Each member of the population has an equally likely chance of being selected

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:
  1. 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

  • Computer generated random numbers table
  • Draw numbers for box (hat)
  • Bingo #=s
  • 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.

    When each member of a population has an equally likely chance of being selected as sample unit this is called?

    4b. Simple random sampling from a population means that each member of a population has an equal chance of being randomly selected.

    When each member of population has an equal?

    1. Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected.

    When everyone gets an equal chance of being selected it is called as?

    If in a sampling process, each individual has an equal chance of being selected from a population, the sampling is called random sampling.

    In which selection all items in the population have an equal chance of getting selected?

    The random sampling method is the sampling method, in which each item in the population has an equal chance of being selected in the sample.