Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

geog 109 LE 2 > Lecture 7 (Sampling Notes) > Flashcards

Lecture 7 (Sampling Notes) Flashcards

1
Q

the process of selecting a representative portion of a population

A

Sampling

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

representative means __

A
  • reproduces the important characteristics of the population
  • Uses the EPSEM method (Equal Probability of Selection Method)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

uncertainty that arises from working with a sample rather than the entire population

A

Sampling error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

A large sample is more likely to include a true cross-section of the population (t or f)

A

t

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

A large sample is more likely to include a true cross-section of the population (t or f)

A

t

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

When the procedures used to select the sample tend to favor the inclusion of individuals in the population with certain population characteristics

A

Sampling bias

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

why sample?

A
  • It is not necessary to take a complete census
  • Sampling requires less costs, time, and effort
  • The population may be infinite
  • The population may be empirically definable, but not practically available
  • Sample also allows for a wider scope and more in-depth study
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

sampling process

A

Define the population
Construct the sampling frame
Select the sampling design
specify the info to be collected
collect data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

difference between descriptive and inferential

A

inferential draw conclusion/generalize a larger population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

the population to whom a researcher wishes to generate the results of a study

A

target

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

the smaller portion of the target population to whom the researcher actually has access to

A

study

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

the group of people selected to be part of the study

A

sample

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

ordered list of sampling units representing the population from which the sample will be drawn
- it must include all individuals in the population (it must be exhaustive)
- each individual element of the population must appear once and only once

A

Sampling frame

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

procedure used to select individuals from the list of samples

A

Sampling design

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

extended test of data collection procedures to be used in a study in advance of the main data collection
- to check instruments, data loggers and all other logistics
- sometimes reveal deficiencies

A

pilot or pre-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

the probability of any individual member of the population being picked for the sample can be specified

A

probability sampling

17
Q

advantage of probability sampling

A

sampling error can be calculated

18
Q

each element in the pop. has an equal probability of being selected as a sample

A

SRS/Simple Random Sampling

19
Q

adv and disadv of SRS

A

ADVANTAGE: simple and easy to apply when the pop. is small
DISADVANTAGE: cumbersome for large pop., not the most statistically efficient method
- Possible not to get a good representation due to “luck” of the draw
- randomness does not guarantee representativeness

20
Q

every kth element starting from a randomly chosen point

A

Systematic Random Sampling

21
Q

subset of the population that has at least one common attribute

A

Stratum

22
Q

Sample is obtained by forming classes, or strata, in the population and then selecting a simple random sample from each

A

Stratified Random Sampling

23
Q

adv and disadv of Stratified Random Sampling

A

ADVANTAGES: reduces sampling error, decreasing the likelihood of obtaining an unrepresentative sample
- Assumes representation of key subgroups
DISADVANTAGES: still cannot guarantee representativeness if the sampling interval corresponds to a pattern

24
Q

adv and disadv of stratified random sampling

A

ADVANTAGES: reduces sampling error, decreasing the likelihood of obtaining an unrepresentative sample
Assumes representation of key subgroups of the pop.
DISADVANTAGE
May require additional prior info about the population and strata

25
Q

proportionate allocation vs optimum allocation

A

PA = Size of sample selected from each stratum is proportional to the size of the stratum in the entire population
- ADVANTAGE: composition of the sample in terms of its representativeness can be considered
OA = proportionality of the sample size to the stratum is not considered
- LIMITATION: cannot generalize directly to the population without applying weights

26
Q
  • The population is first divided into mutually exclusive and exhaustive clusters, then selecting a simple random sample from each.
  • Clusters should be Small scale representation of the population
  • Ideally, each cluster should be internally heterogeneous
A

Cluster Sampling

27
Q

adv and disadv of cluster sampling

A

ADVANTAGES: efficient when the population to be sampled is geographically dispersed over a large area
- Restricting the sampling to a small number of clusters
DISADVANTAGES:
Often gives poor results; sample error is higher

28
Q

sampling is done in a hierarchy of stages with the other four techniques applied in various combinations

A

Multi-stage Sampling

29
Q

adv and disadv of Multi-stage Sampling

A

ADVANTAGES: convenient and efficient
DISADVANTAGES: higher sample error
- Entails much planning before sampling is done

30
Q

sample are selected in some non-random manner

A

non-probability / non random sampling

31
Q

adv and disdv of non-probability / non random sampling

A

ADVANTAGE: useful when time is limited, a sampling (frame) not available, budget is tight

DISADVANTAGE: cannot generalize the general population

32
Q

only convenient or accessible, members of the population are selected
- Useful for pilot or pre-testing

A

Convenience sampling

33
Q

Personal judgment is used to decide which individuals of a population are to be included in the sample
- Useful when there is a limited number of people that have expertise in the area being studied
- likely to overweigh samples that are more readily accessible

A

Purposeful or Judgemental sampling

34
Q

an attempt to obtain a representative sample by acquiring quotas from given sample

A

Quota sampling

35
Q

Sample consists of individuals who self-select from the population, rarely representative
- Individuals are usually more motivated or have a higher interest in the topic

A

Voluntary sampling

36
Q

respondents are accumulated by using each individual as an informant or source of sample

A

snowball sampling

37
Q

The probability distribution of a sample stat/sampling distribution

A

CLT

38
Q

Taking all possible samples of size n from a population, calculating the statistic for each sample, and drawing the distribution of those values

A

CLT

39
Q

adv and disadv of systematic RS

A

ADVANTAGES: simpler than SRS, gives a good spread across the pop.
DISADVANTAGES: still cannot guarantee representativeness if the sampling interval corresponds to a pattern

geog 109 LE 2 (6 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions