Marketing research has three roles: descriptive, diagnostic, and predictive.
1. Descriptive: What is the historic sales trend in the industry? What are consumers’ attitudes toward a
product?
2. Diagnostic/ Explanatory: What was the impact on sales after a change in the package design?
3. Predictive: “What if questions,” such as how can descriptive and diagnostic research be used to
predict the results of a planned marketing decision?
Questionnaires contain three basic types of questions:
1. Open-ended questions
2. Closed-ended questions
3. Scaled-response questions.
Sampling
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Once the researchers decide how to collect primary data, the next step is to select the sampling
procedures being used. Not all possible users of a new product can be interviewed, therefore a
firm must select a sample or a subset of the larger population.

The population or universe must first be defined. Then it is determined if the sample must be
representative of the population. If the answer is yes, a probability sample is needed.

The most desirable feature of a probability sample is that statistical rules can be used to ensure
that the sample represents the population.

One type of probability sample is the random sample—where every element of the population has
an equal chance of being selected as part of the sample.

A nonprobability sample is a sample where little or no attempt is made to get a representative
cross section of the population.

A common form of nonprobability sample is the convenience sample, a selection of convenient
respondents such as employees, relatives, or friends. Because of their lower cost, nonprobability
samples are the basis of much marketing research.

Whenever a sample is used in marketing research, major types of errors may occur:
measurement error and sampling error.

Frame error arises if the sample drawn from a population differs from the target population.

Random error occurs when the selected sample is an imperfect representation of the overall
population.
A summary of statistical techniques
Univariate Statistics
A. Statistics about single variable
B. Measures of Central Tendency- Mean, Median, Mode
C. Measures of Dispersion-variance, standard deviation and interquartile.
Multivariate Statistics
Involves more than one variable
Dependency techniques- Involves dependent and independent variable: Example- Regression, Analysis of
Variance (ANOVA), Discriminant Analysis, Conjoint Analysis
Interdependency techniques: Involves understanding relationship among groups of variables.
Example: Factor Analysis, Cluster Analysis
Data
A.
B.
C.
D.
Nominal: No order. Telephone numbers
Ordinal: Ranked ordering- Different Education levels- High School, College, Masters, Doctoral
Interval: Equally space intervals, arbitrary zero points- Temperature in Celsius
Ratio: True Zero point.
To analyze A & B, we use non-parametric techniques. To analyze C&D, we use parametric techniques.