1. Descriptive and Causal Research Designs
Chapter 5
Descriptive and Causal Research Designs
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Descriptive and Causal Research Designs
Quantitative methods of collecting primary data that involve larger samples
Primary method is the survey, but other methods are possible.

3. Descriptive Research Designs
Describing something with data (as opposed to showing a cause-effect relationship).
Example: Creating a segment profile (age, income, education levels, typical purchases of segment)
Description goals must be clear at the outset unlike exploratory research
Much of marketing research is descriptive
No “manipulations”
Cannot prove causality

4. Survey Methods
Survey research methods: Research procedures for collecting large amounts of data using question-and-answer formats
Goals of Survey Research:
Make accurate predictions about relationships between market factors and behaviors
Gain insights into understanding the relationships between variables and differences between groups
Verify or re-validate existing relationships

5. Advantages and Disadvantages of Survey Research Designs

6. Types of Errors in Surveys
Sampling errors
The difference between the findings based on the sample and the true values for a population
Caused by:
(1) the method of sampling used, and
(2) the size of the sample
Can be reduced by:
Increasing sample size
Using the appropriate sampling method

7. Non-sampling Errors Respondent Errors Nonresponse Error Response Error
Consist of both nonresponse error and response error
Nonresponse Error
A systematic bias that occurs when the final sample differs from the planned sample due a systematic failure to respond by a segment of targeted sampling elements (occurs before survey administration).
Response Error
When respondents have impaired memory or do not respond accurately (occurs during survey administration).

8. Major Types of Survey Research Methods

9. Advantages and Disadvantages of Person-Administered Surveys

10. Advantages and Disadvantages of Self-Administered Surveys

11. Selecting the Appropriate Survey Method
Situational characteristics
Budget
Completion time frame
Quality requirements
Completeness of data
Data generalizability: How projectable to the population is the sample data?
Data precision

12. Selecting the Appropriate Survey Method
Task characteristics
Task difficulty
Required stimuli
Amount of information asked from respondents
Topic sensitivity: The degree to which a survey question leads the respondent to give a socially acceptable response

13. Selecting the Appropriate Survey Method
Respondent characteristics
Diversity
Incidence rate: The percentage of the general population that is the subject of the market research
Respondent participation
Ability to participate: The ability of both the interviewer and the respondent to get together in a question-and-answer interchange
Willingness to participate: The respondent’s inclination or disposition to share his or her thoughts
Knowledge level: Degree to which the selected respondents feel they have knowledge of or experience with the survey’s topics

14. Causal Research Designs
Causal research: Studies that enable researchers to assess “cause-effect” relationships between two or more variables
Independent variables: Variables whose values are directly manipulated by the researcher
Dependent variables: Measures of effects or outcomes that occur as a result of changes in levels of the independent (causing) variable(s)

15. Causal Research Designs
Researchers use experimental designs in causal research
Experiment: An empirical investigation that tests for hypothesized relationships between dependent variables and manipulated independent variables
Test and control cases
What do we mean by “manipulated” variables?
Discrete vs. continuous variables
Levels of a variable

16. The Nature of Experimentation
Experiments can show and explain cause-and-effect relationships between variables and/or constructs.
Variable: A measurable element that can vary or have more than one value
Examples: Age, Gender, Income
Construct: A collection of variables which, when measured and analyzed as a group (i.e. using a scale or multiple scales), produce a meaningful super-ordinate variable
Examples: Intelligence, Brand Loyalty, Satisfaction

17. The Nature of Experimentation
Control variables:
Not usually as important as the main IVs to the research
Affect the DV but (ideally) not the IVs
Are often used as segmentation variables in marketing research
Extraneous variables: Any variables that experimental researchers do not measure or control that may affect the dependent variable (if it does affect the DV, it is called a Confounding Variable)

18. Validity Concerns with Experimental Research
Validity: The extent to which the conclusions drawn from an experiment are true
Internal validity: Extent to which the research design accurately identifies causal relationships (example: Does our study reveal consumers’ true preferences?)
External validity: Extent to which a causal relationship found in a study can be expected to be true for the entire target population (example: Are the “true” preferences uncovered by the experiment representative of the entire target market’s preferences?)

19. Reliability Concerns with Experimental Research
Reliability: The extent to which the research instrument yields the same or similar outcomes on repeated usage occasions
Example:
Reliable GMAT scores for someone: 650, 640, 650
Unreliable GMAT scores for someone: 650, 750, 450
Measured by Split-half Reliability Coefficient or Chronbach’s Alpha Coefficient
Experiments must be both valid and reliable in order for their conclusions to be trustworthy.

20. Comparing Laboratory and Field Experiments
Laboratory (lab) experiments: Causal research designs conducted in an artificial setting
Field experiments: Causal research designs conducted in a natural or “real” setting
IVs can be manipulated in either alternative, but it is much easier to do so in lab experiments.

21. Test Marketing
Using controlled field experiments to gain information on specified market performance indicators
Most common type of field experiment
Used to help launch new products or develop new markets
Problems with competitive interference
Don’t neglect generalizability (i.e. external validity) concerns