Nonprobability Sample Designs 1. Convenience samples 2. purposive or judgmental samples 3. snowball samples 4.quota samples
Unrepresentative sample Some characteristics are overrepresented or underrepresented
Typical Problems in sampling frames 1. Incomplete frames-units are missing from list 2. cluster of elements-listed in clusters rather than individually-city blocks 3. blank foreign elements-listing is outdated
True Experiments must have at least 3 things 1. An experimental and control group 2. variation in the independent variable before assessment of change in the dependent variable(treatment) 3. random assignment to two groups
The Classic Experimental Design Experimental group, control group randomization pretest posttest
Target population A set of elements larger than or different from the population sampled and to which the researcher would like to generalize study findings.
Systematic sampling Select every kth element in a population,where k is determined by dividing the population sixe by the desired sample size. Select a random number between 0 and k and picking that element in the population,systematically pick every kth element
Survey Sampling Sampling designed to produce information about particular characteristics of a finite population.
Stratified samples Done by dividing the population into groups(strata) that are homogeneous on one or more traits,then sampling from each of these groups
Stratified Proportionate sample The number of elements selected from each stratum is proportional to that stratum s representation in the population The same number of sampling units from each stratum or a uniform sampling fraction (n/n)
Stratified Disproportionate sample Chosen to yield numbers in a stratum to allow intensive analysis of that particular stratum Variable sampling fractions,total number in each stratum is different,population parameters have to be weighted by the number of each stratum
Standard error Allows the researcher to determine the probability that a given sample estimate is close to the actual population value. S.E.=standard error,the distribution of all samples about the mean of the samples is S.E.Calculate standard deviation and estimate the S. E.
Simple random sampling Numbering all population elements,then selecting enough random numbers to complete a sample of the desired size.it is simple but inconvenient with large populations
Scale Type of composite measure composed of several items that have logical or empirical structure among them Take account of differing intensity of indicators e.g. Likert scale, Guttman scale
Sampling Theory Major objective is to provide accurate estimates of unknown parameters in population from sample statistics Population=parameter sample=statistic
Sampling Frame A list of all elements or other units containing the elements in a population
Sampling Error -contd The larger the sampling error,the less representative the sample.
Sampling Error Any difference between the characteristics of a sample and the characteristics of a population
Sampling distribution When an infinite number of independently selected sample values such as the means are placed in a distribution,the distribution is called the sampling distribution Its standard deviation is the standard error
Sample generalizability Refers to the ability to generalize from a sample,or subset of a larger population to that population itself.
Sample A subset of a population that is used to study the population as a whole. Subset=sample
Representative sample A sample that looks like the population from which it was selected in all respects that are potentially relevant to the study.
Random selection procedures Ensure that every sampling unit of the population has an equal and known probability of being included in the sample,the probability is n/n n=sample, N=population
Random Selection Each element has an equal chance of selection independent of any other event in the selection process
Quota sample Select respondents such that quotas of various types of people are filled in proportion to their prevalence in the population
Quasi-experimental design Subjects are not randomly assigned to to the experimental and control or comparison group
Purposive or judgmental sample Select a sample that, in their subjective judgment,is representative of the population
Procedures of Control 1. Randomization or random assignment-removes bias from the assignment process by relying on chance-flipping coin or random number table assures that case has an equal probability of being assigned to either group 2. matching- or pairwise matching,for each case in experimental group, another one with identical characteristics is selected for the control group
Probability vs. Nonprobability Sampling Probability sample allows estimates to population from sample Nonprobability sample-list of sample population is unavailable-e;g, illegal residents, drug addicts
Probability Sample Designs 1. random sample 2. systematic samples 3. stratified samplesproportionate, disproportionate 4. cluster samples 5. multistage samples
pretests Measures the dependent variables prior to the experimental intervention,they provide a direct measure of how much the experimental and comparison groups changed over time,tests effects of intervention
PPS-probability proportionate to size Type of multistage cluster sample in which clusters are selected,not with equal probabilities(epsem) but with probabilities proportionate to their sizes
Population-finite or infinite Finite population-contains a countable number of sampling units Infinite population-consists of an endless number of sampling units,an unlimited number of coin tosses
Population The entire set of individuals or other entities to which study findings are to be generalized Whole=population
Weighting Assigning different weights to cases that were selected into a sample with different probabilities of selection.,each case given weight equal to the inverse of its probability of selection