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ZZ1 > Sampling > Flashcards

Sampling Flashcards

1
Q

What is a sample?

A

A smaller set of cases a researcher selects from a larger pool and generalizes to the population

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2
Q

What is a sampling element?

A

The name for a case or single unit to be selected (eg. person, group, organization)

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3
Q

What is a population?

A

Name for the large general group of many cases from which a researcher draws a sample and which is usually stated in theoretical terms (EVERYONE)

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4
Q

What is a target population?

A

Large general group of many cases from which a sample is drawn and which is specified in very concrete terms (eg. women in undergraduate residences)

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5
Q

What is a sampling frame?

A
  • A specific list of cases in a population, or the best approximation of it
  • Operational definition of an abstract concept (changing population)
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6
Q

What is a parameter?

A

A characteristic of the entire population that is estimated from a sample (eg. % of people who smoke)

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7
Q

What is a statistic?

A

A numerical estimate of a population parameter computed from a sample

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8
Q

What is a sampling ratio?

A

The number of cases in a sample divided by number of cases in the population

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9
Q

What is nonprobability sampling?

A

Sampling elements are selected using something other than a mathematically random process

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10
Q

What are the types of nonprobability sampling? If possible, give examples.

A
  1. Haphazard/Convenient (eg. TV interviewers on the street)
  2. Quota
  3. Purposive (eg. seeking out dropouts who are from stable two-parent, rich families)
  4. Snowball (eg. studying members of an organized crime family)
  5. Sequential or Theoretical
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11
Q

What is the principle, pros, and cons of haphazard/convenient sampling?

A
  • Researcher selects anyone they happen to come across
  • Can produce ineffective and highly unrepresentative samples
  • Cheap and quick
  • Not recommended
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12
Q

What is the principle, pros, and cons of quota sampling?

A
  • Get a preset number of cases in each of the several predetermined categories that will reflect the diversity of population, using haphazard methods
  • Still possible to misrepresent a population; you pay attention to number in sample or face value of participants (ie. “do I have a minority here, check!”) but less attention to DEPTH of information (saturation)
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13
Q

What is the principle, pros, and cons of purposive sampling? Discuss deviant case sampling.

A
  • Get all possible cases that fit particular criteria (often specific and difficult-to-reach population), using various methods
  • Until time, resources, or energy is exhausted
  • Expert uses judgement in selecting cases; never knows whether cases selected represent population
  • Generalizability is not really the goal
  • Often used in exploratory and field research
  • May result in stereotyping and false sense of security in representation [SAME AS QUOTA]
  • Deviant case sampling: researcher selects unusual or nonconforming cases (outliers) purposely as a way to provide greater insight into social processes or a setting
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14
Q

What is the principle, pros, and cons of snowball sampling? What is a sociogram?

A
  • Get cases using referrals from one or a few cases, and then referrals from those cases, and so forth (NETWORK; multi-stage technique!)
  • Selection/volunteer bias may apply, can be exclusionary
  • Sociogram: a diagram that shows the network of social relationships, influence patterns, or communication paths among a group of people/units
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15
Q

What is the principle, pros, and cons of sequential/theoretical sampling? Discuss theoretical saturation.

A
  • Get cases until there is no additional information or new characteristics (often used with other sampling methods)
  • Sample size is determined when data reach theoretical saturation (point at which no new themes emerge from data and sampling is complete)
  • Requires that the researcher continuously evaluate all collected cases
  • Expensive, time-consuming, hard
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16
Q

What is the experience sampling method (daily diary study method)? Discuss a limitation.

A
  • Intensive longitudinal approach involving DAILY reports on thoughts, feelings, experiences, behaviours, context, environment
  • Can address response fallacies (social desirability bias, forgetting)
  • More of a data collection technique; but can be considered sampling as recruits must be willing
  • Difficult to analyze/code!
17
Q

What is probability/random sampling?

A

Researcher uses mathematical random process so that each sampling element in the population will have an equal probability of being selected

18
Q

What is sampling error?

A
  • Relevant to random sampling

- How much a sample deviates from being representative of the population

19
Q

What is margin of error?

A
  • Relevant to random sampling

- An estimate about the amount of sampling error that exists in a survey’s results

20
Q

What must you consider to get a representative sample if your sample is small?

A
  • It is possible to get a representative but small sample using the right sampling methods/frame
  • The smaller the population, the bigger the sampling ratio (more in the sample) has to be more an accurate sample
21
Q

What are the types of probability sampling?

A
  1. Simple Random
  2. Systematic
  3. Stratified
  4. Cluster
  5. Random Digit
22
Q

What is the principle, pros, and cons of simple random sampling?

Define:

  • Sampling distribution
  • Sampling distribution of sample means
  • Central limit theorem
A
  • Researcher creates a sampling frame and uses a pure random process (ie. random number table) to select cases
  • Sampling distribution: a distribution created by drawing many random samples from the same population; to get true idea of population
  • Sampling distribution of sample means: a distribution of sample means created by drawing many random samples from the same population
  • Central limit theorem: a law-like mathematical relationship stating that when many random samples are drawn and plotted, a normal distribution forms whose centre is equal to its population parameter
  • Allows for generalization as researcher can calculate probability of statistic being off from parameter
23
Q

What is the principle, pros, and cons of systematic sampling?

Define sampling interval and discuss how it is calculated.

A
  • Researcher selects every kth case in the sampling frame using a sampling interval
  • Sampling interval: tells researcher how to select elements from a simpling frame by skipping elements in the frame before selecting one for the sample; calculated as the inverse of the sampling ratio
  • Likely yields equivalent results are simple random sample
  • Cannot be used if elements in a sample are organized in a pattern
24
Q

What is the principle, pros, and cons of stratified sampling?

A
  • Divides population into strata and then draws random sample from each subpopulation using simple random or systematic sampling
  • Fixes proportion of different strata within a sample to guarantee representiveness (if stratrum information is correct)
25
Q

What is the principle, pros, and cons of cluster sampling? Define cluster.

A
  • Done in multiple stages and is often used to cover wide geographic areas in which clusters are randomly selected; samples are then drawn from the sampled clusters
  • Cluster: unit that contains final sampling elements but can be treated temporarily as a sampling element itself
  • Eg. if no list of residents in a city, sample city blocks, households, and then residents
  • Less expensive and can be used when there is no sampling frame for dispersed population
    BUT each stage introduces sampling errors
26
Q

What is the principle, pros, and cons of random digit sampling?

A
  • General public is interviewed by telephone
  • Population is telephone numbers, not people with telephones
  • Numbers are randomly dialled (not directory)
  • Cost effective way to reach many
27
Q

What are the 2 methods of cluster sampling?

A
  1. Proportionate/unweighted cluster sampling
    - Size of each cluster is the same
  2. Probability proportionate to size (PPS)
    - An adjustment made in cluster sampling when each sample does not have the same number of sampling elements
    - Eg. giving each university an equal chance of being selected when each university has different numbers of students
28
Q

Discuss statistical power.

A
  • The desired sample size depends on how high we need statistical power to be to analyze data
    1. Degree of accuracy required
    2. Degree of diversity in a population (homogenous = less error)
    3. # of variables being analyzed
29
Q

What is inferential statistics?

A

Researcher makes precise statements about level of confidence they have in results of a random sample being equal to population parameter

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