Quantitative And Qualitative Sampling ! Reading Notes *

Front 1

Sample:

Back 1

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

-small collection of units from a larger collection that a researcher can use to study to make generalizations of the larger group

Front 2

Two motivations to use random sampling?

Back 2

1) save time and cost (sampling 2000 people verses 20 million ppl is cheaper)

2) Accuracy, (results of well designed sample will produce results that are the same as trying to reach every single person in a whole population)

Front 3

Census:

Back 3

An attempt to count everyone in a target population

Front 4

Nonprobability Sampling

Nonrandom Sampling:

Back 4

A type of sample in which the sampling elements are selected using something other than a mathematically random process

Front 5

Types of Nonprobability samples

Back 5

Principles

Front 6

Haphazard:

Back 6

get any cases in any manner that is convenient

Front 7

Quota:

Back 7

get a present number of cases in each of several predetermined categories that will reflect the diversity of the population, using haphazard methods

Front 8

Purposive:

Back 8

get all possible cases that fit particular criteria, using various methods

Front 9

Snowball:

Back 9

get cases using referrals from one or a few cases, and then referrals from those cases and so forth

Front 10

Sequential:

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Get cases until there is no additional information or new characteristics (often used with other sampling methods)

Front 11

Definitions of Non probability Sampling :

Back 11

Oh yaaaa … Remember these bad boys ;)

Front 12

Haphazard sampling:

Back 12

A type of non random sample in which the researcher selects anyone he or she happens to come across

-can produce ineffective, unrepresentative samples (not recommended)

Front 13

Quota sampling:

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a type of nonrandom sample in which the researcher first identifies general categories into which cases or people will be selected, then he or she selects a predetermined number of cases in each category
-better then haphazard sampling
-to do this you identify relevant categories of people first, then decide how much people in each category to sample
-it is good because there can be differences in the sample

Front 14

Purposive Sampling:

Back 14

a type of nonrandom sample in which the researcher uses a wide range of methods to locate all possible cases of a highly specific and difficult to reach population

-used in situations in which an expert uses judgement in selecting cases with a specific purpose in mind
-exploratory research/ field research

Front 15

Good in three situations… What are the three situations?

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1) researcher uses it to select unique cases that are information

Front 16

Situation Purposive Sampling #2

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2) researcher may use purposive sampling to select members of a difficult to reach, specialized population (eg.wants to study prostitutes, but uses subjective info e.g location where prostitutes solicit, social groups with whom prostitutes stay with, and experts..police, other prostitutes to identify sample of prostitutes)

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Situation Purposive Sampling #3

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3) researcher wants to identify particular types of cases for in-depth investigation
(not to generalize to larger population, but to gain deep understanding)

Front 18

Deviant case sampling:

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A type of nonrandom sample, espeacially used by qualitative researchers, in which a researcher selects unusal or nonconforming cases purposely as a way to provide greater insight into social processes or a setting

-goal is to locate unsual, different cases that are not representative of the whole

Front 19

Snowball Sampling:

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A type of nonrandom sample in which the researcher begins with one case, then, based on information about interrelationships from that case, identifies other cases, and then repeats the process again and again.

-using cases in a network, one comes after the other comes after the other ☺

-crucial feature is that each person/unit is connected with another through direct or indirect linkage

Front 20

Researchers illustrate Snowball Sampling by drawing a
Sociogram:

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A diagram or map that shows the network of social relationships, influence patterns or communication paths among a group of people or units

Front 21

Sequential Sampling:

Back 21

A type of non random sample in which a researcher tries to find as many relevant cases as possible, until time, financial resources, or his or her energy are exhausted, and there is no new information or diversity from the cases

-researcher garthers cases until the amount of new information or diversity of cases is filled

Front 22

Theoretical sampling:

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An interative sampling technique associated with the grounded theory approach in which the sample size is determined when the data reach theoretical saturation

-researchers who use grounded theory techniques usually use theorectical sampling
theory comes from the sample

Front 23

When Researchers continue to look for new data until none comes, this is called Theoretical Saturation:

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A term associated with the grounded theory approach that refers to the point at which no new themes emerge from the data and sampling is considered complete

Front 24

Probability Sampling
(there is special language when it comes to probability sampling)

Back 24

The Language :)
Populations, elements, and sampling elements

Front 25

Sampling element:

Back 25

The name for a case or single unit to be selected
-unit of analysis or case in a population

Front 26

Population:

Back 26

The name for the large general group of may cases from which a researcher draws a sample and which is usually stated in theoretical terms
-to define the population, researcher states the unit being sampled, geographical location, and temporal boundaries of populations

Front 27

Target Population:

Back 27

The name for the large general group of many cases from which a sample is drawn and which is specified in very concrete terms
-refers to specific pool of cases that he or she wants to study

Front 28

Sampling Ratio:

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The number of cases in the sample divided by the number of cases in the population or the sampling frame, or the proportion of the population in the sample.

-ratio of the size of sample to the size of the target population
-population is abstract, it is always changing (people dying, people getting born)

Front 29

Sampling frame:

Back 29

A list of cases in a population, or the best approximation of it

-developing specific list that closely approximates all the elements in the population
eg. Telephone directories, tax records, drivers license records
-mismatch between sampling frame and conceptually defined population can be major source of error (invalid sampling)
-sampling frames are usually always inaccurate

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-any characteristic of population is a population….

Parameter:

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A characteristic of the entire population that is estimated from a sample

Eg. Average height of all women over the age of 21
-true characteristic of the population

Front 31

Statistic:

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A numerical estimate of a population parameter computed from a sample
-use statistic to estimate population parameter
parameters relate to population
statistics relate to samples

Front 32

Why random?

Back 32

-random samples are more likely to represent population
-random samples let researcher statistically calculate relationship between sample and population … the size of

Front 33

Sampling error:

Back 33

how much a sample deviates from being representative of the population

Front 34

Margin of error:

Back 34

an estimate about the amount of sampling error that exists in a surveys result

Front 35

Random sample:

Back 35

a type of sample in which the researcher uses a random number table or similar mathematical random process so that each sampling element in the population will have an equal probability of being selected

Front 36

Types of Probability samples

Back 36

:)

Front 37

Simple Random Sampling:

Back 37

a type of random sample in which a researcher creates a sampling frame and uses a pure random process to select cases. Each sampling element in the population will have an equal probability of being selected

-after numbering all elements in sampling frame, researchers uses a list of random numbers to decide which elements to select
-researcher can get these numbers from…

Front 38

Random number table:

Back 38

A list of numbers that has no pattern in it and that is used to create a random process for selecting cases and other randomization purposes

-this is used so that any number can be used by random process, each has an equal probability of appearing in any position

Front 39

Sampling distribution:

Back 39

A distribution created by drawing many random samples from the same population

Front 40

Central limit theorem:

Back 40

A law like mathematical relationship stating that whenever many random samples are drawn from a population and plotted, a normal distribution is formed, and the centre of such a distribution for a variable is equal to its population parameter

Front 41

Confidence Intervals:

Back 41

: a range of values, usually a little higher and lower than a speicifc value found in a sample, within which a researcher has a specified and high degree of confidence that the population parameters lie

-allows a researcher say with a high level of confidence that the true parameter lies within a certain range.

Front 42

Systematic Sampling:

Back 42

a type of random sampling in which a researcher selects every kth (eg 12) case in the sampling frame using a sampling interval

Front 43

Sampling interval:

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the inverse of the sampling ratio, which is used in systematic sampling to select cases. The sampling interval (i.e I in k, where k is some number) tells the researcher how to select elements from a sampling frame by skipping elements in the frame before selecting one for the sample

Eg. You want a Sample 300 names from 900. You use random starting point, and from that point use every 3rd name to get sample of 300

Front 44

Stratified sampling:

Back 44

a type of random sample in which the researcher first identifies a set of mutually exlusive and exhaustive categories, then uses a random selection method to select cases for each category

Front 45

Stratified sampling continuted :)

Back 45

-researcher divides population into subpopulations (strata) on basis of supplementary information.
-researcher then gets random sample from each strata
-researcher controls relative size of strata, rather then random process
-usually are more representative of population verses simple random sampling

Front 46

Cluster sampling:

Back 46

a type of random sample that uses multiple stages and is often used to cover wide geographic areas in which aggregated units are randomly selected; samples are then drawn from the sampled aggregated units, or clusters

Front 47

Cluster sampling targets two problems

Back 47

1)researchers lack good sampling frame for a dispersed population

2)the cost to reach a sampled element is very high
-cluster is a unit that contains final sampling elements but can be treated temp. as a sampling element itself
-researcher first samples clusters, then samples elements within the cluster

Front 48

Three stages to Cluster Sampling

Stage 1

Back 48

random sampling of big clusters

Front 49

Stage 2

Back 49

random sampling of small clusters within each selected big cluster

Front 50

Stage 3

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sampling of elements from within the sampled small clusters
-less accurate then simple random sampling
-Design with more clusters is better (elements within clusters tend to be more similar)

Front 51

2 methods to Cluster Sampling

Method 1

Back 51

Probability Proportionate to Size (PPS) As adjustment made in cluster sampling when each cluster does not have the same number of sampling elements
-it is proportionate because the size of each cluster (number of elements at each stage) is the same.

Front 52

Method 2

Back 52

Random Digit Dialing (RDD) A method of randomly selecting cases for telephone interviews that uses all possible telephone numbers as a sampling frame

Front 53

Hidden Populations

Back 53

people who engage in clandestine, deviant, or concealed activities and who are difficult to locate and study

Front 54

How large should a sample be?

Back 54

-it all depends.. the kind of data analysis researcher plans, how accurate the sample has to be for researchers purpose, population characteristics
-one principle: the smaller the population, the bigger the sampling ratio has to be
-bigger population, smaller sample size (as population grows, the returns in accuracy shrink he he )

Front 55

Drawing inferences:

Inferential statistics:

Back 55

A branch of applied mathematics of statistics based on a random sample. It lets a researcher make precise statements about the level of confidence he or she has in the results of a sample being equal to the population parameter.
-researcher samples so he or she can draw inferences from the sample to the population
-researchers take their sample and infer it to population (gap between the two since)