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.

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.

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

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

Advantages and Disadvantages of Survey Research Designs

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

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).

Major Types of Survey Research Methods

Advantages and Disadvantages of Person-Administered Surveys

Advantages and Disadvantages of Self-Administered Surveys

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

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

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

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)

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

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

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)

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?)

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.

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.

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