Lecture 3

Question 1

Data coding

Answer 1

Specifying how the information should be categorized to
facilitate the analysis. The main purpose is to transform the data into a
form suitable for the analysis

Question 2

Data matching

Answer 2

Task of identifying, matching and merging records that
correspond to the same entities from several databases or even within
one database

Question 3

Data imputation

Answer 3

Process of estimating missing data and filling these

values in into data set

Question 4

Data adjusting

Answer 4

Process to enhance the quality of the data for the data

analysis (e.g., weighting, variable respecification, scale transformation)

Question 5

Weighting

Answer 5

Procedure by which each observation (e.g. consumer responses) in the database is
assigned a number according to some pre-specified rule

Question 6

Variable respecification

Answer 6

Procedure in which the existing data are modified to create new variables, or in which a large number of
variables are reduced into fewer variables

For example, six categories are summarized in four categories

Question 7

Scale

transformation

Answer 7

Procedure to adjust the scale to ensure comparability with other scales. Like grading systems in different countries

Question 8

How to identify causal relationships

Answer 8

Evidence for a strong association (e.g. correlation) between two variables.

Evidence that no rival explanation (other correlated parameter) exists for the observed association of the variables.

Changing of the cause variable precedes changing of the result variable (e.g. through a time lag).

Question 9

Experimental group

Answer 9

Test subjects who are exposed to the experimental stimulus, e.g. a new
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Question 10

Control group

Answer 10

Test subjects who are not exposed to the experimental stimulus

Question 11

Randomizing

Answer 11

Random assignment of test subjects to experimental / control groups

Question 12

Matching

Answer 12

Test subjects in experimental and control groups share specific criteria (e.g.
gender, age)

Question 13

Stimulus

Answer 13

Variation of a variable that should trigger a behavioral reaction in people (e.g.
response to price changes)

Question 14

Entity extraction

Answer 14

Which words people write about?

Question 15

Topic Modelling

Answer 15

What topic people write about?

Topics in movie reviews, motivations to host on AirBNB

Question 16

Sentiment analysis

Answer 16

How positive/negative is the text?

Question 17

Relationship between entities? (words)

Answer 17

How do words relate to each other (What side effects are mentioned with the drug?)

Question 18

Writing style

Answer 18

What is the writing style between the words? (Identifying personality traits of social media users)

Question 19

Mode

Answer 19

Low data requirements
Intuitive understanding

Ambiguous if multiple mode values exist
Cannot be used with advanced statistical methdos

Question 20

Median

Answer 20

Low Data Requirements
Low sensitivity to outliers

Can’t be used with advanced statistical methods

Question 21

Mean

Answer 21

Most popular location parameter
Basis for many advanced statistical analyses

Sensitive to outliers
High scale requirements (interval scaling)

Question 22

What is causality?

Answer 22

Causality is variable X causing a change in variable Y. WE need to consider control variables to claim causality

Question 23

How to identify causal releationships?

Answer 23

Evidence for strong association between two variables

Changing of the cause variable precedes changing of the result variable

Evidence that no rival explanation exists for the observed association of the variables