L4 Statistical techniques and sampling designs Flashcards Preview

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Flashcards in L4 Statistical techniques and sampling designs Deck (36)
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1

Descriptive statistics

Methods of summarizing the data in an informative way
- central tendency: mean, median, mode
- dispersion: range, stdev, variance, interquartile range

2

Inferential statistics

Methods to draw conclusions (or to make inferences, test hypotheses)
• Mean difference test
• Chi-square test
• Analysis of variance (ANOVA)
• Regression analysis
• Logit analysis

3

Four types of scales

- Nominal (qualitative)
- Ordinal (qualitative)
- Interval (quantitative)
- ratio (quantitative)

4

Nominal scale

allows classifying data into groups/categories
e.g. gender

5

Ordinal scale

rank orders in a meaningful way
e.g. education level

6

Interval scale

Meaningful differences between values, but no natural zero point --> zero means something (0 degrees)

7

Ratio scale

Meaningful differences and ratios between values due to a natural zero point --> zero is actually nothing (0 dollar is no money)

8

Choosing between inferential statistics:
IV=nominal/ordinal DV=nominal/ordinal

Chi-square test

9

Choosing between inferential statistics:
IV=nominal/ordinal DV=interval/ratio

T-test, Anova

10

Choosing between inferential statistics:
IV=interval/ratio DV=nominal/ordinal

logit analysis

11

Choosing between inferential statistics:
IV=interval/ratio DV=interval/ratio

regression analysis

12

When to perform T-Test vs Anova

T-Test --> compare two means (two levels of IV)
Anova --> compare more than two levels

13

Rating scales

- Likert scale: strongly agree/disagree
- Semantic differential: Cold warm

TREATED AS INTERVAL/RATIO so that you can use regression

14

What is a population?

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

15

What is a sample?

A subset of the population of interest

16

What is a subject?

Single member

17

What is low representativeness?

= properties of the population are over- or underrepresented in the sample
= high sampling error

18

The sampling process

1. define population
2. determine sampling frame
3. determine sampling design
4. determine sample size

19

1. define population

e.g. students TISEM, dutch organ donors

20

2. determine sampling frame

“Physical” representation of the target population
- where you can reach out to e.g. Donorregister

21

coverage error

sampling frame ≠ population
• Under-coverage: true population members are excluded
• Miss-coverage: non-population members are included

22

solutions to coverage error

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

23

3. determine sampling design

probability vs non-probability sampling

24

Probability sampling

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

-->Results generalizable to population
BUT more time and resource intensive

25

Nonprobability sampling

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

--> less time and resource intensive
BUT results not generalizable to population

26

Probability sampling techniques

- Simple random sampling (SRS)
- Systematic sampling
- Stratified sampling
- Cluster sampling

27

Simple random sampling (SRS)

Each population element has an equal chance of being chosen
e.g. out of a hat

--> Highest generalizability
BUT costly?

28

Systematic sampling

Select random starting point and then pick every nth element

--> simplicity
BUT low generalizability if there happens to be a systematic difference between every nth observation

29

Stratified sampling

Divide the population in meaningful (homogenous) groups, then apply SRS within each group
e.g. level of income

--> All groups are adequately sampled, allowing for group comparisons
BUT more time consuming and Requires homogenous subgroups

30

Cluster sampling

Divide the population in heterogeneous groups, randomly select a number of groups and select each member within these groups
e.g. geographic clusters (areas)

--> Geographic clusters
BUT Subsets of naturally occurring clusters are typically more homogeneous than heterogeneous

31

Nonprobability sampling

- Convenience sampling
- Quota sampling
- Judgment sampling
- Snowball sampling

32

Convenience sampling

Select subjects who are conveniently available
e.g. random on the street

--> Convenient (inexpensive and fast)
BUT lower generalizability

33

Quota sampling

Fix quota for each subgroup (percentage in population)

--> When minority participation is critical
BUT lower generalizability

34

Judgment sampling

Select subjects based on their knowledge/professional judgment
e.g. experts

--> Convenient (inexpensive and fast) when a limited # of people has the info you need
BUT Lower generalizability

35

Snowball sampling

“Do you know people who...”
e.g. people with rare disease

--> For rare characteristics (“experts”)
BUT first participants strongly influence the sample

36

Rules of thumb for sample size

• Sample size ≥ 75, < 500
• Multivariate research: ≥ 10 x parameters to be
estimated
• Subsamples (e.g., male/female): ≥ 30 per subsample