Download 1.0 INTRODUCTION Architecture is primarily concerned with the

1.0
INTRODUCTION
Architecture is primarily concerned with the development of the physical environment
(built and natural) for optimum use by man. It is defined as the art and science building which is
concerned with individual houses, large composite building complexes and even a whole city.
Ultimately the art and science of organizing spaces for use by man.
Research is simply the process of arriving at dependable solutions to problems through
the planned and systematic collection, analysis, and interpretation of knowledge of his
environment, to accomplish his purpose and to resolve his conflicts.
Architectural research in Nigeria is geared towards improving the quality of architectural
products in the country and in achieving there is a need to focus on areas having such defects in
qualitative architecture within the country. The sampling methods are used for proper navigation
and focus on areas needing improvement.
Sampling is the process of selecting units (e.g., people, organizations) from a population
of interest so that by studying the sample we may fairly generalize our results back to the
population from which they were chosen.
There is no concept as fundamental to the conduct of research and the interpretation of its
results as in sampling. Except when a complete census is taken, research is almost invariably
conducted by means of a sample, o the basis of which generalizations applicable to the
population from which the sample was obtained are reached. (Osuala 1987).
In research work, sampling has become very important because of the extreme difficulty
involved in listing, observing and reaching every element in the population about which every
opinion is to be expressed. It is the process of selecting representative elements (samples) from a
given population. The process is such that it enables an investigator to choose elements which in
number and character sufficiently reflects the relevant features of the population from which they
are drawn. The purpose of this process is to provide a realistic basis upon which generalizations
about the population may be drawn from sample characteristics. (Olu Ojo 2003).
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1.1
BASIC TERMINOLOGIES IN SAMPLING
(a)
Population: This consists of all conceivable elements, subjects and observations that are
of primary interest to a researcher or a study. (Olu Ojo 2003)
(b)
Study Population: The aggregation of elements from which the sample is actually
selected. (Olu Ojo 2003)
(c)
Sample: This consists of selected elements or observations from a population for the
purpose of study with the hope that the sample is representing the population. (Olu Ojo
2003)
(d)
Subject / Element: The individual member of a population or sample. (Olu Ojo 2003)
(e)
Sampling Unit: The element or set f elements considered for selection from a sampling
population. (Olu Ojo 2003)
(f)
Sampling Frame: The actual list of sampling units from which the sample, or some
stage of the sample selected. (Olu Ojo 2003)
(g)
Observation Unit: This is an element of aggregation of elements from which
information is collected. (Olu Ojo 2003)
(h)
Variable: This is a set of mutually exclusive attributes ; sex, age, religion, race, etc. (Olu
Ojo 2003)
(i)
Parameter: The summary description of a given variable in a population. (Olu Ojo 2003)
(j)
Statistics: Summary description of a given variable in a sample. (Olu Ojo 2003).
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2.0
SAMPLING METHODS
Sampling methods involves a number of activities which enhances and increases the
effectiveness of the entire process. These activities include:
(i)
Problem Definition:
Sampling is undertaken with a view to choosing from a population a number of elements,
which reflects the relevant features of the population. It is thus only logical that the first
test in any sampling activity is to define the variables that are crucial to the kind of
information about the population. These variables reflect characteristics that will enable
the investigator to obtain data at all aspects of the desired information. (Olu Ojo 2003)
(ii)
Data Collection:
Having defined the problem, it is important to proceed to actual sample selection on the
basis of determined characteristics. Sample selection in this regards follow from whatever
information that is available about the population, research variables and the number of
elements to be drawn to represent the population. Then relevant observation is made of
the sample drawn based on the research variables. From the observations made, analysis
is made to obtain relevant information regarding trends and relationship and making
generalizations and conclusion there upon. It is also possible at this stage to ascertain true
analysis whether or not sample drawn and data gathered are sufficient, reliable and
relevant to the problem at hand.
(iii)
Generalization:
Since the ultimate aim of sampling is to provide reasonable basis for the drawing of
conclusion about a population from the samples collected from it, generalization becomes
a determining point in sampling undertaken. Although it does not form part of sampling,
it holds a critical implication in that it is the ultimate basis for the evaluation of the
representativeness, completeness and reliability of the sample.
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2.1
PRINCIPLES OF SAMPLING
Sampling is governed by two basic laws which ensures their representativeness. These
laws include:
(i)
Principle of Statistical Regularity:
This principle states that the more randomly a sample is drawn from a population, the
more the likelihood the sample reflects the characteristic of that population. In other
words, it cautions that for sample to truly reflect population characteristics, such must be
selected through a process that guarantees each element the probability of being included
in the sample.
(ii)
The Principle of Inertia of Large Number:
It holds that the larger the number of elements (samples) drawn to represent a population
the greater the likelihood of that sample reflecting the true features of that population.
2.2
TYPES OF SAMPLING METHODS
Sampling methods generally describe a particular means of drawing samples from a
given population, that is, techniques and procedures adopted to provide a basis for generalizing
about a population through a sample taken from it. Typically divided into probability an nonprobability techniques. (Olu Ojo 2003).
2.2.1 PROBABILITY SAMPLING METHOD
These are those designed to guarantee every element of a population an equal and
independent chance of being included in the sample drawn. These methods are generally referred
to as unbiased and objective considering them against the against the non-probabilistic methods.
That is the researcher has preference of selection. (Olu Ojo 2003). There are four basic types f
probability sampling methods and these include: simple random sampling, systematic sampling,
stratified sampling and cluster/ area sampling.
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(1)
SIMPLE RANDOM SAMPLING:
This is the basic method on which all other probability sampling methods are built and
they all involve it at some stage of the process. This requires that each element of the population
have an equal chance of being selected. A simple random sample is one selected not on the basis
of any criteria, or set of criteria but o the basis that the researcher closes his eyes and mind but
randomly picks elements that constitute the sample. It is a method in which samples are drawn
from a comprehensive list of population elements without any pre-specified order capable of
destroying the balance and opportunity of including every element in the population. This is
done through one of the following methods:
(a)
Balloting or open basket: This is to identify all the elements, give each element a name or
number, writing such name/number on a small piece of paper. Squeezing the paper,
picking is done one after the other until the required sample number is gotten.
(b)
By giving numbers or names to all the elements and writing the numbers or names on
cards shuffling them at intervals and each time the cards are shuffled picking the card on
top until the required number for the sample is gotten.
(c)
The use of table of random numbers or digits. A table of random numbers specially
designed for sampling purposes is found in most books on statistics. By this method, all
elements of the population should be numbered. Digits are selected from the table in any
systematic way and these elements whose numbers coincide with random digits are
included in the sample. This process is continued until the required sample number is
gotten.
Advantages of Simple Random Sampling
(i)
This procedure ensures that every sampling unit of the population has an equal chance of
being included in the sample.
(ii)
Makes easy in analyzing data and computing statistics.
Disadvantages of Simple Random Sampling
(i)
Requires the researcher to have knowledge of population and listing it.
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(ii)
Requires locating and indentifying each chosen element for study. This can consume
much of researcher’s time, money and other resources.
(2)
SYSTEMATIC SAMPLING
This is an extension of the simple random sampling method.
Systematic random
sampling involves the selection of every Nth item from serially listed population subject or units
after the first sampling unit is selected at random from the total of sampling units. Nth item refers
to elements of the population that are lying at each interval point in the list of elements.
Systematic sampling proceeds to choose the sample by first determining the sample
interval, that is, the standard distance between elements selected I the sample, which is obtained
by dividing the study population by the sampling size. (Olu Ojo 2003).
Advantages of Systematic Sampling
(i)
It is more convenient than simple random sampling because it makes sample very simple
to draw.
(ii)
It is also more amenable for use with very large populations or when large samples are to
be selected.
(iii)
If population list is ordered with a variable related t what is being studied, systematic
sampling has the effect of stratification on that variable.
(iv)
Another advantage is that in an alphabetized list of names, this approach will avoid
repeating sampling from the same family.
Disadvantage of Systematic Sampling
(i)
It may result to non-representativeness if the Nth is related to a periodic ordering in the
population listing.
(ii)
System sampling is not completely random once a random starting point has been
selected, all other elements are pre-determined.
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(iii)
Neglecting to take stratification effect into account in the statistics, systematic sampling
will yield estimates of accuracy that are too low.
(3)
STRATIFIED SAMPLING
This is an applied random sampling method. It is a modification of simple random and
systematic sampling techniques. It attempts to overcome the problems of these techniques by
categorizing elements of the population into strata on the basis of vital characteristics likely to
affect the sampling design. By this method, the researcher identifies the key characteristics
which will be of importance to the research. The population is then divided into groups along
these characteristics and each group is known as a stratum. Thereafter, it proceeds to select
samples for each stratum in proportion to sample’s share of the total population based on the
simple random or systematic criteria. This is known as proportionate stratified sample because
the sample size drawn from each sample is proportional to the population size of the stratum.
(Olu Ojo 2003).
Advantages of Stratified Sampling
(i)
It ensures representativeness of whatever characteristics is used to classify units into
strata.
(ii)
Facilitates analysis of strata and hence of subgroups of population; and eases comparison
of subgroups.
(iii)
It is practically convenient and considerably reduces considerably reduces the cost of
execution.
(iv)
The overall picture or the conclusion drawn for the whole population is likely to be more
accurate than if we had simply taken a sample without stratification.
Disadvantages of Stratified Sampling
(i)
It requires accurate information on proportion of population in each stratum, or else error
is increased.
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(ii)
If information for stratification is not available, it may make it costly to obtain and
prepare lists.
(iii)
Risks improper classification of individual in strata due to clerical error or poor
measurement.
(iv)
An extensive sampling frame is necessary and those strata levels of importance can only
be selected subjectively.
(4)
CLUSTER OR AREA SAMPLING
This is a sampling method which can be employed where no sampling frame exists, and
often for a population which is distributed over some geographical areas. I t employs principles
similar to those of stratification. However, rather than apply these principles on the basis of
feature of population elements, it applies them to the study region by dividing it into blocks and
clusters for selection purposes. From each cluster is selected sample to reflect key features of
cluster. This is therefore appropriate where research is conducted in a large territory across
which samples are to be selected. The technique involves selecting one or more geographical
areas and sampling all the members of the target population that can be identified. In other
words, cluster sampling is a sampling procedure in which the units of analysis in the population
are grouped into clusters and a sample of cluster serves as a basis for sampling rather than a
sample of individual.
Advantages of Cluster Sampling
(i)
It reduces travel if clusters are used for interviews.
(ii)
It reduces construction of sampling frames.
(iii)
It permits studies of individual clusters and comparison of clusters.
(iv)
It can use other persons for follow-up within a cluster if cluster was sampled, or can use
other clusters.
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Disadvantages of Cluster Sampling
(i)
It may not be efficient. The closeness of clusters may affect representativeness.
(ii)
It may result in larger errors in estimating population values than other probability
sampling methods.
(iii)
It requires that each member of population be assigned uniquely to cluster, otherwise it
may omit or duplicate cases.
2.2.2 NON-PROBABILITY SAMPLING METHODS
The distinguishing characteristics of non probability sampling techniques are the absence
of each element being included in the sample selected. The sampling technique therefore adopted
rests squarely on subjective criteria determined by particular purpose served or specific
circumstance in which the sample has to be selected. Although sometimes, all the elements end
up having equal opportunity. This may be the result of accident and not by design because the
population elements are not deliberately given equal chance of being selected. (Olu Ojo 2003).
Four of the non-probability sampling techniques commonly used shall be examined in
this write-up and they include; purposive or judgmental sampling, accidental or convenience
sampling, quota sampling and snowball sampling.
(1)
PURPOSIVE OR JUDGEMENTAL SAMPLING:
This is a non-probability sampling technique in which the selection of sample elements
from a given population is based entirely on the subjective choice of the researcher. The sample
selection is therefore the result of the researcher’s opinion as to which element best provide
desired basis and probability of good outcome. Experience, wisdom, research purpose and
solicited opinions are usually relied upon on sample selection by purposive technique. This
technique is also referred to as judgment or judgmental sampling. (Olu Ojo 2003).
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(2)
ACCIDENTAL OR COVENIENCE SAMPLING:
This is the type of sampling adopted whereby elements are drawn into sample on first
come first serve basis. This is usually employed where the population size is unknown and is
impossible or difficult to determine with accuracy. Consequently, elements are selected as they
are observed. In other words, sample elements are selected on the bass of ease of data collection,
data analysis or both. That is, rather research purpose or considered opinions regarding
representativeness, it is the ease of procedure and convenience of the researcher that forms the
criteria for selection. This type of sampling is most useful when a researcher simply wants to
have a smattering idea about a phenomena and not anything accurate. Accidental or convenience
sampling is very cheap and does not consume much time. However it is most time unrealistic
and reliable and lacks precision. There caution should be exercised in using accidental or
convenience sampling. (Olu Ojo 2003).
(3)
QUOTA SAMPLING:
Quota is to non-probability sampling method what stratified sampling is to probability
sampling method. All he procedures are similar for both sampling and stratified methods. The
only difference is that in quota sampling after the stratification is made, a non random method is
applied in selecting the elements of the sample. In quota sampling, population elements are
drawn into sample in specific sub-sections representing vital population characteristics or
features relevant to the problem at hand.(Olu Ojo 2003).. This is obtained in two stages:
(a)
A matrix of the population is drawn based on the specified characteristics or features to
be reflected in the sample selection.
(b)
From each of the matrices, a sample is drawn that is proportionate to the size of that
section relative to the population.
The application of the quota sampling gives, semblance of representativeness though it is
a non-probability sampling method. Quota sampling has many inherent problems.
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First, the quota frame (the proportion that different cells represent) must be accurate and
it is often difficult to get up-to-date information for this purpose.
Second, biases may occur in the selection of ample elements with a given cell-even
though its proportion in the population is accurately estimated.
(4)
SNOWBALL SAMPLING:
The method in snowball sampling is to reach is to reach other sample elements through
initial elements previously included in the sample. Snowball sampling is a method through which
the researcher develops an ever-increasing set of observations. The researcher asks one
participant in the event under study to recommend others for interviewing, and each of the
subsequently interviewed participants is asked for further recommendations. For instance, if you
wish to learn the patter of recruitment into a religious organization over time, you might begin y
interviewing fairly recent converts, asking them who introduced them into the group. You might
the interview the persons named, asking them who introduced them into the group. You might
then interview the persons named asking, in part, who introduced them and so forth. In this case
your sample would “snowball” as each of your interviewees suggests others. (Olu Ojo 2003).
2.3.3 WHEN TO USE NON-PROBABILITY SAMPLING TECHNIQUES
In general, probability sampling is more preferable to non-probability sampling because
the sampling error is smaller in probability sampling. In practice, there are researches or
sampling situations, where only non-probability sampling methods can be used especially, where
there are enough justification for it or where probability sampling method is not feasible.
Situations where non-probability sampling methods could be used include:
(a)
When it is impossible to reach some elements within the population or inexact and
unknown population where the population elements can only be imagined.
(b)
When there is a possibility that a probability sampling may not give a fair representation
of a population.
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(c)
Where generalization of results is not necessary or may not be intended. In such a case
the researcher may not be concerned whether or not the sample is a representative of the
population.
(d)
Cost and time are required to conduct the study using probability sampling are much
more than that required by non-probability sampling. Enough money and time may not be
available for the researcher.
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3.0
OTHER SAMPLING METHODS
In addition to the sampling methods discussed so far, there are other sampling methods
that can be useful in certain circumstances with any of the probability or non-probability
sampling methods aforementioned. These include: multistage sampling, sequential sampling,
double sampling, key informant sampling and representation sampling.
(i)
Multistage Sampling:
This is an extension of cluster sampling which involves selecting samples in two or more
stages. Multistage sampling method can be employed in a situation where the researcher
finds out that the population is so complex that he needs more than one sampling
technique to select his sample. He definitely has to result to sampling in stages. This
method requires the researcher to choose his sample in stages until he gets his required
sample. (Olu Ojo 2003).
(ii)
Sequential Sampling:
Here the sample size is not fixed in advance. Sequential sampling allow one to start with
a small sample and then continue sampling until a criterion of adequacy is met. Typically,
in sequential sampling, an initial sample is taken, and the data are analyzed to see if the
needed statistical precision has been obtained or whether a larger sample is needed. If the
latter, we obtain additional cases until the desired precision is reached. (Olu Ojo 2003).
(iii)
Double Sampling:
The use of double sampling can be used to obtain a more representative sample in
situations where sizeable non-respondent group exist. This increases the likelihood of
obtaining a more representative sample. Double sampling permits a check on reliability
of information obtained from the sample survey of a large sample, another sample can be
drawn from the same group for a more comprehensive investigation through sampling
intensity. (Olu Ojo 2003).
(iv)
Key Informant Sampling:
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Here key informants are selected from the total population as part of the sample. The
elements so selected must possess certain features or characteristics which others do not
have. For instance they must possess knowledge, skill and willingness to give out
information. (Olu Ojo 2003).
(v)
Representation Sampling:
This is a situation where each element in a sample represents an identified group. For
example in a cooperative society, a researcher an talk to the president, secretary, treasurer
and two other members of the cooperative as representatives of the group. (Olu Ojo
2003).
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4.0
CONCLUSION
In conclusion architectural research in Nigeria carried out by researchers employ one or
more of the above discussed sampling methods in other to focus properly on target population,
geographical location and other related architectural problems so that at the end research is done
more effectively, saving time and cost.
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REFERENCES
Ogunsote O.O (2009) Lecture note on research methods in architecture, F U T Akure, Ondo
state
Olu .O (2003) Fundamentals of research methods, Nelson Clammy press, Ibadan, Oyo state.
Osuala E.C (1987) Introduction to research methodology, Africana-Fep publishers, Uyo, Akwa
ibom state.
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