Comprehensive summary for research descriptive methodology statistics of unit 19, 23, 20 and 22. The most important keywords have been added with explanations.
When you observe something, we always think of variables (our theore9cal construct) and
units, that are described by the variable.
® The rela9onship between the theore9cal variable and data is best described by
conceptualiza9on, opera9onaliza9on, and measurement.
® The rela9onship between the units and the data has to do with sampling.
How well do the data reflect my units of analysis?
® Evaluate our data by thinking about the sampling process.
Sampling > a technique that involves taking a small number of par9cipants from a much bigger
group.
When sampling?
If not, all units men9oned in our research ques9on can be studied, we need to ‘sample’.
® Studying a smaller set of units with the aim to say something about all units.
1
,What is sampling?
This is called the sampling process
® The rela9onship between the sampling frame and the sample is called sampling.
Distor/ons in the process
In all steps in the sampling process, we could find distor9ons.
® If the response rate is 100% > everyone we selected in the sample is actually
interviewed.
2 different types of sampling procedures
To dis9nguish these two types, you have to ask this ques9on: is the chance that a specific unit
from the sampling frame is included in the study, known?
• No: Non-probability sampling
• Yes: Probability sampling
Example > simple random sample from the popula<on registry.
Selected units reflect the popula?on.
Assessing sampling
We always make sampling mistakes.
Two types of mistakes:
o Sampling bias > sampling invalidity
o Sampling error > sampling unreliability
Sampling bias
Bias > not being typical for the popula9on. Studying the wrong group of people.
Example > how many people in the Netherlands currently support the EU?
Using snowball sampling > interviewing a person and asking that person for names who also
are be willing to interviewed.
Sampling error
Sampling error is a consequence of sample size and characteris9cs of the popula9on.
Example > how many people in the Netherlands currently support the EU?
Sample size 5 and sample size 400
Probability sampling
® No bias > sample size affects sampling error
19.2. Sample and popula2ons
It is almost impossible to ask all ‘students’ > so you decide to make a sample.
Computa/ons of the sample
Univariate analyses > mode, means, standard devia9on, etc.
Bivariate analyses > Pearson’s R or regression analyses.
® All numerical summaries resul9ng from these computa9ons are fully based on the
sample > called sta9s9cs.
3
, The goal > make statements about the en9re popula9on.
® We use inferen9al sta9s9cs to draw conclusions about the corresponding popula9on
parameters.
® Sta9s9cs are displayed by Roman leZers
® Parameters are displayed by Greek leZers
You want to know the value of popula9on parameter μ (Greek leZer ‘mu’).
® Methods of inferen9al sta9s9cs can help us answer such ques9ons.
5.3. Sampling
Inferen<al sta<s<cs > reverse to methods to draw conclusions about a popula9on based on
data coming from a sample.
Sample > sub set of a popula9on
® You want a representa9ve sample > a micro version of the en9re popula9on
o You can use a simple random sample > each subject has a same chance of being
selected.
Simple random sample
1. Popula9on > you have to make clear what the popula9on is look like
2. Sampling frame > make a list of all subjects
3. Sampling > you ask a computer to randomly select ‘200 students’ out of this list
4. Respondents > how you going to approaches your ‘200 students’
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