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METHODOLOGICAL INSIGHT INTO THE APPLICATION OF EXPERIMENTAL RESEARCH IN ARCHITECTURE

Authors:

Abstract

Experiment, being one of the methodologies applicable in research, has not been given appropriate attention in architecture unlike other disciplines such as education, psychology and sociology. This may be connected to the view that architecture is a core design discipline with little interest in research writing and its multidisciplinary nature with relatively diverse methodological approaches. This study, using a desktop research strategy, presents an overview of the background with methodological insight into the use of experimental research in architecture. Types and sources of data for experimental research, basic concepts involved in experimental studies such as relationship between independent and dependent variables, treatment, causality and randomization are explored. From problem identification to documentation of the findings, steps to conducting an experimental research to writing the paper for publishing in journals and conferences are suggested. The study concludes that the knowledge and appropriate application of experimental research could support design evolution in architecture.
The ARCON Architects Colloquium 2021- ARCHITECTURE AND THE NATIONAL DEVELOPMENT
AGENDA X111, 9TH- 12TH August, 2021, Abuja, Nigeria.
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METHODOLOGICAL INSIGHT INTO THE APPLICATION OF EXPERIMENTAL
RESEARCH IN ARCHITECTURE
by
Authors: Edidiong Ukpong1; Joseph Uwah2 and Udomiaye Emmanuel2
Department of Architecture, University of Nigeria.
Corresponding author’s contact1: 08060061292, email: edidiongukpong1983@gmail.com
ABSTRACT
Experiment, being one of the methodologies applicable in research, has not been given
appropriate attention in architecture unlike other disciplines such as education, psychology and
sociology. This may be connected to the view that architecture is a core design discipline with
little interest in research writing and its multidisciplinary nature with relatively diverse
methodological approaches. This study, using a desktop research strategy, presents an
overview of the background with methodological insight into the use of experimental research
in architecture. Types and sources of data for experimental research, basic concepts involved
in experimental studies such as relationship between independent and dependent variables,
treatment, causality and randomization are explored. From problem identification to
documentation of the findings, steps to conducting an experimental research to writing the
paper for publishing in journals and conferences are suggested. The study concludes that the
knowledge and appropriate application of experimental research could support design
evolution in architecture.
Keywords: experimental research, architectural research, methodological research, design,
causality, treatment, paper writing.
1.0 INTRODUCTION
Experimental research has its bedrock in experiment. Shadish, Cook, and Campbell (2002)
defines an experiment as “a study in which an intervention is deliberately introduced to observe
its effects”. Experimental research design is centrally concerned with constructing research that
is high in causal (or internal) validity (Mitchell, Vogel and Folger 2015). An experimental
research is a scientific study in which the researcher manipulates or treats, controls the level of
some independent variable(s) and then measures the outcome. Experiments are powerful
The ARCON Architects Colloquium 2021- ARCHITECTURE AND THE NATIONAL DEVELOPMENT
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techniques for evaluating cause-and-effect relationships between two or more variables called
independent and dependent variables. Many researchers consider experiments the "gold
standard" against which all other research designs should be judged (Antonakis, 2017; Eden,
2017; Hauser, Linos, & Rogers, 2017; Holmes, 2014). Experiments are conducted both in the
laboratory and in real life situations. It is also called hypothesis testing or deductive research
method (Babbie, 1998).
The multidisciplinary nature of architecture makes a strong case for the application of
multidisciplinary methodologies in architectural research. Experiment, being one of the
methodologies applicable in global research, has not been given appropriate attention in
architecture unlike other disciplines such as education, psychology and sociology. This could
be as a result of the inadequate interest for research or the application of the methodology in
architecture. Many suggest that architecture is a core design discipline and as such the
application of any other research approach in the course is secondary and this may have serious
impact on architects’ interest in other research methodologies. To address this gap, this study,
using a desktop research strategy, presents a methodological insight into the use of
experimental design in architecture.
2.0 TYPES AND SOURCES OF DATA COLLECTED IN EXPERIMENTAL
RESEARCH
Experimental research can be a pre-experiment or a true experiment done in a laboratory and
field setting through a quasi-experiment. Data in experimental research must be able to be
quantified or measured. Data collected could be relative humidity (Nimlyata, Kandara, &
Sediadi, 2015), light intensity (Idowu & Humphrey, 2018), sound intensity (Barrett, Davies,
Zhang, & Barrett, 2015), temperature (Palanivelraja & Manirathinem, 2009). However, the
entity should be carefully observed qualitatively, or described using medium such as words
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and photographs. How does the entity look, smell, sound, feel, and taste (when appropriate)?
These types of observations help supplement the measurements taken throughout the
experiment.
The various channels for sourcing data for experiment are from animate and inanimate
participants in the research. The animate participants include human beings used for evaluation
of their responses. Inanimate participants include instruments for measurement such as
illuminance meter (Reinhart & Weissman, 2012), thermometer for temperature reading
(Figueiro & Rea, 2010), different sensors for reading of indoor air quality (Stabile, Frattolillo,
Dell’Isola, Massimo, & Russi, 2015), electrical lighting, acoustics, etc.
3.0 BASIC CONCEPTS OF EXPERIMENTAL RESEARCH
3.1 Independent and Dependent Variables
Beyond discovering causal relationships, experimental research further seeks out how much
cause will produce how much effect; in technical terms, how the independent variable will
affect the dependent variable. The cause is defined as the independent variable while the effect
is defined as dependent variable. A study investigated indoor environmental quality (IEQ)
performance in hospital buildings in Nigeria (Nimlyata et al., 2015). The study experimented
on teaching and specialist hospital ward buildings by comparing their indoor thermal, visual
and acoustic comfort levels. In this study, the design strategies in teaching and specialist
hospital buildings were the independent variables while the comfort levels were the dependent
variables. Experimental research is strong in internal validity but weak in external validity.
3.2 Treatment and Hypothesis
The term treatment refers to either removing or adding a stimulus in order to measure an effect
(such as turning the knob a little or a lot, or reducing the noise level a little or a lot).
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Experimental researchers want to know how varying levels of treatment will affect what they
are studying. As such, researchers often have an idea, or hypothesis, about what effect will
occur when they cause something. Few experiments are performed where there is no idea of
what will happen. From past experiences in life or from the knowledge architects possess in
architectural field of study, architects know how some actions cause other reactions.
Experiments confirm or reconfirm this fact. In the IEQ study (Nimlyata et al., 2015), the
variations of the design strategies were the treatments.
3.3 Causality
What is so crucial in experimental method is not so much about the observation of the result
and the product, but the design and implementation of the experimental set up that yield causal
relationship in a reliable manner (Lewis, 2019). Experimentation becomes more complex when
the causal relationships they seek aren't as clear as in the stereo knob-turning or IEQ study
examples. Questions such as: will the use of north-west and south-east facing windows as
daylighting strategies give adequate indoor daylighting adequacy? or ‘can the use of autocad
in drafting make an architect a better designer’? these questions present more to be considered.
In the first case, some other factors can make the strategies to under-perform, example,
proximity to trees, inter-building effect, the use of window curtains, burglar proof. The
researcher who wants to show that a specific daylighting strategy gave a specific performance
reading must ensure that nothing affected the capability of the daylighting strategy in giving
its actual indoor reading. To achieve this, several factors must be in serious control.
Given some conditions like
the use of laboratory settings where relevant variables can be easily controlled;
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dependent variables that are in many instances inert, and therefore not likely to change
except as a consequence of the treatment; and
instruments that are calibrated to measure such effect.
Causality can often be assumed without much discussion or argument in certain situations like
experiment in laboratory compared to field experiment. In the IEQ study, the study was done
for a period of three months with repeated measurements and not a one-time process.
3.4 Matching and Randomization
Groups can be created in two ways: matching and randomization. In a daylight study the
strategies can be matched according to orientation, sky conditions to test their differences.
Matching may be problematic, though, because it "can promote a false sense of security by
leading [the experimenter] to believe that [the] experimental and control groups were really
equated at the outset, when in fact they were not equated on a host of variables". Unfortunately,
however, matching participants on one (or a few) characteristic(s) does not guarantee that all
potential confounds are controlled (Holmes, 2014; Shadish et al., 2002).
Randomization, then, is preferred by some researchers to matching. This method is based on
the statistical principle of normal distribution. Theoretically, any arbitrarily selected group of
adequate size will reflect normal distribution. Differences between groups will average out and
become more comparable. The principle of normal distribution states that in a population most
individuals will fall within the middle range of values for a given characteristic, with
increasingly fewer toward either extreme (graphically represented as the ubiquitous "bell
curve").
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4.0 STEPS TO CONDUCTING EXPERIMENTAL RESEARCH
4.1 Research problem identification- First step
The process starts by clearly identifying the problem you want to study and considering what
possible methods will affect a solution. Then you choose the method you want to test, and
formulate a hypothesis to predict the outcome of the test.
For example, a researcher may want to improve architecture student essays, but he doesn’t
believe that lecturers’ feedback is enough. He hypothesizes that some possible methods for
writing improvement include peer workshopping, or reading more example essays. Favouring
the former, the experiment would try to determine if peer workshopping improves writing in
architecture students. He states his hypothesis: peer workshopping prior to turning in a final
draft will improve the quality of the architecture student's essay.
4.2 Planning an experiment- Second step
How to test the hypothesis is next. Consideration of several factors is vital. For example, how
generalizable do you want your end results to be? Do you want to generalize about the entire
population of architecture students in all the schools of architecture in Nigeria, or just the
particular population of architecture students at a specific school? This will determine how
simple or complex the experiment will be. The amount of time and funding you have will also
determine the size of your experiment.
Continuing the example from step one, you may want a small study at one school involving
three lecturers, each teaching two sections of the same course. The treatment in this experiment
is peer workshopping. Each of the three lecturers will assign the same essay assignment to both
classes; the treatment group will participate in peer workshopping, while the control group will
receive only teacher comments on their drafts.
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4.3 Conducting an experiment- Third step
The control and treatment groups must be selected at the start of an experiment. Whereas the
"hard" sciences have the luxury of attempting to create truly equal groups, educators often find
themselves forced to conduct their experiments based on self-selected groups, rather than on
randomization. This makes such study a quasi-experiment since the researchers cannot control
all the variables.
For the peer workshopping experiment, let's say that it involves six levels and three lecturers
with a sample of students randomly selected from all the classes. Each lecturer will have a class
for a control group and a class for a treatment group. The essay assignment is given and the
lecturers are briefed not to change any of their teaching methods other than the use of peer
workshopping. You may see here that this is an effort to control a possible variable: teaching
style variance.
4.4 Data Analysis- Fourth step
The fourth step is to collect and analyze the data. This is not solely a step where you collect the
papers, read them, and say your methods were a success. You must show how successful. You
must devise a scale by which you will evaluate the data you receive; therefore, you must decide
what indicators will be, and will not be important.
Continuing our example on essay writing, the lecturers' grades are first recorded, then the
essays are evaluated for a change in sentence complexity, syntactical and grammatical errors,
and overall length. Any statistical analysis is done at this time to find the difference between
the two groups. Notice here that the researcher has made judgments on what signals improved
architectural writing. It is not simply a matter of improved lecturer grades, but a matter of what
the researcher believes constitutes improved use of the language for architectural presentation.
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In another study on the investigation of thermal comfort of the occupants of learning spaces
using varied ventilation strategies (Lau, Zhang, & Tao, 2018), the study was based on four
measured environmental parameters - air temperature, mean radiant temperature, relative
humidity, and air velocity. One-way ANOVA statistical test was used to compare the means
of the different strategies.
4.5 Paper writing- Fifth step
Once you have completed the experiment, you will want to share findings by publishing
academic paper in journals or conferences. These papers usually have the following format,
but it is not necessary to follow it strictly. Sections can be combined or not included, depending
on the structure of the experiment, and the journal to which you submit your paper.
Abstract: Summarize the project: its aims, participants, method, results, and conclusion. State
some keywords.
Introduction: Set the context of the experiment.
Review of Literature: Provide a review of the literature in the specific area of study to show
what work has been done. Should lead directly to the author's purpose for the study.
Statement of Purpose: Present the problem to be studied.
Participants: Describe in detail participants involved in the study; e.g., how many, etc. Provide
as much information as possible.
Materials and Procedures: Clearly describe materials and procedures. Provide enough
information so that the experiment can be replicated, but not so much information that it
becomes unreadable. Include how participants were chosen, the tasks assigned them, how they
were conducted, how data were evaluated, etc.
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Results: Present the data in an organized fashion. If it is quantifiable, it is analyzed through
statistical means. Avoid interpretation in this section.
Discussion: After presenting the results, interpret what has happened in the experiment. Base
the discussion only on the data collected and the study objective with interpretation. Relate it
to past works in support or discord. Hypothesizing is possible here.
Limitations: Discuss factors that affect the results. Here, you can speculate how much
generalization, or more likely, transferability, is possible based on results. This section is
important for quasi-experimentation, since a quasi-experiment cannot control all the variables
that might affect the outcome of a study. You would discuss what variables you could not
control.
Conclusion: Synthesize all the above sections.
References: Document works cited in the correct format for the journal.
5.0 FURTHER EXAMPLES OF EXPERIMENTAL RESEARCH
a) Abdelhakim, M., Lim, Y., & Kandar, Z. (2019). Optimum glazing configurations for
visual performance in Algerian classrooms under Mediterranean climate.
b) Nocera, F., Faro, A. Lo, & Costanzo, V. (2018). Daylight performance of classrooms
in a Mediterranean school heritage building.
c) Grobe, L. O., Geçİt, B. H., Sevİnç, Z., Altinkaya, G., Aksakarya, G., Ergİn, M.,
Kazanasmaz, T. (2017). Scale-model and simulation-based assessments for design
alternatives of daylight redirecting systems in a side-lighting educational room.
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d) Costanzo, V., Evola, G., Marletta, L., & Panarelli, D. (2017). Static and dynamic
strategies for improving daylight use in side-lit classrooms: a case study.
e) Del Ama Gonzalo, F. (2019). Introducing a new ICT tool in an active learning
environment course: performance consequences depending on the introduction design.
f) Hamat, B., Eisenbart, B., Badke-Schaub, P. & Schoormans, J. (2019). The influence of
a designers’ mind-set on their design process and design outcomes.
g) Chang, Y. S., Chen, M., Chuang, M. & Chou, C. (2019). Improving creative self-
efficacy and performance through computer-aided design application.
h) Wingler, D., Joseph, A., Bayramzadeh, S., & Robb, A. (2019). Using virtual reality to
compare design alternatives using subjective and objective evaluation methods.
6.0 CONCLUSION
This study has been able to give a methodological insight into the application of experiment in
architectural research. Before now, inadequate attention has been given to experiment as a
methodology applicable in architecture. This must have been emboldened with the
complexities associated with experimental research. To take care of this, this study has ensured
that experimental research has been treated from its definition, types and sources of
experimental data, concepts, and steps on how to conduct an experimental research.
Experiment has strong internal validity but weak in external validity. This is because of the
causal relationship determination between IV and DV. Due to this restriction, generalisation of
the findings to a greater setting is found wanting. The knowledge of experimental research and
its appropriate application and output can help in the concept evolution of design and solving
design problems in architecture.
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This study is limited to the expansion of knowledge for beginners in the field of architecture
willing to use experiment as a strategy for conducting research. Further study of experimental
research is recommended.
REFERENCES
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performance in Algerian classrooms under Mediterranean climate. Journal of
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Antonakis, J. (2017). On doing better science: From thrill of discovery to policy implications.
The Leadership Quarterly, 28, 521.
Barrett, P., Davies, F., Zhang, Y., & Barrett, L. (2015). The impact of classroom design on
pupils’ learning: Final results ofaholistic, multi-level analysis. Building and
Environment, 89, 118133. https://doi.org/10.1016/j.buildenv.2015.02.013
Chang, Y. S., Chen, M., Chuang, M. & Chou, C. (2019). Improving creative self-efficacy and
performance through computer-aided design application. Thinking Skills and
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del Ama Gonzalo, F. (2019). Introducing a new ICT tool in an active learning environment
course: performance consequences depending on the introduction design. The
International Journal of Engineering Education, 35(1), 360-371.
Eden, D. (2017). Field experiments in organizations. Annual Review of Organizational
Psychology and Organizational Behavior, 4, 91122.
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Figueiro, M., & Rea, M. (2010). Evening daylight may cause adolescents to sleep less in spring
than in winter. Chronobiology International, 27(6), 12421258.
https://doi.org/10.3109/07420528.2010.487965.Evening
Grobe, L. O., Geçİt, B. H., Sevİnç, Z., Altinkaya, G., Aksakarya, G., Ergİn, M.,
Kazanasmaz, T. (2017). Scale-model and simulation-based assessments for design
alternatives of daylight redirecting systems in a side-lighting educational room. METU
Journal of Faculty of Architecture, 34(2), 3558.
https://doi.org/10.4305/METU.JFA.2017.2.1
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designers’ mind-set on their design process and design outcomes. International Journal
of Technology and Design Education, 1-17.
Hauser, O. P., Linos, E., & Rogers, T. (2017). Innovation with field experiments: Studying
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Holmes, W. M. (2014). Using propensity scores in quasi-experimental designs. Thousand
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Idowu, O., & Humphrey, S. (2018). Aesthetics and day-lighting correlation: An experimental
study of form and placement of windows on buildings. Journal of Art and
Architecture Studies (JAAS), 7(1), 110.
Lau, S. S. Y., Zhang, J., & Tao, Y. (2018). A comparative study of thermal comfort in
learning spaces using three different ventilation strategies on a tropical university
campus. Building and Environment. https://doi.org/10.1016/j.buildenv.2018.11.032
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Lewis, J. (2019). Experimental design: Ethics, integrity and the scientific method. Springer
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environmental quality (IEQ) performance in hospital buildings in Nigeria. Jurnal
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Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental
designs for generalized causal inference. New York, NY: Houghton Mifflin.
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Stabile, L., Frattolillo, A., Dell’Isola, M., Massimo, A., & Russi, A. (2015). Air permeability
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https://doi.org/10.1016/j.egypro.2015.11.772
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PROGRAMME OF EVENTS: AUGUST 9TH 12TH, 2021
DAY ONE
MONDAY,
AUGUST 9,
2021
ARCHITECTURE AND THE NATIONAL DEVELOPMENT
AGENDA XIII
PRESENTERS
8:00 - 10:30am
Arrival/ Registration
10:30 - 11:00am
ARCON OVERVIEW
Documentary DAY 1
Arc. Jacinta Lewa-
Samson
Arc. Mohammed
Abdulkadir
11:00
11:20am
The Colloquium Rationale
Director of Colloquium Speech
Arc. M. J. Faworaja
11:20
11:30am
Sponsor 1 - NTA
Arc. Jacinta Lewa-Samson
11:30 12:30
pm
Students Master class
Arc.
Emeka/Arc.
Karaye
Arc. Murnai
Arc. Lewis
Arc. Jolaoso
12:30 1:50pm
Registered Architect Master Class
Dr.. Tony Alabi & Dr. Sada
1:50 2:00pm
Sponsor 2 Baze University
Dr. Muhammad-Oumar
2:30 3:00pm
LUNCH! LUNCH! LUNCH!
3:00 4:00pm
Panel Discussion:
Architectural Education in
Nigeria
Prof. E. O.
Ibem
Edidiong
Ukpong
Joseph
Uwah
Udomiaye
Emmanuel
Registrar
ARCON
8 Nos
University
HODs
4:00 4:10pm
Briefing by ARCON Registrar
Registrar Arc. Umar Murnai
04:10 4:30pm
DAY 1 CLOSES
DAY TWO
TUESDAY,
APRIL 10, 2021
ARCHITECTURE AND THE NATIONAL DEVELOPMENT
AGENDA XI
PRESENTERS
OPENING CEREMONY - MAIN HALL
Start 8:00 am
Arrival/ Registration
9:30 - 10:00am
ARCON OVERVIEW
Documentary Highlights of Day 1
Arc. Jacinta Lewa-Samson
9:45 -
Arrival of Dignitaries
9:50
Arrival of Chief Host
10:05
National Anthem
10:10 - 10:25am
Welcome Address by PARCON
10:25
10:45am
Goodwill Messages - Digital
10:45
11:45am
Address by Special Guest of Honour
Others
10:45
11:55am
Vote of Thanks VP ARCON
11:55 12:00
noon
National Anthem
12:00 1:00 pm
Tea Break/Lunch
1:00 1.45 pm
Lead Paper Impact of Covid 19 Pandemic on Housing
Adequacy in Nigeria: Matters Arising
Dr. M. Sada
1:45 2:20 pm
Architectural response towards the coronavirus disease
(Covid 19) as a catalogue for National security in Nigeria
Okonta, Ebere Donatus
Okonta Emeka Martin
Ohaemenyi Uchechi
Grace
2:20 2:50 pm
Indoor Air Quality in post pandemic Architecture: a new
normal for studio classroom design for Architectural student in
Nigeria
Abubakar Salisu
Usman A. Mohammed
2:50 3:20 pm
A review on adopting sustainability and greening to hospital
building design
Ibukun Taldo
Adebayo Amusan
Kayode O. James
3:20 4:00 pm
Panel discussion
4:00 4:30 pm
Question and answer session
04:30pm
END OF DAY SESSION
DAY THREE
WEDNESDAY,
11TH AUGUST,
2021
ARCHITECTURE AND THE NATIONAL DEVELOPMENT
AGENDA XIII
PRESENTERS
MAIN HALL
Start 8:00 am
Arrival/ Registration
8:30 - 9:00am
ARCON OVERVIEW
Documentary Highlights of Day 2
9:00 9:45 am
9:45 10:00 am
Lead Paper The Role of Architects in supporting
sustainable security in Nigeria
Question and Answer
Arc. Kyaagba Joseph Adom
10:00 10:30
am
Tea Break
10:30 11:00
11:30 12 noon
Educational roots and repercussion of insecurity in
Nigeria
Question and answer
Prof Abdul Raheem A.
Lawal VC University of Offa
12:00 - 1:00 pm
Lunch Break
1:00 1:30 pm
1:30 2:00 pm
Unethical geotechnical practice, building collapse and
failure and infrastructure in Nigeria
Question and Answer
Engr. Moshood N. Tijani
Fidelis A. Abijai
2:00 2:45 pm
2:45 3:30 pm
Panel discussion
Question and Answer
Arc. Muhammad-Oumar
Arc. Yusuf Kazaure
Arc. Kyaagba
DAY FOUR
THURSDAY,
12TH AUGUST,
2021
ARCHITECTURE AND THE NATIONAL DEVELOPMENT
AGENDA XIII
PRESENTERS
MAIN HALL
9:00 - 11:00am
Syndicate Groups
2 Groups
11:00 12:00
Tea Break
12:00 12:30
pm
Communiqué/Presentation/Reactions
12:30 1:00 pm
Director’s Vote of Thanks
1:00 1:30 pm
Press Conference/Interaction with Journalists
1:30 pm
CLOSING CEREMONY
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Chapter
Full-text available
Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central concepts associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research.
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