Ch. 6 Sampling (study Guide)
What is the major reason for using a stratified sample?
Reduces sampling error for a given sample size to a level lower than that of an SRS of the same size.
How area/multistage samples are/become representative? (how do they become representative)
Small, more numerous, heterogeneous clusters
Stratified Sampling
Stratified sampling changes this by dividing the population into smaller subgroups called strata, before drawing separate random samples from each of the strata.
Organizational list: name a problem
in some cases the sampling frame may not completely reflect the full theoretical concept intended for ex: a study on poverty could operationalize the concept "poor" as those receiveing public assistance, however many people with little or no income do not receive public assistance.
Strengths of Simple random sampling
Although it has a wide application, the basic sampling procedure on which statistical theory is based; and it is the standard against which other sampling procedures are measured.
Sample frame
A listing of all the elements in a population.
Provide an example in which Area/mult-stage sampling should be used and why?
A needs assessment survey that would determine the extent and distribution of preschool children with educational deficiencies in a large urban area. Simple random and systematic samples cannot be used because no sampling frame listing all such children exists. Area sampling enables us to draw a probability sample without having a complete list of all elements in the population.
Area/multistage sampling
A procedure in which we obtain the final units to include in the sample by first sampling among larger units, called clusters that contain the smaller sampling units. A series of sampling stages are involved, working down in scale from larger clusters to smaller ones.
Sampling with diverse populations (Use representative samples)
A representative sample should be used which has all the same characteristics as the population. This allows for conclusions that are based on them to be legitimately generalized to the populations from which they are drawn.
Systematic Sampling
A variation of simple random sampling, which involves taking every kth element listed in a sampling frame.
Limitation of convenience/Availability sample
Not representative of population where it was drawn from.
Representative sample (Used for Diverse populations)
Accurately reflects the distribution of relevant variables in the target population. In a sense, a small reproduction of the population. ex. the success of unwed teenage mothers in raising their children; the research sample drawn should contain the same proportion of unwed teenage mothers at each age level, educational level, and SES status that exists in the community as whole. Should have all the same characteristics as the population
Despite the complexity of area sampling why is it used?
Allows us to draw highly accurate probability samples from populations that because of their size of geographical spread, we could not otherwise sample.
How Stratified sampling and area/multistage sampling differ in regard to sampling error?
Area-the larger the initial areas and the greater the homogeneity, the greater the sampling error. while: Stratified-greater homogeneity leads to less error Remember: with stratified we select a sample from each stratum, but with area sampling we draw from samples ONLY from a few areas. If a few areas in a sample are homogeneous in comparison with others then they are less representative.
When using Systematic sampling, what technique should be used?
Assessing the sample frame carefully for any cyclical pattern that might confound the sample and, if necessary, rearrange the list to eliminate the pattern. OR use SRS instead of systematic sampling
Why is a Purposive sample used?
Choosing a sample that specifically excludes certain types of people, because their presence might confuse the research findings.
Sample
Consists of one or more elements selected from a population. The manner in which we select elements for the sample has enormous implications for the scientific utility of the research based sample. To select a good sample, we need to clearly define the populations from which to draw the sample.
A good sample: clearly defines the population and should specify four things. What 4?
Content: refers to the particular characteristics that the members of the population have in common. Units: Indicates the unit of analysis. Extent: of the population refers to geographic coverage Time: temporal period during
How is disproportionate sampling different from most probability samples?
Most probability samples achieve representativeness by giving every element in the population an equal chance of appearing in the sample. Whereas:
Simple Random Sampling
Each element in the population has an equal probability of inclusion of the sample. Treats the target population as a unitary whole. The simplest technique for drawing probability samples
Probability samples
Each element in the population has some chance of inclusion in the sample. The investigator can determine the chances or probability of each element's inclusion-probabilities can be equal or different, but each element's probability of inclusion is nonzero or unknown. Enables calculation of sampling error Permits the researcher to estimate precisely the likelihood that a sample differs from the true population
disproportionate sampling definition
Each element of a stratum has an equal chance of appearing in the sample of that stratum, but the elements in some strata have a better chance of appearing in the overall sample than the elements of other strata do.
Non probability samples: T/F is it appropriate for all studies?
False: not required or appropriate for all studies but: Researchers use in assessing client functioning and evaluating the effectiveness of intervention strategies.
When to use a convenience/availability sample
For research when it is difficult or impossible to develop a complete sampling frame. More common forms of sampling used in human service research, because it is less expensive and the impossibility of developing an exhaustive sampling frame.
When is snowball sampling useful?
Hard to identify or reach populations; subcultures, or in the investigation of sensitive topics, such as child abuse or drug use, where the perpetrators or victims might hesitate to identify themselves if approached by a researcher, but open to someone they know shares their experience or deviant stauts.
Limitations of snowball sampling
Individuals will be similar, not diverse. Although it taps into people involved in social networks, it misses people who are isolated from them.
What does snowball sampling allow researchers to accomplish? Why is this relevant?
Interactive sampling: sampling people who interact with each other. Social science theories stress the impact of associates on behavior, these additional influences researchers can study combining snowball sampling with a probibility sample.
Convenience/availability
Involves the researcher's taking whichever elements are readily available. Based on person's convenience
A Purposive sample is stronger than what kind of sample?
Is stronger than a non-probability sample.
What is crucial to determining the quality of the sampling fame?
The adequacy of the sampling frame and the degree to which the sampling frame includes all members of the population.
Non probability sampling: What are the 3 reasons that a researcher would want to use non probability sampling?
The goal of research is to see whether a relationship exists between independent and dependent variables, with no intent to generalize the results beyond the sample to a larger population ex. experimental research In qualitative research: to understand the social process and meaning structure of a particular setting or group. When it is impossible a develop a sampling frame of a population, such as hidden populations who try to hide themselves from detection because they engage in illegal activity. Rather than give up they use non-probability sampling.
Non probability samples
The investigator does no know the probability of each population element's inclusion in the sample. Have some important uses:
Purposive sampling (define)
The investigators use their judgement and prior knowledge to choose for the sample who best serve the purposes of the study. (assumption you know the pop)
Why does a stratified sample reduce sampling error for a given sample size to a level lower than that of an SRS of the same size?
The more homogeneous a population on the variables under study, the smaller the sample size needed to represent it accurately.
area/multistage sampling: other factors affecting sampling error
The size of the areas initially selected and their degree of homogeneity.
Probability samples are used in what type of research?
Used in some types of human service research, such as needs assessment and evaluation research.
When is disproportionate sampling used
Used when various subgroups in a population is relatively uncommon
What does disproportionate sampling mean?
We do not sample the strata proportionately to their presence in the population.
Snowball sampling
We start with a few cases of the type we want to study, and we let them lead us to more cases, which leads to more, so on. Like the rolling snowball the sample builds up as we continue to add cases.
How can systematic sampling produce a biased sample?
When the sampling frame consists of a population list that has a cyclical or recurring pattern, called periodicity.
How is Stratified Sampling different from simple random sampling and systematic sampling?
With SRS and systematic sampling methods, we treat the target population as a unitary whole when sampling from it whereas:
What are the Limitations of non probability samples: Three
Without the use of probability in the selection elements for the sample we can make no claim of representativeness. Which greatly limits the ability to to generalize findings beyond the level of the sample case The degree of sampling error remains unknown and unknowable. Researchers must use caution when using statistical tests of significance which indicate to the researcher whether relationships found in sample data are sufficiently strong to generalize to the whole population.
Systematic sampling producing a biased sample: example
ex. a sampling households in a large apartment building. Each floor has a corner apartment is a penthouse apartment with higher rent, if you began counting the interval which included the higher rent apartment then you would have a bias smaple in favor of the more expensive apartments.
examples of sample frames
lists of members in an organization (organizational list) Samples for large populations: telephone numbers: RDD random-digit dialing address based sampling: traditional listing method US Postal service computerized sequence file: list all addresses served by USPS List of customers from a local electric utility City directories, criss-cross directories, available in libraries
Limitations of Simple Random sampling
often impractical due to cost. There are alternatives which are more efficient in terms of providing a high degree of representativeness with a smaller sample. Limited to fairly small scale projects with populations modest in size.
limitations of area/multistage sampling, and how to overcome these limitations
selected blocks within an area often contain vastly different numbers of people from high density inner-city areas to the lower density suburbs. We must adjust the number of blocks and the number of households per block that are selected into the sample to take into account the differing population densities. Estimation of sampling error: The many stages of sampling involved make error estimation exceedingly complex due to error introduced at each stage of sampling.