Research Subjects Flashcards
Population and Samples
- population: the larger group to which the results are generalized
- accessible population: potential participants within target population that is accessible to the investigators
- sample: subgroup of accessible population; serves as a reference group for drawing conclusions about the population
- target population: overall group to which the research to intends to generalize the study
Subjects
- subset of accessible population
- group of subjects is called a sample
- primary data collected from subjects in real-time
- secondary data is collected during routine business or a prior research activity
- not restricted to humans
Sample
- assume that the response of the sample members represents how the population would respond in similar circumstances
- heterogenous: presence of variations (physical, behavioral, psychological)
- a good sample reflects the variations of the population in same proportions they exist within the population
Sampling Bias
- when individuals selected for a sample over-represent or under-represent certain population attributes related to the phenomenon under study
- conscious or unconscious
- methods of sampling are needed to make unbiased sample selections
Samples and Validity of the Study
- a valid study is one that finds the truth
- 2 components
- external validity: the degree to which results of the study can generalize to individuals or settings outside the sample
- internal validity: refers to the adequacy of the study design, degree of control used when gathering the data
How is a study’s design evaluated?
- identified and recruited
- selected
- managed
Inclusion Criteria
-characteristics that individuals from target population must possess to be eligible for study
Exclusion Criteria
-characteristics that will make individuals ineligible for the study
Subject Selection
- recruitment will depend on definition of accessible population, cost, and requirements
- once potential subjects are identified a selection process must be identified
- common selection methods are categorized as probabilistic or nonprobabilistic
Probabilistic Sampling Methods
- method for randomly selecting subjects for participation in a study
- every individual in population has equal chance of being chosen
- every individual has equal chance of having some of the characteristics that are present throughout the population
- minimize sampling error as well as bias
- sample is considered representative of population
- types: simple random sample, systematic sampling, stratified random sampling, cluster sampling
Simple Random Sampling
- unbiased selection of sample
- draw names out of a hat
- table of random numbers
Systematic Sampling
- used when accessible population is not or cannot be numbered in a list
- divide total number of individuals in accessible population by number to be selected
- 1000 in accessible population and 100 to be selected so divide 1000 by 100 get 10 and select every 10th individual in accessible population
Stratified Sampling
- stratification is process of grouping individuals in population into groups based on characteristic
- sub-groups
- want to sample 100 so divide by male and female and sample 50 of each
Cluster Sampling
- method to get random sampling when population is large
- divide population into large subgroups or clusters
- then randomly select sample from each of the subgroups
Nonprobabilistic Sampling Methods
- used when it is difficult to obtain a true random sample
- subjects selected without randomization
- easier to implement, lower cost, greater sampling error and potential bias
- types: convenience sample, snowball sampling, purposive sampling
Convenience Sampling
- subjects are chosen on basis of availability
- consecutive sampling: recruiting all patients who meet inclusion and exclusion criteria as they become available
- volunteers
Purposive Sampling
-researcher hand picks subjects on basis of specific criteria to determine if they fit study
Snowball Sampling
- subjects are successively recruited by referrals from other subjects
- identify a few subjects who meet inclusion/exclusion criteria
- these subjects asked to identify others who have required characteristics
Subject Management Within the Study
- assignment processes allow subjects to be divided into groups of equal size and similar characteristics
- balanced groups are important to isolate the effect of the intervention rather than differences in group characteristics
- assignment may be random or non random
Random Assignment Methods: Assignment by an Individual
- pull number from hat
- may lead to unequal number in groups
- groups without similar characteristics
Random Assignment Methods: Block Assignment
- randomly assign subjects to one group until predetermined number is attained
- repeat for next group
Random Assignment Methods: Systematic Assignement
- use list of subjects
- repeatedly count off group numbers
Random Assignment Methods: Matched Assignment
- divide subjects into subgroups based on characteristics
- assign each member of subgroup to study group
Nonrandom Assignment
- subjects are often members of preexisting gropus of interest OR
- investigators determine which subject goes into which group
- commonly seen in retrospective studies
- group assignment determined by presence or absence of characteristic of interest
- statistical analysis tests are used to adjust for variation due to nonrandom assignment
Other SUbject Managment Issues
- standard protocols for processing subjects within a study: scripts for instructions provided to subjects, specific sequence in testing methods
- control of subjects normal daily routines: avoid changing daily routines
- masking or blinding subjects to group assignment: avoid changes in behavior due to expectation
- masking or blinding investigators to subject’s group assignment: avoid changes in investigator behavior toward group members
Sample Size
- number of subjects needed to insure statistical differences may be detected
- power is the probability that a statistical test will identify a relationship or difference
- insufficient sample size may lead to false negative findings or a type II error
- accurate power calculations provide the minimum number of subjects needed