Sampling Design CHAPTER FOUR research method
•
4.
Sampling Design
CHAPTER FOUR
4.
Sampling Design
CHAPTER FOUR
•Sampling Design
1.Census and Sample Survey ( Opinion
poll, survey, poll ,)
2.Implications of a Sample Design
3.Steps in Sampling Design
4.Criteria of Selecting a Sampling
Procedure
5.Characteristics of a Good Sample
Design
6.Different Types of Sample Designs
7.How to Select a Random Sample?
8.Random Sample from an Infinite
Universe
9.Complex Random Sampling Designs
10.Conclusion
●
•WHAT
IS SAMPLE DESIGN
•WHAT
IS SAMPLE DESIGN : A sample design is the framework, or road map,
that serves as the basis for the selection of a survey sample and
affects many other important aspects of a survey as well. In the theory of
limited population sampling, a sampling design specifies
for every possible sample its probability of being drawn.
• The
selected respondents represent what is technically called a ‘sample’ and
the selection
process is called ‘sampling technique.’ The survey so conducted is known as
‘sample survey’. Algebraically, let the population size be N
and
if a part of size n (which is < N) of
this population is selected according to some rule for studying some
characteristic of the population, the group consisting of these n
units
is known as ‘sample’. Researcher must prepare a sample design for his study
i.e., he must plan how a sample should be selected and of what size such a
sample would be.
•
•Census
and Sample Survey
•What
is Census ? Census
is
official counting of regions, or Nation’s people and collections of economic,
social and other data, usually for formulation of development politics and
plans and demarcating constituencies of election or survey of entire population
as opposed t a sample survey.
•Census
includes the total process of collecting, compiling, analyzing, evaluating,
publishing and disseminating statistical data regarding the population and
housing and their geographical location. Population characteristics include
demographic, social and economic data and are provided as of a particular date
(reference period).
•A census is
the procedure of systematically acquiring and
recording information about the members of a given population. It is a
regularly occurring and official count of a particular population.
•The term
is used mostly in connection with national population and housing censuses;
other
common censuses include agriculture, business, and traffic censuses.
•
•Continue
- census
•Census
is an official
enumeration
or inventory or details or record or explanation of the
population, with details as to age, sex, occupation, in the
registration of citizens and their property, for purposes of taxation. an
official periodic count of a population including such information as sex, age,
occupation.
•Census Methods: Population
censuses typically use one of two approaches:
1. De facto –
meaning enumeration of individuals as of where they are found in the census,
regardless of where they normally reside.
2. De jure -
meaning enumeration of individuals as of where they usually reside, regardless
of where they are on census day.
•A sample
survey
•What
is sample survey ?
•A
sample survey is a study that obtains data from a subset of a population, in
order to estimate population attributes .
•Sample
survey is Cross-sectional study aimed at producing
summary statistics such
as averages, means, and percentages.
•A
sample survey is a process for collecting data on a sample of observations
which are selected from the population of interest using a probability-based
sample design.
•
In
sample surveys, certain methods are often used to improve the precision and
control the costs of survey data collection.
•These
methods
introduce a
complexity to the
analysis, which must be accounted for in order to produce unbiased estimates
and their associated levels of precision.
•Complex Sample Designs
•Statistical
methods for estimating population parameters and their associated variances are
based on assumptions about the characteristics and underlying distribution of
the observations.
•
•Continue
- A sample
survey
qsurvey
sampling describes
the process of selecting a sample of elements from a target population to
conduct a survey. The
term "survey
"may refer
to many different types or techniques of observation.
q In
survey sampling it most often involves a questionnaire used to measure the
characteristics and/or attitudes of people.
qDifferent
ways
of contacting members of a sample once they have been selected is the subject
of survey data collection.
q The
purpose of sampling is to reduce the cost and/or the amount of work
that it would take to survey the entire target population.
qA survey
that measures the entire target population is called a census.
qSurvey
samples can be broadly
divided
into two types:
1.probability
samples
:In a
probability sample (also called "scientific" or "random"
sample
2. non-probability
samples:
In non-probability
samples the relationship between the target population and the survey sample is
immeasurable and potential bias is unknowable
•Terms of definition( terminology)
qWhat
is population ? A population can be defined as including all people or items
with the characteristic one wishes to understand.
q
Because there is very rarely enough time or money to gather information from
everyone or everything in a population, the goal becomes finding a
representative sample (or subset) of that population.
vA
population are
all of the objects/items in the sample space.(defined groups of human beings or
other entities.)
vWhat
is sample ? A sample is
(s) subset of the population.
vWhat is sampling ? Sampling is
the process by which we select our subset of the population, i.e. sample.
•
•
•Terms
of definition( terminology) continue
vIdentify the population of interest.
A population is
the group of people that you want to make assumptions or hypothesis about.
vSpecify a sampling frame. A sampling
frame is
the group of people from which you will draw your sample.
v
Specify
a sampling method.
There are basically two ways to choose a sample from a sampling frame: randomly
or non-randomly.
There are benefits to both. Basically, if your sampling frame is approximately
the same demographic makeup as your population, you probably want to randomly
select your sample
v
Determine
the sample size.
In general, larger samples are better, but they also require more time and
effort to manage.
vImplement the plan.
Once you know your population, sampling frame, sampling method, and sample
size, you can use all that information to choose your sample
•
•Continue
– sampling
•What is sampling design : A
sample design is the framework, or road map, that provides as the basis for the
selection of a survey sample and affects many other important aspects of a
survey as well.
•Why sample?
qResources
(time, money) and workload
qGives
results with known accuracy that can be calculated mathematically
•Sample
frame :
•Why sample?
qResources
(time, money) and workload
qGives
results with known accuracy that can be calculated mathematically
•Define the target population
•The
target population is the collection of elements or objects that possess the
information sought by the researcher and about which inferences are to be made.
The target population should be defined in terms of elements, sampling units,
extent, and time.
1.Element: is
the object about which or form which the information is desired, ex,respondent.
2.Sampling Units: is
element or a unit containing the element that is available for selection at
some stage of sampling process.
3.Extent
refers to the geographical boundaries
4.Time is the time period under consideration
5.A sampling frame is
the group of people from which you will draw your sample
Implications of a Sample Design
qImplication
is sample design is a exact plan for
obtaining a sample from a given population. It refers to the technique or the
procedure the researcher would adopt in selecting items for the sample. Sample
design may as well lay down the number of items to be included in the sample
i.e., the size of the sample. Sample design is determined before data are
collected.
qImplication
of
sampling design is a detailed outline of which dimensions or scope will
be taken at what times, on which material, in what method,
and
by whom. Sampling plans should be designed in such a way that the resulting
data will contain a representative sample of the parameters of interest and
allow for all questions, as stated in the goals, to be answered.
qA
sample design is a definite plan for obtaining a sample from a given
population. It refers to the technique or the procedure the researcher would
adopt in selecting items for the sample.
qThe
researcher
must keep in mind the following points while preparing a sample design.
Universe, Sampling unit, Source list, Sample size, Parameters of interest,
budgetary constraint, sampling procedure.
•Boards
and online .
•The steps involved in implication of
sampling plan are:
•The steps involved in implication of
sampling plan are:
qidentify
the parameters or limitation to be
measured, the range of possible values, and the required resolution
qdesign
a sampling scheme that details how and when samples will be taken
qselect
sample sizes
qdesign
data storage formats
qassign
roles and responsibilities
qImplications
of Sample Design Size Requirements
qdiscovery
Data and Safety Monitoring
•Continue
1.Reliability and Sample Size Requirements
2. Multiple Endpoints and Sample Size
Requirements
•Sample
design may as well lay down the number of items to be included in the sample
i.e., the size of the sample.
•Sample
design is determined before data are collected. There are many sample designs
from which a researcher can choose.
•Some
designs are relatively more precise and easier to apply than others.
•Researcher
must select/prepare a sample design which should be reliable and appropriate
for his research study.
•Steps
for
Sample Design
•The researcher
must keep in mind the following points while preparing a sample design.
1.Universe
2.Sampling
unit
3.Source
list
4.Sample
size
5.Parameter
of interest
6.Budgetary
constraint
7.Sampling
procedure
--------------
•
•Steps for Sample Design – continue
1.Universe
;(
general population ) - While preparing a sample design, it is important
required to define the set of objects to be studied which can be finite
(limited) or infinite.
2.Sampling
unit - is
element or a unit containing the element that is available for selection at
some stage of sampling process
3.Source
list -
sampling frame all items of a universe (finite universe only).
4.Sample
size -
This is the number of items, selected from the universe, representing a sample
5.Parameters
of interest -
consider the question of the specific population limitations like to estimate
the proportion of persons with some specific attributes in the population or
other measure concerning the population.
6.Budgetary
constraint -
Cost considerations have a major impact upon the decisions concerning not only
the sample size but also the sample type.
7.Sampling
procedure -
decides the techniques to be used in selecting the items for the sample.
• further additional explanation see below
but this are precise and understanding steps of sample design.
•Steps
for Sample Design – additional explanation
1.Universe:
While preparing a sample design, it is leading required to define the set of
objects to be studied. Technically, it is also known as the Universe,
which
can be finite (limited) or infinite. In case of a finite universe, the number
of items is limited. Whereas, in an infinite universe the number of items is
limitless.
2.Sampling
unit: It is necessary to decide a sampling unit before selecting a sample. It
can be a geographical one (state, district, village, etc.), a construction unit
(house, flat, etc.), a social unit (family, club, school, etc.), or an
individual.
3.Source
list: In other words, it is called the ‘sampling frame’ from which the sample
is drawn. It comprises the names of all items of a universe (finite universe
only). If source list/sampling frame is unavailable, the researcher has to
prepare it by himself.
•
•Continue
-
Steps for Sample Design
4. Sample size: This is the number of items,
selected from the universe, constituting a sample. The sample size should not
be too large or too small, but optimum(best possible). In
other words, an optimum sample accomplishes the requirements of efficiency,
representativeness, reliability and flexibility.
5.
Parameters of
interest: While determining a sample design, it is required to consider the
question of the specific population limitations (limitation)of interest.
For example, we may like to estimate the proportion of persons with some
specific attributes in the population, or we may also like to know some average
or other measure concerning the population.
6.
Budgetary constraint:
Practically, cost considerations have a major impact upon the decisions
concerning not only the sample size but also the sample type. In fact, this can
even lead to the use of a non-probability sample
7.
Sampling procedure:
The researcher, at last, decides the techniques to be used in selecting the
items for the sample. In fact, this technique/procedure stands for the sample
design itself. Apparently, such a design should be selected, which for a
provided sample size and cost, has a smaller sampling error.
•
•The Definition of Sample Size
•The Definition of Sample Size :
•What is sample size? Sample size is the
sub-population to be studied in order to make an inference or conclusion to a
reference population (A broader population to which the findings from a study
are to be generalized.
•In census, the sample size is equal to
the population size. However, in research, because of time constraint and
budget, a representative sample is normally used.
•The larger the sample size the more
accurate the findings from a study
•Availability of resources sets the upper
limit of the sample size
• While the required accuracy sets
the lower limit of sample size
•Therefore, an optimum or best sample size is an essential component
of any research
•Sample
size
•What
is your population of interest?
•To whom do you want to generalize
your results?
–All doctors
–School children
–Minority
–Women aged 15-45 years
–Other
•Can
you sample the entire population?
•
•Sample
size
•Sampling
•CRITERIA
OF SELECTING A SAMPLING PROCEDURE
•Sampling
criteria is the list of characteristics of the elements that we have determined
beforehand that are essential for eligibility to form part of the sample.
•We
determine what these criteria (or essential characteristics) are as a result of
the research problem - or the purpose of the research.
•In
other words, we look at what we are studying for our research project, and then
we decide what essential characteristics the elements (often people) would need
in order for us to be able to look at the problem.
•For
example, these criteria could include (and we are only thinking in terms of
human subjects here):
•Age
(elderly, children, middle-aged, neonates, etc.);
•Gender
(male/female);
•Marital
status;
•Ethnic
status;
•Type
of disease that they may have;
•Type
of treatment that they are undergoing;
•Ability
to understand English/Urdu/German, etc.;
•Ability
to write; and so on.
•
•CRITERIA
OF SELECTING A SAMPLING PROCEDURE
•In
this context one must remember that two costs are involved in a sampling
analysis :
1.The cost of collecting the data
2. The cost of an incorrect implication
resulting from the data.
•
Researcher must keep in view the two causes of incorrect inferences
(suggestion)viz., systematic bias and sampling error.
•Systematic
bias results from errors in the sampling procedures, and it cannot be
reduced or eliminated by increasing the sample size.
•
At best the causes responsible for these errors can be detected and corrected.
•Usually
a systematic bias is the result of one or more of the following factors:
•
•
•Continue
- factors of systematic bias
Factor
systematic bias in criteria of selecting a sampling procedure
1.Inappropriate sampling frame
2.Defective measuring device
3.Non-respondents
4.Indeterminacy principle ( the quality
standard )
5.Natural bias in the reporting of data
---------------------------
•Inappropriate sampling frame: In
case the sampling frame is inappropriate (a biased representation of the
universe), it results in a systematic bias.
•Defective measuring device: When
the measuring device shows constant error, it results in systematic bias. In a
survey, if the questionnaire or the interviewer is biased, it results in
systematic bias. Similarly, if the physical measuring device is defective, it
shows systematic bias in the data collected through such a measuring device.
•
•Continue
•
•Non-respondents: If
all the individuals included in the sample are not involved, it might cause
systematic bias. This is because, in such a situation the possibility of
establishing contact from an individual is often correlated or show relationship with what is to be
estimated.
•Indeterminacy principle: (means the
quality or state of being indeterminate) Individuals act differently when kept
under observation compared to non-observed situations. For instance, if workers
are aware that they are being watched during a work study (which will determine
their average length of time to complete a task and quota or share for piece work), they generally be
likely to work quite slowly. Thus, the indeterminacy principle may also be the
cause of systematic bias.
•Natural bias in data reporting: Natural
bias of respondents often causes systematic bias in many inquiries. We can find
a downward bias in the income data collected by government, whereas we find an
upward bias in the income data collected by some social organization. People
tend to understate their income if asked about it for tax purposes. But, they
overstate the same when it is a question of their social status.
•
•CHARACTERISTICS
OF A GOOD SAMPLE DESIGN
•characteristics
of a good sample design is a
definite or exact plan for obtaining a
sample from a population. It refers to the technique or the procedure for
obtaining a sample from a given population. The
list down the characteristics of a good sample design as under:
(a) Sample design must result in a truly
representative sample.
(b) Sample design must be such which
results in a small sampling error.
(c) Sample design must be viable or visible in the context of funds available
for the research study.
(d) Sample design must be such so that
systematic bias can be controlled in a better way.
(e) Sample should be such that the
results of the sample study can be applied, in general, for the universe with a
reasonable level of confidence.
•
•DIFFERENT
TYPES OF SAMPLE DESIGNS
The sampling Process
The sampling Process
•The
sampling process comprises several stages:
–Defining
the population of concern
–Determining
the sample size
–Implementing
the sampling plan
–Sampling
and data collecting
–Reviewing
the sampling process
•
•DIFFERENT
TYPES OF SAMPLE DESIGNS
There is different type of sample design
based on two factors:
•Sampling
designing are basically two types: or sampling method
•Non
probability sampling
•Probability
sampling
•Probability
samples: In a probability sample (also called "scientific" or
"random" sample.
•A probability
sampling
scheme is one in which every unit in the population has a chance (greater than
zero) of being selected in the sample, and this probability can be accurately
determined.
•When
every element in the population does have the same probability of selection,
this is known as an 'equal probability of selection' (EPS) design. Such designs
are also referred to as 'self-weighting' because all sampled units are given
the same weight, Probability sampling includes:
•Simple
Random Sampling,
•Systematic
Sampling,
•Stratified
Random Sampling,
•Cluster
Sampling
•Multistage
Sampling.
•Multiphase
sampling
•Continue
Non-probability
samples: In non-probability samples the relationship between the target
population and
the survey
sample is
immeasurable and potential bias is unknowable. Non probability design if the
characteristics of non response are not well understood, since non response
effectively modifies each element's probability of being sampled.
•Non
probability is Any sampling method where
some elements of population have no chance of selection (these are sometimes
referred to as 'out of coverage'/'under covered'), or where the probability of
selection can't be accurately determined.
• It
involves the selection of elements based on assumptions regarding the
population of interest, which forms the criteria for selection. Hence, because
the selection of elements is nonrandom, no probability sampling not allows the
estimation of sampling errors. No probability Sampling includes:
•Accidental
Sampling,
•Quota
Sampling and
•Purposive
Sampling.
•In
addition, no response effects may turn any probability design into a no
probability design if the characteristics of no response are not well
understood, since no response effectively modifies each element's probability
of being sampled.
•Sampling Methods/ Designs
•COMPLEX
RANDOM SAMPLING DESIGNS
•Systematic
random sampling
•Systematic
random sampling
•Stratified
random sampling
•Cluster
random sampling
•Multistage
random sampling
•Area random sampling
•Convenience
random sampling
•Restricted
random sampling
•unrestricted
random sampling;
•purposive
random sampling;
•Sequential random sampling
•
•SIMPLE
RANDOM SAMPLING
•SIMPLE
RANDOM SAMPLING is Applicable when population is small, homogeneous &
readily available
•All
subsets of the frame are given an equal probability. Each element of the frame
thus has an equal probability of selection.
•It
provides for greatest number of possible samples. This is done by assigning a
number to each unit in the sampling frame.
•A
table of random number or lottery system is used to determine which units are
to be selected. Estimates are easy to calculate.
•Simple
random sampling is always an EPS design, but not all EPS designs are simple
random sampling.
•Disadvantages
•If
sampling frame large, this method impracticable.
•Minority
subgroups of interest in population may not be present in sample in sufficient
numbers for study.
•SYSTEMATIC
SAMPLING
•Systematic sampling relies
on arranging the target population according to some ordering scheme and then
selecting elements at regular intervals through that ordered list.
•Systematic
sampling involves a random start and then proceeds with the selection of every
k th
element from then onwards. In this case, k=(population size/sample size).
•It
is important that the starting point is not automatically the first in the
list, but is instead randomly chosen from within the first to the k th
element in the list.
•A
simple example would be to select every 10th name from the telephone directory
(an 'every 10th' sample, also referred to as 'sampling with a skip of 10').
•ADVANTAGES:
•Sample
easy to select
•Suitable
sampling frame can be identified easily
•Sample
evenly spread over entire reference population
•DISADVANTAGES:
•Sample
may be biased if hidden periodicity in population coincides with that of
selection.
•Difficult
to assess precision of estimate from one survey.
•STRATIFIED
SAMPLING
•STRATIFIED
SAMPLING Where
population squeezes a number of different categories, the frame can be
organized into separate "strata.“ or section Each division is then sampled as an
independent sub-population, out of which individual elements can be randomly
selected.
•Every
unit in a stratum has same chance of being selected.
•Using
same sampling fraction for all strata ensures proportionate representation in
the sample.
•Adequate
representation of minority subgroups of interest can be ensured by
stratification & varying sampling fraction between strata as required.
• Finally,
since each stratum is treated as an independent population, different sampling
approaches can be applied to different strata.
•
•
•CLUSTER
SAMPLING
•
First stage a sample of areas is chosen;
•
Second stage a sample of respondents within
those areas is selected.
•
Population divided into clusters of homogeneous units, usually based on
geographical contiguity.
•Sampling
units are groups rather than individuals.
•A
sample of such clusters is then selected.
•All
units from the selected clusters are studied.
•Advantages
:
•Cuts
down on the cost of preparing a sampling frame.
•This
can reduce travel and other administrative costs.
•Disadvantages:
sampling error is higher for a simple random sample of same size.
•Often
used to evaluate vaccination coverage in EPI
•
•MULTISTAGE
SAMPLING
•Multistage
sampling used frequently when a complete list of all members of the population
not exists and is inappropriate.
•Moreover,
by avoiding the use of all sample units in all selected clusters, multistage
sampling avoids the large, and perhaps unnecessary, costs associated with
traditional cluster sampling.
•This
technique, is essentially the process of taking random samples of preceding
random samples.
•Not
as effective as true random sampling, but probably solves more of the problems
inherent to random sampling.
•
An effective strategy because it banks on multiple randomizations. As such,
extremely useful.
• Part
of the information collected from whole sample & part from subsample.
•In
Tb survey in all cases – Phase I
•X
–Ray chest +ve
cases – Phase II
•Sputum
examination in X – Ray +ve
cases - Phase III
•Survey
by such procedure is less costly, less laborious & more purposeful
•
•QUOTA
SAMPLING
•The
population is first segmented into mutually exclusive sub-groups, just as in
stratified sampling.
•Then
judgment used to select subjects or
units from each segment based on a specified proportion.
•For
example, an interviewer may be told to sample 200 females and 300 males between
the age of 45 and 60.
•It
is this second step which makes the technique one of non-probability sampling.
•For
example interviewers might be tempted to interview those who look most helpful.
The problem is that these samples may be biased
because not everyone gets a chance of selection. This random element is its
greatest weakness and quota versus probability has been a matter of controversy
for many years
•
•Area
sampling:
•Area sampling: If clusters happen to be
some geographic subdivisions, in that case cluster
•sampling
is better known as area sampling. In other words, cluster designs, where the
primary sampling unit represents a cluster of units based on geographic area,
are distinguished as area sampling.
•The
plus and minus points of cluster sampling are also applicable to area sampling.
•CONVENIENCE
SAMPLING
•Sometimes
known as grab or opportunity
sampling or accidental or haphazard sampling.
•A
type of non probability sampling which involves the sample being drawn from
that part of the population which is close to hand. That is, readily available
and convenient.
•The
researcher using such a sample cannot scientifically make generalizations about
the total population from this sample because it would not be representative
enough.
•
For example, if the interviewer was to conduct a survey at a shopping center
early in the morning on a given day, the people that he/she could interview
would be limited to those given there at that given time, which would not
represent the views of other members of society in such an area, if the survey
was to be conducted at different times of day and several times per week.
•This
type of sampling is most useful for pilot testing.
•In
social science research, snowball sampling is a
similar technique, where existing study subjects are used to recruit more
subjects into the sample.
•
•
CHART SHOWING BASIC SAMPLING DESIGNS
CHART SHOWING BASIC SAMPLING DESIGNS
•How to select a random sample :
•How
to select a random sample : this is a process Random sample technique where we
select a group of subjects (a sample) for study from a larger group (a
population).
•Each
individual is chosen entirely by chance and each member of the population has
an equal chance of being included in the sample.
•Each
unit in the sampling frame has an equal chance of being selected
•It
does not require additional information on the frame (such as geographical
area) example
•Lottery
–sample drawn from the box
•Table
of random numbers
•Computer
generated random numbers
•---
•Fortunately,
we can take a random sample in a relatively easier way without taking the
trouble of enlisting all possible samples on paper-slips as explained above.
Instead of this, we can write the name of each element of a finite population
on a slip of paper, put the slips of paper so prepared into a box or a bag and
mix them thoroughly and then draw (without looking) the required number of
slips for the sample one after the other without replacement.
•How to select a random sample :
•In
doing so we must make sure that in successive drawings each of the remaining
elements of the population has the same chance of being selected.
•This
procedure will also result in the same probability for each possible sample.
•
We can verify this by taking the above example.
•
Since we have a finite population of 6 elements and we want to select a sample
of size 3, the probability of drawing any one element for our sample in the
first draw is 3/6, the probability of drawing one more element in the second
draw is 2/5, (the first element drawn is not replaced) and similarly the
probability of drawing one more element in the third draw is 1/4. Since these
draws are independent, the joint probability of the three elements which
constitute our sample is the product of their individual probabilities and this
works out to 3/6 × 2/5 × 1/4 = 1/20.
•
BASIC TERMS
BASIC TERMS
•N = the number of cases in the sample
space (frame)
•n = the number of cases in the sample
event
•f = n/N = the sampling fraction
(probability)
Simple Random sampling
qA
basic probability sampling design.
q
qEvery
member/element of the population has an equal and independent chance of being
selected.
q
qIt
requires a listing of the total research population.
q
qRandomness
can be accomplished by either lottery
•HOW
TO SELECT A RANDOM SAMPLE ?
•How
to select a random sample.
•Objective: To select n
units out of N
considering that each has an equal chance of being selected.
An example.
Suppose you would want to chose 30
schools out of 100 schools in Mogadishu city.
•Continue ---HOW TO SELECT A RANDOM SAMPLE ?
For
instance
•Number all the 100 units in the
population
•Place corresponding numbers on slips of
paper
•Slips are put in a container or box and
mixed thoroughly.
•Draw a slip and record the number on the
sheet
•The process can be repeated till the
required sample is obtained.
Random
number generator/computer, tables can also be used.
•
•
•
chapter Questions
1. What do you mean by ‘Sample
Design’? What points should be taken
into consideration by a researcher in developing a sample design for this
research project.
2.How would you differentiate between
simple random sampling and complex random sampling designs?
3.Why probability sampling is
generally preferred in comparison to non-probability sampling? Explain the procedure
of selecting a simple random sample.
4. Under what circumstances
stratified random sampling design is considered appropriate? How would you select
such sample?
•chapter Questions
•5. Distinguish between:
•(a)
Restricted and unrestricted sampling;
•(b)
Convenience and purposive sampling;
•(c)
Systematic and stratified sampling;
•(d)
Cluster and area sampling.
•6. Under what circumstances would you
recommend:
•(a)
A probability sample?
•(b)
A non-probability sample?
•(c)
A stratified sample?
•(d)
A cluster sample?
•7. Explain the procedure of selecting a
random sample.
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