lecture 4 summary

Question 1

statistical data editing

Answer 1

is the process of checking observed data and correcting them if necessary

Question 2

error localization

Answer 2

determines which values are erroneous

Question 3

we recognize the following types of errors

Answer 3

interviewer error: interviews may not be giving the respondents the correct instruction

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

AMbiguity: a response might not be legible, or it might be unclear

Inconsistncies: sometimes two responses can be logically inconsistent

lack of cooperation: in the long questionnaire with hundreds of attitude questions, a respondent might rebel and checkthe same response in a long list of questions

Ineligible respondent: an inappropriate respondent may be included in the sample (e.g. underage respondents)

Question 4

Data coding

Answer 4

is 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 5

Data matching

Answer 5

is the task of identifying, matching and mergin records that correspond to the same entities from severaldatabases or even within one database

Question 6

Data imputation

Answer 6

is the process of estimating missing data and filling these valuees into the dataset

Question 7

Data adjusting

Answer 7

refers to the process to enhance the quality of the data for the data analysis

Question 8

Weighting

Answer 8

is the procedure by which each observation in the database is assigned a number according to some pre-specified rule

Question 9

Variable re-specification

Answer 9

is the 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

Question 10

scale transformation

Answer 10

is the procedure to adjust the scale to ensure comparability with other scales

Question 11

The model

Answer 11

is the value in a measurement series (category) with maximum frequency (multiple mode values are possible)

Question 12

Median

Answer 12

is the value that lies in the middle of a frequency distribution (same number of instances above and below the median)

Question 13

discrete distributions

Answer 13

such as binomial distribution, poisson distributions, and multinomial distributions.

Question 14

Continuous distributions

Answer 14

such as normal distributions, log-normal distributions, t-distributions and f-distributions

Question 15

A positive correlation and negative correlation reflects

Answer 15

a positive correlation reflects a tendency for a high value in one variable to be associated with a high value in a second variable.

A negative correlation reflects an association between a high value in one variable and a low value in a second variable

Question 16

Correlation analysis

Answer 16

is a measurement of the linear association strength between two metrically scaled variables. Values are comparable across different variables due to restrictions to the interval. It recognizes the following limitations:

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 is possible if background variables are not controlled for

Question 17

Causality means

Answer 17

that a change in one variable will produce a change in another. If we can claim for sure that x causes y, we can talk about a causal relationship. If there are theoretical reasons why different variables such as z cause a change in x and y, we need to control for this variable (e.g. strategy to increase ice cream prices), otherwise we cannot claim causality. Furthermore, we need to make sure that x causes y and not that y causes x. So the first approach to determine the direction of causation is to draw from logic and previous theories. Theory always comes first. The second approach to determine the direction of causation is to consider that there is usually a tome lage between cause and effect, and so if such time lage can be postulated a causal relationship can be identified.

Question 18

There are three ways to identify causal relationship

Answer 18

there exists theoretical evidence for a strong association, or correlation between two variables

Changing of the cause variable precedes changing of the result variable

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

Question 19

Experiment

Answer 19

features a formulation of a causal relationship (hypothesis) it is an evaluation of the directional influence of one or more independent variables on one or more dependent variables

Question 20

Experimental group

Answer 20

test subjects who are exposed to the experimental stimulus eg. a new advertisement

Question 21

Control group

Answer 21

test subjects who are not exposed to the experimental stimulus

Question 22

Radnomizing

Answer 22

random assignment of test subjects to experimental / control groups

Question 23

Matching

Answer 23

test subjects in experimental and control groups share specific criteria

Question 24

stimulus

Answer 24

variation of a variable that should trigger a behavioural reaction in people

Question 25

Laboratory experiment

Answer 25

is a performance of the experiment in an artificial (laboratory) environment. Test subjects are aware that they are participating in a test

Question 26

Field experiment

Answer 26

a performance of the experiment in a natrual environment. Test subjects are not aware that they are part of an experiment

Question 27

limits of correlation analysis

Answer 27

only linear relations can be depicted

no evidence for causal relationship

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

spurious association is possible if background variables are not accounted for