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MC Research part 2

MC Collins from midterm to final

target population population for which study outcomes are aimed & which generalizations would like to be made
accessible population the portion of the target population which the researchers has reasonable access
sample selected group of people or elements from which data are collected for a study
what is taken from accessible population sample
sampling selecting a group of people, events, behaviors, or other elements with which to conduct a study
element an individual unit of a population
examples of elements person, event, behaviors
sampling criteria characteristics essential for inclusion in the target population
representativeness sample, the accessible population, and the target population are alike in as many ways as possible
need to evaluate what for representativeness setting, characteristics of the subjects, distribution of values on variables measured in the study
simple random sampling -most basic type -every element has an equal & independent chance of being chosen -sample selected from sampling frame through completely random measures -not subject to bias
simple random sampling - not subject to bias - no guarantee will be representative -ensure differences in the attributes - laborious & inefficient - rarely possible to get complete list
stratified random sampling -population 1st divided into 2 or more strata or subgroups -aim to enhance represetnativeness
cluster sampling multi-stage sampling
cluster sampling - successive random sampling of units ie: - all nursing students - random sample of schools - random sample of students from schools - states, cities, districts, blocks, households
systematic sampling selection of every Kth element from a list or group - determine desired sample size (n) - determine size of population (N) - divide N/n = sampling interval - 1st element selected randomly - essential identical to simple - list arranged randomly no g
k =1000/100 = 10 sampling interval
random sampling equal, independent chance of being selected to participate in the study
random assignment random allocation of subjects to a control or experimental group
probability sampling types - simple random - stratified random - cluster - systematic
non-probability sampling nonrandom
non-probability sampling types - convienience - purposive - network
non-probability sampling - less likely to produce accurate & representative samples - most research samples are this type
convenience sampling - use of conveniently available people or objects meeting study criteria - available subjects maybe atypical of population -stop people on street, run advertisements
purposive sampling -theoretical or judgmental sampling -researcher hand picks subjects -qualitative research -instrument testing -needs assessments
network sampling -early sample members identify & refer others who meet study criteria -specific traits difficult to identify -expedient but has bias
factors influencing sample size effect size, # of variables, homogeneity of the population, attrition, sub-group analysis, measurement sensitivity
effect size an estimate of the magnitude of the relationships between the research variables
which need larger samples sizes to detect effect modest relationships
power analysis review notes about probability of null rejected, level of significance ect.
number of variables the greater number the larger sample size
homogeneity of the population -a small sample may be adequate -greater sampling error when heterogenous -biophysical processes with small amounts of variation may use smaller samples -nursing studies: safer to assume hetero unless prior research or experience contrary
attrition -factor in which determing # of subjects needed -issue for all studies longitudinal -large time periods between data collection -mobile or hard-to-locate population -high risk, vvulnerable, or diable
sub-group analysis -testing hypotheses for specific sub-group of the sample
measurement sensitivity when measuring tool is impercise & susceptible to errors, larger sample sizes are needed to test adequately
sampling error definition differences between population values & sample values
sampling error -impossible to have a perfectly representative sample & 1 that contains no sampling error -probability sampling permits estimates of degree of expected error
random variation expected difference in values that occur when different subjects form the sample sample are examined. difference is random because some values will be higher & others lower than the average population values.
systematic variation (bias) consequence of selecting subjects whose measurement values differ in some specific way from those of the population. these values do not vary randomly around the population mean
external validity extent to which study results can be generalized from the study sample to other subjects, populations, settings, etc.
implementing sampling plan 1. identify target population 2. identify accessible population 3. specify eligibility criteria 4. specify sampling plan 5. recruit the sample
recruiting the sample identify eligible candidates & persuade them to participate.
things to keep in mind during recruiting the sample - face to face vs letter or phone call - manners - recruiter/sample characteristics - persistence - incentives
make sure recruits know -explanation of research benefits -offer summary of results -make participation convenient -study endorsement -assurance of research integrity -document for reasons for non-participation
Created by: midnight1854