M5 - Research Design

Question 1

Building a sample - selection

Answer 1

Random - non random
Unrestricted random - restricted random
Restricted random : stratified, cluster, multi-stage sampling

2. conscious - arbitrary
   Selection of extreme/ typical cases
   Concentration / snowball/ ratio method

Question 2

Random selection - unrestricted random

Answer 2

Each element has the same probability to be part of the sample

+ easy to impelement

Question 3

Random selection - stratified sampling

Answer 3

Classification of the population into disjoint groups “strata”
then unrestricted random within the groups

+ greater precision with the same effort

E.g: you have 3 locations and wnat 1/3 of each lication

• complex extrapolation

Question 4

Randlm selection - cluster sampling

Answer 4

Random selection of clusters within the population

+ cheaper than oure randon
- large errors

Question 5

Random selection - multi-sage sampling

Answer 5

Sequence of random sampling

E.g.: random selection lf electoral districts, then random selection of voters, then random selection of …)

• large errors
• complex extapolation
  + cheaper

Question 6

Non random selection - arbitrary

Answer 6

Select cases of the population that are easilyaccesible

Question 7

Non random - Conscious selection :

Answer 7

Dependent on survey object

• -> snowball
• –> quota
• -> concentration method

Question 8

Non random - conscious - snowball method

Answer 8

Selection of members of rare & unknown populations

Question 9

Non random - conscious - concentration method

Answer 9

Selection of cases for which a certain feature is so distinct that the distribution of it is thought to be alone in the pop.

Question 10

Non random - conscious - quota method

Answer 10

The sample fulfills certain quotas that are known from the pop.

Question 11

Which is representative?

Random or non-random?

Answer 11

Representativeness is only ensured by random selection!

Question 12

Central Limit Theorem

Answer 12

The distribution of mean values of the size N that were drawn from the population converges with increasing N to a normal distribution.

Implication: for samples of a size >=30, probabilties can be quantified as estimates of the mean

Question 13

Central Limit Theorem implication

O rule
2o rule

Answer 13

For samples of above 30, probabilities can be quantified as estimates of the mean

O-rule: with a probability of 68% the mean of a random variable is in the range of
y +- o

2O-rule: with a probability of 95.5% the mean of a random variable is in the range of
y +- 2o

Question 14

How does missing data occur?

Answer 14

• unrecorded items/data
• item non-response : some variables for a survey unit are not indicated
• unt non-response: survey unit not included

Question 15

Why are random missing data points no problem?

Answer 15

Only systematic missings are a problem, because the characteristics of the object cause the non-response –> biased result

Question 16

How to handle missing data?

Answer 16

Economically model the missings through correction terms

Invest in high response rate

Question 17

Cross sectional analysis

Characteristics

• adv
• disadv

Answer 17

• 1 point in time
• several units of obs
• 1/more variables

Adv:
Cheap, quick, simoel extrapolation

Disadv:
no testing of causal rel-ships
Statements restricted to a date –> limits external validity

Question 18

Jow to choose the type of date set?

Question
Criteria

Answer 18

At what time(s) and for how many units do i collect?

Criteria:
Time horizon
Fundings, resources
Type of research question (static/ snapshot)
Type of hypotheses (difference / correlation / change)

Question 19

Internal validity

Answer 19

Is achieved when the treatment is actually responsible for the variation of the dependent variable

Question 20

External validity

Answer 20

Possibility of generalization of the experimental results to other studies/ situations/ people.

Question 21

Longitudinal analysis

• what
• adv
• disadv

Answer 21

• several points in time
• one unit
• 1/more variables
  Used in finance markets, macroeconomics

Adv:
High internal validity
Often easily performed by use of historical data

Disadv:
Data collection takes time
Low external validity

Question 22

Heterogeneity

Answer 22

Uneinheitlichkeit der elemente hinsichtlich eines/mehrerer merkmale

–> difference between properties in the dataset

Question 23

Endogeneity

Answer 23

Occurs when the explanatory variable is correlated with the error term

Can cause omitted variables, measurement errors and autoregression

Question 24

Panel design

What
Adv
Disadv

Answer 24

-several points in time
- one/more variables
- several units
Used in marketing, labor economics

Adv:
Good internal and external validity
Good control over latent heterogeneity within the units

Disadv: 
Cost
Data collection takes time
Panel mortality - unit drops out
Unit non-response in certain time periods

Question 25

Experimental studies

What
Adv
Disadv

Answer 25

Possible random assignment of subject to ‘treatments’ and repetition
–> psychological research

Adv:
High internal validity
Used for testing causal hypotheses

Disadv:
Low external validity (no reality)
High cost
Laboratory artifacts possible

Question 26

Natural experiments

What
Adv
Disadv

Answer 26

Exogeneous effects on objects of study in real life

Adv:
Low cost
No endogeneous interferences
Use of exogeneous produced variation

Disadv
Rare
Suitable for testing vausal hypotheses

Question 27

Homogeneity

Answer 27

Similarity between properties in datasets