WEEK 1 ● Discriminant validity
Marketing research process ● Nomological validity
- Problem definition (pg.16) R pre-requisites: data transformation
- Research approach - New datasets based on existing datasets
3 fundamental types of relationships/variables - Data transformation within dataset
- Main effect ● New variable from old variable
- Moderation effect ● Old variable into new variable
- Mediation (pg. 17) R application for reliability
Research design types WEEK 4 (Casual research designs - experiments)
- Exploratory design (pg. 19) Experimental design
- Conclusive design - Condition for causality
Data collection - Between vs. within subjects design
- Secondary data (pg. 19) What is regression?
- Primary data (pg. 21) Analyzing experimental data
Introduction to R - (pg. 21) Main effect
WEEK 2 ● Descriptive statistics
(pg.4) Level of measurement ● Regression results
- Categorical, nominal - (pg. 23) Covariates & multiple main effects
(pg.4) Sampling, and inference - (pg. 24) Main effect with N>2 conditions
- Sampling & sampling error - (pg. 25) Moderation
- Sampling bias ● What is moderation?
- Sampling techniques ● Moderating effect (2x2 factorial
- Statistical inference design)
- N0 and N1 ● (pg. 28) quantitative moderator
- P-value → interaction effect
- Confidence interval → conditional effects
(pg.6) Statistical analyses - (pg. 30) Mediation
- Univariate tests (pg.6) ● Normal mediation
● One sample t-test ● Parallel mediators
● One sample chi-square - (pg. 32) Mediated moderators
- Bivariate analysis (pg.8) - (pg.33) Validity of experiments
● Correlation analysis ● Types of experiments
● Chi-square ● Extraneous effects jeopardise
● Independent sample t-test internal validity
- What do we do with bad data (pg10) ● Ethics in experiments
WEEK 3 WEEK 5 (Multiple item measurements)- pg. 34
(pg.11) Measurement and marketing - S1: Correlation matrix
- Latent construct - S2: determine n. Of factors
- Comparative vs. non-comparative scaling - S3: Delete weak items
(pg.11) Multi-item measurement vs. single item - S4: Rotate initial solution
- True score vs. observed score - S5: interpret rotated solution
- Reliability - S6: Calculate Cronbach alpha
- Validity - S7: calculate factor scores
(pg. 13) Assigning reliability WEEK 6 (Qualitative research)
- Internal consistency - (pg. 40) Goals of qualitative research
- Test re-test - Common techniques
(pg.13) Assessing validity ● In-depth interviews
- Content validity ● Projective techniques
- Criterion validity ● Observation techniques:
- Construct validity ethnography
● Convergent validity - Qualitative data analysis
*Notes in green starting with * include extra information, or my explanation
, Consumer marketing research
WEEK 1
Marketing research → systematic and objective identification, collection, analysis and dissemination of information
for improving decision-making related to the identification and solution of problems and opportunities in marketing.
Collect & organize data → analyze it → evaluate & distribute it accurately and on time → to marketing decision
makers
Marketing research Process:
Problem definition:
- Decision problem (focus on action): how should we position our product on supermarket shelves to attract
marketing consumer attention
- Research problem (focus on understanding): how does shelf positioning affect consumer attention?
Research approach:
1. An exploratory research goal may be rule-based or data-drive
2. With an exploratory research goal, a conceptual model should be guided by theory. → A research model
determined the relationships between different variables
3 fundamental types of relationships/variables
1. Main effect
2. Moderation
3. Mediation
1- MAIN EFFECT: does the IV influence the DV?
Hypothesis must clearly include (a) all variables and (b) direction of the relationship
e.g.
(-) portion size influences consumption amount (no direction = no testable is it positive or is it a negative
impact?
(+) portion size POSITIVELY influences consumption amount
(+) consumption amount will be greater with a large portion compared to a small portion
2- MODERATION EFFECT
The direction or strength of the effect of IV on DV is affected by a moderating variable?
1
, (-) food healthiness moderates the influence of portion size on consumption amount (no direction = not
testable)
(+) the positive effect of portion size on consumption amount is greater for unhealthy foods than for healthy
foods
3- MEDIATION EFFECT
Moderations specify WHEN certain effects will hold, mediators speak to HOW or WHY such effect occur.
(-) value for money mediates the effect of portion size on consumption amount
(+) value for money mediates the positive effect of portion size on consumption amount: (vs. smaller)
portion provides higher value for money, which will in turn increase consumption amount
Research design: determines the information that’s needed to answer a specific RQ, or to test the developed
conceptual model and hypothesis.
It takes into account:
- Nature of the issue: common behavior (spontaneous response), personal or sensitive issue, repressed
tendency
- Nature of respondents: age, background, previous participation
- Context: cultural norms, ease of data collection
- Exploratory design (getting insights)
qualitative research
quantitative research (e.g. getting data from Airbnb and exploring what it has to say)
- Conclusive design (testing hypothesis)
descriptive research (surveys)
casual research (experiment)
Data collection:
1. Secondary data – collection for some other purpose than problem at hand:
- External: governmental, non-governmental data
- Internal: customer data
Web scraping: systematically collecting data by either building code on python or use apps
2. Primary data - collected primarily for the purposes of the problem at hand):
- Quantitative methods: surveys, panels, descriptive data
2
, - Qualitative methods: in-depth interviews, focus groups, ethnography, observation
- Casual research methods (lab and field experiments)
Examples of quantitative methods:
- Basic analysis, analysis of variance and covariance, survey techniques, item analysis, factor analysis,
regression, cluster analysis, multidimensional scaling, conjoint analysis
Examples of qualitative methods:
- Content analysis, semiotics
INTRODUCTION TO R
Terms to know:
Running code
Assigning (=saving)
Objects:
- Values
- Vectors (= several values or one row/column)
function: c()
- Dataframe (=several vectors or rows/columns)
Functions
Inputs
Outputs
R is case sensitive: it will give errors if:
- Capital letters
- Spaces were not needed
To find out what is your value (e.g. character, numeric…): class()
To create a data frame: df <- data.frame()
Once you create a data frame, you will not see it until you open it → to open it: double-click on df
Get frequencies:
3
Marketing research process ● Nomological validity
- Problem definition (pg.16) R pre-requisites: data transformation
- Research approach - New datasets based on existing datasets
3 fundamental types of relationships/variables - Data transformation within dataset
- Main effect ● New variable from old variable
- Moderation effect ● Old variable into new variable
- Mediation (pg. 17) R application for reliability
Research design types WEEK 4 (Casual research designs - experiments)
- Exploratory design (pg. 19) Experimental design
- Conclusive design - Condition for causality
Data collection - Between vs. within subjects design
- Secondary data (pg. 19) What is regression?
- Primary data (pg. 21) Analyzing experimental data
Introduction to R - (pg. 21) Main effect
WEEK 2 ● Descriptive statistics
(pg.4) Level of measurement ● Regression results
- Categorical, nominal - (pg. 23) Covariates & multiple main effects
(pg.4) Sampling, and inference - (pg. 24) Main effect with N>2 conditions
- Sampling & sampling error - (pg. 25) Moderation
- Sampling bias ● What is moderation?
- Sampling techniques ● Moderating effect (2x2 factorial
- Statistical inference design)
- N0 and N1 ● (pg. 28) quantitative moderator
- P-value → interaction effect
- Confidence interval → conditional effects
(pg.6) Statistical analyses - (pg. 30) Mediation
- Univariate tests (pg.6) ● Normal mediation
● One sample t-test ● Parallel mediators
● One sample chi-square - (pg. 32) Mediated moderators
- Bivariate analysis (pg.8) - (pg.33) Validity of experiments
● Correlation analysis ● Types of experiments
● Chi-square ● Extraneous effects jeopardise
● Independent sample t-test internal validity
- What do we do with bad data (pg10) ● Ethics in experiments
WEEK 3 WEEK 5 (Multiple item measurements)- pg. 34
(pg.11) Measurement and marketing - S1: Correlation matrix
- Latent construct - S2: determine n. Of factors
- Comparative vs. non-comparative scaling - S3: Delete weak items
(pg.11) Multi-item measurement vs. single item - S4: Rotate initial solution
- True score vs. observed score - S5: interpret rotated solution
- Reliability - S6: Calculate Cronbach alpha
- Validity - S7: calculate factor scores
(pg. 13) Assigning reliability WEEK 6 (Qualitative research)
- Internal consistency - (pg. 40) Goals of qualitative research
- Test re-test - Common techniques
(pg.13) Assessing validity ● In-depth interviews
- Content validity ● Projective techniques
- Criterion validity ● Observation techniques:
- Construct validity ethnography
● Convergent validity - Qualitative data analysis
*Notes in green starting with * include extra information, or my explanation
, Consumer marketing research
WEEK 1
Marketing research → systematic and objective identification, collection, analysis and dissemination of information
for improving decision-making related to the identification and solution of problems and opportunities in marketing.
Collect & organize data → analyze it → evaluate & distribute it accurately and on time → to marketing decision
makers
Marketing research Process:
Problem definition:
- Decision problem (focus on action): how should we position our product on supermarket shelves to attract
marketing consumer attention
- Research problem (focus on understanding): how does shelf positioning affect consumer attention?
Research approach:
1. An exploratory research goal may be rule-based or data-drive
2. With an exploratory research goal, a conceptual model should be guided by theory. → A research model
determined the relationships between different variables
3 fundamental types of relationships/variables
1. Main effect
2. Moderation
3. Mediation
1- MAIN EFFECT: does the IV influence the DV?
Hypothesis must clearly include (a) all variables and (b) direction of the relationship
e.g.
(-) portion size influences consumption amount (no direction = no testable is it positive or is it a negative
impact?
(+) portion size POSITIVELY influences consumption amount
(+) consumption amount will be greater with a large portion compared to a small portion
2- MODERATION EFFECT
The direction or strength of the effect of IV on DV is affected by a moderating variable?
1
, (-) food healthiness moderates the influence of portion size on consumption amount (no direction = not
testable)
(+) the positive effect of portion size on consumption amount is greater for unhealthy foods than for healthy
foods
3- MEDIATION EFFECT
Moderations specify WHEN certain effects will hold, mediators speak to HOW or WHY such effect occur.
(-) value for money mediates the effect of portion size on consumption amount
(+) value for money mediates the positive effect of portion size on consumption amount: (vs. smaller)
portion provides higher value for money, which will in turn increase consumption amount
Research design: determines the information that’s needed to answer a specific RQ, or to test the developed
conceptual model and hypothesis.
It takes into account:
- Nature of the issue: common behavior (spontaneous response), personal or sensitive issue, repressed
tendency
- Nature of respondents: age, background, previous participation
- Context: cultural norms, ease of data collection
- Exploratory design (getting insights)
qualitative research
quantitative research (e.g. getting data from Airbnb and exploring what it has to say)
- Conclusive design (testing hypothesis)
descriptive research (surveys)
casual research (experiment)
Data collection:
1. Secondary data – collection for some other purpose than problem at hand:
- External: governmental, non-governmental data
- Internal: customer data
Web scraping: systematically collecting data by either building code on python or use apps
2. Primary data - collected primarily for the purposes of the problem at hand):
- Quantitative methods: surveys, panels, descriptive data
2
, - Qualitative methods: in-depth interviews, focus groups, ethnography, observation
- Casual research methods (lab and field experiments)
Examples of quantitative methods:
- Basic analysis, analysis of variance and covariance, survey techniques, item analysis, factor analysis,
regression, cluster analysis, multidimensional scaling, conjoint analysis
Examples of qualitative methods:
- Content analysis, semiotics
INTRODUCTION TO R
Terms to know:
Running code
Assigning (=saving)
Objects:
- Values
- Vectors (= several values or one row/column)
function: c()
- Dataframe (=several vectors or rows/columns)
Functions
Inputs
Outputs
R is case sensitive: it will give errors if:
- Capital letters
- Spaces were not needed
To find out what is your value (e.g. character, numeric…): class()
To create a data frame: df <- data.frame()
Once you create a data frame, you will not see it until you open it → to open it: double-click on df
Get frequencies:
3
