SOLUTION: University of South Florida Ethical Context and Ethical Decision Making Paper

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CA1.3 White Paper Assignment – Article Identification
Name:
Field/Major: Mechanical Engineering
Interest Area: Business Ethics
Article Title:
Author(s):
Journal:
Date of Publication:
doi number:
Statistical method(s)
Ethical Context and Ethical Decision Making
Valentine, S., Nam, S. H., Hollingworth, D., & Hall, C.
Journal of Business Ethics
18 September 2013
DOI 10.1007/s10551-013-1879-8.
cluster and discriminant analyses
Definition of method(s)
The method uses a combination of cluster and discriminant analysis. Cluster analysis
explores the homogeneity and heterogeneity. Therefore it explores these two while
discriminate research looks at discrete and what distinguishes members of a group.

Architecture by the Numbers
Probability and Statistics for the Best Places
Intelligent Building Design, Inc.
November, 2020
Architecture by the Numbers
Table of Contents
Introduction: Your Best Places Using Probability &
Statistics ………………………………………………………………… 1
Case 1: Review of a Statistical Analysis of Worker
Satisfaction with Workspaces …………………………………. 2
Case 2: Using Statistical Analysis to Evaluate A New
Method for Future Office Space Demands in Central
Business Districts ……………………………………………………… 7
Case 3: The Use of Statistical Analysis for Evaluating
Spaces for Graduate Student Education ………………. 10
Case 4: Instructions……………………………………………….. 13
Conclusion: Probability and Statistics Contribute to
Better Buildings ……………………………………………………. 15
Literature………………………………………………………………. 16
Intelligent Building Design, Inc.
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Architecture by the Numbers
Introduction
Your Best Places Using Probability & Statistics
The procurement and upkeep of the places we congregate in are significant capital investments for
your organizations, institutions, or government agencies. Those of you responsible these places
know any decisions you make have many long-term implications for your organization, institution,
or government agency. Occupants, market segment, resource needs, proximity to workers and users,
real estate markets, space uses, and local infrastructures are all considerations that go into making
these decisions (Brill, Weidemann, & BOSTI Associates, 2001; Horgen, Joroff, Porter, & Schön,
1999; O’Mara, 1999). All these factors make these evaluating your options difficult. It takes knowing
the questions to ask, the data needed to answer them, and the best inferential statistical approach for
evaluating all of these factors to make the right decision for your organization. At Intelligent
Building Design, we have the knowledge and resources you need to make these decisions in a way
that will move your organization, institution, or government entity successfully into the future. Let
us help you get to your best place.
In the cases we present below, you will see a range of projects completed by architects and
architectural researchers that use statistics to help clients like you understand the choices you are
making when investing in the buildings and spaces your organization needs. Their analyses will
understand what workspace qualities impact worker satisfaction, processes for evaluating the market
demands for commercial office space, and using faculty and student feedback to determine how to
create the best spaces for learning. Each case provides us the opportunity to present a different way
we are use probability and statistics as part of designing the places central to your organization. Each
case also discusses the optimal way to present these analyses for their best use in your decisionmaking processes.
What is the purpose of this section? This section needs to:





Speak to potential clients/customers – Address what they care about (go look at the
marketing materials of large organizations in your field. How do they speak to clients?
What do they indicate is important? Cite these sources.)
Connect the things being analyzed by your articles to what clients in the field care
about. This may involve looking at industry publications.
Connect the things your articles discuss together professionally and organize them
logically.
Promote the value of probability and statistics analyses as stand-alone services or
elements of other client services.
Demonstrate professional competence in your attention to details: Make the graphics
consistent, use clear professional language, and run a grammar and spelling check
before you finish your document.
Intelligent Building Design, Inc.
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Architecture by the Numbers
Case 1
Review of a Statistical Analysis of Worker
Satisfaction with Workspaces
By Sally Messer
This case study reviews the article Workspace
satisfaction: The privacy-communication
trade-off in open-plan offices by Jungsoo Kim
and Richard de Dear.
Study Objective
This study examines the influence of workspace
enclosure on worker satisfaction with their
workspaces. It evaluates data on 16 factors of
indoor environmental quality dimensions to
understand what perceptions of a workspace
environmental qualities are most influenced by
enclosure, and what workspace environmental
qualities most influence dissatisfaction and
satisfaction with workspaces.
Figure 1: Indoor Environmental Quality
and Worker Satisfaction with Workplaces
Source: This Photo by Unknown Author is
licensed under CC by SA-NC
Study Outcome
The study examined the responses to post-occupancy surveys completed by workers to understand
the influences of the types of enclosure and various environmental qualities on workers’ satisfaction
with their workspaces. First, data was examined across all respondents to look at their response to
the various enclosure types and environmental qualities. Researchers then reviewed the data in more
detail, separating responses from workers in various enclosures types to understand the relationship
of various indoor environmental quality factors (IEQ) on both satisfaction and dissatisfaction of
workers with their workspaces.
The study found that the amount of space workers had was the most significant factor in
determining worker satisfaction with their workspaces when measured for all workspace types. The
study verified what many workers have long claimed that enclosed private offices increase workers’
satisfaction with their workspace. The data indicated the most significant dissatisfaction related to
the distractions from noise and lack of privacy workers faced in open office layouts.
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Architecture by the Numbers
Data Used for the Research
The data set used for the analyses was from the Center for the Built Environment at the University
of California. It is part of a Post-Occupancy Evaluation database they established in 2000 to collect
data from various building types. The data analyzed in this study are from the data collected in the
office building category, which included 42,764 survey respondents from 303 office buildings in the
United States (U.S.). This study’s data are organized by the level of workspace enclosure, which are
in the five categories shown in Table 1.
Table 1: Space Types Included in the Research
Category
Figure
Additional Detail
Enclosed Private Office
2
Enclosed Shared Office
3
Cubicles with High Partitions
4
Five feet high or taller
Cubicles with Low Partitions
5
Lower than five feet
Open Office with no or limited Partitions
6
Figure 2: Enclosed Private Office
Source: This Photo by Unknown Author is
licensed under CC BY
Figure 3: Enclosed Shared Office
Source: Knoll Website, Shared Office
Figure 4: High Partitioned Cubicles
Source: Steelcase, Answer Panel Systems Product website
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Architecture by the Numbers
Figure 5: Low Partition Cubicles
Source: This Photo by Unknown Author is
licensed under CC BY
Figure 6: Minimal Partition Open
Office Source: Steelcase Products Tour
Workspace Website
Statistical Analysis Review
The researchers used three different types of inferential statistical analyses to examine employee
workplace satisfaction data for four workspace enclosure types. The use of inferential statistical
analyses enabled the researchers to project these data onto the likely workplace satisfaction for all
U.S. workplaces. The researchers examined three questions critical for design and real estate
decisions:
1. Does the type of workspace enclosure influence user satisfaction?
2. Is dissatisfaction with some IEQ factors more important than others for workspace
satisfaction of workers working in different workplace enclosure types?
3. How much does the dissatisfaction or satisfaction with particular IEQs impact users’
satisfaction with various workplace enclosure types?
The first question was if users in various workspace enclosure types responses to different IEQ
factors were different. The researchers evaluated the percent of users in each space type dissatisfied
with a particular IEQ factor against the mean satisfaction score. While some findings were expected,
such as private office satisfaction being the highest across all factors, other findings were surprising,
such as sound privacy rating lower in cubicle spaces than in fully open offices.
The second question evaluated the relative role of user dissatisfaction with each IEQ factor in the
satisfaction with the different workspace enclosure types. Using multiple regression analyses, the
researchers quantitatively evaluated the strength of the relationships between each of the 15 IEQ
factors satisfaction ratings and overall workplace satisfaction. Their goal was to determine if one or
more of the IEQ factors correlate to the overall workplace satisfaction and indicate the strength of
the predictive value of the individual IEQ factors on workplace satisfaction for different enclosure
types. Sound privacy, visual privacy, and temperature dissatisfaction respectively proved to have the
strongest relationship to dissatisfaction. This evaluation provides designers and researchers with a
tool for understanding which of these factors deserve the most focused design consideration and
investment if the desired outcome is high workplace satisfaction.
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Architecture by the Numbers
The third question assessed the positive and negative impacts of each IEQ factor on overall
satisfaction with different workspaces. The researchers rang multiple regression analyses using
generalized categories of satisfied, neutral, or dissatisfied (called dummy variables). Then each IEQ
factor was run against overall satisfaction ratings to evaluate how much satisfaction or dissatisfaction
with each category impacted overall user satisfaction with workspace enclosure types. The
satisfaction/dissatisfaction rating for the amount of space has the most impact on users’ satisfaction
ratings of their overall workspaces for all types of enclosures. However, the remaining IEQs that
were most significantly related to overall workplace satisfaction ratings varied by enclosure type.
What is so valuable about these analyses is twofold. First, we can infer these outcomes as having
validity for workspace use throughout the U.S. Second, with a smaller data set from an organization
or industry, we can check for any statistical variation specific to your organization.
Evaluation of the Article’s Text
While this article would be useful to professionals designing workspaces, the technical language used
in the analysis section of this assignment would make it difficult for someone now familiar with
statistics to understand. This research could have significant value for those designing workplaces
and professionals responsible for making real estate use decisions within or for organizational
workplaces. To make this work accessible to these professionals, the analysis part of the research
would need to indicate what was accomplished with the statistical analysis rather than the detailed
technical presentation of the analysis process. If they were to write the document in this way, this
important work would be accessible to people who could put the findings into decision making for
future workplaces.
Evaluation of the Article’s Graphics
This article incorporates several types of graphics to assist in presenting the material, including
tables, bar graphs, and a radar graph. The researchers included well-organized tables, focusing on a
specific point, using consistently aligned columns and clearly presented table headers to make them
easy to read. The bar graphs are less easy to read (see Figure 5) as they contain a large amount of
data crammed into a small layout. What they are trying to accomplish would have better achieved in
a 3-D bar chart or by switching the axes of this graph and expanding it to a full page. Some of the
tables and bar charts are not located on the same page on which they are discussed. This separation
can make them inconvenient to locate and create confusion for readers. Even when challenging to
read or find, the graphics are all helpful in understanding the data and findings.
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Architecture by the Numbers
Figure 6: Example of Bar Graph from Research Publication (IEQ Questionnaire Dissatisfaction by
Office Layout Configuration)
Source: Kim & de Dear, 2013, p. 22
Contribution of the Research
This document makes a big picture contribution to the field in addition to demonstrating how
statistical methods can be valuable to the work architects do. It makes use of a publicly available
database to evaluate what makes U.S. workers satisfied or not with the workspaces provided for
them. Earlier research by Shujahat and colleagues (2018) documented that increases in knowledge
worker workplace satisfaction increases knowledge creation, knowledge sharing, and innovation. As
this research documents what IEQ factors are most connected to overall workplace satisfaction, it
provides valuable insight into the most important investments to make when developing your
workplaces. For this case, the most important thing this work does it to demonstrate how we, as
architects can use statistical methods to prioritize the financial investments that we can make on
your project to maximize the benefit of your investment in space for your organization, institution
or agency.
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Architecture by the Numbers
Case 2
Using Statistical Analysis to Evaluate A New
Method for Future Office Space Demands in
Central Business Districts
By Janetta Caldwell
This case study reviews the article Office
Space per Worker: Evidence from Four
European Markets by Jacco Hakfoort and
Robert Lie.
Study Objective
Figure 7: Frankfurt Germany Central Business District
This study tests a model that forecasts the
Source: This Photo by an Unknown Author is licensed under
demand for space in central business
CC Attribution 2. Generic license.
districts (CBDs). This model expands on
the traditional approach of evaluating employment forecasts to include a range of other economic
factors believed to impact the space demand in these locations. These factors include rent costs,
lease period, expected firm growth, and the uncertainty of that growth, substitution possibilities, and
search and adjustment costs.
Study Outcome
The statistical analysis completed did not show strong enough results to claim that the model
worked. However, the findings did suggest directions for further research. Based on their findings,
the researchers are modifying their model. They believe that further research with a larger data set is
needed to demonstrate the effectiveness of their revised model in forecasting CBD space demand.
The modifications would be to add more market imperfections to the model and find a way of
evaluating space allocation for employees other than education, which was not conclusive.
Data Used for the Research
The researchers used real estate data from governmental and industry sources, and data from their
survey to complete the study. Both sets of data presented challenges for comparison due to their
origins from different markets, and their use of different measurement parameters. The data from
external sources included: space included in measurable lettable floorspace by city in the study, and
space per worker in square feet. The survey included interviews with executives in four European
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Architecture by the Numbers
cities: London, Frankfort, Brussels and Amsterdam. Survey participant were included from industry
segments in proportion to each industry in the local market and were identified through local
chambers of commerce. This study utilized survey data that identified office space allocations per
workers in each city and industry sector and asked executives to predict the impact they believed
increases in rents of 10%, 25% and 100% would have on their use of space and choice of staying in
the Central Business District.
Statistical Analysis Review
Statistics were used in this study to evaluate the viability of each hypotheses that founded each
element of the researchers’ model for predicting CBD space demand. In order to make visible the
data for their evaluation of each hypothesis the researchers used descriptive statistics presented in
tables and on graphs.
Researchers used tables to make data visible to disprove two hypotheses and offer support for one.
First, researchers identified space demand trends in the cities using available data from government
and industry. Before presenting the data, the researchers clarified with a table the ways in which the
numbers would and would not be comparable. They then used a table to present the average square
feet per workers in each market in each industry sector. This table was used to make data easily
visible which was intended to support the hypothesis that there would be industry sector differences
in space use. Yet, it clearly shows this is not the case, thus, using industry sectors to weigh space
demand would not work. Researchers used the same approach to evaluate the second hypothesis
that they would be able to use occupations to weigh their calculations of space use demand. The
proxy they selected to represent this was the percent of the workforce with higher education by
industry. Again, presenting the data on the table made it clear this did not work. With the final table
researchers evaluated the size of the office building as being predictive of the amount of space used.
They did find some support for the space for worker being higher in smaller buildings.
A scatter plots was used to visualize the data for the hypothesis that rent would be predictive of the
amount of space firms used. To make this work the researchers converted the rents to equivalent US
dollars. When plotted the data make it clear that the higher the rent, the less the space would be
allocated to each worker.
The final data visualization used a 3-dimentional bar graph to represent the educated projections of
executives that their organizations would make chances in space use or office location if their rent
increased by different percentages. While this visualization in general indicated that the more rent
increased the more likely firms were to make changes to their space use or location. However, this
data was not consistent.
The use of descriptive statistics to represent the data being used for evaluation of the hypotheses in
this study made the data easy to see and understand which made it an effective approach for
presenting the significant quantity of data reviewed for this study.
Intelligent Building Design, Inc.
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Architecture by the Numbers
Evaluation of the Article’s Text
This article was published in a real estate journal and used language that w …
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