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TERM PAPER
ON
SAMPLING METHODS IN ARCHITECTURAL
RESEARCH IN NIGERIA
COURSE:
CODE:
CLASS:
RESEARCH METHODOLOGY
ARC 805
MTECH 1, 2007/2008
LECTURER:
PROF. OLU OLA OGUNSOTE
SUBMITTED TO THE DEPTARMENT OR ARCHITECTURE
SCHOOL OF ENVIRONMENTAL TECHNOLOGY, FEDERAL
UNIVERSITY OF TECH, AKURE
BY: ADERIBIGBE OLADUNNI .O
ARC/01/0418
APRIL 2008
1
Abstract
Research is a crucial tool that architects must master in order to effectively
address the technical, aesthetic, and behavioral issues that arise in their
work. This paper covers the use of sampling methods in research
methodology. It is specifically targeted to help professional designers and
researchers better conduct and understand research. The basic research
issues and concepts, relating to methods of sampling in research will be
explained in details. , The paper will discuss briefly the seven types of
research, including historical, qualitative, correlational, experimental,
simulation and modeling, logical argumentation, and case study but
emphasis will be on sampling methods. Architectural Research Methods is
an essential reference for architecture students and researchers as well as
architects, interior designers, landscape architects, and building product
manufacturers.
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Introduction
The aim of this paper is to provide an introductory guide into the use of
sampling methods in architectural research. This paper will enumerate types
of research and will focus mainly on sampling methods.
The word research derives from the French recherche, from rechercher, to
search closely where "chercher" means "to search" (see French language); its
literal meaning is 'to investigate thoroughly'
Research is a human activity based on intellectual investigation and aimed at
discovering, interpreting, and revising human knowledge on different
aspects of the world. Research can use the scientific method, but need not do
so Research methods
The goal of the research process is to produce new knowledge, which takes
three main forms:

Exploratory research, which structures and identifies new problems

Constructive research, which develops solutions to a problem

Empirical research, which tests the feasibility of a solution using
empirical evidence
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Research can also fall into two distinct types:
1. Scientific research relies on the application of the scientific method, a
harnessing of curiosity. This research provides scientific information
and theories for the explanation of the nature and the properties of
humans. It makes practical applications possible. Scientific research is
funded by public authorities, by charitable organisations and by
private groups, including many companies. Scientific research can be
subdivided into different classifications.
2. Historical research is embodied in the historical method.
3. Basic research has as its primary objective the advancement of
knowledge and the theoretical understanding of the relations among.
Types of Architectural research.
 Qualilative
 Quantitative
Qualitative technique: refer to the methods of collecting data in qualitative
research. Qualitative research makes use of data that comes in form of
words,pictures and sounds. Qualitative research are usually
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unstructured,where respondent/concern populations are free to express their
thought freely. Qualitative data are usually categories into two types: namely
interative and non-interative methods
Fig 1 Qualitative Research
Techniques
Interviews
Interactive
Un-Structured Interviews
Non-Interative
In-depth interviews
Focus groups
observation
Participant Observation
Non-participant
observation
Arcival documents
Artifacts and Buildings
Written documents
Drawings/Photographs
Arctifacts
Quanlitative techniques involves variable whose value are numerical.
Quantitative research use structured research. The research results are
detailed as behaviour, attitudes and motivation, the resluts are based on
larger sample sizes that are representative of a selected population..
Fig 2 Quantitative technique
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Research Phase
Structured
Larger Sample
Survey technique
Sampling methods
Sampling is a part of statistical practice concerned with the selection of
individual observations intended to yield some knowledge about a
population of concern, especially for the purposes of statistical inference.
Each observation measures one or more properties (weight, location, etc.) of
an observable entity enumerated to distinguish objects or individuals.
Results from probability theory and statistical theory are employed to guide
practice.
The sampling process consists of seven simple stages:

Defining the population
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
Specifying a sampling frame (a set of items or events possible to
measure )

Specifying a sampling method (selecting items or events from the
frame )

Determining the sample size

Implementing the sampling plan

Sampling and data collecting

Reviewing the sampling process
Population definition
Successful statistical practice is based on focused problem definition.
Focusing can be on selected population, sometimes the population of
concern can be difficult to specify. However, in all cases, time spent in
making the population of concern precise is often well spent, often because
it raises many issues, ambiguities and questions that would otherwise have
been overlooked at this stage.
Sampling frame
identify and measure every single item in the population which may be
included in the sample sample. a sampling frame must have the property that
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can be identify.. For example, in an opinion poll, possible sampling frames
include:

Electoral register

Telephone directory
The sampling frame must be representative of the population and this is a
question outside the scope of statistical theory demanding the judgment of
experts in the particular subject matter being studied.
In defining the frame, practical, economic, ethical, and technical issues need
to be addressed. The need to obtain timely results may prevent extending the
frame far into the future.
"Nature has established patterns originating in the return of events but only
for the most part. New illnesses flood the human race, so that no matter how
many experiments you have done on corpses, you have not thereby imposed
a limit on the nature of events so that in the future they could not vary."
Having established the frame, there are a number of ways for organizing it to
improve efficiency and effectiveness.
It is at this stage that the researcher should decide whether the sample is in
fact to be the whole population and would therefore be a census.
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Types of Sampling method
Within any of the types of frame identified above, a variety of sampling
methods can be employed, individually or in combination.
1. Quota sampling
In quota sampling, the population is first segmented into mutually exclusive
sub-groups, just as in stratified sampling. Then judgment is used to select the
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. In quota sampling the selection of the sample is non-random. 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.
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2. Simple random sampling
In a simple random sample of a given size, all such subsets of the frame are
given an equal probability. Each element of the frame thus has an equal
probability of selection: the frame is not subdivided or partitioned. random
probability of being chosen
3. Stratified sampling
Where the population embraces a number of distinct categories, the frame
can be organized by these categories into separate "strata." A sample is then
selected from each "stratum" separately, producing a stratified sample. The
two main reasons for using a stratified sampling design are [1] to ensure that
particular groups within a population are adequately represented in the
sample, and [2] to improve efficiency by gaining greater control on the
composition of the sample. In the second case, major gains in efficiency
(either lower sample sizes or higher precision) can be achieved by varying
the sampling fraction from stratum to stratum. The sample size is usually
proportional to the relative size of the strata. However, if variances differ
significantly across strata, sample sizes should be made proportional to the
stratum standard deviation. Disproportionate stratification can provide better
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precision than proportionate stratification. Typically, strata should be chosen
to:

have means which differ substantially from one another

minimize variance within strata and maximize variance between
strata.
4. Cluster sampling
Sometimes it is cheaper to 'cluster' the sample in some way e.g. by selecting
respondents from certain areas only, or certain time-periods only.
Cluster sampling is an example of 'two-stage sampling' or 'multistage
sampling': in the first stage a sample of areas is chosen; in the second stage a
sample of respondent within those areas is selected.
This can reduce travel and other administrative costs. It also means that one
does not need a sampling frame for the entire population, but only for the
selected clusters. Cluster sampling generally increases the variability of
sample estimates above that of simple random sampling, depending on how
the clusters differ between themselves, as compared with the within-cluster
variation.
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5. Random sampling
In random sampling, also known as probability sampling, every combination
of items from the frame, or stratum, has a known probability of occurring,
but these probabilities are not necessarily equal. With any form of sampling
there is a risk that the sample may not adequately represent the population
but with random sampling there is a large body of statistical theory which
quantifies the risk and thus enables an appropriate sample size to be chosen.
Furthermore, once the sample has been taken the sampling error associated
with the measured results can be computed. With non-random sampling
there is no measure of the associated sampling error. While such methods
may be cheaper this is largely meaningless since there is no measure of
quality. There are several forms of random sampling. For example, in simple
random sampling, each element has an equal probability of being selected.
Another form of random sampling is Bernoulli sampling in which each
element has an equal probability of being selected, like in simple random
sampling. However, Bernoulli sampling leads to a variable sample size,
while during simple random sampling the sample size remains constant.
Bernoulli sampling is a special case of Poisson sampling in which each
element may have a different probability of being selected. Other examples
of probability sampling include stratified sampling and multistage sampling..
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6. Matched random sampling
A method of assigning participants to groups in which pairs of participants
are first matched on some characteristic and then individually assigned
randomly to groups. The Procedure for Matched random sampling can be
briefed with the following contexts,
a) Two samples in which the members are clearly paired, or are matched
explicitly by the researcher. For example, IQ measurements or pairs of
identical twins.
b) Those samples in which the same attribute, or variable, is measured twice
on each subject, under different circumstances. Commonly called repeated
measures. Examples include the times of a group of athletes for 1500m
before and after a week of special training; the milk yields of
5.0
Sampling and data collection
Good data collection involves:

Following the defined sampling process

Keeping the data in time order

Noting comments and other contextual events

Recording non-responses
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Most sampling books and papers written by non-statisticians focus only in
the data collection aspect, which is just a small part of the sampling process.
Conclusion
This paper discusses sampling methos in architectural research. This method
is considered appropriate especially where a large population is to be
analysed to achieve a measurable results.
The discussion also focus on types of sampling methods use in architectural
research.
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References

Baddie, E (1986): the practice of Socail Researh 4th Ed., Wadsworth
Publishing Co. Belmont California.

Brown, K.W., Cozby, P.C., Kee, D.W., & Worden, P.E. (1999).
Research Methods in Human Development, 2d ed. Mountain View,
CA : Mayfield. ISBN 1-55934-875-5

Cochran, W G (1977) Sampling Techniques, Wiley, ISBN 0-47116240-X

Flyvbjerg, B (2006) "Five Misunderstandings About Case Study
Research." Qualitative Inquiry, vol. 12, no. 2, April 2006, pp. 219245. [2]

Linda G, and David W, Architectural Resarch methods

Lohr, H (1999) Sampling: Design and Analysis, Duxbury, ISBN 0534-35361-4

Sarndal, Swenson, and Wretman (1992), Model Assisted Survey
Sampling, Springer-Verlag, ISBN 0-387-40620-4

Stuart, Alan (1962) Basic Ideas of Scientific Sampling, Hafner
Publishing Company, New York
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