Lecture 5

Question 1

In data sets rows capture

Answer 1

Obersvations (on e.g. consumers or firms)

Question 2

Columns display

Answer 2

Variables. A variable can take on different values for different subjects

Question 3

Dummy variables

Answer 3

Variables that only take on the values 0 or 1

Question 4

codebook

Answer 4

A list of all the codes used in a dataset

Question 5

The variables in your data set need to match the unit of analysis in a study. Specifically:

Answer 5

The dependent variable is measured at the level of the unit of analysis. So are mediator vairables

Independent and moderator vairables are measured at the level of the unit of analysis or at a more aggregate level

Question 6

Population

Answer 6

Entire group of people, firms, events, or things of interest for which you would like to make inferences

Question 7

Sample

Answer 7

A subset of the population of interest

Question 8

Why use samples in the first place

Answer 8

Impossible to study the entire population

Question 9

The sampling process consists of the following steps

Answer 9

1) define the population you are interested in
2) Determine the sampling frame. The sampling frame is the physical representation of the pupulation through which one can reach out to that population
3) Decide on the sampling design

Question 10

How to define the target population and choose the sampling frame

Answer 10

1) Define the target population: (Students at tisem, employees at philips etc.)

2) Determine the sampling frame

-Physical representation of the target population (Examples: students at Tisem –> Database students TiSEM)

3) Determine the sampling design

Question 11

Coverage error

Answer 11

Sampling frame =/ population

Question 12

Under coverage

Answer 12

Ture population members are excluded

Question 13

Miss-coverage

Answer 13

Non population members are included

Question 14

Solution to coverage error

Answer 14

If small, recognize but ignore
If large, redefine the population in terms of the sampling frame

Question 15

Probability sampling

Answer 15

Each element of the population has a known chance of being selected as a subject

Results generalizable to population

More time and resource intensive

Question 16

Nonprobability sampling

Answer 16

The elements of the population do not have a known chance of being selected as a subject

Less time and resource intensive

Results not generalizable to population

Question 17

Probability sampling: simple random sampling

Answer 17

Each population element has an equal chance of being chosen

highest generalizability but costly

Question 18

Systematic sampling

Answer 18

Select random starting point then pick every ith element (e.g every third starting from person 5)

Simplicity (adds a degree of system or process)

Low generalizability if there happens to be a systematic difference between every nth observation

Question 19

Stratified sampling (probability sampling)

Answer 19

Divide the population in meaningful (homogenous) groups, then apply SRS withing each group

All groups are adequately sampled, allowing for group comparisons

More time consuming and requires homogenous subgroups

Question 20

Cluster sampling

Answer 20

Divide the population in heterogeneous groups, randomly select a number of groups and selsct each member within these groups

Cluster population –> sample (clusters)

Geographic clusters

Subsets of naturally occuring clusters are typically more homogeneous than heterogeneous

Question 21

Classification of sampling designs

Answer 21

Sampling of sampling designs

1) probability:
simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling

2) Nonprobability
Convenience sampling
Quota sampling
Judgement sampling
Snowball sampling

Question 22

Convenience sampling (nonprobability sampling)

Answer 22

Select subjects who are conveniently available

Convenient (inexpensive and fast)

Lower generalizability

Question 23

Nonprobability sampling (quota sampling)

Answer 23

Fix quota for each subgroup

E.g. do you think dog owners should pay taxes for their pet

Household with dog (mainly no)
household with no dog (mainly yes)

When minority participation is critical (good)
Lower generalizability

Question 24

Nonprobability sampling: judgement sampling

Answer 24

Select subjects based on t hier knowledge/professional judgement

Convenient (inexpensive and fast) when a limited # of people has the info you need

Lower generalizability

Question 25

Nonprobability sampling (snowball sampling)

Answer 25

Do you know people who…

Good for rare characteristics (experts)

First participants strongly influence the sample

Question 26

Measurement or operationalization means

Answer 26

Turning abstract conceptual variables into measurable observations

Question 27

Nominal scales

Answer 27

A scale that allows you to classify your data into categories

E.g. states in the united states that are either democrat or repubican
You assign 1 to democrat and 2 to republican

Question 28

Ordinal scale

Answer 28

Ranked or ordered

Rank orders he categories in a meaningful way
More information than a nominal scale; here three is more than 2

E.g. Best to worst, first to last etc.

Question 29

Interval scale

Answer 29

Allows you to compare differences between values

Meaningful differences between values, but no natural zero point

E.g. IQ

Compared to ranked order; 1 –> 2 is not the same as 2 –> 3 when ranking chili peppers. Iq is standardized and comparable

Question 30

Ratio scales

Answer 30

Meaningful differences and ratios between values due to a natural zero point

Ratios are meaningful for this scale

E.g. Distance

Zero point is possible

Question 31

Measures of central tendency

Answer 31

Mean (average), median(central variable in an ordered group of variables) or mode( most common variable)

Question 32

Measures of dispersion

Answer 32

Range, standard deviation, variance or interquartile range

Question 33

Indiffeential statistics

Answer 33

Methods to draw conclusions (or to make inferences)

E.g. Mean difference tests

Question 34

Choosing between descriptive statistics

Answer 34

Nominal scale
Measure of central tendency: mode
Measure of dispersion —

Ordinal scale
Measure of central tendency: median
Measure of dispersion (interquartile range)

Interval scale
Measure of central tendency: mean
Measure of dispersion (standard deviation, variance)

Ratio
Measure of central tendency: mean
Measure of dispersion (standard deviation, variance)

Question 35

Choosing between inferential statistics:

Answer 35

Check slides

Question 36

When there are multiple IVs in a study, with different measurement scales:

Answer 36

The highest scale determines the statistical technique

Question 37

Choosing inferential statistics: T-test or ANOVA

Answer 37

T-test: compaares two means (two levels of an IV)

Anova: can compare more than two levels

Choice, as such:

Depends on the number of IVs
Depends on the number of levels (conditions or groups) of the IV

Question 38

Choosing inferential statistics: rating scales (Likert scale)

Answer 38

strongly disagree, disagree, undecided, agree and strongly agree

Question 39

Choosing inferential statistics: rating scales (semantic differential)

Answer 39

Organized _ _ _ _ _ _ _ Unorganized
Cold _ _ _ _ _ _ _ _ Warm
Modern _ _ _ _ _ _ _ _ old fashioned

Treated as interval scales

Question 40

From a statistical point of view, a moderator is

Answer 40

Also considered an IV.

Question 41

To test the moderating effect of M on the relationship between X and Y you have to include three IVs in your regression model:

Answer 41

The main effect of X

The interaction effect between X and M (=X*M) to capture the moderating effect of M on the relationship between X and Y

The main effect of M (to statistically control for the impact of M on Y; if you would not include M, the effect of X*M would not be correctly estimated)