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The entire group of people or objects to which the researcher wishes to generalize the study findings
Meet set of criteria of interest to researcher
Examples
All institutionalized elderly with Alzheimer's
All people with AIDS
All low birth weight infants
All school-age children with asthma
All pregnant teens
Accessible population
the portion of the population to which the researcher has reasonable access; may be a subset of the target population
May be limited to region, state, city, county, or institution
Examples
All institutionalized elderly with Alzheimer's in St. Louis county nursing homes
All people with AIDS in the metropolitan St. Louis area
All low birth weight infants admitted to the neonatal ICUs in St. Louis city & county
All school-age children with asthma treated in pediatric asthma clinics in university-affiliated medical centers in the Midwest
All pregnant teens in the state of Missouri
Samples
Terminology used to describe samples and sampling methods
Sample = the selected elements (people or objects) chosen for participation in a study; people are referred to as subjects or participants
Sampling = the process of selecting a group of people, events, behaviors, or other elements with which to conduct a study
Sampling frame = a list of all the elements in the population from which the sample is drawn
Could be extremely large if population is national or international in nature
Frame is needed so that everyone in the population is identified so they will have an equal opportunity for selection as a subject (element)
Examples
A list of all institutionalized elderly with Alzheimer's in St. Louis county nursing homes affiliated with BJC
A list of all people with AIDS in the metropolitan St. Louis area who are members of the St. Louis Effort for AIDS
A list of all low birth weight infants admitted to the neonatal ICUs in St. Louis city & county in 1998
A list of all school-age children with asthma treated in pediatric asthma clinics in university-affiliated medical centers in the Midwest
A list of all pregnant teens in the Henderson school district
Randomization = each individual in the population has an equal opportunity to be selected for the sample
Representativeness = sample must be as much like the population in as many ways as possible
Sample reflects the characteristics of the population, so those sample findings can be generalized to the population
Most effective way to achieve representativeness is through randomization; random selection or random assignment
Parameter = a numerical value or measure of a characteristic of the population; remember P for parameter & population
Statistic = numerical value or measure of a characteristic of the sample; remember S for sample & statistic
Precision = the accuracy with which the population parameters have been estimated; remember that population parameters often are based on the sample statistics
Probability Sampling Methods
Also called random sampling
- Every element (member) of the population has a probability greater than) of being selected for the sample
- Everyone in the population has equal opportunity for selection as a subject
- Increases sample's representativeness of the population
- Decreases sampling error and sampling bias
Types of probability sampling - see table in course materials for details
Simple random
- Elements selected at random
- Assign each element a number
- Select elements for study by:
- Using a table of random numbers in book
A table displaying hundreds of digits from 0 to 9 set up in such a way that each number is equally likely to follow any other
See text for random sampling details & table of random numbers
Stratified random
Population is divided into subgroups, called strata, according to some variable or variables in importance to the study
Variables often used include: age, gender, ethnic origin, SES, diagnosis, geographic region, institution, or type of care
Two approaches to stratification - proportional & disproportional
Proportional
Subgroup sample sizes equal the proportions of the subgroup in the population
Example: A high school population has
15% seniors
25% juniors
25% sophomores
35% freshmen
With proportional sample the sample has the same proportions as the population
Disproportional
Subgroup sample sizes are not equal to the proportion of the subgroup in the population
Example
Class
Population
Sample
Seniors
15%
25%
Juniors
25%
25%
Sophomores
25%
25%
Freshmen
35%
25%
With disproportional sample the sample does not have the same proportions as the population
Cluster random sampling
A random sampling process that involves stages of sampling
The population is first listed by clusters or categories
Procedure
Randomly select 1 or more clusters and take all of their elements (single stage cluster sampling); e.g. Midwest region of the US
Or, in a second stage randomly select clusters from the first stage of clusters; eg 3 states within the Midwest region
In a third stage, randomly select elements from the second stage of clusters; e.g. 30 county health dept. nursing administrators from each state
Systematic
A random sampling process in which every kth (e.g. every 5th element) or member of the population is selected for the sample after a random start is determined
Example
Population (N) = 2000, sample size (n) = 50, k=N/n, so k = 2000 ) 50 = 40
Use a table of random numbers to determine the starting point for selecting every 40th subject
With list of the 2000 subjects in the sampling frame, go to the starting point, and select every 40th name on the list until the sample size is reached. Probably will have to return to the beginning of the list to complete the selection of the sample.