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CYC 323 Steven > Chapter 4 Sampling > Flashcards

Chapter 4 Sampling Flashcards

1
Q

Sampling

A

The process of selecting units from the population of interest (the selection of subjects)

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2
Q

The process of selecting units from the population of interest (the selection of subjects).

A

Sampling

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3
Q

Population

A

The group you want to generalize and sample in a study.

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4
Q

The group you want to generalize and sample in a study.

A

Population

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5
Q

Theoretical Population

A

A group which, ideally, you would like to sample from and generalize to. This is usually contrasted with (compared to) the accessible population.

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6
Q

A group which, ideally, you would like to sample from and generalize to. This is usually contrasted with (compared to) the accessible population.

A

Theoretical Population

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7
Q

Accessible Population

A

A group that reflects the theoretical population of interest that you are able to get access to when sampling. This is usually contrasted with (compared to) the theoretical population.

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8
Q

A group that reflects the theoretical population of interest that you are able to get access to when sampling. This is usually contrasted with (compared to) the theoretical population.

A

Accessible Population

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9
Q

Sampling Frame

A

The list from which you draw your sample. In some cases, there is no list, so you draw your sample based upon an explicit rule.

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10
Q

The list from which you draw your sample. In some cases, there is no list, so you draw your sample based upon an explicit rule.

A

Sampling Frame

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11
Q

Sample

A

the actual units (individuals/entities) you select to participate in your study.

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12
Q

The actual units (individuals/entities) you select to participate in your study.

A

Sample

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13
Q

Bias

A

A systematic error in an estimate. A bias can be the result of any factor that leads to an incorrect estimate.
When bias exists, the values that are measured do not accurately reflect the true value.

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14
Q

A systematic error in an estimate. A ____ can be the result of any factor that leads to an incorrect estimate. When _____ exists, the values that are measured do not accurately reflect the true value.

A

Bias

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15
Q

Generalizing/Generalizability

A

The process of making an inference that the results observed in a sample would hold in the population of interest. If such an inference or conclusion is valid, we can say that it has _________.

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16
Q

The process of making an inference that the results observed in a sample would hold in the population of interest. If such an inference or conclusion is valid, we can say that it has ___________.

A

Generalizing/Generalizability

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17
Q

Sampling
Model

A

A model for generalizing in which you identify your population, draw a fair sample, conduct your research, and finally generalize your results from the sample population.

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18
Q

A model for generalizing in which you identify your population, draw a fair sample, conduct your research, and finally generalize your results from the sample population.

A

Sampling Model

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19
Q

Proximal Similarity Model

A

A model for generalizing from your study to other contexts based upon the degree to which the other context is similar to your study’s context.

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20
Q

A model for generalizing from your study to other contexts based upon the degree to which the other context is similar to your study’s context.

A

Proximal Similarity Model

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21
Q

Gradient of Similarity

A

The dimensions along which context of your study can be related to other potential contexts that you might wish to generalize. Contexts that are closer to your study along the _________ __ ________ of place, time, people, and so on can be generalized to with more confidence than ones that are further away.

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22
Q

The dimensions along which the context of your study can be related to other potential contexts that you might wish to generalize. Contexts that are closer to your study along the __________ __ ________ of place, time, people and so on can be generalized to with more confidence than ones that are further away.

A

Gradient of Similarity

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23
Q

Probability Sampling

A

Method of sampling that utilizes some form of random selection.

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24
Q

Method of sampling that utilizes some form of random selection.

A

Probability Sampling

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25
Nonprobability Sampling
Method of sampling that does not involve random selection.
26
Method of sampling that does not involve random selection.
Nonprobability Sampling
27
Random Selection
Process or procedure that assures that the different units in your population are selected by chance.
28
Process or procedure that assures that the different units in your population are selected by chance.
Random Selection
29
Confidence Intervals (CI)
A _________ _________ is used to indicate the precision of an estimate of a statistic. The ___ provides the lower and upper limits of the statistical estimate at a specific probability level. For example, if a statistic has an estimate mean/average ________ ___________ of 95% it means that there is a 95% chance the true mean is likely to occur.
30
A _________ _________ is used to indicate the precision of an estimate of a statistic. The ___ provides the lower and upper limits of the statistical estimate at a specific probability level. For example, if a statistic has an estimate mean/average ________ ___________ of 95% it means that there is a 95% chance the true mean is likely to occur.
Confidence Intervals (CIs)
31
Modal Instance Sample
Sampling for the most typical/Frequent case
32
Sampling for the most typical/frequent case.
Modal Instance Sampling
33
Mode
Most frequent occurring value in distribution.
34
Most frequent occurring value in distribution.
Mode
35
Expert Sampling
A sample of people with known or demonstrable experience and expertise in some area.
36
A sample of people with known or demonstrable experience and expertise in some area
Expert Sampling
37
Quota Sampling
Any sampling method where you sample until you achieve a specific number of sample units for each subgroup of a population (for example race or gender).
38
Any sampling method where you sample until you achieve a specific number of sample units for each subgroup of a population (for example race or gender).
Quota Sampling
39
Proportional Quota Sampling
A sampling method where you sample until you achieve a specific number of sampled units for each subgroup of a population, where the proportions in each group are the same.
40
A sampling method where you sample until you achieve a specific number of sampled units for each subgroup of a population, where the proportions in each group are the same.
Proportional Quota Sampling
41
Nonproportional Quota Sampling
Nonproportional quota sampling: A sampling method where you sample until you achieve a specific number of sampled units for each subgroup of a population, where the proportions in each group are not the same.
42
A sampling method where you sample until you achieve a specific number of sampled units for each subgroup of a population, where the proportions in each group are not the same.
Nonproportional Quota Sampling
43
Heterogeneity Sampling
Sampling for diversity or variety
44
Sampling for diversity or variety.
Heterogeneity Sampling
45
Snowball Sampling
A sampling method in which you sample participants based upon referral from prior participants.
46
A sampling method in which you sample participants based upon referral from prior participants.
Snowball Sampling
47
Respondent-driven Sampling
A nonprobability sampling method that combines chain-referral or snowball sampling, with a statistical weighting system that helps compensate for the fact that the sample was not drawn randomly.
48
A nonprobability sampling method that combines chain-referral or snowball sampling, with a statistical weighting system that helps compensate for the fact that the sample was not drawn randomly.
Respondent-driven Sampling
49
Response
A specific measurement value that a sampling unit supplies.
50
A specific measurement value that a sampling unit supplies.
Response
51
Statistic
A value that is estimated data.
52
A value that is estimated data.
Statistic
53
Population Parameter
The mean/average you would obtain if you were able to sample the entire population.
54
The mean/average you would obtain if you were able to sample the entire population.
Population parameter
55
Sampling Distribution
The theoretical distribution of an infinite number of samples of the population of interest in your study.
56
The theoretical distribution of an infinite number of samples of the population of interest in your study.
Sampling Distribution
57
Standard Deviation
An indicator of the variability of a set of scores in a sample around the mean of that sample.
58
An indicator of the variability of a set of scores in a sample around the mean of that sample.
Standard Deviation
59
Standard Error
The spread of the averages around the average of averages in a sampling distribution.
60
The spread of the averages around the average of averages in a sampling distribution.
Standard Error
61
Sampling Error
Error in measurement associated with sampling.
62
Error in measurement associated with sampling.
Sampling Error
63
Bell curve/Normal curve
A type of distribution where the values of a variable have a smoothed histogram or frequency distribution that is shaped like a bell. In a normal distribution, approximately 68 percent of cases occur within one standard deviation of the mean or center, 95 percent of the cases fall within two standard deviations, and 99 percent are within three standard deviations.
64
A type of distribution where the values of a variable have a smoothed histogram or frequency distribution that is shaped like a bell. In a normal distribution, approximately 68 percent of cases occur within one standard deviation of the mean or center, 95 percent of the cases fall within two standard deviations, and 99 percent are within three standard deviations.
Bell curve/Normal curve
65
Simple Random Sampling
A method of sampling that involves drawing a sample from a population so that every possible sample has an equal probability of being selected.
66
A method of sampling that involves drawing a sample from a population so that every possible sample has an equal probability of being selected.
Simple Random Sampling
67
Stratified Random Sampling
A method of sampling that involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup.
68
A method of sampling that involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup.
Stratified Random Sampling
69
Systematic Random Sampling
A sampling method where you determine randomly where you want to start selecting the sampling frame and then follow a rule to select every (X)-th element in the sampling frame list (where ordering of the list is assumed to be random). For example, starting in a random spot on a list of names and then picking every 10th person.
70
A sampling method where you determine randomly where you want to start selecting the sampling frame and then follow a rule to select every (X)-th element in the sampling frame list (where ordering of the list is assumed to be random). For example, starting in a random spot on a list of names and then picking every 10th person.
Systematic Random Sampling
71
Cluster Random Samplin/Area Random Sampling
A sampling method that involves dividing the population into groups called clusters, randomly selecting clusters, and then sampling each element in the selected clusters. This method is useful when sampling a population that is spread across a wide are geo-graphically.
72
A sampling method that involves dividing the population into groups called clusters, randomly selecting clusters, and then sampling each element in the selected clusters. This method is useful when sampling a population that is spread across a wide are geo-graphically.
Cluster Random Samplin/Area Random Sampling
73
Multistage Sampling
The combining of several sampling techniques to create a more efficient or effective sample than what any individual sampling type can achieve on its own.
74
The combining of several sampling techniques to create a more efficient or effective sample than what any individual sampling type can achieve on its own.
Multistage Sampling
75
Threats to External Validity
Any factors that can lead you to make an incorrect generalization from the results of your study to other persons, places, times or settings.
76
Any factors that can lead you to make an incorrect generalization from the results of your study to other persons, places, times or settings.
Threats to External Validity
77
Replicate/Replication
A study that is repeated in a different place, time or setting.
78
A study that is repeated in a different place, time, or setting.
Replicate/Replication
79
N
Number of cases in the sampling frame.
80
Number of cases in the sampling frame.
N
81
n
Number of cases in the sample.
82
Number of cases in the sample.
n
83
NCn (small "N", full sized "C", small "n").
The number of combinations (subsets) of n from N.
84
The number of combinations (subsets) of n from N.
NCn (small "N", full sized "C", small "n").
85
F= n/N
The sampling fraction.
86

CYC 323 Steven (2 decks)

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