|1. General Marketing Research Process 3. Problem Formulation 5. Data Collection 7. Measurement 9. Sample Size 11. Multi Regression Analysis 13. FACTOR Analysis||2. Total error 4. Research Design 6. Data Collection Form 8. Sampling 10. Data Examination 12. ANOVA 14. Structured Equation Model|
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
2. Total ErrorBack to Index
Larger sample does not necessary increase accuracy. Larger sample may increase total error.
3. Problem FormulationBack to Index
1) Translate Decision Problem into Research Problem
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
4. Research DesignBack 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
6.Data Collection Form Back to Index
7. MeasurementBack to Index
Preference of brands
Graded quality of lumbers
Number of purchasers
Probability of purchase
Eliminate errors as much as we can.
1) Classification of Errors
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)
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 itemss I=variance s t=total variance
8. Sampling Back to Index
Sample drawing procedure
9.Sample Size Back to Index
When a population variance is unknown:
The half of the interval inference = zs x‾
When population is probability,
10 Data examinationBack to Index
11. Multiple regressionBack 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
Assess whether the difference between group means is significant or not
Dependents must be independent among each other.
Equality of covariance matrices
Find factors which highly correlate to variables
14. Structured Equation modelBack to Index