Descriptive and Causal Chapter 6 Descriptive and Causal Research Designs

Descriptive Research Research design in which the major emphasis is on determining the frequency with which something occurs or the extent to which two variables covary.

Descriptive research rests on one or more specific hyphotheses. Whereas an exploratory design of marketing research is characterized by its flexibility, descriptive design can be considered rigid. Descriptive study of marketing research require a clear specification of the who, what, when, where, why, and how of the research.

USE OF DESCRIPTIVE RESEARCH Transparency 6.1 Use of Descriptive Research USE OF DESCRIPTIVE RESEARCH To describe the characteristics of certain groups. For example, based on information gathered from known users of our particular product, we might attempt to develop a profile of the “average user” with respect to income, sex, age, educational level, and so on. 2. To estimate the proportion of people in a specified population who behave in a certain way. We might be interested, say, in estimating the proportion of people within a specified radius of a proposed shopping complex who would shop at the center. 3. To make specific predictions. We might be interested in predicting the level of sales for each of the next five years so that we could plan for the hiring and training of new sales representatives.

Sample Survey True Panel Longitudinal Omnibus Panel Descriptive Transparency 6.2 Classification of Descriptive Studies Descriptive Studies Cross Sectional Sample Survey Longitudinal Omnibus Panel True Panel

Longitudinal study Investigation involving a fixed sample of elements that is measured repeatedly through time. Cross-sectional study Investigation involving a sample of elements selected from the population of interest which are measured at a single point in time.

There are two types of panels; True panels: A fixed sample of respondents who are measured repeatedly over time with respect to the same variables. Omnibus panel; fixed sample of respondents who are measured repeatedly over time but on variables that change from measurement to measurement.

(Cross-sectional study) Transparency 6.8 Sample Survey SAMPLE SURVEY (Cross-sectional study) Cross-sectional study in which the sample is selected to be representative of the target population and in which the emphasis is on the generation of summary statistics such as averages and percentages. Surveys can be designed to capture a vide variety of information on many diverse topics and subjects. Disadvantages of the sample survey; Superficial analysis of the subject, High Cost and Technical sophistication

Blue collar and use the product Blue collar and don’t use the product Example: Management thinks that occupation is an important factor determining the consumption of its product. Proposition: “ White collar workers are more apt to use the product then the blue collar workers.” White collar and use the product White collar and don’t use the product Blue collar and use the product Blue collar and don’t use the product

Causal research Sometime to be able to explain some events / problems cause –and- effect hypotheses may be required. For example, if a price change is planned, marketing manager may want to test the hypothesis : “A 5 percent increase in the price of the product will have no significant effect on the amount of the product that customers will buy” Or If a new package design is planned: “A redesign of the package will improve consumer attidues toward the product”. This situation represents a problem for causal analysis. For testing cause –and – effect relationships between two variables, causal research is arranged.

Concept of Causality The concept of causality is complex. X- is the cause of another thing (Y) suggests that there is a single cause of an event. If a change in X, causes Y to change in the hypothesized way, then we are tempted to say X causes Y.

Experiments are defined as studies in which conditions are controlled so that one or more independent variable(s) can be manipulated to test a hypothesis about a dependent variable. In other words, in experimental research, the researcher manipulates the independent variable and then measures the effect of this manipulation on the dependent variable.

The key principle of experimental work is manipulation of a treatment variable ( say X), followed by observation of response variable (say Y). If a change in X, causes Y to change in the hypothesized way, then we are tempted to say X causes Y.

What are causal relationships? A concern with causality appears throughout marketing decision making. For example, What effect have recent price increases had on product class sales? Does the number of sales calls per month affect the size of the order placed? Underlying the research questions given is the need to understand a causal relationship between an action and a probable outcome. These could be actions taken in the past or predictions about future actions.

Given the causation concept, that a change in one variable will produce a change in another, it is reasonable to conclude that, if two variables causally linked, they should be associated. Thus, an association between attitude (A) and behavior(B) is evidence of a causal relationship. Attitude behavior If a causal link between two variables is thought to exist , a reasonable question is : Which variable is the causal (independent) variable and which is the “caused” (dependent) variable?

One approach to determining the direction of causation is to draw on logic and previous theory. In this context it is useful to observe whether one of the variables is relatively fixed and unalterable. Variables like sex, age, and income are relatively permanent. If, for example, an association is found between age and attendance at rock concert, it would be unrealistic to claim that attendance at rock concerts causes people to be young. In this case age could not be a “caused” variable because it is fixed in this context. Asecond approach is to consider the fact that there is usually a time lag between cause and effect. (ıf such a time lag can be postulated , the causal variable should have a positive association with the effect variable lagged in time.)

Copyright © 2001 by Harcourt, Inc. Transparency 6.9 Types of Evidence That Supports a Causal Inference Concomitant variation--evidence of the extent to which X and Y occur together or vary together in the way predicted by the hypothesis Time order of occurrence of variables--evidence that shows X occurs before Y Elimination of other possible causal factors—evidence that allows the elimination of factors other than X as the cause of Y X -- the presumed cause Y -- the presumed effect Copyright © 2001 by Harcourt, Inc. All Rights Reserved

Laboratory Experiment Transparency 6.10 Types of Experiments Laboratory Experiment Research investigation in which investigator creates a situation with exact conditions so as to control some, and manipulate other, variables Experiment Scientific investigation in which an investigator manipulates and controls one or more independent variables and observes the dependent variable for variation concomitant to the manipulation of the independent variables Field Experiment Research study in a realistic situation in which one or more independent variables are manipulated by the experimenter under as carefully controlled conditions as the situation will permit

Transparency 6.11 Internal Validity INTERNAL VALIDITY One criterion by which an experiment is evaluated; the criterion focuses on obtaining evidence demonstrating that the variation in the criterion variable was the result of exposure to the treatment, or experimental, variable.

Transparency 6.12 External Validity EXTERNAL VALIDITY One criterion by which an experiment is evaluated; the extent, to what populations and settings, to which the observed experimental effect can be generalized.

MARKET TEST (test-marketing) Transparency 6.13 Market test (test-marketing) MARKET TEST (test-marketing) A controlled experiment done in a limited but carefully selected sector of the marketplace; its aim is to predict the sales or profit consequences, either in absolute or relative terms, of one or more proposed marketing actions.

CRITICAL PROBLEMS OF TEST MARKETING Transparency 6.14 Critical Problems of Test Marketing CRITICAL PROBLEMS OF TEST MARKETING COST TIME CONTROL

Transparency 6.15 Relationship Among the Various Types of Test Markets Simulated Test Market Promising Not Promising Abort Controlled Test Market Promising Not Promising Abort Standard Test Market Not Promising Abort Promising National Rollout