OPRE 6301 Test 2 Questions and
Answers| Latest Update
methods of collecting data
direct observation
experiments
surveys- solicits information from people
why we take samples
To contact the whole population would be time consuming
The cost of studying all the items in a population may be prohibitive
The physical impossibility of checking all items in the population
The destructive nature of some tests
The sample results are adequate
*simple random sample
each item or person in the population has the same chance of being included
names of 25 employees being chosen out of a hat from a company of 250
employees
,*systematic random sampling
The items or individuals of the population are arranged in some order. A random
starting point is selected and then every kth member of the population is selected
for the sample.
A population consists of 845 employees of Nitra Industries. A sample of 52
employees is to be selected from that population.
First, k is calculated as the population size divided by the sample size. For Nitra
Industries, we would select every 16th (845/52) employee list. If k is not a whole
number, then round down. Random sampling is used in the selection of the first
name. Then, select every 16th name on the list thereafter
*stratified random sampling
A population is first divided into subgroups, called strata, and a sample is selected
from each stratum
Useful when a population can be clearly divided in groups based on some
characteristics
If we only have sufficient resources to sample 400 people total, we would draw
100 of them from the low income group...
*cluster sampling
A population is divided into clusters using naturally occurring geographic or other
boundaries. Then, clusters are randomly selected and a sample is collected by
randomly selecting from each cluster.
Subdivide the state into small units—either counties or regions, then take samples
of the residents in each of these regions and interview them.
,sample size
The number of subjects used in an experiment or study
the larger the better
*sampling error
differences between the sample and the population that exist only because of the
observations that happened to be selected for the sample
increasing sample size will reduce this error
*non-sampling error
more serious and are due to mistakes made in the acquisition of data or due to
the sample observations being selected improperly
Errors in data acquisition
Nonresponse errors
Selection bias
increasing sample size will not reduce this error
*errors in data acquisition
recording of incorrect responses, due to:
— incorrect measurements being taken because of faulty equipment,
, — mistakes made during transcription from primary sources,
— inaccurate recording of data due to misinterpretation of terms, or
— inaccurate responses to questions concerning sensitive issues
*nonresponse error
error (or bias) introduced when responses are not obtained from some members
of the sample, i.e. the sample observations that are collected may not be
representative of the target population
response rate
the proportion of all people selected who complete a survey
key survey parameter and helps in the understanding in the validity of the survey
and sources of nonresponse error
*selection bias
the sampling plan is such that some members of the target population cannot
possibly be selected for inclusion in the sample
*continuous random variable
one that can assume an uncountable number of values
cannot list the possible values because there is an infinite number of them
because there is an infinite number of values, the probability of each individual
value is virtually 0
Answers| Latest Update
methods of collecting data
direct observation
experiments
surveys- solicits information from people
why we take samples
To contact the whole population would be time consuming
The cost of studying all the items in a population may be prohibitive
The physical impossibility of checking all items in the population
The destructive nature of some tests
The sample results are adequate
*simple random sample
each item or person in the population has the same chance of being included
names of 25 employees being chosen out of a hat from a company of 250
employees
,*systematic random sampling
The items or individuals of the population are arranged in some order. A random
starting point is selected and then every kth member of the population is selected
for the sample.
A population consists of 845 employees of Nitra Industries. A sample of 52
employees is to be selected from that population.
First, k is calculated as the population size divided by the sample size. For Nitra
Industries, we would select every 16th (845/52) employee list. If k is not a whole
number, then round down. Random sampling is used in the selection of the first
name. Then, select every 16th name on the list thereafter
*stratified random sampling
A population is first divided into subgroups, called strata, and a sample is selected
from each stratum
Useful when a population can be clearly divided in groups based on some
characteristics
If we only have sufficient resources to sample 400 people total, we would draw
100 of them from the low income group...
*cluster sampling
A population is divided into clusters using naturally occurring geographic or other
boundaries. Then, clusters are randomly selected and a sample is collected by
randomly selecting from each cluster.
Subdivide the state into small units—either counties or regions, then take samples
of the residents in each of these regions and interview them.
,sample size
The number of subjects used in an experiment or study
the larger the better
*sampling error
differences between the sample and the population that exist only because of the
observations that happened to be selected for the sample
increasing sample size will reduce this error
*non-sampling error
more serious and are due to mistakes made in the acquisition of data or due to
the sample observations being selected improperly
Errors in data acquisition
Nonresponse errors
Selection bias
increasing sample size will not reduce this error
*errors in data acquisition
recording of incorrect responses, due to:
— incorrect measurements being taken because of faulty equipment,
, — mistakes made during transcription from primary sources,
— inaccurate recording of data due to misinterpretation of terms, or
— inaccurate responses to questions concerning sensitive issues
*nonresponse error
error (or bias) introduced when responses are not obtained from some members
of the sample, i.e. the sample observations that are collected may not be
representative of the target population
response rate
the proportion of all people selected who complete a survey
key survey parameter and helps in the understanding in the validity of the survey
and sources of nonresponse error
*selection bias
the sampling plan is such that some members of the target population cannot
possibly be selected for inclusion in the sample
*continuous random variable
one that can assume an uncountable number of values
cannot list the possible values because there is an infinite number of them
because there is an infinite number of values, the probability of each individual
value is virtually 0
