lecture 4

Question 1

overview of brand perception data

Answer 1

several market research companies track the perception of firms and brands

This includes variables such as attitudes towards the firm, customer satisfactions, reputations, quality perceptions etc

Indication for how strong a brand is in the hearts and minds of consumers

Question 2

Some potential limitations (brand perception data)

Answer 2

No actual purchase behaviour (e.g. sales)

Response bias

Sampling bias

Question 3

Overview of stock return data

Answer 3

Refers to the current price that a share of s tock is trading for on the market

A companys stock price reflects investor perception of its ability to earn and grow its profits in the future

Issues within and outside of a company may cause a stock price to move in either direction

Stock price data is available for free (e.g. yahoofinance.com)

Question 4

Some potential limitations (stock price data)

Answer 4

Sampling bias (i.e. only available for traded companies)

Only focus on investors

Question 5

Text data

Answer 5

Huge amounts of text data: online reviews, social media posts, texts, customer service calls, open-ended survey questions, firm annual reports, advertisements, newspaper articles, movie scripts, song lyrics, etc.

Question 6

Text data: textual data goes beyond social media

Answer 6

firm to firm

consumer to consumer

society to society

Question 7

Cleaning big data

Answer 7

most time consuming and least enjoyable data science task, surve says

Question 8

What data scientists spend the most time doing

Answer 8

building and training sets 3%

Cleaning and organizing data 60%

Collecting data sets 19%

Mining data for patterns 9%

Refining algorithms 4%

other 5%

Question 9

Statistical data editing

Answer 9

observed data generally contains errors and missing values. Thus, the data must undergo preliminary preparation before the data can be analyzed

Process of checking observed data, and, when necessary, correcting them

Question 10

Essential tasts (statistical data editing)

Answer 10

Error localization: determine which value are erroneous

Correction: correct missing and erroneous data in best passible way

Consistency: adjust values such that all edits become satisfied

Question 11

Interviewer error

Answer 11

interviewers may not be giving the respondents the correct instructions

Question 12

Omissions

Answer 12

respondents often fail to answer a single question or a section of the questionnaire, either deliberately or inadvertently

Question 13

Ambiguity

Answer 13

a response might not be legible or it might be unclear

Question 14

Inconsistencies

Answer 14

Sometimes two responses can be logically inconsistent. For example, a respondent who is a lawyer may have checked a box indicating that he or she did not compmlete high school

Question 15

Lack of cooperation

Answer 15

In a long questionnaire with hundreds of attitude questions, a respondent might rebel and check the same response in a long list of questions

Question 16

Ineligible respondent

Answer 16

An inappropriate respondent may be included in the sample (e.g. underage respondents)

Question 17

data coding

Answer 17

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 18

Data matching

Answer 18

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

Question 19

Data imputation

Answer 19

Process of estimating missing data and filling these values in into data set

Question 20

Data adjusting

Answer 20

Process to enhance the quality of the data for the data analysis (e.g. weighting, variable respecification, scale transformation)

Question 21

Common procedures for statistically adjusting data

Answer 21

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

For example, Weighting is used to make the sample data more representative

Variable respecification: procedure in which the existing data are modified to create new variables or in which a large number of variables ar ereduced into frewer variables

For example, six categories are summarized into four categories

Scale transformation: procedure to adjust the scale to ensure comparability with other scales

For example, some respondents (e.g. from different cultures) may consistently use the lower end of the rating scale, whereas other may consistently use the upper end. These differences can be corrected for

Question 22

two main ways we can use text data

Answer 22

Language FEFLECTS

Text reflects intentions, actions, relationships, context and more

Eg. People tweet about events near vs far using difference in concreteness

Brand positioning maps

Customer service that uses “I” vs “we” can have greater impact on customer satisfaction

Language AFFECTS

Text affects perceptions, firm outcomes and more

Eg. Online chatter increases stock value

narrative reviews are more persuasive than non narrative reviews

Frames impact implicit attitudes about consumption pratice

Question 23

types of scales and informative statistics location parameter (dispersion parameter)

Answer 23

nominal (mode)

Ordinal (median, mode)

Interval + ratio (mean, median, mode) + (variane and SD)

Question 24

Mode

Answer 24

value in a measurement series (category) with maximum frequency

Question 25

Median

Answer 25

value that lies in the middle of a frequency distribution

Question 26

mode meaning and limits

Answer 26

low data requirements (nominal scaling) + intuitive understanding

Limits: ambiguous in interpretation if multiple mode values exist

Cannot be used for analysis with advanced statistical methods

Question 27

Median meaning and limits

Answer 27

low data requirements (ordinal scale) + low sensitivity to outliers

Limits: cannot be used for analysis with advanced statistical methods

Question 28

mean (meaning and limits)

Answer 28

most popular location parameter

basis for many advanced statistical analyses (t-test, variance analysis etc)

limits: sensitive to outliers

high scale requirements (interval scaling))

Question 29

discrete distribution

Answer 29

Name: binomial distribution, poisson distribution, multinominal distribution

E.g. Customer retention rate, frequency of purchase, brand selection probability

Question 30

Continuous distribution

Answer 30

name: normal distribution, log-normal distribution, x^2 distribution, t-distribution

e.g. image ratings, scales, special distribution in inferential statistics

Question 31

Empirical distribution

Answer 31

Many features exhibit normal distribution in reality (e.g. body size)

Question 32

Distribution model for statistical parameters

Answer 32

statistical parameters such as mean and variance exhibit normal distribution upon multiple sampling

Question 33

mathematical base distribution

Answer 33

Distributions in inferential statistics are derived from normal distribution

Question 34

Distribution in error theory

Answer 34

random errors in repeated measurements exhibit normal distribution

Question 35

explanatory power and limits of correlation analysis

Answer 35

measurement of the linear association strength between two metrically scaled variables

Direction of the correlation is visible

Values are comparable across different variables due to restriction to interval (-1,+1)

No dependence on the sample size

Strength of the correlation in thesense of the explained variance can be identified (r^2 = Explained variance)

prerequisite for statistical verification of (linear) causal relationships

Limits: only linear correlations can be depicted

no sufficient evidence for the presence of a causal relationship

strength of the correlation in the sense of a leverage effect cannot be identified

Spurious association possible if background variables are not controlled for (-> partial correlation coefficient as workaround)

Question 36

How to identify causal relationships

Answer 36

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

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

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

Experiments establish (the best) conditions that make it possible to determine causal relationships

Question 37

Features of an experiment

Answer 37

1) formulate a causal relationship

2) evaluation of the directional influence of one or more independent variables on one or more dependent variables
(definition of the independent variables to be manipulated, definition of the dependent variables to be measured, definition of the variation steps (manipulation) of the independent variables)

3) controlling of all disturbing influences (control variables) to exclude distortion of the results
(selection of the test subjects and assignment to the groups, Controlling of the selection bias, minimizing the inlfuence of other external variables

Question 38

experimental group

Answer 38

test subjects who are exposed to t he experimental stimulus, e.g. a new advertisement

Question 39

Control group

Answer 39

test subjects who are not exposed to the experimental stimulus

Question 40

Randomizing

Answer 40

random assignment of test subjects to experimental/ control groups

Question 41

Matching

Answer 41

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

Question 42

Stimulus

Answer 42

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

Question 43

Lab experiment

Answer 43

performance of the experiment in an artificial laboratory environment

Test subjects are aware that they are participating in a test

Advantages: higher internal validity because stimuli can be more effectively manipulated and exetnal factors better cotnrolled, lower costs

Disadvantages: test subjects do not react as in a natural environment, making generalizations and predictions of the effect difficult

Lower external validity

Question 44

Field experiment

Answer 44

Performance of the experiment in a natural environment

Test subjects are not aware that they are part of an experiment

e.g. INtroduction of a new sales promotion plan to retailers

Advantages: higher external validity because test subjects are acting under real conditions

Easier to predict and generalize the effect

Disadvantages:
cost intensive

Activities visible to competitors

less manipulation freedom (e.g. limits to changes in the price)

More difficult to control extraneous factors