1. General Marketing Research Process Back to Index
1) Formulate marketing research problem
2) Determine research design (Exploratory / Causal)
3) Determine data collection method (Secondary / Primary)
4) Design data collection forms
5) Design sample and collect data
6) Analyze and interpret the data
7) Prepare the research report
Larger sample does not necessary increase accuracy. Larger sample may increase total error.
3. Problem Formulation Back to Index
1) Translate Decision Problem into Research Problem
2) Research Proposal
1.Tentative project title
2.Statement of the marketing problem
3.Purpose and limits of the project
4.Outline (tentative framework of the project)
5.Data sources and research methodology
6.Estimate of time and personnel requirements
7.Cost estimates
4. Research Design Back to Index
Exploratory Research (General an starting research)
Descriptive Research (Relationship finding research)
Causal Research (Cause & effect finding research)
1) Secondary data
2) Primary data
a. Communication
b. Observation
6. Data Collection Form Back to Index
Scales

Scale 
Examples 

Nominal 
Male/Female User/nonuser Occupations 

Ordinal 
Preference of brands Social class Graded quality of lumbers 

Interval 
Temperature scale 

Ratio 
Units sold Number of purchasers Probability of purchase Weight 
Eliminate errors as much as we can.
1) Classification of Errors
Xt=TRUE SCORE
Xo=OBSERVED SCORE
Es=SYSTEMATIC ERROR (a constant error, ex. measure is not accurate)
Er=RANDOM ERROR (a transient error, ex. shoes on or off when we measure height)
Es+Er=TOTAL ERROR
Xt=Xo+Es+Er
When a measure is VALID, Es+Er=0 ® Xt=Xo
When a measure is RELIABLE, Er=0 ® Xt=Xo+Es
2) Assessment of Validity
Content validity: The adequacy with which the domain of the characteristic is captured by the measure.
Construct validity: Assessment of how well the instrument captures the construct, concept or trait it is supposed to be measuring.
Convergent validity: The confirmation of relationship by independent measurement procedures
® Independence of each measurement procedure
Discriminant validity: Requirement that the measure of construct does not correlate too highly with another measures from which it is supposed to differ
® Independence of each construct
3) Assessment of Reliability
Stability: Small difference between two different time points of the identical construct.
Equivalence: Adequate correlation among all items answered by the one person.
Coefficient a : Summary of intecorrelations among a set of items.
k=# of items s _{I}=variance s _{t}=total variance
Sample drawing procedure
Sampling procedure
When a population variance is unknown:
The half of the interval inference = z s _{x‾}
When population is probability,
10 Data examination Back to Index
Outliers
Normality
Heteroscedacity
Skewness
Linearity
11. Multiple regression Back to Index
R square: coefficient of determination, how much the variation from the regression can be explained by X.
Beta coefficient: relative impact of each coefficient (coefficient for each standard error)
Variables ® a multiple variate
Assumptions:
Linearity
Normality
Assess whether the difference between group means is significant or not
Ttest:
Assumptions:
Dependents must be independent among each other.
Normality
Equality of covariance matrices
Explanatory research
Find factors which highly correlate to variables
14. Structured Equation model Back to Index