Download Nonprobability Sample Designs

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foreign elements-listing is outdated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