Marketing research methods
Lecture 1
Academic thinking: Academic writing:
- Critical Precise & objective
- Literature Structure indicators
- Conceptualization Argumentation
- Operationalization References
- Methodology First write yourself,
use AI to polish only
- Results vs conclusions
- Conclusions vs implications
Conceptualization:
Drawing boundaries around terms to make them tangible. Elimination of
vagueness and ambiguity. (use the same word for the same meaning).
Outcome -> a conceptual model
Conceptual model
What should be specified/defined in your conceptual model?
- Concepts. A variable in the model is something that varies and is
measurable
- Relations - (in)dependent, antecedents/outcomes,
moderating/mediating
Measurements:
- Nominal – colours - mode
- Ordinal – school level - +median
- Interval – temperature - +mean
- Ratio – height – all
The same concept can be measured by different scales. It depends on
how you frame the question.
,Biased scales
Scales can be leading towards certain outcomes.
0 = impossible to drink, 5 = OK, 7 = tasty, 10 = excellent
Research design types
Exploratory:
- Discover ideas, insights, understanding processes
- Experts, qualitative primary + secondary data
Descriptive:
- Describing important characteristics/ markets
- Surveys, panels, quantitative primary + secondary data
Causal:
- Determine cause-effect relations (X & Y variables)
- Cleanest: experiments
The occurrence of X makes the occurrence of Y more probable. No 100%
prove that X is a cause of Y.
3 conditions for causality
- Concomitant variation
Extent to which a cause X and an effect Y occur together or
vary in the way predicted by the hypothesis
- Time order or occurrence
First X, then Y
- Absence of other possible causal factors
Realistic?
So: control other factors (or hold constant)
,Random assignment to groups
Experiments
- Manipulation of one or more independent variables
- Measurement of the effect on one or more dependent variables
Advantage -> you can make statements about causality because of
experimental control: nothing except the independent variable is
changing.
Focal questions
- What independent variables are to be manipulated
How are you going to test if the manipulation worked?
- What dependent variables are to be measured?
- How are the extraneous variables to be controlled?
- How about the test units?
Within-subjects design
Each participant provides data for all the levels of the independent
variable
- “Repeated Measures design”
Advantages
- Holds subject variables constant
- Increase statistical power by reducing random variation
- Reduces the number of subjects needed
Disadvantages
- Potential threats to validity
Maturation (getting older)
Instrumentation
Testing (people start to understand overtime)
, Between-subjects design
Each participant receives only one level of the independent variable
- Example: Group 1: Ad 1, Group 2: Ad 2
Advantages
- Avoids between-experiments comparisons
Disadvantages
- Large designs require a large number of pps
Rule of thumb: each cell needs 75 pps;
A-priori power analyses better to determine required cell size
- Must address selection issue (random assignment)
- Lack of statistical power (compared to within-subjects design)
Analyse with power analyses
Validity in experiments
Internal Validity:
- Did the independent variable actually cause the effect on the
dependent variable?
- No effect of extraneous variables?
External Validity:
- Can the results of the experiment be generalized in terms of:
Population
Geographic areas
Product categories
Higher Internal Validity often means lower External Validity
- But: Field experiments
Experimental designs
True experiments
- Lab, online, fully controlled
- Allows for measuring the underlying process
Field experiments
Lecture 1
Academic thinking: Academic writing:
- Critical Precise & objective
- Literature Structure indicators
- Conceptualization Argumentation
- Operationalization References
- Methodology First write yourself,
use AI to polish only
- Results vs conclusions
- Conclusions vs implications
Conceptualization:
Drawing boundaries around terms to make them tangible. Elimination of
vagueness and ambiguity. (use the same word for the same meaning).
Outcome -> a conceptual model
Conceptual model
What should be specified/defined in your conceptual model?
- Concepts. A variable in the model is something that varies and is
measurable
- Relations - (in)dependent, antecedents/outcomes,
moderating/mediating
Measurements:
- Nominal – colours - mode
- Ordinal – school level - +median
- Interval – temperature - +mean
- Ratio – height – all
The same concept can be measured by different scales. It depends on
how you frame the question.
,Biased scales
Scales can be leading towards certain outcomes.
0 = impossible to drink, 5 = OK, 7 = tasty, 10 = excellent
Research design types
Exploratory:
- Discover ideas, insights, understanding processes
- Experts, qualitative primary + secondary data
Descriptive:
- Describing important characteristics/ markets
- Surveys, panels, quantitative primary + secondary data
Causal:
- Determine cause-effect relations (X & Y variables)
- Cleanest: experiments
The occurrence of X makes the occurrence of Y more probable. No 100%
prove that X is a cause of Y.
3 conditions for causality
- Concomitant variation
Extent to which a cause X and an effect Y occur together or
vary in the way predicted by the hypothesis
- Time order or occurrence
First X, then Y
- Absence of other possible causal factors
Realistic?
So: control other factors (or hold constant)
,Random assignment to groups
Experiments
- Manipulation of one or more independent variables
- Measurement of the effect on one or more dependent variables
Advantage -> you can make statements about causality because of
experimental control: nothing except the independent variable is
changing.
Focal questions
- What independent variables are to be manipulated
How are you going to test if the manipulation worked?
- What dependent variables are to be measured?
- How are the extraneous variables to be controlled?
- How about the test units?
Within-subjects design
Each participant provides data for all the levels of the independent
variable
- “Repeated Measures design”
Advantages
- Holds subject variables constant
- Increase statistical power by reducing random variation
- Reduces the number of subjects needed
Disadvantages
- Potential threats to validity
Maturation (getting older)
Instrumentation
Testing (people start to understand overtime)
, Between-subjects design
Each participant receives only one level of the independent variable
- Example: Group 1: Ad 1, Group 2: Ad 2
Advantages
- Avoids between-experiments comparisons
Disadvantages
- Large designs require a large number of pps
Rule of thumb: each cell needs 75 pps;
A-priori power analyses better to determine required cell size
- Must address selection issue (random assignment)
- Lack of statistical power (compared to within-subjects design)
Analyse with power analyses
Validity in experiments
Internal Validity:
- Did the independent variable actually cause the effect on the
dependent variable?
- No effect of extraneous variables?
External Validity:
- Can the results of the experiment be generalized in terms of:
Population
Geographic areas
Product categories
Higher Internal Validity often means lower External Validity
- But: Field experiments
Experimental designs
True experiments
- Lab, online, fully controlled
- Allows for measuring the underlying process
Field experiments
