistics, all of which are based on frequency counts, are permissible, e.g., percentages, and mode.

Illustration of Primary Scales of Measurement

Nominal Ordinal RatioScale Scale Scale

Preference $ spent last No. Store Rankings 3 months

1. Parisian2. Macy’s3. Kmart4. Kohl’s5. J.C. Penney 6. Neiman Marcus 7. Marshalls8. Saks Fifth Avenue 9. Sears 10.Wal-Mart

IntervalScale Preference Ratings

1-7 11-17

7 79 5 15 02 25 7 17 2008 82 4 14 03 30 6 16 1001 10 7 17 2505 53 5 15 359 95 4 14 06 61 5 15 1004 45 6 16 010 115 2 12 10

Primary Scales of MeasurementOrdinal Scale

• A ranking scale in which numbers are assigned to objects to indicate the relative extent to which the objects possess some characteristic.

• Can determine whether an object has more or less of a characteristic than some other object, but not how much more or less.

• Any series of numbers can be assigned that preserves the ordered relationships between the objects.

• In addition to the counting operation allowable for nominal scale data, ordinal scales permit the use of statistics based on centiles, e.g., percentile, quartile, median.

Primary Scales of MeasurementInterval Scale

• Numerically equal distances on the scale represent equal values in the characteristic being measured.

• It permits comparison of the differences between objects.

• The location of the zero point is not fixed. Both the zero point and the units of measurement are arbitrary.

• Any positive linear transformation of the form y = a + bx will preserve the properties of the scale.

• It is not meaningful to take ratios of scale values.

• Statistical techniques that may be used include all of those that can be applied to nominal and ordinal data, and in addition the arithmetic mean, standard deviation, and other statistics commonly used in marketing research.

Primary Scales of MeasurementRatio Scale

• Possesses all the properties of the nominal, ordinal, and interval scales.

• It has an absolute zero point.

• It is meaningful to compute ratios of scale values.

• Only proportionate transformations of the form y = bx, where b is a positive constant, are allowed.

• All statistical techniques can be applied to ratio data.

Primary Scales of Measurement

Scale Basic Characteristics

Common Examples

Marketing Examples

Nominal Numbers identify & classify objects

Social Security nos., numbering of football players

Brand nos., store types

Percentages, mode

Chi-square, binomial test

Ordinal Nos. indicate the relative positions of objects but not the magnitude of differences between them

Quality rankings, rankings of teams in a tournament

Preference rankings, market position, social class

Percentile, median

Rank-order correlation, Friedman ANOVA

Ratio Zero point is fixed, ratios of scale values can be compared

Length, weight Age, sales, income, costs

Geometric mean, harmonic mean

Coefficient of variation

Permissible Statistics Descriptive Inferential

Interval Differences between objects

Temperature (Fahrenheit)

Attitudes, opinions, index

Range, mean, standard

Product-moment

A Classification of Scaling Techniques

Likert Semantic Differential

Stapel

Scaling Techniques

NoncomparativeScales

Comparative Scales

Paired Comparison

Rank Order

Constant Sum

Q-Sort and Other Procedures

Continuous Rating Scales

Itemized Rating Scales

A Comparison of Scaling Techniques

• Comparative scales involve the direct comparison of stimulus objects. Comparative scale data must be interpreted in relative terms and have only ordinal or rank order properties.

 • In noncomparative scales, each object is scaled

independently of the others in the stimulus set. The resulting data are generally assumed to be interval or ratio scaled.

Relative Advantages of Comparative Scales

• Small differences between stimulus objects can be detected.

• Same known reference points for all respondents.

• Easily understood and can be applied.

• Involve fewer theoretical assumptions.

• Tend to reduce halo or carryover effects from one judgment to another.

Relative Disadvantages of Comparative Scales

• Ordinal nature of the data

• Inability to generalize beyond the stimulus objects scaled.

Comparative Scaling TechniquesPaired Comparison Scaling

• A respondent is presented with two objects and asked to select one according to some criterion.

• The data obtained are ordinal in nature.

• Paired comparison scaling is the most widely-used comparative scaling technique.

• With n brands, [n(n - 1) /2] paired comparisons are required.

• Under the assumption of transitivity, it is possible to convert paired comparison data to a rank order.

Obtaining Shampoo Preferences Using Paired Comparisons

Instructions: We are going to present you with ten pairs of shampoo brands. For each pair, please indicate which one of the two brands of shampoo you would prefer for personal use. Recording Form:

Jhirmack Finesse Vidal Sassoon

Head & Shoulders

Pert

Jhirmack 0 0 1 0 Finesse 1a 0 1 0 Vidal Sassoon 1 1 1 1 Head & Shoulders 0 0 0 0 Pert 1 1 0 1 Number of Times Preferredb

3 2 0 4 1

aA 1 in a particular box means that the brand in that column was preferred over the brand in the corresponding row. A 0 means that the row brand was preferred over the column brand. bThe number of times a brand was preferred is obtained by summing the 1s in each column.

Paired Comparison SellingThe most common method of taste testing is paired comparison. The consumer is asked to sample two different products and select the one with the most appealing taste. The test is done in private and a minimum of 1,000 responses is considered an adequate sample. A blind taste test for a soft drink, where imagery, self-perception and brand reputation are very important factors in the consumer’s purchasing decision, may not be a good indicator of performance in the marketplace. The introduction of New Coke illustrates this point. New Coke was heavily favored in blind paired comparison taste tests, but its introduction was less than successful, because image plays a major role in the purchase of Coke.

A paired comparison taste test

Comparative Scaling TechniquesRank Order Scaling

• Respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion.

• It is possible that the respondent may dislike the brand ranked 1 in an absolute sense.

• Furthermore, rank order scaling also results in ordinal data.

• Only (n - 1) scaling decisions need be made in rank order scaling.

Preference for Toothpaste Brands Using Rank Order Scaling

Instructions: Rank the various brands of toothpaste in order of preference. Begin by picking out the one brand that you like most and assign it a number 1. Then find the second most preferred brand and assign it a number 2. Continue this procedure until you have ranked all the brands of toothpaste in order of preference. The least preferred brand should be assigned a rank of 10.

No two brands should receive the same rank number.

The criterion of preference is entirely up to you. There is no right or wrong answer. Just try to be consistent.

Preference for Toothpaste Brands Using Rank Order Scaling

Brand Rank Order

1. Crest _________

2. Colgate _________

3. Aim _________

4. Gleem _________

5. Sensodyne _________6. Ultra Brite _________

7. Close Up _________

8. Pepsodent _________

9. Plus White _________

10. Stripe _________

Form

Comparative Scaling TechniquesConstant Sum Scaling

• Respondents allocate a constant sum of units, such as 100 points to attributes of a product to reflect their importance.

• If an attribute is unimportant, the respondent assigns it zero points.

• If an attribute is twice as important as some other attribute, it receives twice as many points.

• The sum of all the points is 100. Hence, the name of the scale.

Importance of Bathing Soap AttributesUsing a Constant Sum Scale

Instructions

On the next slide, there are eight attributes of bathing soaps. Please allocate 100 points among the attributes so that your allocation reflects the relative importance you attach to each attribute. The more points an attribute receives, the more important the attribute is. If an attribute is not at all important, assign it zero points. If an attribute is twice as important as some other attribute, it should receive twice as many points.

Form Average Responses of Three Segments Attribute Segment I Segment II Segment III1. Mildness2. Lather 3. Shrinkage 4. Price 5. Fragrance 6. Packaging 7. Moisturizing 8. Cleaning Power

Sum

8 2 4 2 4 17 3 9 7

53 17 9 9 0 19 7 5 9 5 3 20

13 60 15 100 100 100

Importance of Bathing Soap AttributesUsing a Constant Sum Scale