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25 Cards in this Set

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probability sampling
Based on the principle of randomness and probability theory

Objective: to obtain a sample from a population that will provide useful information about the total population

Objective: To obtain a representative sample


Used when researchers want precise, statistical descriptions of large populations.

A sample of individuals from a population must contain the same variations that exist in the population.
types of probability sampling
Simple random sampling (SRS)
Systematic sampling
Stratified sampling
Multistage cluster sampling
simple random sampling
It is the most basic probability sampling method.
It can be done by a computer.
It needs an accurate sampling
systematic sampling
It is basically SRS with some modifications.
The first step is to number all the elements in the sampling frame.
Start out with a random number and then select every kth element
Example: a sampling frame with 900, and you need 300 elements, the interval (or kth) will be 3 (900/300), so you select every 3rd element from the list
Arrangement of elements in the list can result in a biased sample because of periodicity
stratified sampling
Grouping of units composing a population into homogenous groups before sampling.
This procedure, which may be used in conjunction with simple random or systematic sampling, improves the representativeness of a sample in terms of the stratification variables.
Rather than selecting sample for population at large, researcher draws from homogenous subsets of the population.
Results in a greater degree of representativeness by decreasing the probable sampling error.
The researcher ensures that appropriate numbers of elements are drawn from homogenous subsets of the population.
Example: personnel in a school
Multistage Cluster
Used when it's not possible or practical to create a list of all the elements that compose the target population (sampling frame).
Involves repetition of two basic steps: listing and sampling.
The researcher draws several samples in stages (introducing error every time)
Highly efficient but costly.
Example: Immigrant survey in
Non-Probability sampling
Technique in which samples are selected in a way that is not suggested by probability theory.
When there is no sampling frame available
When studies have different objectives, such as how people understand certain things, meaning, etc.—closely aligned with qualitative methods.
Examples include reliance on available subjects as well as purposive (judgmental), quota, and snowball sampling.
types of non-prob sampling-
Reliance on available subjects:
Only justified if more accurate sampling methods are not possible.
Researchers must exercise caution in generalizing from their data when this method is used.
Example: Taking a poll in a street, using classmates in a class
snowball sampling
Appropriate when members of a population are difficult to locate.
Researcher collects data on members of the target population she can locate, then asks them to help locate other members of that population.
Example: study of stay
Purposive or judgmental sampling
Selecting a sample based on knowledge of a population, its elements, and the purpose of the study.
Used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors
Example: study of immigrants
Quota sampling
Begin with a matrix of the population.
Data are collected from people with the characteristics of a given cell in the matrix.
Data should represent the total population.
Example: hair study
Illistrations
looking at actual sampling design used to select a sample of university students. followi9ng steps and decisions involved in selecting that sample
study population and sampling frame- illistrations
Sampling frame: the actual list from which the elements will be selected. Examples: directories, registry lists, phone books

If the sample is to be representative of the population, it is essential that the sampling frame include all (or nearly all) members of the population.

Unit of observation: the element from which the observation is collected that provides the basis for analysis
stratification- illistrations
stratification by college class would be sufficient, although the students mikght have been further stratified within class, if desired, by gender, college, mahor...
sample selection- illistrations
once students have been arranbged by class, a systematic sample was selected across the entire rearranged list. once the sample has been selecdted, the computer was instructed to pring each student's name and mailing address
sample modification
before mailing questionares, researchers discovered that unexpeced expenses in the production of the questionaries made it impossible to cover the costs of mailing all 1100. so it was reduced
sampling unit
Element or set of elements considered for selection in some stage of
parameter
Summary description of a given variable in a population.

Any characteristic of the population, such as percentage of people of different ethnicities in a city, the average income of a country.

Importantly, the parameter is never known with accuracy for a large population; we must estimate it on the basis of samples
stastic
Summary description of a variable in a sample.

A characteristic or characteristics (variables) by which we can describe a sample. They are used to estimate population parameters
sampling error
The degree of error of a given sample design.
It is the deviation between sample results and population parameters due to random process.
Sampling error size is affected by the size of the population and variation in the population.
A large sample produces a smaller sampling error and a homogeneous population produces smaller a sampling error
(Sampling error is often used to assess the quality of estimates: margin of error)
population
Study population: the larger pool or universe that he researcher is interested in. It is a theoretically specified aggregation or population

Study population: the pool or an aggregation of elements from which the sample will be collected
probability theory
If many independent random samples are selected from a population, the sample statistics provided by those samples will be distributed around the population parameter in a known way (bell curve)

If we were to select a large number of good samples, we would expect them to cluster around the true value (50%), but given enough such samples, a few would fall far from the mark.
probability theory
Researchers usually draw only one sample, but can use the principles of probability theory to estimate how close (or far) they have come to the actual population parameter because of the use of random sampling
area under the curve
68% of the area lies within +_ 1 standard error from the population parameter

95% of the area lies within +_ 2 standard error from the population parameter

99% of the area lies within +_ 3 standard error from the population
area under the curve
95% of samples will give estimates within the confidence interval of +_2 standard error

I am 95% confident that my estimate will fall within the interval of +_ 2 standard errors

Or a sample has a 95% chance of falling within +_2 standard errors (our confidence interval).