Marketing Research


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 Error  Back to Index

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)

      1. Longitudinal (Continue to pick up the same categories)
      1. Cross Sectional (Pick up different categories & different persons at any time)

Causal Research (Cause & effect finding research)

 


  1. Data Collection Back to Index

1) Secondary data

2) Primary data

a. Communication

b. Observation


6. Data Collection Form Back to Index


7. Measurement 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


8. Sampling Back to Index

Sample drawing procedure

  1. Define the population
  2. Identify the sampling frame
  3. Select a sampling procedure
  4. Determine the sample size
  5. Select the sample elements
  6. Collect the data from the designated elements

Sampling procedure

  1. Probability samples
    1. Simple random
    2. Stratified
    1. Cluster
  1. Non probability samples
    1. Convenience (accidental) samples
    2. Judgement samples: select the sample which is believed to represent a population.
    3. Quota: use a proportionate element with a parent population to draw samples.
  1. Sample distribution

 


9. Sample Size Back to Index

 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

 


  1. ANOVA Back to Index
  2. Assess whether the difference between group means is significant or not

    T-test:

    Assumptions:

    Dependents must be independent among each other.

    Normality

    Equality of covariance matrices


  3. FACTOR Analysis Back to Index

Explanatory research

Find factors which highly correlate to variables


14. Structured Equation model Back to Index