Flashcards in L4 Statistical techniques and sampling designs Deck (36)
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Descriptive statistics
Methods of summarizing the data in an informative way
- central tendency: mean, median, mode
- dispersion: range, stdev, variance, interquartile range
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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
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Four types of scales
- Nominal (qualitative)
- Ordinal (qualitative)
- Interval (quantitative)
- ratio (quantitative)
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Nominal scale
allows classifying data into groups/categories
e.g. gender
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Ordinal scale
rank orders in a meaningful way
e.g. education level
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Interval scale
Meaningful differences between values, but no natural zero point --> zero means something (0 degrees)
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Ratio scale
Meaningful differences and ratios between values due to a natural zero point --> zero is actually nothing (0 dollar is no money)
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Choosing between inferential statistics:
IV=nominal/ordinal DV=nominal/ordinal
Chi-square test
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Choosing between inferential statistics:
IV=nominal/ordinal DV=interval/ratio
T-test, Anova
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Choosing between inferential statistics:
IV=interval/ratio DV=nominal/ordinal
logit analysis
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Choosing between inferential statistics:
IV=interval/ratio DV=interval/ratio
regression analysis
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When to perform T-Test vs Anova
T-Test --> compare two means (two levels of IV)
Anova --> compare more than two levels
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Rating scales
- Likert scale: strongly agree/disagree
- Semantic differential: Cold warm
TREATED AS INTERVAL/RATIO so that you can use regression
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What is a population?
Entire group of people, firms, events, or things of interest for which you would like to make inferences
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What is a sample?
A subset of the population of interest
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What is a subject?
Single member
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What is low representativeness?
= properties of the population are over- or underrepresented in the sample
= high sampling error
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The sampling process
1. define population
2. determine sampling frame
3. determine sampling design
4. determine sample size
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1. define population
e.g. students TISEM, dutch organ donors
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2. determine sampling frame
“Physical” representation of the target population
- where you can reach out to e.g. Donorregister
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coverage error
sampling frame ≠ population
• Under-coverage: true population members are excluded
• Miss-coverage: non-population members are included
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solutions to coverage error
• If small, recognize but ignore
• If large, redefine the population in terms of the sampling frame
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3. determine sampling design
probability vs non-probability sampling
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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
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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
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Probability sampling techniques
- Simple random sampling (SRS)
- Systematic sampling
- Stratified sampling
- Cluster sampling
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Simple random sampling (SRS)
Each population element has an equal chance of being chosen
e.g. out of a hat
--> Highest generalizability
BUT costly?
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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
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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
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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
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Nonprobability sampling
- Convenience sampling
- Quota sampling
- Judgment sampling
- Snowball sampling
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Convenience sampling
Select subjects who are conveniently available
e.g. random on the street
--> Convenient (inexpensive and fast)
BUT lower generalizability
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Quota sampling
Fix quota for each subgroup (percentage in population)
--> When minority participation is critical
BUT lower generalizability
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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
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Snowball sampling
“Do you know people who...”
e.g. people with rare disease
--> For rare characteristics (“experts”)
BUT first participants strongly influence the sample
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