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Summary Glossary - Introduction to Research in Marketing (Spring 2022)

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Tilburg University (UVT)

Glossary/summary of the introduction lecture and the 6 topics discussed in class: ANOVA, Linear Regression, Factor Analysis, Cluster Analysis, Logistic Regression, Conjoint Analysis. Professor: Els Gijsbrechts. Written: spring 2022.

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Geupload op 9 december 2022
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Geschreven in 2021/2022
Type Samenvatting

2022
anova
linear regression
factor analysis
cluster analysis
logistic regression
conjoint analysis
els gijsbrechts
introduction to research in marketing

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Glossary
Introduction to Research in Marketing
(Spring)

Contents
Introduction lecture ............................................................................................................................. 2
ANOVA .................................................................................................................................................. 3
Linear Regression................................................................................................................................. 4
Factor Analysis...................................................................................................................................... 5
Cluster Analysis .................................................................................................................................... 6
Logistic Regression.............................................................................................................................. 6
Conjoint Analysis.................................................................................................................................. 7

1

, Introduction lecture
Multivariate analysis - All statistical methods that simultaneously analyze multiple measurements on each
individual or object under investigation.
- Any simultaneous analysis of two or more variables.
- Examining relationships between/among more than two variables.
Nominal - Characteristics: Unique definition/identification
(non-metric) - Phenomena: E.g. brand name, gender, student number
- Methods of analysis: %, mode, Chi square test
Ordinal - Characteristics: Indicate ‘’order’’, sequence
(non-metric) - Phenomena: Preference ranking, level of education
- Methods of analysis: Percentiles, median, rank correlation, + previous methods
Interval (metric) - Characteristics: Arbitrary origin
- Phenomena: Attribute scores, price index, celcius
- Methods of analysis: Arithmetic average, rane, SD, + previous methods
Ratio (metric) - Characteristics: Unique origin (starts at zero)
- Phenomena: Age, cost, number of customers
- Methods of analysis: Geometric average, coefficient of variation, + previous methods
Error Reliability The degree to which the observed variables measure the ‘’true’’ value or is error free; if
the measure is asked repeatedly, more reliable measures will show greater consistency
than less reliable measures.
→ Opposite of measurement error
→ Test and retest to make sure data is reliable
Error Validity The degree to which a measure accurately represents what it is supposed to; does the
measure capture the concept it is supposed to measure?
Measurement error The degree to which the observed values are not representative of the ‘’true’’ values.
→ When variables with measurement error are used to compute correlations or means,
the ‘’true’’ effect is partially masked by the measurement error, causing correlations to
weaken and the means to be less precise.
Type 1 error (α) The probability of a test showing statistical significance when it is not present.
- Measurement error: false positive
- The probability of incorrectly rejecting the null hypothesis.
- Saying a correlation exists when it actually does not.
Type 2 error (β) The probability of incorrectly failing to reject the null hypothesis.
- Measurement error: false negative
- Not reject the null hypothesis when it is actually false.
- The chance of not finding a correlation of means difference when it does exist.
Power (1-β) The probability of a test showing statistical significance when it is present.
- The probability of correctly rejecting the null hypothesis when it should be rejected.
- Limiting type 1 error: Use a stricter decision rule
- Cutoff: Set it in such way that alpha is smaller than 0.05
Dependence - One or more variables can be identified as dependent variables and the remaining as
techniques independent variables.
(Multivariate - Choice of dependence technique depends on the number of dependent variables
method) involved in analysis.
- When looking for a causal relationship; causes and outcome variables.
- Either metric or non-metric.
Interdependence - Whole set of interdependent relationships is examined to find the underlying structure.
techniques - Further classified as having focus on variables or objects.
(Multivariate - No single variable or group of variables is defined as being dependent or
method) independent.
- No distinction between causes and outputs, just looking for links without grouping
them into dependent and independent variables.
Outliers Observations with a unique combination of characteristics identifiable as distinctly
different from the other observations.
Missing data When you have collected data but some of the questions have not been answered by
some of the respondents.
MAR Missing At Random. Whether Y is missing depends on the level of X. Yet, within level of
(Missing data) X is MAR (most harmful).
MCAR Missing Completely At Random. Whether Y missing is truly random. Is independent of Y
(Missing data) or any other variable X. There is no pattern (least harmful, doesn’t decrease sample size).
2

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