CALIFORNIA ENERGY COMMISSION Daylighting In Schools: Reanalysis Report

Abstract

This is a follow on report to the first PG&E Schools study, testing for teacher assignment bias and other variables. It synthesizes the general trend for a 'daylighting effect'

Full text

CALIFORNIA ENERGY
COMMISSION
Daylighting In Schools:
Reanalysis Report
TECHNICAL REPORT
October 2003
500-03-082-A-3
Gray Davis, Governor

CALIFORNIA
ENERGY
COMMISSION
Prepared By:
Heschong Mahone Group
Lisa Heschong, Project Director
Fair Oaks, California
Managed By:
New Buildings Institute
Cathy Higgins, Program Director
White Salmon, Washington
CEC Contract No. 400-99-013
Prepared For:
Donald Aumann,
Contract Manager
Nancy Jenkins,
PIER Buildings Program Manager
Terry Surles,
PIER Program Director
Robert L. Therkelsen
Executive Director
DISCLAIMER
This report was prepared as the result of work sponsored by the
California Energy Commission. It does not necessarily represent
the views of the Energy Commission, its employees or the State
of California. The Energy Commission, the State of California, its
employees, contractors and subcontractors make no warrant,
express or implied, and assume no legal liability for the
information in this report; nor does any party represent that the
uses of this information will not infringe upon privately owned
rights. This report has not been approved or disapproved by the
California Energy Commission nor has the California Energy
Commission passed upon the accuracy or adequacy of the
information in this report.

ACKNOWLEDGEMENTS
The products and outcomes presented in this report are part of the Productivity and Interior
Environments research project. The reports are a result of funding provided by the California Energy
Commission’s Public Interest Energy Research (PIER) program on behalf of the citizens of
California. Heschong Mahone Group would like to acknowledge the support and contributions of the
individuals below:
Heschong Mahone Group, Inc.: Principal in Charge: Lisa Heschong. Project Director: Lisa
Heschong. Project staff: Puja Manglani and Rocelyn Dee.
Subcontractors: Jack A. Paddon and James L. Engler of Williams + Paddon Architects + Planners
Inc., Marshall Hemphill of Hemphill Interior technologies, and James Benya of Benya Lighting
Design.
Review and Advisory Committee: We are greatly appreciative of the following people who
contributed to the review of this report: William Beakes of Armstrong Industries, Jerry Blomberg of
Sunoptics, Pete Guisasola of City of Rocklin Building Department, Rob Samish of Lionakis
Beaumont Design Group, Michael White of Johnson Controls, Chuck McDonald of USG, John
Lawton of Velux, John Mors of Daylite Company, Joel Loveland of Lighting Design Lab, Anthony
Antonelli of Ecophon, Steve Fuller and Martin Powell of Albertsons, Jehad Rizkallah of Stop and
Shop, Paul McConocha of Federated Departments, JimVan Dame of My-Lite Daylighting Systems
and Products, Doug Gehring of Celotex, Ivan Johnson of TriStar Skylights, Robert Westfall of
Solatube International Inc., Leo Johnson of PJHM Architects, George Loisos of Loisos/Ubbelohde
Architects, Jim Kobs of Chicago Metallics, Steve Ritcher of Crystollite, Jackie Stevens of So-
Luminaire, Peter Turnbull of PG & E, Sean Flanigan of WASCO Products, Richard Schoen of
Southern California Roofing, Mike Toman and Jeff Guth of Ralphs and Food for Less, and Lori
Johnson of Target.
Project Management: Cathy Higgins, New Buildings Institute; Don Aumann, California Energy
Commission.

PREFACE
The Public Interest Energy Research (PIER) Program supports public interest energy research and
development that will help improve the quality of life in California by bringing environmentally safe,
affordable, and reliable energy services and products to the marketplace.
This document is one of 33 technical attachments to the final report of a larger research effort called
Integrated Energy Systems: Productivity and Building Science Program (Program) as part of the
PIER Program funded by the California Energy Commission (Commission) and managed by the New
Buildings Institute.
As the name suggests, it is not individual building components, equipment, or materials that optimize
energy efficiency. Instead, energy efficiency is improved through the integrated design, construction,
and operation of building systems. The Integrated Energy Systems: Productivity and Building Science
Program research addressed six areas:
 Productivity and Interior Environments
 Integrated Design of Large Commercial HVAC Systems
 Integrated Design of Small Commercial HVAC Systems
 Integrated Design of Commercial Building Ceiling Systems
 Integrated Design of Residential Ducting & Air Flow Systems
 Outdoor Lighting Baseline Assessment
The Program’s final report (Commission publication # P500-03-082) and its attachments are intended
to provide a complete record of the objectives, methods, findings and accomplishments of the
Integrated Energy Systems: Productivity and Building Science Program. The final report and
attachments are highly applicable to architects, designers, contractors, building owners and operators,
manufacturers, researchers, and the energy efficiency community.
This attachment (#A-3) provides supplemental information to the program’s final report within the
Productivity and Interior Environments research area. It includes the following report:
 Daylighting in Schools: Reanalysis Report. This study expands and validates previous
research by Heschong Mahone Group that found a statistical correlation between the amount
of daylight in elementary school classrooms and the performance of students on standardized
math and reading tests.
The Buildings Program Area within the Public Interest Energy Research (PIER) Program produced
these documents as part of a multi-project programmatic contract (#400-99-413). The Buildings
Program includes new and existing buildings in both the residential and the non-residential sectors.
The program seeks to decrease building energy use through research that will develop or improve
energy efficient technologies, strategies, tools, and building performance evaluation methods.
This report is Attachment A-3 (Product 2.2.5) to the Final Report on Integrated Energy Systems:
Productivity and Building Science Program (Commission Publication #P500-03-082). For other
reports produced within this contract or to obtain more information on the PIER Program, please visit
www.energy.ca.gov/pier/buildings or contact the Commission’s Publications Unit at 916-654-5200.
All reports, guidelines and attachments are also publicly available at www.newbuildings.org/pier.

ABSTRACT
The “Daylighting in Schools: Reanalysis Report” is part of the Productivity and Interior
Environments research project, one of six research elements within the Integrated Energy Systems:
Productivity and Building Science Program. The Program was funded by the California Energy
Commission’s Public Interest Energy Research (PIER) Program.
This study expands and validates previous research by Heschong Mahone Group that found a
statistical correlation between the amount of daylight in elementary school classrooms and student
performance. The researchers reanalyzed student performance data from two school districts to
answer questions raised by the previous study. The reanalysis found that:
 Elementary school students in classrooms with the most daylight showed a 21% improvement
in learning rates compared to students in classrooms with the least daylight.
 There was no teacher assignment bias that might have skewed the original results; more
experienced or more educated teachers were not significantly more likely to be assigned to
classrooms with more daylighting.
 The daylighting effect does not vary by grade.
 Physical classroom characteristics (daylighting, operable windows, air conditioning, portable
classrooms) do not have an effect on student absenteeism. This seems to contradict claims
that have been made about the health effects of daylight or other environmental conditions, as
reflected in absenteeism rates of building occupants.
These results, which are consistent with the original findings, affirm that daylight has a positive and
highly significant association with improved student performance. These findings may have
important implications for the design of schools and other buildings.
Author: Lisa Heschong, Heschong Mahone Group
Keywords: Daylight, Productivity, Student Performance, Window, Skylight, Absenteeism,
Attendance, Health, Classroom Condition, School Design

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TABLE OF CONTENTS
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TABLE OF CONTENTS
EXECUTIVE SUMMARY ___________________________________________V
1. INTRODUCTION _______________________________________________ 1
1.1 Study Tasks________________________________________________ 2
1.2 Literature Review of Research on Teachers' Influence_______________ 2
1.2.1 Differences with Our Study _______________________________ 5
1.3 Summary of Previous Study ___________________________________ 5
2. TEACHER SURVEY ____________________________________________ 9
2.1 Methodology _______________________________________________ 9
2.1.1 Survey Structure ______________________________________ 10
2.2 Teacher Characteristics______________________________________ 11
2.2.1 Years of Experience____________________________________ 11
2.2.2 Education Level, Certificates and Honors ___________________ 12
2.2.3 Classroom Preferences _________________________________ 14
2.2.4 Criteria for Classroom Selection __________________________ 15
2.2.5 Permanent vs. Portable Classroom Preference_______________ 17
2.2.6 Classroom Energy Management __________________________ 18
2.3 Conclusions _______________________________________________ 20
3. TEACHER BIAS ANALYSIS _____________________________________ 23
3.1 Hypothesis________________________________________________ 23
3.2 Methodology ______________________________________________ 23
3.2.1 Teacher Credentials____________________________________ 23
3.2.2 Assignment Bias ______________________________________ 24
3.2.3 Decision to Focus on Daylight Code Only ___________________ 26
3.2.4 Daylight Code as a Dependant Variable ____________________ 26
3.2.5 Teacher Assignment Bias Models _________________________ 27
3.3 Findings __________________________________________________ 27
3.4 Conclusion________________________________________________ 29
3.5 Discussion ________________________________________________ 29
3.5.1 Findings of Different Study Population Models _______________ 30
3.5.2 Conclusions of Different Study Population Models ____________ 33
4. GRADE LEVEL ANALYSIS______________________________________ 35
4.1 Hypothesis________________________________________________ 35
4.2 Methodology ______________________________________________ 36

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TABLE OF CONTENTS
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4.3 Findings __________________________________________________ 36
4.4 Conclusions _______________________________________________ 38
5. ABSENTEEISM ANALYSIS _____________________________________ 39
5.1 Hypothesis________________________________________________ 40
5.2 Methodology ______________________________________________ 41
5.3 Findings __________________________________________________ 42
5.3.1 Absenteeism Findings __________________________________ 42
5.3.2 Tardiness Findings_____________________________________ 43
5.4 Conclusions _______________________________________________ 43
5.5 Discussion ________________________________________________ 44
6. RE-ANALYSIS CONCLUSIONS __________________________________ 47
6.1 Grade Level Analysis________________________________________ 47
6.2 Absenteeism Analysis _______________________________________ 47
6.3 Teacher Survey ____________________________________________ 47
6.4 Bias Analysis ______________________________________________ 48
6.5 Re-Analysis Report _________________________________________ 49
7. APPENDICES ________________________________________________ 51
7.1 Statistical Terminology ______________________________________ 51
7.2 Teacher Survey ____________________________________________ 53
7.2.1 Three Most Important Criteria in Selection of Classroom _______ 55
7.2.2 Permanent v. Portable Classrooms ________________________ 63
7.2.3 Additional Comments___________________________________ 67
7.3 Bias Analysis Models________________________________________ 71
7.4 Grade Level Models ________________________________________ 81
7.5 Absenteeism Models ________________________________________ 89

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TABLE OF CONTENTS
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TABLE OF FIGURES
Figure 1: Daylight Code Definitions ___________________________________ 6
Figure 2: Surveyed vs. Original Population Distribution by Daylight Code ____ 10
Figure 3: Number of Years Teaching for Survey Respondents _____________ 12
Figure 4: Teacher Education Level __________________________________ 13
Figure 5: Teacher Certificates and Honors (Recognition) _________________ 14
Figure 6: Most Preferred Attributes of Classrooms ______________________ 17
Figure 7: Permanent vs. Portable Classroom Preference _________________ 17
Figure 8: Teachers' Energy Management of Classrooms _________________ 19
Figure 9: Teachers' Lighting Management of Classrooms_________________ 20
Figure 10- Correlation of Teacher Variables to Daylight Variables, Student level
Analysis, Capistrano __________________________________________ 25
Figure 11 - Change in Capistrano Math Model with Addition of Teacher
Variables ___________________________________________________ 28
Figure 12 - Change in Capistrano Reading Model with Addition of Teacher
Variables ___________________________________________________ 28
Figure 13: Surveyed, Original, and Expanded Populations ________________ 30
Figure 14: Daylight Affect for Different Populations, with and without Teacher
Variables, on Reading Tests in Capistrano _________________________ 31
Figure 15: Daylight Affect for Different Populations, with and without Teacher
Variables, on Math Tests in Capistrano____________________________ 31
Figure 16: Teacher Variables and Daylight effect on Reading for the Three
Populations Compared ________________________________________ 32
Figure 17: Teacher Variables and Daylight effect on Math for the Three
Populations Compared ________________________________________ 33
Figure 18: Capistrano Grade Level Models with Interactive Variables
Summary ___________________________________________________ 37
Figure 19- Seattle Grade Level Models with Interactive Variables Summary __ 38
Figure 20- Distribution of Absences and Tardies ________________________ 41
Figure 21- Equation for natural log of attendance data ___________________ 42
APPENDICES:
Figure 22 - Capistrano Reading Models, Original Population, with and without
Teacher Variables ____________________________________________ 71
Figure 23 - Capistrano Math Models, Original Population, with and without
Teacher Variables ____________________________________________ 72
Figure 24 - Capistrano Reading Model, Teacher Survey Population, with and
without Teacher Variables ______________________________________ 73

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TABLE OF CONTENTS
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Figure 25 - Capistrano Math Model, Teacher Survey Population, with and without
Teacher Variables ____________________________________________ 74
Figure 26 - Capistrano Reading Model, Expanded Population, with and without
Teacher Variables ____________________________________________ 75
Figure 27 - Capistrano Math Model, Expanded Population, with and without
Teacher Variables ____________________________________________ 76
Figure 28 - Descriptive Statistics, Capistrano Original Population ___________ 77
Figure 29 - Descriptive Statistics, Capistrano Teacher Survey Population ____ 78
Figure 30 - Descriptive Statistics, Capistrano Expanded Population _________ 79
Figure 31- Capistrano Grade Level Interaction, Reading Daylight___________ 81
Figure 32- Capistrano Grade Level Interaction, Math Daylight _____________ 82
Figure 33- Seattle Grade Level Interaction, Reading Daylight ______________ 83
Figure 34 - Seattle Grade Level Interaction, Math Daylight ________________ 84
Figure 35- Descriptive statistics, Capistrano Grade Level, Reading and Math _ 85
Figure 36- Descriptive statistics, Seattle Grade Level, Reading ____________ 86
Figure 37- Descriptive statistics, Seattle Grade Level, Math _______________ 87
Figure 38 - Capistrano Absenteeism Model ____________________________ 89
Figure 39 - Capistrano Tardiness Model ______________________________ 90
Figure 40 - Capistrano Absenteeism/Tardiness Descriptive Statistics________ 91

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT EXECUTIVE SUMMARY
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EXECUTIVE SUMMARY
This report is a follow-on study to the Daylighting in Schools study1 that was completed
in 1999, which found a compelling statistical correlation between the amount of
daylighting in elementary school classrooms and the performance of students on
standardized math and reading tests. This re-analysis of the original study data was
intended to answer key questions raised by the peer review of the earlier study, and
expand our understanding of methodological choices for further work.
The original findings potentially have very important implications for the design of
schools and other buildings where people live, work and play. Daylight used to be
common and even required in schools, homes and offices, but fully daylit buildings
became increasingly rare as electric lighting became more the norm. This re-analysis
study helps to provide greater certainty for the original findings.
For this re-analysis study HMG conducted four tasks:
The Teacher Survey collected information from a sample of teachers in the Capistrano
school district about their education and experience levels, preferences for classroom
features and operation of those features. The primary purpose of the survey was to
provide input to a subsequent "assignment bias" analysis. In addition, we learned some
useful information about teacher preferences, attitudes and behaviors in response to
classrooms conditions.
While the teachers we surveyed generally had a preference for windows, daylight and
views in their classrooms, these preferences were not found to be driving classroom
preferences. Far more important was an almost universal desire for more space, a good
location, quiet, lots of storage and water in the classroom.
Environmental control was also found to be an important issue for teachers, especially
for those who did not have full control. Teachers seemed to hold a basic expectation that
they would be able to control light levels, sun penetration, acoustic conditions,
temperature and ventilation in their classrooms. They made passionate comments about
the need for improvement if one or more of these environmental conditions could not be
controlled in their classroom.
The Teacher Bias Analysis further examined information from the Teacher Survey. The
survey data was coded into variables and statistically analyzed in relation to both
assignment to daylit classrooms and the student performance models. The goal of the
Bias Analysis was to discover if the original study had over-inflated the effect of daylight
on student learning by not accounting for a potential "assignment bias" of better teachers
to more daylit classrooms.
We conclusively found that there was not an “assignment bias” influencing our results.
None of the individual teacher characteristics we identified were significant in explaining
assignment to a daylit classroom in the Capistrano District. Considering all teacher
characteristics together only explained 1% of the variation in assignment to daylit
classrooms. We did find that a few types of teachers, those with more experience or
1 Heschong Mahone Group (1999). Daylighting in Schools. An investigation into the relationship between
daylight and human performance. Detailed Report. Fair Oaks, CA.
(http://www.h-m-g.com/Daylighting/daylighting_and_productivity.htm)

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT EXECUTIVE SUMMARY
vi
honors, were slightly more likely (1%-5%) to be assigned to classrooms with more
windows or some types of skylights.
When we added the teacher characteristics to the original student performance models,
the daylight variables were not reduced in significance. Further analysis of other sub-
populations repeated these findings. Among twelve models considered, we identified a
central tendency of a 21% improvement in student learning rates from those in
classrooms with the least amount of daylight compared to those with the most.
In the Grade Level Analysis, we re-analyzed the original student test score data for
both Capistrano and Seattle by separate grade level, instead of aggregating the data
across the four grade levels (2-5). Our goal was to determine if this method would more
accurately explain the relationship of student performance to daylighting. We tested for
statistical significance and correlation, and we looked at any patterns discovered in the
analysis.
The data did not show any significant patterns between a daylight effect and the
separate grade levels, neither an increase or decrease in daylight effects by grade level.
Thus, we conclude that there do not seem to be progressive effects as children get
older, nor do younger children seem to be more sensitive to daylight than older children.
Allowing the results to vary by grade did not noticeably improve the accuracy of the
models. Therefore, we conclude that looking at data across grade levels is a sufficiently
accurate methodology.
In the Absenteeism Analysis, we used absenteeism and tardiness data in the original
Capistrano data set as dependent variables and evaluated them against the full set of
explanatory variables from the original study, plus the new information on teacher
characteristics. These models would allow us to assess whether daylighting or other
classroom physical attributes potentially impacted student health, as measured by
changes in student attendance.
Student attendance data is certainly not the best indicator of student health. Yet to the
extent that attendance data does reflect student health, our findings do not suggest an
obvious connection between physical classroom characteristics and student health.
Notably, daylighting conditions, operable windows, air conditioning and portable
classrooms were not found to be significant in predicting student absences.
Overall, the strength of the daylight variable in predicting student performance stands
out sharply across all of these re-analysis efforts.
This analysis also demonstrated that the findings of these models are more strongly
dependent upon the sample population then the subtleties of the explanatory variables.
Thus, we believe that it will be more informative to replicate this study with a different
population, to continue to try to refine the models with further detail in the explanatory
variables.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
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1. INTRODUCTION
The Daylighting in Schools study1 completed in 1999 by the Heschong Mahone
Group on behalf of the California Board for Energy Efficiency found some a
compelling statistical correlation between the amount of daylighting in elementary
school classrooms and the performance of students on standardized math and
reading tests.
The study was reviewed by a panel of experts, recruited by Lawrence Berkeley
National Laboratory and involved a wide range of disciplines related to the study.
In general the review panel was satisfied with the soundness of the basic
methodology and the rigor of the statistical analysis. An additional “classroom
level analysis” (included in the Appendix of the detailed version) verified the
robustness of the initial results. The peer reviewers, however, expressed two
primary concerns2 that could only be addressed in follow-up studies. These are:
1. The results might be confounded by a potential bias whereby "better"
teachers might be more likely to be assigned to more daylit classrooms
2. The analysis might be more accurate if performed by grade level,
rather than aggregating data from four grade levels together
The study described in this report, supported through the California Energy
Commission's Public Interest Energy Research (PIER) program, was designed to
address these two concerns, while also expanding other areas of our knowledge
about the interaction of students, teachers and daylighting. The series of four
tasks described in this report were the necessary first steps in resolving
remaining questions about the Daylighting and Schools study. The results of
these initial re-analysis studies will also be used to inform the methodology and
data collection for the forthcoming PIER productivity studies in schools, retail,
manufacturing, and offices.
This report discusses the re-analysis of the 97-98 school year student
performance data on standardized math and reading tests from the Capistrano
Unified School District in Southern California and the Seattle Public School
District in Seattle Washington. The re-analysis of the original study data was
intended to answer key questions raised by the peer review of the earlier study,
and expand our understanding of methodological choices for further work.
1 Heschong Mahone Group (1999). Daylighting in Schools. An investigation into the relationship between
daylight and human performance. Detailed Report. Fair Oaks, CA.
2 Heschong Mahone Group (1999). Daylighting and Productivity. An investigation into the relationship
between daylight and human performance. Review Report. Fair Oaks, CA.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
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1.1 Study Tasks
Four study tasks were defined, which are briefly summarized here, and described
fully later:
• Teacher Survey
• Teacher Bias Analysis
• Grade Level Analysis
• Absenteeism Analysis
The Teacher Survey surveyed a sample of teachers in the Capistrano school
district to determine their years of teaching experience, education level, and
other characteristics that might be associated with being a "better" teacher. While
we were conducting a survey, we decided to include a few additional questions to
learn more about the teacher's perspective on classroom assignments, their
preferences for the physical qualities of classrooms, and how they operated their
classrooms.
The survey fed into the second task Teacher Bias Analysis. The teacher
information from the survey was coded into variables that could be analyzed
statistically. First we looked at the assignment bias, to see if some types of
teachers were more likely to be assigned to more daylit classrooms in the
Capistrano District. Next, we added the information about the teachers to the
original Capistrano student test score models to see if accounting for teacher
characteristics would impact the significance or magnitude of the daylight
variables.
In addition to the tasks described above, we also re-analyzed the original data in
two other ways. The Grade Level Analysis looked at the original student test
score data for both Capistrano and Seattle by grade level to see if this was a
more accurate way to study the relationship of student performance to
daylighting.
The original Capistrano data set also included information on student
attendance--both absences and tardiness. This gave us the opportunity to see if
daylighting, or other physical characteristics of the classrooms in Capistrano,
were associated with changes in attendance. For the Absenteeism Analysis
task, we set student absenteeism and tardiness as dependent variables, and
used the full set of explanatory variables used in the original study, plus the new
information on teacher characteristics, to see if daylighting or other classroom
attributes were associated with student attendance.
1.2 Literature Review of Research on Teachers' Influence
We looked to research by educational researchers in our effort to understand
how teacher characteristics might be described and included in our models.
Various educational researchers have analyzed the relationship between teacher
performance and student achievement, and have identified a number of teacher

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
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characteristics that seem to fairly reliably predict student learning in the
classroom. Factors that have been found to be significant in previous studies
include a teacher's general intelligence, teaching experience, certain personality
traits, knowledge of the subject matter, knowledge of teaching strategies,
continuing education, and certification1. The following summary is based on an
extensive literature review by Prof. Linda Darling-Hammond of the Stanford
University School of Education (Darling-Hammond 2000) of the recent research
on the relationship between teacher performance and student achievement. The
reader is referred to her report for specific citations or further detail on studies.
This literature review helped inform the classification of teacher characteristic
variables for in this study. The discussion below includes both the approach of
other researchers to define variables of interest and a brief summary of some of
their findings.
General intelligence: General intelligence as measured by IQ test or college
grade point average shows the weakest performance as a predictor of
subsequent student performance. While early studies in the 40's positively
correlated teachers' intelligence and student achievement, these correlations are
generally statistically insignificant and have not held up over time. Two meta-
reviews of these studies performed in the 80's found little or no correlation.
Teaching experience: Researchers have usually measured teaching experience
by the number of years a teacher has spent in the profession. While various
studies have found a positive relationship between teachers' experience and
student learning, this relationship is not always significant or linear. Although
many studies conclude that inexperienced teachers generally perform less well
than those with more experience, the benefits of experience tend to level off after
approximately five years. This seems, however, to be dependent on the
organizational structure of the school district: in districts that emphasize the
importance of continuing education, long time teachers are more likely to improve
throughout their career.
Teacher personality traits: Studies have found scant correlation between
student learning and various teacher personality traits. One exception is a set of
personality traits variously defined as "flexibility," "creativity," or "adaptability."
This would seem to be consistent with a theory that a teacher's ability to
creatively adjust their teaching methods to fit the needs of the students and the
instructional goals would correlate positively with student learning. Some
researchers have found that "flexibility" is also closely correlated to variables
measuring a teacher's professional education, implying that teachers who have
studied formally are more likely to be able to adjust teaching strategies for
students' different learning styles.
Knowledge of subject matter: Knowledge of the subject matter to be taught, as
measured by number of college classes taken or by scores on a subject matter
1 Darling Hammond, L. (2000). Teacher Quality and Student Achievement: A review of state policy evidence.
Education Policy Analysis Archives, Vol. 8, number 1, available on-line, http://epaa.asu.edu/epaa/v8n1/

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
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test, has been found to be less important than might be expected. A variety of
studies have shown small, statistically insignificant relationships, both positive
and negative. One recent study found that teachers' coursework in the subject
field relates positively to student achievement in mathematics and science, but
that the number of courses show diminishing returns above a certain threshold
level (Monk, 1994). A teacher's knowledge of the subject was found to be more
important for higher-level classes and higher-achieving students (Hawk, Coble, &
Swanson, 1985). Thus, a certain level of subject matter knowledge appears
important, but above that point, other factors, such as the ability to effectively
convey this knowledge, become more important to student achievement.
Teaching strategies: Knowledge of teaching strategies has been measured by
number of education classes taken in teaching methods and level of college
degree (BA or MA). These variables generally capture variance in teacher
performance more effectively than the variables discussed above. Ferguson and
Womack (1993) studied 200 graduates of one teacher education program. They
concluded that the amount of education coursework was responsible for more
than four times the variance (16.5 %) in teacher performance than measures of
content knowledge, as determined by National Teacher Examination subject
matter test scores and GPA in the major (4.5 %).
Continuing education: It is also seems to be important that teachers continue to
refresh and update their knowledge through continuing education. Greater
student achievement has been linked to mathematics teachers' opportunities to
participate in sustained professional development courses. Similar results have
been suggested for literature-based instruction. Not only is the amount of
ongoing education important, but also how recent it is.
Certification: Standard certification usually requires a teacher to graduate from
an accredited teacher training program, have a major or minor in the field to be
taught, and pass a test on basic skills and teaching strategies. Therefore,
certification status (standard certification vs. emergency, temporary or provisional
certification issued to those lacking the above credentials) is a measure of both
knowledge of the subject and of teaching skills. Linda Darling-Hammond
compiled data from all 50 states using the 1993-94 Schools and Staffing Surveys
(SASS) and the National Assessment of Educational Progress (NAEP). She
found that at the state level, the percentage of well-qualified teachers (with full
certification and a major in their field) was the strongest, consistently positive
predictor of student achievement (.61 < r < .80, p<.001) while the percentage of
newly hired, uncertified teachers was the strongest, most consistently negative
predictor of student achievement (-.63 < r < -.40, p<.05).
Scores on state licensing examinations: Another variable that combines
several important factors are scores on state licensing examinations, which test
both basic skills and teaching knowledge. Ronald Ferguson (1991) examined
900 Texas school districts, controlling for student background and district
differences, and found that a combination of teacher qualification variables –
scores on a licensing examination, education level, and years of experience --
explained more of the inter-district variation in students' reading and mathematics

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
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achievement gains than student socioeconomic status. The strongest of these
variables were the scores on the state licensing exam.
1.2.1 Differences with Our Study
These studies formed a context of our work. However, the goal of our study was
not to determine the effect of teachers’ credentials, qualifications, and experience
on student performance. Our goal, rather, was to discover whether daylighting in
classrooms remained a significant indicator of student performance even when
teacher characteristic variables were included in a statistical regression model.
Thus, our study differed from those discussed above in several important ways.
First of all, our data collection procedure of teacher variables was limited, due to
privacy concerns, to the variables we could reliably measure through self-
reporting. We had to exclude original sources such as transcripts, college or
licensing board test scores, or classroom observations.
Second, the data in other studies was often aggregated to the district or state
level. We, on the other hand, analyzed the data at the student and classroom
level, which may yield different results or emphasize different factors.
1.3 Summary of Previous Study
For the original schools study we identified three study sites of large school
districts that had a range of daylighting conditions in their classrooms. We
collected test scores and demographic information for all second through fifth
graders in the district, and classified their classrooms for the amount and quality
of daylight available. We choose to work with data on elementary school children
since they typically spend all year in one classroom. Thus, we could directly
isolate the effects of that one classroom. We also specifically selected districts
that had a number of classrooms lit from above with skylights or roof monitors
(“toplighting”). We reasoned that daylight provided through windows might have
a number of complicating factors, such as the quality of view, whereas daylight
provided from above typically had fewer other qualities that might influence
results, thus we would be more likely to be looking a pure “daylighting” effect.
The three districts were located in San Juan Capistrano, (Southern) California;
Seattle, Washington; and Fort Collins, Colorado. These three districts have very
different climates, different school building types, different curriculums and
different testing protocols. The districts also provided us with information about
student demographic characteristics, special school programs, size of schools,
etc.
We added information to these data sets about the physical conditions of the
classrooms to which these children were assigned. We reviewed architectural
plans, aerial photographs and maintenance records and visited a sample of the

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
6
schools in each district to classify the daylighting conditions in over 2000
classrooms. Each classroom was assigned a series of codes on a 0-5 scale (see
Figure 1) indicating the size and tint of its windows, the presence and type of any
skylighting, and a holistic daylighting code indicating the overall quality and
quantity of daylight expected from both windows and toplighting combined. In
Capistrano, the skylights were given a variable type (A, AA, B, C, D) rather than
a scalar. The configuration of these skylight types is described in the original
report. The Daylight Code, which is used predominately for reporting findings in
this report, was based on the following qualitative criteria, with foot candle levels
at midday conditions are provided as an illustration rather than a criteria.
Daylight Code 5 Classroom is adequately and uniformly lit with daylight, such that
teacher could successfully instruct with electric lights off, for
most of the school year. 50± footcandles on most desks.
Daylight Code 4 Classroom has major daylight component, and could
occasionally be operated without any electric lights. Daylight may
have strong gradient. 30± footcandles on many desks.
Daylight Code 3 Classroom has adequate levels in limited areas, such as near
windows. Some, but not all, electric lights could occasionally be
turned off. 15± footcandles at some desks.
Daylight Code 2 Classroom has poor and/or very uneven daylight. Not likely to
ever operate without electric lights fully on. 10± footcandles in
limited areas.
Daylight Code 1 Classroom has minimal daylight. Very small and/or darkly tinted
windows or inadequate toplighting. Not possible to operate
without electric lights. 5± footcandles in limited areas.
Daylight Code 0 Classroom has no daylight.
Figure 1: Daylight Code Definitions
Ultimately the study analyzed test scores performance for 8000 to 9000 students
per district. We looked at both math and reading scores in all three districts, and
analyzed each separately, alternately using the holistic daylight code and the
separate window and skylight codes, for a total of twelve statistical models.
The Capistrano Unified School District proved to be our most interesting study
site for a number of reasons. The District administers standardized tests both in
the fall and spring, allowing us to compare the change in students’ math and
reading test scores while they spent the year in one classroom environment.
Because the District, like most in California, has a number of standardized
portable classrooms at every elementary site, we were able to use these
portables as a standardized condition controlling for the influence of individual
school sites or neighborhoods. We also collected additional information at this
district about the HVAC and ventilation conditions of the classrooms, which was
also included in the analysis.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
7
In Capistrano, using a regression equation that controlled for 50 other variables,
we found that students with the most daylighting in their classrooms progressed
20% faster on math tests and 26% on reading tests in one year than those with
the least. Similarly, students in classrooms with the largest window areas were
found to progress 15% faster in math and 23% faster in reading than those with
the least. Students that had a well-designed skylight in their room, one that
diffused the daylight throughout the room and which allowed teachers to control
the amount of daylight entering the room, also improved 19-20% faster than
those students without a skylight. Classrooms with a skylight that allowed direct
beam sunlight into the classroom and did not provide the teacher with a way to
control the amount of daylight were actually seen to have a negative association
with student performance. In addition, in three of the four Capistrano models, the
presence of an operable window in the classroom was also seen to have a
positive effect on student progress, associated with 7-8% faster learning. These
effects were all observed with 99% statistical certainty.
The Seattle and Fort Collins school districts administer only one standardized
test at the end of the school year. In these districts, the study used the final
scores on math and reading tests at the end of the school year and compared
the results to the district-wide average test score. In both of these districts we
also found positive and highly significant (99%) effects for daylighting. Students
in classrooms with the most daylighting were found to have 7% to 18% higher
scores than those with the least.
The three districts have different curricula and teaching styles, different school
building designs, and very different climates. And yet, the results of the studies
show consistently positive and highly significant effects. This consistency across
such diverse school environments persuasively argues that there is a valid and
predictable effect of daylighting on student performance.
These models explained from 25% to 44% of the variation in student scores (R2=
.25 to .44). Thus another 56% to 75% of the variation might be explained by
other factors not included in our equation such as teacher quality, home life,
health, nutrition, individual talents and motivation, etc. There always remains the
possibility that some other variable left out of the equation is influencing results
on the variable of interest.
Reviewers of the original school study specifically asked if “better” teachers were
more likely to be assigned to the more daylight classrooms, thus influencing the
results. Additionally, they asked if the analysis might be more accurate if
performed by grade level rather than aggregating data from four grade levels
together. This follow-on study addresses those concerns by re-examining our
most detailed models for the Capistrano district.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT INTRODUCTION
8

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
9
2. TEACHER SURVEY
The first task for the follow-up study was to collect additional information about
the teachers that could be added to the original models. We choose to work with
the Capistrano Unified School District for three reasons: they had provided us
with the most detail in the original study, they were willing to cooperate with us on
further studies, and they were physically the closest district to us.
2.1 Methodology
We asked the District the best way to compile additional information about the
specific teachers in the study that would be useful in our re-analysis. The District
was unable to provide us with information about their teachers directly due to
confidentiality restrictions. However, they agreed that we could solicit such
information from the teachers, in a survey. A survey gave the teachers an
opportunity to decline to participate, and allowed us to collect additional
information that could be kept confidential from the District.
We agreed that the District would review and approve the instrument, and also
help us to locate the teachers in our sample for distribution of the survey. A two-
page survey was developed and reviewed by the District and members of our
Technical Advisory Committee. A draft version was tested on a number of local
elementary teachers for ease of use and clarity.
The final survey, with a explanatory cover letter from the District office, was
distributed to a stratified sub-sample of teachers from our original data set. We
identified 14 schools with a balanced sample of all window and skylight
conditions found in the original 27 elementary schools included in the 97-98
database. Our goal was to achieve a sufficient population of teachers in each
daylighting condition, in order to have the best chance to achieve statistical
certainty in our new analysis. We provided the District a list of all teacher names
used in mapping the data for those 14 schools. The District then located these
teachers for us. Over the two year period, between the survey and the original
data mapping, about 17% of the teachers had left the district or moved to non-
teaching jobs and about 6% had re-located to a different school in the district. As
a result, our sub-sample of teachers now resided at every elementary school in
the district.
Surveys were mailed to each school office, with a list of teachers to whom they
were to be distributed. After two days, the responses were collected in a
confidential master envelope and returned to us for analysis. Ultimately, we
received completed surveys from 68% of the teachers on our distribution list, or
206 teachers, representing 3900 students in our data set. Some school offices
disregarded our list and distributed the survey to all of their teachers, so we
received responses from an additional 44 teachers who were not in our original
study, for a total of 250 responses.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
10
0%
10%
20%
30%
40%
50%
01233.544.55
Daylight Code
Pe rce ntage of Students
O r igin a l Surveyed
Figure 2: Surveyed vs. Original Population Distribution by Daylight Code
Figure 2 shows the resulting distribution of students by Daylight Code for the
surveyed population compared to the original population of the study. The two
populations are reasonably similar. There is a slight increase in the proportion of
teachers in the higher daylight codes (3.5+) due to our concern that our sample
include enough teachers to support statistically significant analysis. The
reduction in Daylight Code 2 reflects a lower sampling of teachers in portable
classrooms.
2.1.1 Survey Structure
The two-page survey instrument, provided in Appendix 7.1, contains both
structured and open-ended questions. The primary purpose of the survey was to
collect information about teacher characteristics that could be included in our
models of student performance in daylit classrooms. Thus, the survey first asked
for the classroom and grade assignment for both the current year and the 97/98
school year so that we could verify our data mapping. It then asked for the
teacher’s education level, certificates, additional coursework, special honors, and
years of teaching experience—in the current school, district and total.
In addition, we collected information about the teachers’ perception of any
“assignment bias,” their preferences for classroom selection, and additional
information about how they operated their classrooms. While this information was
not part of the primary intent of the survey, it was hoped that such information
might provide valuable insight in future analysis.
Thus, the survey was designed to answer the following questions:

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
11
1. What are the educational qualifications and experience of the teachers
who taught in classrooms included in the 97-98 data set?
2. Did they believe that they have been allowed to choose their own
classroom or have any influence on where they are assigned?
3. If they could choose a classroom, what attributes of a classroom would
they give top priority in their selection?
4. How do these teachers operate the energy-using features of their
classrooms? For example, if they have operable windows, how often
do they open them?
2.2 Teacher Characteristics
The survey responses were categorized, cleaned and entered into a database.
Information from open-ended questions was coded for analysis. The teacher
characteristic information was eventually transformed into variables for inclusion
in the statistical models of later tasks in this study.
The Capistrano Unified School District tries to maintain uniformly high education
and training standards among its teachers, which tends to reduce the variation in
teacher quality across classrooms. In discussions with Capistrano administrators
prior to the survey, we were told that the District was not hiring teachers with
provisional or emergency credentials. Beyond requiring all of their teachers to be
certified, the district highly values continuing education for all teachers. A sliding
salary scale rewards additional college education, in addition to years of
experience. The District also provides opportunities for on-site training classes
that are specifically tailored to the curriculum needs of the district.
2.2.1 Years of Experience
The 250 teachers who responded to the survey varied in their teaching
experience from one or two years to more than 40 years. They averaged 11
years of teaching in the CSUD district and 13.5 years of teaching in total (see
Figure 3).

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
12
Teachers Experience
0
5
10
15
20
25
30
35
40
45
No. of Years Teaching
Years teaching in district
Years teaching total
Figure 3: Number of Years Teaching for Survey Respondents
2.2.2 Education Level, Certificates and Honors
Teachers were asked to report their highest educational degree, plus additional
college course work, training programs, and special certificates and honors. This
information was described qualitatively by the teacher respondents, thus we
needed to classify the responses into meaningful categories that could be used
to analyze the data. The first step was to understand the educational
requirements for elementary school certification in California, and similarly the
District’s standards for hiring and promotion.
There are two levels of accreditation in California elementary schools. A
Preliminary Credential is good for the first five years of teaching. It requires as a
minimum completion of a bachelor's degree and a teacher preparation program,
knowledge of the US Constitution, plus additional certification in teaching
reading, passing a standardized test of knowledge (CBEST) and the multiple
subject assessment for teachers (MSAT). The second level of accreditation is
called the Professional Clear. It requires an additional fifth year of study beyond
the bachelor's including course work in computer, health and special education.
Based on interviews with the District personnel officers and review with our
Technical Advisory Group, we decided to group the teachers’ education levels for
analysis into two simple categories, BA and MA, with three sub-categories, as
follows:

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
13
• "BA" indicated any teacher with a bachelors degree
• "Clear only" indicated teachers who had been teaching for 7 years or
more, but had not pursued any continuing education beyond that
necessary for their professional clear credential.
• "BA Plus" indicated teachers who listed college credits beyond the
minimum required for certification
• "MA" indicated those with a masters, or doctorate (one case)
• "MA Plus" identified teachers with college credits beyond a master's
degree.
In our sample of surveyed teachers (Figure 4), 58% had Bachelor degree, of
which 12% had only a BA and had taught for 6 years or less, 12% were grouped
in the Clear Only category, 34% were grouped in the Bachelor Plus category;
42% of the teachers reported having a Masters degree, of which 29% had just an
MA, and 13% were grouped in the Masters Plus category,
Teacher Education
MA Plus
13%
MA
29%
BA Plus
34%
Clear Only
12%
BA
12%
Figure 4: Teacher Education Level
In addition to their qualifications, teachers also reported other credentials that
identified if they have received any special certificates or honors. From this
information we defined two other analysis categories:
• The Certificates category included teachers, who reported special
certificates beyond those required for the CLEAR credential, such
as a certificate in bilingual or gifted and talented education.
• The Honors category grouped together all teachers who reported
special awards or honors, such as being named a mentor teacher
or Teacher of the Year.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
14
Figure 5 shows the proportion of surveyed teachers who were classified into
these two categories.
Teachers with Certificates
77%
Cetificates
23%
Teacher Recognition
60%
Recognition
40%
Figure 5: Teacher Certificates and Honors (Recognition)
2.2.3 Classroom Preferences
We pursued a number of different methodologies to understand if there was an
intentional or unintentional bias in assigning some teachers to more daylit
classrooms. In our original study we had interviewed administrators and
principals in the district, who assured us that there was no obvious mechanism or
practice of assigning "better" teachers to more daylit classrooms. Given the rapid
growth of the district, frequent reassignment of classrooms to accommodate new
school openings and added portable classrooms tended to randomize teacher
classroom assignments on a fairly regular basis. In addition, it was reported that
each school site follows its own administrative criteria in assigning teachers to
classrooms, using criteria such as clustering of grade levels or special interest
teaching teams.
From the Teacher Survey we found a slightly different story. Of the teachers
surveyed, 32% felt that they may have had some influence on the selection of
their classrooms within the past year (a yes answer to Question 14) and 41%
answered yes or maybe. Similar percentages reported that they may have had
past influence. Thus, the teachers seemed to feel that they could influence
classroom selection.
When asked to indicate their top criteria for selection of a classroom, if they were
to have a choice (Question 15), 8% of the sample ranked windows or natural light
as their top criteria, and 27% mentioned windows, natural light or view within
their top three choices. Lumped together, these three criteria would have placed
fourth in importance as a classroom selection criteria, after classroom size
(53%), convenient location (36%), and storage capacity (30%). (See Figure 6
and discussion in Section 2.2.4 below.) Thus, while windows and associated
qualities light natural light and view are important to teachers, they are not the
most important criteria that teachers claim drive their choices.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
15
In addition to the structured questions, teachers were given the opportunity to
write any comments they wished. Over two hundred, or 80%, took the
opportunity to write informative comments, while three complained about not
enough time to respond. (See Appendix 7.1.3 to read the un-edited comments)
Their comments read as a loud plea for better physical conditions in the
classroom. The reader should realize that many of the comments are referring to
class-size reduction measures that were instituted in the District to increase the
number of teaching spaces, but unfortunately have compromised physical
comfort and control. The passion for control of physical conditions--lighting,
acoustics, ventilation and thermal comfort--is also very evident in these
comments. The list of comments should make compelling reading for anyone
managing or designing school facilities.
2.2.4 Criteria for Classroom Selection
The survey, in an open-ended question, asked what were the three most
important criteria that the teacher would use to select a classroom, if they were
given the choice. We grouped the qualitative responses into the following
categories, reported in the order of their frequency of mention within the top three
criteria:
• Size indicated teachers’ preference for larger classrooms and was
most frequently listed in the top three criteria, mentioned by 53% by
respondents. It was also the most frequently listed as the top
preference.
• Location of the classroom within the school layout was the second
most common criteria in determining their classroom choice (36%),
and was also second as the top criteria. The location preferences
included close proximity to the school entrance, administrative offices,
playground, library, or other elements of the school plan.
• Storage space inside the classroom in the form of closets or cupboards
was the third most mentioned criteria.
• Water or the availability of a sink in the room was among the top four
most mentioned criteria. Comments typically emphasized the primary
importance of water in the classroom for student hygiene, and
secondarily for class projects.
• Quiet captured criteria such as “lack of noise” and “being in a quiet
zone.” It was the fifth most common criteria (23%) mentioned in any of
the top three preferred classroom attributes by teachers, and third
criteria in terms of teachers’ top preference (after classroom size and
location).
• Windows were mentioned by 20% of the respondents.
• HVAC indicated a preference for air conditioning in the classroom, or
control of temperature, or acceptable thermal comfort conditions.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
16
• Door indicated a preference for full enclosure or the availability of a
door to close off the classroom from other activity areas.
• Proximity indicated a preference for a classroom close to particular
colleagues, either by grade level or shared teaching responsibilities.
• Condition indicated a preference for better physical conditions, such as
new paint, furniture or carpet, or good maintenance.
• Ventilation indicated a preference for fresh air or good air circulation.
• Lighting indicated preference for a good lighting quality in the
classroom or control of the lighting levels.
• Natural light indicated a preference for natural light from windows or
skylights.
• Walls indicated a preference for lots of wall surfaces for display.
• Bathroom indicated a preference for a bathroom close by.
• Views indicated a preference of a good view from the classroom.
• Whiteboards indicated a preference for lot of whiteboard surfaces.
• Phone indicated a preference for a telephone available in the
classroom.
• Workroom indicated a preference for being adjacent to a teacher
workroom.
Classroom Selection Criterea
0%
10%
20%
30%
40%
50%
60%
size
locatio n
storage
water
quiet
w indow s
HVAC
door
proximity
condition
ventilation
lighting
natural light
w alls
bathroom
view s
w hiteboards
phone
w orkroom
Percentage of Responses
Top 3 choices
1st preference

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
17
Figure 6: Most Preferred Attributes of Classrooms
It should be noted that the teachers' preferences for classroom features is largely
a function of what options are, or are not, currently available to them. For
example, a teacher in a school without windows but the option of moving to a
portable with a window may rank windows very high, while a teacher in a
classroom with large windows but no sink, may rank access to water highest.
Thus, we interpret these results to be particular to the context of the Capistrano
Unified School District and the status of current facilities.
2.2.5 Permanent vs. Portable Classroom Preference
The use of portable classrooms in California was mandated by the state for a
number of years as a strategy to accommodate rapidly shifting population
growth. As a consequence, every school site in our Capistrano study had a
substantial number of portables. Portable classrooms have also come under
recent scrutiny for possible poor indoor air quality or other health concerns such
as mold growth. A number of state and national studies are currently trying to
assess the health implications of portable classrooms. Our 1999 study did not
find any negative student performance impacts associated with portables.
Indeed, our models tended to find positive, but not statistically significant
impacts, associated with being in a portable classroom, once we controlled for
daylight, ventilation and all other variables in our equation. To learn more about
teacher’s perceptions of portables we included a question about preference of
portable or permanent classrooms in the survey (Question 16). The answers and
associated comments are fully presented in Appendix 7.1.2.
Permanent vs. Portable Preference
portable
15%
no answer
2%
permanent
68%
no opinion
15%
Figure 7: Permanent vs. Portable Classroom Preference
Sixty eight percent of the teachers surveyed preferred to teach in a permanent
classroom rather than a portable one. (Figure 7). Thirty percent of the

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
18
respondents were divided equally between those who preferred portable
classrooms or reported no preference for either type.
48% of the teachers that preferred portables mentioned that the closed walls of
the portable solved the noise and distraction problems found in the permanent
classrooms of their school created by an open classroom plan or poor acoustics.
24% preferred portables because they were larger than the permanent
classrooms available at their school. Remaining comments mentioned the
presence of air conditioning, better bulletin boards, and better physical condition.
Teachers who preferred permanent classrooms had a much wider range of
reasons why. Larger size, better location, better amenities, less noise were
frequently mentioned. One teacher summed up a preference for permanent
classrooms in the comment: “Feels substantial and lets children know they are
important and that things are not temporary.” 22% of teachers preferring
permanent classrooms specifically mentioned indoor air quality concerns, such
as moldy or musty smells and increased incidence of allergies or colds in
portables.
2.2.6 Classroom Energy Management
In the survey, teachers were asked how they operated a number of energy using
features in their classrooms. The data that we have for the Capistrano
classrooms merely indicates the presence of a feature, such as operable
windows, not whether or how it is used. This set of questions was intended to
provide insight into how their might actually use these features, and provide
some baseline data, admittedly self-reported, that might allow us to estimate the
energy impacts of various features.
Figure 8 highlights the percentages of teachers’ responses for the ten energy
statements surveyed. Positive percentages indicate actions taken, while negative
percentages indicate inability to act, or no action.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
19
Classroom Energy Management
25%
46%
54% 55%
6%
24%
5% 9% 12% 2%
-60%
-41%
-22%
-10%
-42%
-11%-11%
-9%
-5%
-9%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
adjust thermostat
open door (ventilation)
darken room (video)
close w /d for noise
som e lights off
open w indow (ventilation)
all lights off
block sun
fan on
dr aw curt ains
% of re spons es
>10x /y ear
>10x/w eek
Nev er Do
Can ' t Do
Figure 8: Teachers' Energy Management of Classrooms
HVAC control: Over 50% of the teachers’ surveyed reported adjusting the
classroom thermostat on a weekly basis, and almost 90% of them reported doing
this more than 10 times/school year (about monthly).
Acoustic control: Over 80% of the teachers occasionally close the windows or
doors (“close w/d for noise”) to avoid high noise levels from the outside, and 55%
do this frequently.
Ventilation control: 46% open the outside door for ventilation purposes on a
weekly basis and 84% do this at least 10 times every year. 25% of the teachers
surveyed reported doing this on a daily basis. More than 40% of the teachers
surveyed reported they can’t open a window for natural ventilation, while 42% of
the total sample open a window at least 10-times/school year. 12% of the
teachers report using a portable fan, which probably means they brought in their
own personal fan that they purchased themselves to solve a perceived ventilation
problem in their classroom.
In the comments section, one teacher summarized the teaching challenges faced
with in small, poorly ventilated portable: “The students do not have enough
space to move around. Most large projects are eliminated because of lack of
space and no access to water. The room is so small that we use the ramp
outside to set up centers. The door is always open because the poor circulation
in the room gets us sick. We have no water to wash our hands after sneezing
and coughing…we get sick more often and pass colds, flu to each other because
of our close proximity.”

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
20
Lighting control: Darkening the room for TV or video is also very common, done
by over 80% occasionally and 25% frequently. Turning some or all lights off is
also a fairly common activity, while taking measures to block the sun, or close
curtains is much less frequent.
Figure 9 shows further detail on teachers' management of the electric lighting in
their classrooms. This graph shows 54% of the teachers turning some of the
lights off, and 37% of the teachers turning all of the lights off, at various
frequencies during the school year.
Teacher's Operation of Classroom Lights
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
not possible
never do
occassionally, <10x/yr
often, 10+/year
often, 10+/year, per weather
very frequently, 1+x/wk
constantly, 1+x/day
all lights off
some lights off
Figure 9: Teachers' Lighting Management of Classrooms
2.3 Conclusions
The results of the teacher survey on preferences and operation of classrooms
suggest that daylighting and operable windows are indeed important to teachers,
but tend to be secondary to their most pressing concerns, such as adequate size,
location, and water (hygiene) availability in classrooms. Acoustic, thermal and
visual comfort and adequate ventilation are all frequently listed as top priorities.
The optional comments response to the survey was overwhelming. 98% of the
teachers surveyed took the time to write about what was good and bad in their
classrooms. The passion put into the comments on physical comfort in
classrooms makes it clear that teachers are very stressed by any type of poor
physical condition in classrooms where they must work every day with 20-30 very
active children. “Please help California get more square footage per child. It’s
crazy!” pleaded one. “Teaching … without running water makes me feel like it’s
the 1900’s. We carry pails of water!” exclaimed another. One teacher concluded
about the need for cross ventilation: “I believe it is good for myself and students
to breathe in some fresh air. It helps us all think.” While some teachers report
being pleased and comfortable with their classrooms, a sizable group feel they
have overwhelming physical challenges in their classrooms that routinely
interfere with their ability to teach.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
21
There are clearly some important energy use challenges revealed in the survey
that should be carefully considered by school designers and facility managers. In
Figure 8 it is clear that Capistrano teachers are actively trying to increase the
ventilation of their classrooms by opening doors, opening windows, and adding
portable fans. Furthermore, 54% claim to be adjusting the thermostat at least
once a week and 55% also claim to be closing windows or doors at least once a
week specifically to control noise in the classroom, implying that they had
previously opened them, most likely for ventilation. This suggests that teachers’
driving desires for good ventilation, thermal comfort and acoustic comfort tend to
be in conflict with the options allowed by their physical environment. Increasing
ventilation is likely to also increase ambient noise in the classroom and/or reduce
thermal comfort. One teacher summarized this problem with the comment: “I like
being able to adjust the a/c, heat and ventilation. The down side of this is the a/c
unit makes a lot of noise and makes hearing students and teacher more difficult,
so you have to raise your voice, ask for repeats or be very stuffy and
uncomfortable during oral readings and discussions.”
The Capistrano school district is in a relatively mild climate in Southern
California, where ambient temperatures are often in the comfort zone, allowing
natural ventilation without supplementary heating or cooling. However, even in
Capistrano, it is highly probable that substantial energy is wasted running heating
or cooling systems while classroom doors and windows are open. Simply
improving the efficiency of the heating and cooling systems will not solve this
problem. Rather, given teachers’ strong desire for more ventilation, classroom
design should include systems that allow increased ventilation without increasing
energy use for heating or cooling.
Lighting energy use is also an important issue for schools, constituting a large
percentage of overall energy use. The provision of daylighting in classrooms
only saves energy if electric lights are turned off when not needed, either
manually or automatically. The results in Figure 9 suggest that a manual lighting
control scheme has an likelihood of being operated by about half of the teachers
in a school. This behavioral element should be factored into any proposed
lighting control scheme. While automatic systems may be effective more often,
their cost-effectiveness should be compared to manual systems that are
occasionally operated by 50% of the teachers.
The information in the Capistrano teacher survey is not comprehensive enough
to draw any universal conclusions about teacher preferences or behaviors.
However, it is strongly suggestive that the physical environment is a key factor in
teaching effectiveness, and that teacher preferences for classroom operation
need to be given high priority in the design of comfort systems and classroom
controls.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER SURVEY
22

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23
3. TEACHER BIAS ANALYSIS
Once the information in the teacher survey was categorized and compiled into a
database, we were able to analyze the data for a potential bias in teacher
assignment to more daylit classrooms. This task was pursued with a variety of
analytic approaches.
3.1 Hypothesis
For this task we set out to test the hypothesis that the higher rates of learning in
daylit classrooms might be attributable to "better" teachers being located in more
daylit classrooms. For this discussion "better" teachers would be defined as
those who are responsible for faster learning rates in their students, as reflected
in the rate of progress measured by standardized math and reading tests. Daylit
classrooms would be defined by the Daylight Code assigned to each classroom
in the original study.
3.2 Methodology
In order to study this question we needed to 1.) find a way to identify potentially
"better" teachers 2.) determine if the "better" teachers were being differentially
assigned to more daylit classrooms and 3.) determine to what extent the
magnitude or significance of the daylighting effect would change if information
that could predict teacher quality could be included in the model.
Our first step was to define the specific teacher variables to be included in the
models, based on the data we had collected in the early Teacher Survey task. In
order to do this, we needed to understand the basic structure of educational
requirements for a California Elementary School Teacher's credential, along with
the hiring and promotional policies of the district. We collected this information
from the Department of Education web site, the Capistrano District personnel
office, and by interviewing various district administrators.
3.2.1 Teacher Credentials
In discussions with Capistrano administrators prior to the survey, we were told
that the District was not hiring teachers with provisional or emergency
credentials. Beyond requiring all of their teachers to be certified, the district
highly values continuing education for all teachers. A sliding salary scale
rewards additional college education. The District also provides opportunities for
on-site training classes that are specifically tailored to the curriculum needs of the
district.
In the teacher survey we asked teachers to report on their years of teaching in
the current school, district, and total; their highest level of education; additional

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24
course work or certifications; and special awards or recognition. This information
was all self-reported and described in the teacher's own words. We
subsequently categorized this information into the eight variable codes described
below.
Teaching Experience: We defined the variable of Log Yrs Teach as the natural
log of the total number of years teaching. By using a natural log we attempted to
account for the diminishing effect of additional years of experience reported in
the research literature.
Level of Education:
BA indicated any teacher with a bachelors degree. Reported as Teacher
1.
Clear only indicated teachers who had been teaching for 7 years or more,
but had not pursued any continuing education beyond that necessary for their
professional clear credential. Reported as Teacher 5.
BA Plus indicated teachers who listed college credits beyond the minimum
required for certification. Reported as Teacher 2.
MA indicated those with a masters, or doctorate (one case). Reported as
Teacher 3.
MA Plus identified teachers with college credits beyond a master's degree.
Reported as Teacher 4.
Certification: This variable was used to identify teachers who had received any
special certificates or credentials, beyond the minimum required for a California
elementary multi-subject credential. Special certificates for Bilingual Education,
Gifted and Talented Education, Special Education, etc. were grouped together
under one variable. Reported as Teacher 6.
Honors: Many teachers reported receiving special awards, such as Teacher of
the Year, or being selected to be mentor teachers. Because responses varied,
and because we had little way of measuring how prestigious the awards were,
any teacher that reported receiving an award or being chosen to be a mentor
teacher was indicated by the AwarMent variable. Reported as Teacher 7.
The teacher characteristics variables were added back into the master data set.
The surveyed population of teachers represented about 1/2 of the original data
set. Thus, for about 1/2 of the student records we added the information
characterizing their teacher’s years of experience, education level, special
certificates or honors. The remainder of the student records were given an
indicator variable for no teacher information.
3.2.2 Assignment Bias
Once we had defined the teacher characteristic variables, we looked to see if
there were any significant correlations between these teacher characteristics and

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25
the daylight conditions in the classrooms in our Capistrano data set. This was our
first statistical test for a teacher assignment bias. If we found a strong pattern of
correlation between a few teacher variables and a few daylight codes, then it was
likely that some types of teachers were being differentially assigned to more
daylit classrooms. In this first pass at the analysis we included all of the window
related variables, including the daylight code, window code, skylight codes, and
operable windows.
The analysis was based on the data collected in the teacher survey, described in
the preceding section. We used the data from surveys of 206 teachers. These
teachers taught 3,948 of the students included in the original study. To be
consistent with the original study, the first pass statistical analysis was carried out
at the student level. In other words, each student was been taken to be an
observation. Since the number of students per teacher in our data set varied
somewhat independently of the number of total students in a classroom, this
approach has the effect of weighting the results according to the study population
database. Because of the large number of student observations, it also tends to
exaggerate the significance of the correlations.
Looking the student level, we found a statistically significant (2-tailed, p<.10),
correlation among almost all of the variables (see Figure 10). We found no
obvious pattern of any variables less likely to have correlations than others.
Furthermore, the magnitude of correlation was minor throughout. The strongest
correlation, at p=.01, was between Teacher 7 and Skylight Type B (a Pearson
Correlation of .227), implying that 5% (.2272) of classroom assignments might be
explained by this correlation. Nine other combinations had a Pearson Correlation
between 0.1 and 0.2 and all others (61%) were below 0.11, indicating a very
weak magnitude of correlation.
Variable Daylight Window AA Skylight A Skylight B Skylight C Skylight D Skylight Oper. Win.
Teacher 1 Pearson Correlation 0.089 0.068 -0.062 0.111 0.026 -0.001 0.015 -0.106
Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.012 0.905 0.144 0.000
Teacher 2 Pearson Correlation 0.044 0.020 -0.076 0.048 0.085 -0.041 0.064 0.019
Sig. (2-tailed) 0.000 0.050 0.000 0.000 0.000 0.000 0.000 0.067
Teacher 3 Pearson Correlation 0.069 0.112 -0.001 -0.021 0.028 -0.018 0.020 0.004
Sig. (2-tailed) 0.000 0.000 0.914 0.039 0.007 0.082 0.058 0.718
Teacher 4 Pearson Correlation 0.083 0.080 0.109 0.013 0.077 -0.021 -0.025 0.018
Sig. (2-tailed) 0.000 0.000 0.000 0.218 0.000 0.048 0.015 0.081
Teacher 6 Pearson Correlation 0.066 0.051 -0.026 0.087 0.047 -0.039 -0.035 -0.034
Sig. (2-tailed) 0.000 0.000 0.011 0.000 0.000 0.000 0.001 0.001
Teacher 7 Pearson Correlation 0.150 0.147 0.056 -0.096 0.227 -0.067 -0.012 -0.030
Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000 0.000 0.246 0.003
Log yrs teach Pearson Correlation 0.138 0.171 -0.005 -0.007 0.097 -0.071 0.022 0.015
Sig. (2-tailed) 0.000 0.000 0.659 0.499 0.000 0.000 0.033 0.143
N= 3948 students
Figure 10- Correlation of Teacher Variables to Daylight Variables, Student level
Analysis, Capistrano
1 The Teacher 5 variable had not been defined at this time, so was left out of this correlation table.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER BIAS ANALYSIS
26
In a second pass, we also re-calculated the correlations using the 206 teachers
as independent observations. Using the smaller teacher population, un-weighted
for student population in our data base, presented a more extreme test for
significance. Out of the 56 correlations that are reported in Figure 9, ten were
judged to be potentially significant with p-values of .10 or less at the teacher
level. None of the correlations with the Daylight Code were significant. Skylight
Type AA did show a pattern of correlations, but with only 5 surveyed teachers in
this group, we discounted this as a random result. The most interesting finding
was a slight indication that more senior teachers (Log yrs teach) had some
influence being assigned to classrooms with larger window areas, operable
windows, or skylight types A, and that Teacher 7 (honors) were more likely to be
assigned to skylit classrooms type A or B. The magnitude of a possible effect is
minimal, with only 1% to 5% of the variation in assignment to these classroom
types potentially explained by either of these variables.
We concluded from this exercise that there was indeed some potential for an
assignment bias relative to honors or years of experience, but that a two-
dimensional correlation analysis was not a sufficient tool to determine its
magnitude or influence on the results of the multi-variate regression models.
3.2.3 Decision to Focus on Daylight Code Only
For simplicity sake, we choose to work henceforth with just the Daylight Code.
Tracking the change in performance for one variable, instead of eight, reduced
the complexity of the task dramatically. We choose to focus on the Daylight Code
since it was the holistic code that combined the effects of the window and
skylight codes together. It had been very robust in the previous analysis, and
described the classroom characteristic of greatest interest.
By focusing our attention on just the change in the Daylight Code across models,
we were more likely to see patterns across models.
3.2.4 Daylight Code as a Dependant Variable
Next we ran a regression model with the Daylight Code as the dependant, or
outcome, variable and the teacher characteristic variables as the independent, or
explanatory variables. This model was run using only the surveyed teacher
population. This model would tell us more precisely if there was indeed an
"assignment bias," such that some teacher types were more likely to be assigned
to daylit classrooms. It was a more precise test than the correlation tables, since
it allowed the influence of each teacher characteristic variable to be assessed
simultaneously.
From this regression model, we found that there were NO teacher
characteristics, as defined by our variables from the survey data, that were
significant in explaining assignment to more daylit classrooms. The variable that
achieved the highest probability of influence was Teacher 7 (honors) at only 78%
likelihood of significance (p=.22) that there might be a 5% higher assignment in
Daylight Code (A teacher who had received an honor or award had a 78%

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER BIAS ANALYSIS
27
probability of being assigned to a classroom rated 3.15 on the daylight scale
instead of a 3.0). The other variables had a 50% probability or less.
The R2 for this model was only 0.014, indicating that all of the teacher
characteristic variables could explain only 1% of the variation in assignment to
daylight classrooms. When we ran a similar model at the student level, the level
of explanation increased to 2%. Thus, from this exercise we conclude that the
Capistrano Unified School District did not have any marked bias in the
assignment of teachers to more daylit classrooms, based on the teacher
characteristics that we studied.
3.2.5 Teacher Assignment Bias Models
Our final step in the Teacher Bias Analysis was to re-run the original Capistrano
student performance models with the teacher characteristic variables added to
the list of potential explanatory variables. Again, we choose to focus our reporting
on the results of the Daylight Code for simplicity, although we did also run the
separate models with the window and skylight variables. The original models
were re-run for both change in reading and math scores at the student level.
Teacher characteristic variables were added for 42% of the population.
It should be noted that the performance of the observed students within a given
classroom may not be mutually independent. In the original research, we carried
out a special analysis to assess the effect of correlation between students within
a given classroom (See Appendix 6.2 to original report, dated 6/29/1998). This
analysis indicated that the statistical significance of some of our results was
somewhat overstated but the effects of interest were not substantially altered.
However, carrying out the analysis at the student level made it easier to explore
the relationship between characteristics of the student, teacher, room, and
school.
3.3 Findings
Figure 12 and Figure 11 display the findings of these two models, compared to
the original models without the teacher variables. The school site variables and
outliers have been left off of the equations shown here for simplicity, but are
included in the full model detail in the Appendix 7.2. A central column shows the
change in the B coefficient for each variable and the model R2.

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28
New Model Change Old Model
Capistrano, Teacher Analysis - Math Daylight new-old Capistrano, Original Analysis Math Daylight
28-2 (Original population) R^2 C17-md
Model R^2 0.259 0.003 Model R^2 0.256
B Std. Error p (Signif) BB Std. Error p (Signif)
(Constant) 9.045 0.464 0.000 (Constant) 8.026 0.407 0.000
Classroom characteristics Classroom characteristics
Daylight code 0.430 0.072 0.000 -0.075 Daylight code 0.504 0.067 0.000
Teacher characteristics
Teacher 3 -0.933 0.248 0.000
Teacher 5 -0.688 0.335 0.040
Log yrs teaching 0.373 0.077 0.000
Student characteristics Student characteristics
Grade 2 9.624 0.216 0.000 -0.088 Grade 2 9.711 0.215 0.000
Grade 3 5.949 0.220 0.000 0.018 Grade 3 5.931 0.219 0.000
Grade 4 1.802 0.216 0.000 -0.011 Grade 4 1.813 0.216 0.000
Absences unverified -0.263 0.123 0.033 0.000 Absences unverified -0.263 0.123 0.032
Absences unexecused -0.029 0.014 0.043 -0.003 Absences unexecused -0.026 0.014 0.069
GATE program -1.191 0.222 0.000 0.045 GATE program -1.236 0.223 0.000
Language program 0.488 0.205 0.017 -0.001 Language program 0.490 0.205 0.017
School characteristics School characteristics
School Pop-per 500 -0.995 0.000 0.000 -0.483 School Pop-per 500 -0.512 0.000 0.010
Figure 11 - Change in Capistrano Math Model with Addition of Teacher Variables
New Model Change Old Model
Capistrano, Teacher Bias Analysis - Rea ding Daylight ne w-old C apistrano, Or iginal Ana lysis Re ading D aylig ht
28-2 (Original population) R^2 C17-rd
Model R^2 0.248 0.002 Model R^2 0.246
B Std. Error p (Signif) BB Std. Error p (Signif)
(Constant) 3.009 0.303 0.000 (Constant) 3.025 0.298 0.000
Classroom characteristics Classroom characteristics
Daylight code 0.475 0.086 0.000 0.011 Daylight code 0.464 0.085 0.000
Operable windows 0.650 0.212 0.002 0.007 Operable windows 0.643 0.212 0.002
Teacher Characteristics
Teacher 3 -0.917 0.288 0.001
Teacher 5 -1.335 0.388 0.001
Log yrs teaching 0.221 0.090 0.014
Student characteristics Student characteristics
Grade 2 10.823 0.251 0.000 -0.037 Grade 2 10.860 0.251 0.000
Grade 3 4.368 0.255 0.000 0.069 Grade 3 4.298 0.254 0.000
Grade 4 0.944 0.252 0.000 0.008 Grade 4 0.937 0.252 0.000
GATE program -1.432 0.257 0.000 0.020 GATE program -1.452 0.257 0.000
LANG progra
m
0.827 0.239 0.001 -0.011 LANG progra
m
0.838 0.239 0.000
Figure 12 - Change in Capistrano Reading Model with Addition of Teacher
Variables
Even with the addition of the teacher characteristic variables into the original
models, the daylight variable stayed highly significant in both cases. For the
math model, with the outcome variable as the change in fall to spring math
scores, the magnitude of the daylight effect decreased slightly.
For the reading model, the magnitude of the daylight effect actually increased. In
the case of the reading model, operable windows also remained a significant
variable, and also increased slightly in magnitude.
Three of the eight teacher characteristic variables were found to be significant in
both models. (While the significant teacher variables here were consistent, they

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29
were not consistent in the models using window codes and skylight types as
explanatory variables, nor were they consistent in later models that we ran,
discussed later.)
With the addition of information about the teachers, the R2 of the models
increased, but only by a tiny amount, increasing their power of explanation by
less than 1%.
3.4 Conclusion
Thus, we conclude that the strength of the daylight variable showed in the
original analysis was not an inadvertent effect of a “teacher assignment bias.”
We have shown in the regression model of the Daylight Code versus the teacher
characteristic variables, that the teacher characteristics captured in our survey
only explained 1% of the variation of teacher assignment to daylit classrooms.
Furthermore, in the master student performance regression models adding
information about teacher characteristics for 42% of the population did not
reduce the significance of the daylight variables. As might be expected, the
magnitude shifted slightly; in one case down, in one case up.
3.5 Discussion
One potential weakness in the findings above is that we only had teacher
characteristic information for less than half of the study population. We decided it
would be a good test to re-run the models for just the population of students
represented by teachers who responded to the Teacher Survey. That way, we
could look at a model where 100% of the population had information about the
teachers. This “surveyed population” model included 206 teachers and 3948
students, or about 50% of the original population.
We were aware that if we shifted the sample population for a model, we ran the
risk of getting different results. But we wanted to examine the stability of the
daylighting coefficient in our models over different sample populations. We also
wanted to explore the stability of including the information about the teachers.
Thus, we decided to run similar models to the original Capistrano math-daylight
and reading-daylight models, looking at the change in the daylight variable from
one sample to another and with the addition of the teacher characteristic
variables.
We also had one other complexity to account for. In coding the data from the
Teacher Survey it was discovered that three schools had been inadvertently
dropped from the original study population. Criteria for inclusion of a student’s
record in the original analysis had included complete records for test scores,
attendance and demographic data. We did not observe at the time that we had
not been provided with attendance data for three entire schools. Thus, the data
cleaning procedures resulted in inadvertently dropping all students (and all
teachers) from those three schools from the analysis. We were particularly

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30
concerned since two of the dropped schools represented somewhat extreme
daylight conditions, one with many classrooms of Daylight Code 0, and the other
with many Classrooms of Daylight Code 4. Thus, we worried that the exclusion of
these schools from the original analysis may have skewed our results.
We noted that any effect due the missing attendance data could be absorbed to
some degree by the dummy variable that identified the school site that was
missing the attendance information. Thus, we decided to create a new
“expanded” population that included these three schools and provided a
“missing” indicator in the attendance record fields. This “expanded population”
model included 394 teachers and 9200 students, 13% larger than the original
study population.
(number of students in population)
Figure 13: Surveyed, Original, and Expanded Populations
We were interested to see if the daylight variable would remain significant in
models of student performance in these different populations, with and without
the addition of the teacher characteristic variables. The teacher survey
population would present the clearest test of the impact of the teacher
characteristics, since for this population we would have information about teacher
characteristics for 100% of the teachers. The expanded population was likely to
have the truest daylight results, since it represented the full 2-5 grade district
population in 1997/98 school year. For this population we had information on
50% of the teachers.
3.5.1 Findings of Different Study Population Models
Figure 14 and Figure 15 compare the results for the three sets of regression
models; the original model, the expanded model, and the teacher surveyed
model, for the reading and math models. Full detail of all models is included in
the Appendix. In addition to comparing the B coefficient for the Daylight Code,
the significance of the Daylight Code and the R2 of the model, we also report
here on the effective rate of change in the learning rate, and the confidence
interval for that rate.
Expanded
Population (9302)
Surveyed
Population (3949)
Original 1999
Population (8100)

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT TEACHER BIAS ANALYSIS
31
Key
Capistrano
Reading Model,
Study Population
Teacher
Variables
Included in
Model
B Coefficient
for Daylight
Code
p (Signif)
of B Model R2
% Change in
Learning
Rate
confidence
interval
A original no 0.464 0.000 0.247 26% ±10%
B original yes 0.475 0.000 0.248 27% ±10%
Shift from Model A to B 0.011 no change 0.001 1% no change
C expanded no 0.416 0.000 0.238 24% ±9%
D expanded yes 0.418 0.000 0.240 24% ±9%
Shift from Model C to D 0.002 no change 0.002 0% no change
E surveyed no 0.434 0.000 0.239 23% ±12%
F surveyed yes 0.463 0.000 0.243 25% ±12%
Shift from Model E to F 0.029 no change 0.004 2% no change
Figure 14: Daylight Affect for Different Populations, with and without Teacher
Variables, on Reading Tests in Capistrano
Key
Capistrano Math
Model, Study
Population
Teacher
Variables
Included in
Model
B Coefficient
for Daylight
Code
p (Signif) of
B Model R2
% Change in
Learning
Rate
confidence
interval
A original no 0.504 0.000 0.257 20% ±5%
B original yes 0.430 0.000 0.259 17% ±6%
Shift from Model A to B -0.074 no change 0.002 -3% 1%
C expanded no 0.351 0.000 0.250 14% ±5%
D expanded yes 0.301 0.000 0.252 12% ±5%
Shift from Model C to D -0.050 no change 0.002 -2% no change
E surveyed no 0.544 0.000 0.274 21% ±8%
F surveyed yes 0.497 0.000 0.277 19% ±8%
Shift from Model E to F -0.047 no change 0.003 -2% no change
Figure 15: Daylight Affect for Different Populations, with and without Teacher
Variables, on Math Tests in Capistrano
For the reading model, the most conservative estimate of a daylight effect would
be +11% for the surveyed population without teacher variables (23%-12%), while
the most optimistic would be +37% for both the original and surveyed population
with teacher variables (27%+10% and 25%+12% respectively). For the math
model, the most conservative estimate of a daylight effect would be +7% for the
expanded population with teacher variables (12%-5%), while the most optimistic
would be +29% for the surveyed population without teacher variables
(27%+10%). Thus, from worst to best case we can say with a high degree of
confidence, that children with the most daylighting in Capistrano are learning
somewhere from 7% to 37% faster on the District's math and reading curriculum.

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32
With the addition of teacher characteristics to the three sets of models, the
following changes were observed:
• Daylight variables were still significant across all models
• R
2 value increased by 0% to +2% indicating that the models with teacher
characteristics had a slightly better explanatory power for the studied
phenomena.
• Math models indicated a decrease in the effect of daylight on student
performance by 2% to 3%.
• Reading models indicated an increase in the effect of daylight on student
performance by 0% to 2%.
• In general the availability of daylight in classrooms was reliably associated
with an increase in student performance and learning rate of somewhere
within the bounds of 7% to 37%. The central tendency among all these
models would seem to be a 25% improvement in reading and a 16%
improvement in math, or a 21% general improvement between children in
classrooms with the most daylight (code 5) compared to those in
classrooms with the least (code 0). In summary, if the average student in
the district were moved from an average classroom (code 2.5) to a
classroom with maximum daylight (code 5), he or she would be expected
to increase his or her learning rate by 11% (10.5).
• All these results were observed with 99.9% statistical certainty.
In addition, we were interested to understand the change in daylighting effect
among the three populations, the original, expanded, and surveyed, before the
addition of the teacher variables. Figure 16 and Figure 17 compare the changes
when moving from the original population to the expanded population (13%
larger), and from the original to the surveyed (50% smaller) for both reading and
math. These changes were also very modest, with from a 3% to 6% shift in the
net impact of the daylight variable on student learning rates.
Key
Capistrano
Reading Model,
Study Population
Teacher
Variables
Included in
Model
B Coefficient
for Daylight
Code
p (Signif)
of B Model R2
% Change in
Learning
Rate
confidence
interval
A original no 0.464 0.000 0.247 26% ±10%
C expanded no 0.416 0.000 0.238 24% ±9%
Shift from Model A to C -0.047 no change -0.009 -3% -1%
A original no 0.464 0.000 0.247 26% ±10%
E surveyed no 0.434 0.000 0.239 23% ±12%
Shift from Model A to E -0.030 no change -0.008 -3% 2%
Figure 16: Teacher Variables and Daylight effect on Reading for the Three
Populations Compared

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33
Key
Capistrano
Math Model,
Study
Population
Teacher
Variables
Included in
Model
B Coefficient
for Daylight
Code
p (Signif)
of B Model R2
% Change in
Learning
Rate
confidence
interval
A original no 0.504 0.000 0.257 20% ±6%
C expanded no 0.351 0.000 0.250 14% ±5%
Shift from Model A to C -0.153 no change -0.007 -6% -1%
A original no 0.504 0.000 0.257 20% ±6%
E surveyed no 0.544 0.000 0.274 21% ±8%
Shift from Model A to E 0.040 no change 0.017 1% 2%
Figure 17: Teacher Variables and Daylight effect on Math for the Three
Populations Compared
Interestingly, the greatest variability between models, 6%, occurred from the
original to expanded populations for the math model. Earlier, in the Classroom
Level Analysis, included in the Appendix of the 1999 Detailed Report, we had
found much greater variability in the success of math instruction attributable to
individual teachers than reading instruction. Thus, we would also expect greater
volatility in the math results between population samples.
The following findings were observed when comparing the three populations
before adding the teacher variables to them:
• No change in significance of daylight variable
• The explanatory power of the statistical models (i.e., R2) in explaining the
data varies by less than 2%.
3.5.2 Conclusions of Different Study Population Models
The shift in model study populations actually had a greater impact on the R2 of
the models than the addition of the teacher characteristic variables. We also saw
the largest shift in the magnitude of the B coefficient for the Daylight Code
between study populations, rather than with the addition of information about the
teachers. Thus, we conclude that the selection of the study population is more
likely to impact findings about the effect of daylight than is the addition of
information about teachers.
We continue to believe in the importance of the addition of the teachers'
characteristics to the model, both to access the potential for a teacher bias and to
further refine the accuracy of the model. However, it is clear from this exercise
that the study population is likely to have an even greater effect on the results.
This once again argues for the importance of replicating the study in other
districts, and preferably in widely differing geographic regions and cultural
environments.

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34

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT GRADE LEVEL ANALYSIS
35
4. GRADE LEVEL ANALYSIS
The Grade Level Analysis task was intended to answer two of the questions that
were raised from a previous peer review1 of the Daylighting in Schools study.
One question was whether it was might be more appropriate to analyze the data
in single grade cohorts, rather than across grades. It was proposed that
especially in Seattle, for the Iowa Test of Basic Skills (ITBS), results could not
correctly be compared across grades. Creating separate models for each grade
level would solve this problem.
A second question asked whether the daylighting effect might vary by grade
level. The models used in the first analysis constrained the results to a simple
linear expression. It was argued that there might be a progressive effect, again
especially in Seattle, where children were exposed to fairly consistent daylighting
conditions for the duration of their career at a given school. In Seattle, where we
were looking at absolute test scores, exposure to good daylight conditions over
more than one year might result in a cumulative effect. This would be evidenced
by a progressively greater daylight effect in each higher grade. Again, separate
grade level models would allow the daylight effect to change by grade level,
allowing us to identify any patterns as children got older.
In Capistrano, we hypothesized that we would not find any progressive effects
since children are likely to be shuffled back and forth between traditional
classrooms and portable classrooms with each change in grade level. We
confirmed with the District that the churn rate in the Capistrano district is
reasonably low, with about 4% growth per year, and a similar number of students
who relocate to other districts per year. Thus, we estimate about 90% of the
students return to a given school each year. Typically, they would experience at
least two, if not three or four daylight conditions throughout their career at a given
school. Furthermore, since in Capistrano we were looking at the improvement in
schools in one year, from fall to spring, cumulative effects would be less likely to
show up.
4.1 Hypothesis
Given the main objective of this task, it was hypothesized that daylighting may
have a cumulative effect on student scores. This hypothesis would be likely true
if a pattern of progressively stronger effects by grade level was observed in
Seattle, where children typically remain under one school-wide daylighting
condition. A comparative analysis for the test scores in the Capistrano school
district, where students may change between high and low daylighting conditions
1 Daylighting and Schools Peer Review Report, sent to PG&E, July 21, 1999. Not released.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT GRADE LEVEL ANALYSIS
36
during their stay at an elementary school, would corroborate our hypothesis if a
minor or no cumulative effect of daylight was observed in that district.
4.2 Methodology
We re-ran the student performance regression models for both Seattle and
Capistrano, this time allowing the daylighting effect to vary by grade level. This
was achieved by adding grade level interaction variables for each variable in the
model. This is statistically equivalent to running separate models, but simplifies
the reporting and interpretation.
Interaction variables between the grade level of the student and each
explanatory variable were created and added to the original Capistrano and
Seattle models. As in the original study, the Capistrano model used the
difference between fall and spring scores while Seattle’s used the absolute value
of the spring scores.
Since information regarding teacher characteristics was available for the
Capistrano school district, the teacher variables were also included in the
Capistrano math and reading models to strengthen their explanatory power.
4.3 Findings
The data from our interaction models did not show a significant effect for the
interaction variables between daylight and separate grade levels. This indicates
that, for our study populations, we could not support the hypothesis that daylight
has a different or cumulative effect on student performance by each grade. The
full model results are shown in the Appendix 7.3.
We also found that allowing the results to vary by grade did not improve the
accuracy of the models. The R2 of the models increased only very slightly with
the addition of the interaction variables, 4% for the Seattle reading model, and
less than 1% for the other three. (See Figure 18 and Figure 19)
It is important to note, however, that the daylighting effects remained highly
significant even after the addition of the interactive variables. This indicates that
daylight still provides a robust explanation of student performance in math and
reading tests across all grades. For the Capistrano reading model, the magnitude
of the effect (B) declined by 14%, but not the significance.
For the Capistrano math model, we saw a greater impact on both the magnitude
(45% decline) and significance (7% decline). This is the one incidence where the
daylight variable would not pass our threshold criteria of 95% significance or
greater for inclusion in the model. This decline in significance and magnitude
were probably caused most by the addition to this model of the one daylight-
grade level interaction variable that did prove significant: Daylight Code(2nd
grade). This interaction variable was found to increase the daylight effect
considerably for second graders, by more than twice (216%). The interpretation
here would be that second graders in more daylit classrooms were mastering the

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT GRADE LEVEL ANALYSIS
37
math curriculum dramatically faster then those who were not in daylit classrooms,
and also comparatively faster than children in other grade levels in daylit
classrooms.
While this finding about second graders learning math might seem potentially
interesting, the fact that we did not find any other significant interaction effects in
any of the other model tends to discount the validity of this finding. Out of twelve
opportunities, the interaction between grade level and daylight was found to be
significant in only one case. Thus, we tend to doubt that there is any differential
sensitivity to daylight by grade level.
Key Test Interactive
Variables B Model R^2 % impact error bound Signif.
A Reading N 0.464 0.247 26% +/-10% 100.0%
B Reading Y 0.396 0.239 22% +/-9% 100.0%
Shift from Model A to B -14% -0.008 -4% 0%
C Math N 0.504 0.257 20% +/-5% 100.0%
D Math Y 0.275 0.261 11% +/-12% 92.7%
Shift from Model C to D -45% 0.004 -9% -7%
Figure 18: Capistrano Grade Level Models with Interactive Variables Summary
In Seattle, when allowing for grade level interactions with all the other variables,
we saw no declines in significance, and also saw substantial increases in the
magnitude of the daylight effect. In the case of the Seattle reading model, the
magnitude of the daylight effect increased 26%, while in the math model the
magnitude of the daylight effect increased 12%. For the Seattle reading model,
the accuracy of the model (R2 ) increased 4%. This would tend to argue for the
validity of the increase in the magnitude of the daylight effect. Since some of the
significant interaction variables have to do with the physical conditions of the
classroom (school vintage, school size, classroom SF) it is possible that some of
the daylight effect was previously being masked by the imprecision of those
variables without the interaction effects.
Key Test Interactive
Variables B Model
R^2 % impact error bound Signif.
A Reading N 1.883 0.297 16% +/- 8% 100.0%
B Reading Y 2.533 0.337 22% +/- 7% 100.0%
Shift from Model A to B 26% 0.040 6% 0%
CMath N 1.391 0.258 12% +/- 7% 99.9%
DMath Y 1.585 0.257 13% +/- 7% 100.0%
Shift from Model C to D 12% -0.001 2% 0%

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT GRADE LEVEL ANALYSIS
38
Figure 19- Seattle Grade Level Models with Interactive Variables Summary
4.4 Conclusions
The grade level analysis did not increase the accuracy of the models. Further
more, while we did find interaction effects between grade level and other
variables, most notably the demographic variables, we did not find a consistent
interaction between grade level and a daylighting effect. This was true in both
Seattle and Capistrano.
From this exercise, we conclude that our original modeling approach, grouping all
of the data for grades 2-5, was sufficiently accurate. We also note that we did not
find any progressive effect for the daylighting variable, as postulated for Seattle,
nor any other pattern related to the age of the student.

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39
5. ABSENTEEISM ANALYSIS
The Capistrano data set includes information on absences and tardiness per
student. Both of these parameters were included as explanatory variables in our
original daylighting analysis, but not as dependant variables. We did not use
them as dependant variables at the time for two reasons. First, we did not have
this information for all three districts, and our original criteria included consistent
analysis across districts. Second, the absenteeism and tardiness data is much
thinner than student test performance data, since only about 10% of students
had a significant number of absences. Thus, it provided a much less sensitive
metric of performance.
However recent research findings by others, discussed below, suggested that we
should re-examine the Capistrano data set for similar effects. In a number of
studies increased ventilation rates have been found to reduce worker
absenteeism. There has also been increased interest in the effect of classroom
environments, particularly portable classrooms, on student health with a number
of epidemiological studies initiated to look for these links. Finally, many
daylighting proponents have been claiming the daylighting improves student
attendance, and thereby will also increase funding to the schools through
California’s system of ADA (average daily attendance) payments.
Milton et al of Harvard School of Public Health reported that increased ventilation
rates were associated with reductions in sick leave in the Polaroid Company
offices in Massachusetts1. They report: “Based on this latter analysis, 45% of the
sick leave among workers in lower ventilation areas was attributable to lower
outdoor air supply. Similarly, 41% of sick leave was [also] attributable to
humidification, and 39.2% of sick leave…was attributable to the presence of
(IAQ) complaints. This corresponded to 1.4 – 1.5 days of increased sick leave
per person per year attributable to ventilation, and 1.2 – 1.3 days per person per
year attributable to humidification, and 1.1 – 1.2 days per person per year
attributable to IAQ complaints, depending on age and gender.”
Teculescu et al. 2 recently reported that occupants of an air-conditioned building
were more likely to have multiple absences from work than were persons in a
naturally ventilated building. This study was limited, however, by the use of only
two buildings (in northeastern France), and by lack of control for ventilation rates
and individual and group factors that may have confounded the relationship
between building and sick leave.
1 Milton DK, Glencross PM,Walters MD. Risk of Sick Leave Associated with Outdoor Ventilation Level,
Humidification, and Building Related Complaints, Harvard School of Public Health, August 1999
2 Teculescu DB, Sauleau EA, Massin N, Bohadana AB, Buhler O, Benamghar L, Mur JM. Sick-building
symptoms in office workers in northeastern France: a pilot study. Int Arch Occup Environ Health 1998;
71:353-6.

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40
The interest in the effect of classroom construction and maintenance, particularly
portable classrooms, on student health has peaked in recent years. Current
projects in progress include: HP-Woods Institute is studying the relationships
between indoor environment and occupant performance in two elementary
schools, funded by Air Conditioning and Refrigeration Technology Institute’s 21-
CR program; the California Department of Public Health is beginning a study of
the environmental health conditions in portable classrooms, funded by Air
Research Board; a pilot study of indoor air quality in portable classrooms is being
done in Los Angles Count, funded by US EPA; another CEC PIER project is also
looking at exposure to VOCs and thermal comfort in four new portable
classrooms.
Given this level of interest, we concluded that it would be worthwhile to see if our
original Capistrano data set would allow us to make any correlations between
classroom physical conditions and student health. The absenteeism and
tardiness data could be used as a proxy measure of student health, while
daylighting, operable windows, air conditioning, age of classroom and type of
classroom (portable, modular, open, semi-open, traditional) could be used as
explanatory variables.
We choose to look at absences or tardies data as a reasonable potential proxy
for student health. However, our study could not distinguish reasons for
absences or tardies. There are many other powerful factors influencing
elementary school attendance besides the health of the student, such as dentist
or orthodontist appointments, outside activities, poor transportation, parental
health, family obligations, etc. Thus, our absenteeism and tardiness variables
cannot be interpreted as a strong metric of student health, but rather simply as
the best proxy for student health that we had available in our data set.
5.1 Hypothesis
In our earlier Capistrano study, we found that daylight was consistently
associated with enhanced learning rates, and operable windows were associated
(>95% certainty) with enhanced learning rates in three of the four models. In that
original analysis, neither portable classrooms nor the presence or type of air
conditioning had a statistically significant effect.
Based on this finding we hypothesized that daylighting and operable windows
might also be associated with a reduction in student absenteeism and tardiness
in the Capistrano school district.
If this hypothesis were true, operable windows and daylight, as explanatory
variables, would appear to be significant and negative in a regression analysis
with student absenteeism and tardiness as dependant variables.
Since the models also included other descriptions of the physical conditions of
classrooms, we could simultaneously test for the significance of those variables
in relationship to absenteeism or tardiness. We were particularly interested in
the portable classroom (port) and modular classroom (pport) variables. If

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT ABSENTEEISM ANALYSIS
41
portable or modular construction does indeed impact student health, then we
would expect to see these variables show up as significant in the regression
analysis.
5.2 Methodology
A multivariate regression model, using the original data from the 1999 study of
the Capistrano school district with all the school data, including daylight, operable
windows, as well as the addition of the new teacher and school variables, was
run. The student characteristics, teacher characteristics, and school and
classroom characteristics were run as independent exploratory variables against
absenteeism data the dependant outcome variable. A similar model was run with
the same variables against tardy data as an outcome variable.
The data set was redefined to include all those students who attended at least 40
days at the same school. The students, however, were not required to have test
scores. As a result, the population shifted slightly, including more students who
were not present for either the fall or spring tests, but excluding any records
missing attendance data. Thus, the three schools from which we had never
received attendance data were dropped from the population. The resulting
analysis population was 8808 students.
41%
33%
15%
3% 1% 1% 0% 0%
71%
4% 2% 2% 1% 1% 1%
6%
12%
6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40+
Number of Absences and Tardies
% of Students
Absences Tardies
Figure 20- Distribution of Absences and Tardies
The absence variable was defined as a function of the sum of three fields in our
data set: unverified absences, excused absences, and unexcused absences.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT ABSENTEEISM ANALYSIS
42
Absences due to school function were not included. Only the sum of absences
per student was available. We did not have information on the distribution of
absences over time.
Plotting the attendance data in Figure 20 we noted a very strong curve, where
74% of the population were found to have both fewer than 10 absences, and
83% fewer than 10 tardies. In order to properly model this data distribution we
choose to use a natural log function, as expressed in the equation shown in
Figure 21 below. We normalized the absenteeism and tardiness data across the
whole population by adding a ratio of days enrolled to maximum possible days
enrolled:
Ln_Abs = 




×
=Tardies)(or Absences ofnumber
40) (minimum enrolled days ofnumber
days) enrolled ofmaximum(180
ln
Figure 21- Equation for natural log of attendance data
5.3 Findings
The regression models with the log of absences or tardiness as dependant
variables did NOT support the hypothesis that daylight variables, or any other
physical characteristics of the classrooms, have a significant effect on student
absenteeism or tardiness.
While these models included all of the same explanatory variables used in
previous analysis, they proved to be comparatively weak models. The R2 of the
absences model was only 0.05, and that of the tardiness model 0.10, indicating
that only 5% and 10% respectively of the variance in the data was explained by
all of the variables included in the models.
5.3.1 Absenteeism Findings
Physical classroom variables that were considered and found to have NO
significance in the absenteeism model included: daylight code, operable window,
type of classroom (portable, open, traditional), air conditioning, and size of
classroom. In addition, none of the teacher characteristics were found to be
significant.
Variables that were significant included: grade level, student socio-economic
characteristics, special programs, school site, school vintage, and school
population.
Thus, we conclude that student demographic characteristics and school level
characteristics (which might include neighborhood effects, special programs, or
size of school) have the greatest relationship to student absenteeism.

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43
5.3.2 Tardiness Findings
The Tardiness model did find that three physical characteristics of classrooms
had a slight, significant effect on the pattern of tardiness:
• Daylighting had a modest, positive effect p=.000
• 5% reduction
• No Air Conditioning had a slight, negative effect p=.032
• 11% increase
• Portable classrooms had a slight, negative effect p=.037
• 5% increase
R2 = 0.097
These results could be interpreted to predict that the students in the most daylit
classrooms would be likely to have one less tardy per year than those in the least
daylight classrooms (5 daylight codes *.05 per code =25% reduction in norm of 5
tardies per year, or 4 tardies per year.) Likewise, no air conditioning was found to
be associated with a slight increase in tardiness, 11% from the norm of 5 to 5.5
tardies per year, and portable classrooms were found to be associated with a
slight increase in tardiness by 5%, up to 5.25 tardies per year. .
Since tardies are a somewhat subjective measure of student performance (not all
teachers mark a student tardy at the same point of lateness) and since tardies do
not have as a strong economic tie to the performance of the school as does
absenteeism data, we chose to discount these results as not particularly
interesting.
5.4 Conclusions
Student attendance, as measured by absences and tardies, was not predicted by
with the daylight conditions of the classrooms in the Capistrano Unified School
District. Likewise, other physical conditions of the classrooms were not found to
be reliable predictors of student attendance.
From this exercise, we concluded that attendance data is a very difficult outcome
metric to work in trying to understand the effects of the physical environment on
the performance of students, or the productivity of people in general. There are
two basic reasons for this difficulty. First, attendance data can only be a loose
proxy for the health of the student, since so many other events can cause a
student to be absent or tardy besides health effects caused by the physical
environment. Secondly, it is not a very sensitive metric. There is not a very big
range in attendance values among students, with only about 10% of the student
population showing much variation in number of days absent or tardy.
A summary of the findings from the absenteeism analysis is as follows:

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT ABSENTEEISM ANALYSIS
44
• Daylighting variables were not significant indicators of Absenteeism.
Similarly neither operable windows nor portable classrooms variables
were significant.
• Student demographic variables were the only reliable predictors of
absenteeism
• Physical characteristics of classrooms were not predictors of student
attendance
• Attendance data is not particularly useful as a performance metric,
providing meaningful variation for only 10% of students in our fairly large
samples (n= ~ 8800).
• A slight effect of daylight on student tardiness was observed, but not
considered interesting.
5.5 Discussion
Our study could not distinguish reasons for absences or tardies. It was assumed
that overall absence and tardy data might serve as a reasonable proxy for
student health. However, there are many other powerful factors influencing
elementary school attendance besides the health of the student, such as dentist
or orthodontist appointments, outside activities, poor transportation, parental
health, family obligations, etc. Thus, our absenteeism and tardiness variables
cannot be interpreted as a strong metric of student health, but rather simply as
the best proxy for student health that we had available in our data set.
Improved physical conditions in a workplace or school have been postulated by
many to be associated with reduced absenteeism. Indeed, this is a fairly common
assertion made in presentations advocating “green” or “sustainable” buildings—
that an improvement in the quality of the physical environment will result in fewer
absences and thus higher productivity. These claims are most frequently made
for improvements in indoor air quality (IAQ)1, but also variously for natural
ventilation, ventilation rates2, thermal comfort, ergonomic furniture, electric
lighting quality and the presence of daylight.
Our study can only speak to a few of these issues: the potential link between
poor indoor air quality in portable classrooms and increased absenteeism. It is
important to note that this re-analysis study of the Capistrano data did not
substantiate any of these claims.
1 Fisk WJ (2000). Health and productivity gains from better indoor environments and their relationship with
building energy efficiency. Annual Review of Energy and the Environment 25(1): pp. 537-566
2 Milton DK, Glencross PM, Walters MD (2000). Risk of sick leave associated with outdoor ventilation level,
humidification, and building related complaints. Indoor Air, 10(4): pp. 212-21

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT ABSENTEEISM ANALYSIS
45
• Portable classrooms are currently under investigation by a number of
researchers for poor indoor air quality1, which might reduce overall student
health.
• Our study did not find that there was any significant association between
portable classrooms and increased absenteeism among students.
• Operable windows have been associated with a reduction in indoor air quality
complaints2.
• We did not find that operable windows were significantly associated with
any improvement in attendance among elementary school students.
• Claims have been made that daylit schools are associated with improved
attendance among students3.
• We did not find that increased daylight in classrooms was associated with
better attendance.
1 Per Jed Waldman, CA Department of Public Health
2 MP Callahan, DS Parker, WL Dutton, and JER McLivaine, 1997. “Energy Efficiency for Florida Educational
Facilities: the 1996 Energy Survey of Florida Schools.” FSEC-CR-951-97, Florida Solar Energy Center,
Cocoa, Fl.
3 M Nicklas and G Bailey, “Analysis of the Performance of Students in Daylit Schools,” Proceedings of the
American Solar Energy Society, 1997.

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46

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT RE-ANALYSIS CONCLUSIONS
47
6. RE-ANALYSIS CONCLUSIONS
6.1 Grade Level Analysis
The data did not show a significant effect for the interaction variables between
daylight and separate grade levels. Likewise, we did not observe any consistent
patterns of an increase or decrease in daylight effects by grade level. Thus, we
conclude that there do not seem to be progressive effects as children get older,
nor do younger children seem to be more sensitive to daylight than older
children.
Allowing the results to vary by grade did not improve the accuracy of the models;
with one exception, the R2 of the models increased less than 1%. Therefore, we
believe that the extra analysis did not add significantly to our understanding and
future research can proceed looking at data across grade levels.
Furthermore, the daylighting effects remained highly significant even after the
addition of the interactive variables. This indicates that the Daylight Code still
provides a robust explanation of student performance in math and reading tests
across all grades.
6.2 Absenteeism Analysis
The student attendance record regression models did not support the hypothesis
that daylight variables or any other physical characteristics of the classrooms
have a significant effect on student absenteeism or tardiness. Notably,
daylighting conditions, operable windows, and air conditioning were not
significant in predicting absences. The models were comparatively weak; the full
set of 57 variables for the Capistrano data explained only 5% and 10% of the
variance in absences and tardies, respectively.
We chose to look at absences and tardiness data as the best proxy for student
health that we had available. Absenteeism and tardiness cannot be interpreted
as a strong metric of student health, since many other powerful factors influence
elementary school attendance. However, to the extent that attendance data does
reflect student health, our study may indicate only a weak connection between
physical classroom characteristics and student health.
6.3 Teacher Survey
Although the Teacher Survey task was primarily aimed at providing additional
information for other Re-analysis tasks, we did learn some useful information
about teacher preferences, attitudes and behaviors. For example, while the
teachers we surveyed clearly had a preference for windows, daylight and views
in their classrooms, these preferences were not likely to be driving classroom

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48
selection. Far more important in classroom selection was an almost universal
desire for large classrooms, lots of storage and water supply in the classroom.
Environmental control is also an important issue for teachers, especially when
they find that they don't have it in their classroom. Teachers seemed to hold a
basic expectation that they would be able to control light levels, sun penetration,
acoustic conditions, temperature and ventilation in their classrooms. When
control of one or more of these environmental conditions was not available to
them in the classroom, they were passionate and outspoken in their outrage.
We also found that teachers reported using their optional control features
frequently enough to make significant impacts on classroom energy use. Use of
these features by a dedicated minority would seem to be sufficient to justify their
cost effectiveness in terms of energy savings. Of course, their value should also
be considered in terms of classroom comfort and productivity.
In their freely offered comments, the teachers were desperate to be heard about
the need for better physical environments in their classrooms. It is worth taking
the time to review these comments included in the Appendix. Class-size
reduction, in particular, has been responsible for many of their current
challenges. The teachers clearly resent the many inconveniences posed by sub-
optimal classrooms. Capistrano is a well-managed school district with many
beautiful new facilities, a mild climate and a world-class location on the Southern
California coast. Imagine what kind of responses might come from a district
facing far more extreme physical challenges!
6.4 Bias Analysis
We did find that a few types of teachers, those with more experience or honors,
were slightly more likely (1% to 5%) to be assigned to classrooms with larger
window areas, skylights or operable windows. However, a full multivariate
regression of teacher characteristics against the Daylight Code found that none
of the teacher characteristics that we identified were significant in explaining
assignment to daylit classrooms. This model explained only 1% of the variation in
assignment to daylit classroom. We concluded that this assignment bias, while it
does exist, is extremely small.
Similarly, we found that the daylight variables remained highly significant in the
student performance models, even after the addition of information about the
teachers. While a few teacher characteristics did show up as significant
variables in our models of student performance, the daylight variables remained
extremely robust in all models.
Comparing across twelve different models of student performance in Capistrano,
we conclude that the central tendency is for a 21% increase in learning rate
between children in classrooms with minimal daylight compared to those with
maximum daylight.

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49
6.5 Re-Analysis Report
Overall, the strength of the daylight variable in predicting student performance
stands out sharply across all of these re-analysis efforts. The addition of more
information to the models did very little to change the predicted impact of the
Daylight Code on student performance.
Only the exercise to link the Daylight Code to student attendance was
unsuccessful. This is also an extremely important finding, since it contradicts so
many claims have been made about the health effects of daylight or other indoor
environmental conditions, as reflected in absenteeism rates of building
occupants. In this study, in this school district, we did not find that any of the
physical attributes that we had available to us to classify the classrooms could be
linked significantly with student attendance.
It is also very clear from these efforts, as we re-analyzed the original data sets
with additional information, that the findings of these models are much more
strongly dependant upon the particular population studied in the analysis than
upon the subtleties of all the variables included in the models. Thus, we conclude
that it will be much more informative to try to replicate this study with a
completely different population, at a different school district, such as we will
attempt to do in Task 2.4 of this project, than it would be to continue to try to
refine the models and with further detail in the explanatory variables. This
process has been informative as a sensitivity analysis and methodological study.
We look forward to applying these lessons in the next study.

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50

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
51
7. APPENDICES
7.1 Statistical Terminology
The following briefly describes key statistical terms in the report.
Table 1
Term Name Definition
r Correlation
Coefficient
Or
Pearson correlation
Measures the strength of the linear relationship
between two variables
It can take on the values from -1.0 to 1.0, where
-1.0 is a perfect negative (inverse) correlation,
0.0 is no correlation, and 1.0 is a perfect positive
correlation.
On page 6, r is the correlation between well-
qualified teachers, and student performances.
When .61<r<.80, a strong positive relationship is
predicted.
p p-value A p-value is a measure of how much evidence
you have against the null hypothesis, i.e. that the
hypothesis is not true. (In the report on page 6,
the null hypothesis could be interpreted as: r=0).
The smaller the p-value, the more evidence you
have. (On page 6, a very small p-value indicates
that one has very high evidence that the given
correlation is significantly different from 0). The
probability of a false rejection of the null
hypothesis in a statistical test is called the
significance level.
A p-value can vary from >.00 to <1.0. The
significance level is 1-p, expressed as a
percentage. So if a p-value is .01, the
significance level is 99%.
One may combine the p-value with the
significance level to make a decision on a given
test of hypothesis. In such a case, if the p-value
is less than some threshold (usually .05,
sometimes a bit larger like 0.1 or a bit smaller
like .01) then you reject the null hypothesis.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
52
Term Name Definition
R2 Regression
correlation
coefficient
A value between 0 – 1.0 that indicates how well
an X value (or the independent or explanatory
variables in the regression) explains a Y value
(the dependent variable). Technically, the
regression equation is: Y= B0+B1X1+ B2X2+…+
BnXn+e
where B0= intercept, e=error,
so as Xs change, Y, the dependent variable,
also changes., and variations in X values cause
variations in Y.
R2 is defined as the percentage of total variation
in Y explained by the independent variables.
If R2 is equal to 1, then entire variation in Y is
explained by the independent variables, i.e. the
model is very good, and the X variables have
perfect explanatory power (for explaining Y).
So, the higher the value of R2, the better the
model is for that set of data. Models explaining
data that have a high degree of inherent
variation, such as individual behavior, will have a
much lower R2 than models explaining more
predictable events, such as group averages.
B B Coefficient Technically, the regression equation is:
Y= B0+B1X1+ B2X2+…+ BnXn+e
where B0 is the intercept (constant), and
B1 ,B2 ,…,Bn are the slopes of the regression
equation, or the coefficients of the Xs, (or the
independent variables), and e is error.
A particular Bi (i=1,2,…,n) shows how a
particular Xi variable is related to Y. If a Bi
coefficient is a positive number, an increase in Xi
by one unit increases Y by the amount of the Bi
coefficient.
Please refer to Figure 11 for a list of the B
coefficients for each independent variable.

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
53
7.2 Teacher Survey
(format changed slightly to fit two pages in this appendix
CLASSROOM SURVEY
Dear CUSD Teacher,
The Heschong Mahone Group, an architectural consulting firm, has been working with
the Capistrano Unified School District on an innovative study of the relationship of the
physical classroom environment and student performance. We have been funded by the
California Energy Commission to do a follow up study to examine a few methodological
questions. To do this, we need your assistance to collect information about CUSD
teachers and their classrooms.
Please fill out this brief two-page questionnaire and return it today. All individual
responses will remain strictly confidential, and will not be released to the District, or to
anyone outside of our immediate research team. Only summary data will be reported.
Thank you for your help!
Lisa Heschong, Partner, Heschong Mahone Group
A. Please tell us about yourself:
1. Your Name: Grade Level:
2. Your current room number (location): 99/00 School:
3. How many years have you been in this classroom?
(answer questions 4 and 5 below if you have moved your classroom in the past three years)
4. Your room number from 2 years ago (97/98): Grade Level:
5. How many years in that (97/98) classroom? 97/98 School:
6. How many years have you been teaching at this school?
7. How many years have you been teaching in this district?
8. How many years have you been teaching total?
9. Your Gender: Male  Female 
10. Your Age: 20-39  40-59  60+ 
11. Your College Degrees:
12. Additional Coursework:
13. Teaching Awards:

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
54
B. Please tell us about your classroom:
14. Do you feel that you had any influence on the selection of your classroom location?
This past year: Yes  No  Maybe/not sure 
When I first started here: Yes  No  Maybe/not sure 
Anytime in between: Yes  No  Maybe/not sure 
15. If you could select your own classroom, what would be the three most important criteria you
would use to choose? If possible, put them in rank order (1,2,3)
16. Do you prefer teaching in a permanent or portable classroom?
Permanent classroom:  Portable classroom:  No opinion: 
Why?
17. In general, while school is in session, how often do you:
Never Always
(*Please use the scale described below:) N/A 0 1* 2* 3* 4* 5
Open a window for ventilation       
Open a door for ventilation       
Close a door or window to reduce noise       
Turn on a portable fan       
Adjust the thermostat       
Teach with the curtains or blinds closed       
Teach with all the electric lights off       
Teach with some of the lights off       
Darken the room for TV or computer use       
Do something in order to block the sun       
N/A This is not possible in my current classroom
0. I could do this in my room, but I never do
1. I do this occasionally, a few days a year
2. I do this often, more than 10 times per year, depending on the weather
3. I do this often, more than 10 times per year, independent of the weather
4. I do this very frequently, about once a week or more, all year
5. I do this about once a day or more, all year
18. Any comments?
Thank you very much for your time!
If you have any questions about this survey, please contact Lisa Heschong at the address below.

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55
7.2.1 Three Most Important
Criteria in Selection of Classroom
(Answers to Question 15)
fresh paint
location
matching/appropriate furniture
my own 4 walls
water in classroom
more storage
Heating/ventilation/air conditioning
natural light
sound proofing
Quiet
room and light
storage space
walls to separate from other rooms
air conditioning/heater
noise level-
air conditioning
clean air
proximity to facilities (bathroom, cafeteria)
a door that closes
full size walls
equitable room size
brightness/airflow/lighting
size
available water
A good location, off the street and parking lot
Enough room
ventilation, temperature control (see notes)
In main building
air conditioning that works quietly
close proximity to restrooms
quiet
your are in control of noise level
limited distractions i.e. window
windows for natural ventilation and lighting.
bulletin boards,
access to water
Access to water
a 2nd window for cross ventilation/light
sufficient storage
Size
sink
windows
permanent classroom
located near grade level team
noise
size
water in classroom
storage for supplies
in the building
light
new
Inside school
close to team
close to playground access
4.away from noise
in the building
away from the lunch area
in the same pod as the grade level I'm teaching
quality health standards i.e. no asbestos
safety close proximity to school
sink
size
location in school
storage space
quiet
spacious
close to supplies
quiet environment not near the lunch area
good lighting
good ventilation, air circulation
enough space and storage
inside where the main bldg. Provides water, sinks and
center work area
easier computer printer access and classrooms are
better maintained
size
storage boards and white boards/bulletin boards, 4
cleanliness
How large is the room
Is it clean and safe
Does it have communication to 911 or office staff
Proximity to MPR for music activities I do
ventilation - airflow (catches prevailing breeze
size and brightness (windows and skylights
larger in size
keep playground noise to minimum
storage
Adequate lighting
ventilation of fresh air into classroom
room size
sink-washing hands -
science, art
white boards to eliminate dust -
safe/noise
size
water
phone
air conditioned
sink
size
air
storage
close to office
full view of street for safety during weekends
near bathrooms
size,
location
who neighbor teachers are

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Working air conditioner
Big room
water
size
location
quality of ac
more, much more room. My current room has no room,
it is a misnomer
cupboards that opened more than a 30 degree acute
angle
built-in shelves attached to freestanding walls
location
has windows
size
size
storage
noise level
size
location, proximity to same grade teachers, playground,
office
shape
square footage (storage, too)
quiet
access to water, elect. Etc. no water in my portable
quiet surroundings
windows, yet not looking out onto playground
sink with water
location
size
noise level
permanent classroom-completely enclosed
permanent classroom with minimal noise from
neighbors
portable with adequate ventilation
Quietness
space
near bathroom
light-windows
sink
noise level quiet
size
cupboards for storage
location
size
noise level
window
Large room(space for desks, floor space & small group
space
single desks (not large tables or trapezoids
sink and storage area
quiet
sink
larger size
cabinets
sink
room size
4 closed walls
large
windows
Balanced - behavior
academic abilities and
study skills - -
quiet (solid walls
sink
built-in shelves
light-natural
outside door
size/space
outside door
sink
built in cupboards
student friendly
ample room
location
large
quiet
good a/c
size
window
outdoor passage
self contained
adequate space -
self contained classroom /4 walls doors and quiet
an air conditioner that works
larger room to allow for centers
space, present size
windows tinted
storage space closed off by moving white boards
open windows, light
quiet, insulation from other rooms
nearby work room/office
with grade level
windows
access to bathrooms
Windows that open (big windows)
good storage space
carpet
space-lighting
storage
clean carpeting and freshly painted room
size(permanent room with sink)
location(away from playground noise)
windows and natural lighting
away from playground noise
size
near grade level
space (usable)
freshness (clean painted)
location(proximity to playground, office
location
size
age
size
cleanliness
location(near office, restrooms

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
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Size, room to move & do centers
location
water, storage
Large enough for desks and room for center grouping
sink
ample cabinets and drawers for books and supplies
Lots of natural light, windows, skylights
space for kids & materials
grade levels clustered together
windows
source of water
view
close to other teachers at my grade level
close to office/work room
noise level
windows
phone
water
space
light
noise location
next to other second grades
facing courtyard
close to office
location - away from playgrounds and lunch tables
exterior view- students need to work outside at times
-
Enclosed room
room to move around/nice big space
sound proof
location near someone I can team
quiet location - grass, trees, etc
full size-running water
size
air and water
storage
air conditioning and heating system that works
windows-
available water
outside door/window
space
windows
sink
space
outside access
space
sink area
windows
door to outside
air conditioning
location
size
facilities (sink, etc)
size
noise level
close access to library, computer room, etc
air conditioning
light
spaciousness
size- and storage
location in respect to playground bathrooms
location in regards to other grade level classes for
learning
climate control
access to a bathroom
water in room
location to playground
restrooms
office
single desks to lend for flexibility
carpet for sound
tackable wall space
natural light - windows
openness- size
lots of useable/tackable walls
quiet
windows/light(natural)
size
windows
built in cupboard space
sink/space for students to walk around
windows
sinks
space lots of it
Windows, natural light and a view
self-contained and not in the traffic pattern so we're not
interrupted frequently
adequate air conditioning and heating
away from recess area
close enough to workroom, office, library
size though all are the same
Little outside noise, I am next to preschool special ed
play yard
fresh air
room to move freely and for storage
quiet area
close to team teacher
black top and ramp area not in field
size
proximity to front of school
no paneled walls
Black/wipe boards
sink
storage
close to teammates
away from playground
privacy
enclosed (4 walls
close to office or work areas
away from playground
controlled air circulation
windows and door access
physical space

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58
closed in classroom
newer facility
location relative to team teachers
size
doors
location to office
self contained, no open walls
roominess
fully equipped, water, phones, etc
closed classroom
air conditioner that doesn't leave dirt throughout room
wall that can be stapled into
closed - self-contained
lots of bulletin boards and magnetic white boards
control of heat/air
size
location -
ability to isolate class and students from surrounding
noises and other students and other instructions
to control temperature and air flow
proximity to team mates of same grade,
if any choice was available more modern facilities
including storage and water /sink
size
lighting
storage
quiet
accessible
well ventilated
Enough space for children & furniture ( not crammed
together)
cupboard space, built in drawers, sink in room, counters
windows
noise (exterior)
temperature
lighting
two exits
running water
windows that open
size
storage
lighting
size
location away from outside noise, i.e. freeway, lunch
area
storage space
space of students, desks, materials
storage
easy access to playground
space floor space wall space
physical environment ventilation, lighting etc
location, restrooms, drinking fountains
corner room with minimal 'traffic' flow
storage space and inside sink
white board space
windows
storage/cupboards
size/bright/clean/well ventilated
size
natural light
self-contained
away from playground
near bathroom
near office
lots of space
lots of bulletin boards
lots of storage room
size fits 30
windows to the outside
self-contained
space size of classroom and storage space
windows
location
easy accessibility
more built-in bookshelves and counter, cabbies
larger wet area
space for children
sink area
storage space
not too close to a playground
not too close to an eating area
a room with windows that can be opened
4, better lighting
white boards
storage
space
distance from playground
access to pod -
space
storage
layout
size
technology wiring
sink
clean new facility
appropriate lighting
nice size
windows. my classroom at Moulton had none
space to move around the room
sinks with clean running water
size
ventilation
sinks in classroom
sink,
sunlight
location
size
lighting,
windows
a classroom with much more light
new carpet -
Inside bathroom and sink
art area
enough closet space/cubbies for minimum # of children
water
windows

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
59
away from playground noise
have air conditioning
plenty of room
windows that open
built in storage space
thermostat controlled inside temps
space
window/walls
storage
size
location in relation to playground/office
location in relation to others at same grade level
size
storage ac/heat ventilation
windows
running water
built in storage
air conditioning
size
water
space for all students/desks/cupboards
lighting in class
soft walls to hang things
size
running water/sink
condition of classroom
size
light
locations
size
windows
location
student space
storage
location (away from playground etc)
size
wall space
proximity
Lots of space
lots of natural light
adjoins other rooms
windows
space - bigger than a portable
cabinet/storage areas
In the school building
Water
Connected to a pod
storage
windows
sink
location close to same grade level
size
storage space
permanent classroom
quiet location
convenient location
size
location -
size
clustered w/other 5 grades
away from playground distractions
location
space/cabinets
room (sq. footage)
space
light
storage space
physical space
storage
light
Access to natural light (window thick walls so
my students may work noisily at times
cabinet space/technology
3 Pod-
close to office
teaming situation
student work space
materials storage
water
main building
with grade level
work room
storage
location/workroom
windows
Quiet (we have open classrooms
windows to the outside to see out
large enough for desks and centers
space
doors
windows
sink
size
windows
light
compatible teaching style-neighbors
easy accessibility in and out
windows and natural light
good lighting (electrical
ample size
natural light
ventilation
secluded location not central because of foot traffic
portable
room with a window or outside lighting
my own air conditioning controls
enclosed
lots of space
lots of storage space
walls and door
windows
water
centrally located
storage furniture

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
60
Built in sink
close to bathroom
close to the "going ons" with the school
Location away from playground noise
inside main building
near team (grade level)
close to bathroom
size
inside the building
large size
good location-close to copiers, etc
clean
close to another room incase I need another teacher to
keep an eye on my class or vice versa
work area in central pod and area for storage
proximity to copiers
size
noise factor
location to main services
room size
access to water pod area
size
building vs. portable
location
space per student
technology
environment (sink, water a/c etc
space
windows(light)
wallspace (allowing for displays and bulletins
Interior environment (cleanliness etc)
location ( in the school near office, copiers, etc)
storage
size
location
storage
location to office, bathrooms etc
running water
size of room
size
location, front of school, avoid playground
lighting
size
location
storage space
air conditioning
more cupboards
new carpet/paint
large enough
desks in good condition
natural light
location - near grade level
air conditioning
storage
Full size classroom
air conditioning
sink/water in classroom
permanent classroom - large
bulletin board type walls to ceilings
square room (unlike bowling alley I am in
air quality (windows, proper ventilation etc
size not a bowling alley as it is now 2 portables divided
into 3
not a portable
size
air conditioning
condition/cleanliness
size
quality - carpet-paint
air from window flow, very important
windows
water in the classroom
size
space available ventilation
room environment/sink, painted walls in good condition
etc
good ventilation
quiet location
lighting
Internet/electrical/av wiring
windows-light
size
size
location
condition
large room
windows that open/close
bulletin board space
away from the black top room overlooking
courtyard or grass area
carpeting to the door
size
location
storage space
space
location
light
appropriate space for the number of students and
furniture that will occupy it.
Adequate storage for student and teacher resources
condition - everything clean and in working order
size for 33 students
heating/ac
sink & water to drink
sink
air conditioner/heater
spare cupboards
sink, access to water
air conditioning
location to playground (far enough to not be bothered
by noise, close enough so it is not a 10 min hike
conveniently located
size
a/c and heating

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
61
size - larger the better
Location in school "noise factor"
amount of storage and air conditioning
windows - open-ness
light (not dark)
designed - physical lay-out in harmony for
centers/teaching
Storage, there needs to be some
water
windows, fresh air
windows
non toxic
air circulation
In the school building closer to restrooms, labs,
workrooms
running water for science, art, hygiene
space and storage; less noise
Near teammates
in the bldg. W/water, near office and restroom
not used after school
Location
surrounding noise
appearance
Floor space
outside noise level
storage
Adequate floor space with super storage including book
shelves quiet environment away from lunch and
recess noise windows providing natural light
Quiet location
bright
storage
Size (30+ fifth graders need lots of room
location (I'm in what we call cell block B)
windows, (I have one small window makes me feel
claustrophobic and lack of natural light is depressing
size
being in main building -
location
amount storage space
# windows
easy access location to office and spots that will help
w/my student council advisor position
quietness
window/door placement and room design
bulletin board space (sufficient)
storage, ample
sink in room, bathroom and work room closer
air conditioning
size
in main building ( not portable
proximity to office, restrooms, workrooms, lounge area,
running water
proximity to grade level team members
area with maximum sunlight, minimum playground
noise
cleanliness
temperature
classroom relationship to office
sink w/water fountain
amount of space, at least 30' x 30'
window or skylight, door to the outside
size
storage
running water
size
location
lay-out
space
windows
storage
size
storage
cleanliness/brightness
windows that open
not near the playground noise
more natural light
Size
Windows for air
clean
be closer to the office
larger classroom
fresh air flow, open window on one side and the door
on the other

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62

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
63
7.2.2 Permanent v. Portable Classrooms
Answers to Question 16
Preference Why -
depends depends on factors in question 19
depends it depends, if the perm classroom is large and quiet, I prefer perm. If not portable
depends both have positives, permanent rooms have better equipment and sinks, portables seem more private
but are uglier on a campus
no opinion 17 times, no comments
no opinion as long as it is large enough for centers
no opinion both have +/- portable + air - no water and more wall space, permanent no air water more windows
no opinion either is fine, as long as there are walls
no opinion either is good with above options
no opinion haven't tried a portable
no opinion I don't care so long as top 3 choices are met
no opinion I have had both and can adjust to either
no opinion I have taught in both, and as long as they are new and nice esp. portables they are great
no opinion I love portable walls, staple everything up
no opinion If the portable had running water and was large it wouldn't matter
no opinion I've done both they are both fine
no opinion I've never been in a portable
no opinion I've never taught in a portable classroom so I have nothing to compare
no opinion I've only taught in portables
no opinion never taught in permanent but I'd like water
no opinion permanent have storage and feeling of permanence, portables have air - but old ones have mold
no opinion permanent have windows, sinks, portables have great walls and flexibility
no opinion portables are larger, but permanent rooms have water to wash hands, clean paint brushes, etc.
no opinion pros and cons of both
permanent 17 times, no comments -
permanent above reasons/ less adhesives, toxic materials used in construction, light natural air
permanent access to office, library, others at grade level
permanent access to school facilities 'i.e. library, bathroom, water, office
permanent access to water, air quality, size, safety quieter, close to necessities
permanent access to water, especially for younger children
permanent accessibility, central location to services
permanent after teaching in a portable for 12 years I feel the ventilation in a portable is unhealthy
permanent air quality was better and I did not get sinus infections However it is quieter
permanent air quality, allergy problems minimized, learning enhanced
permanent at [my school] they are superior more natural light- more cabinet space and access to a work room
permanent because I have a sink and wonderful storage cupboards
permanent because they seem to get more perks. 'i.e. new carpet
permanent better air circulation, although not sure at this site
permanent cabinets/storage & work room
permanent centrally located to lib, restrooms, water, office etc
permanent classrooms have running water (usually) and the floors are solid and make less noise when walking etc.
permanent cleaner, brighter, doesn't have musty or chemical portable odor
permanent cleaner, more storage, less mildew
permanent closer to copy machines and central pod location for easier access to other classes and work area space
permanent closer to mail building, access to water in room
permanent closer to office, supplies, library/computer lab and other teachers
permanent closer to office/bathroom/workroom/multipurpose room etc.
permanent closer to other rooms and the office
permanent closer to the office, work room (but there is no fresh air flow
permanent closer to things I need, bathrooms, office, copiers-also running water in the classroom
permanent closer to water source, clean hands and room are important to overall health of teachers and students
permanent easier access to office, workroom etc. water availability, noise, no clumping floors, safety when working on
weekends, nights, etc
permanent feels substantial lets children know they are important and that things are not temporary
permanent generally have better location
permanent generally more cupboard space and windows, electrical and plumbing
permanent has water
permanent I am concerned about the health issues for myself and my students
permanent I don't like the potential of mold and the space for re-circulated air. There are many leaks in the two
portables I have been in
permanent I don't mind either on as long as they fit the criteria above

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
64
permanent I feel like a part of the school building
permanent I feel more connected to the rest of the school, and I like having running water. I feel safer.
permanent I feel they are safer in the event of an earthquake
permanent I firmly believe that portables contribute to poor health (colds etc)
permanent I had bad experiences in my portable, allergies, also, I do not like the storage or too rectangular configuration
permanent I have a drain under my portable, I have allergies to mold and mildew
permanent I have had so many bad experiences with the air quality in portables I bought myself an air filter this year and
I have had parents come to me and say their children's health has improved
permanent I haven't had opportunity because of storage
permanent I just like having a sink, If portables had sinks I really wouldn't mind teaching in one
permanent I like being a part of the main bldg.
permanent I like the built in storage
permanent I like the logistics and ability to team with others in a permanent classroom, but I also like the portables
because they are more self contained. I can be noisy and quiet when I choose
permanent I like to keep things clean and orderly, much easier to do w/storage and water
permanent I prefer being close tot he center of the school and portables are usually located out on the playground
permanent I prefer closed classrooms
permanent I teach K with 30 kids and I need a bathroom in my room
permanent If I can open the windows and doors, [at three schools] there is nothing that opens
permanent If I could have a permanent classroom with doors I would prefer that
permanent I'm a male teacher. I like working in open portables because of misunderstandings that could happen
permanent it has more windows, its larger, and it has a sink in the room
permanent it has sinks
permanent it is closer to facilities(office, copiers, restroom) and it is larger than portables
permanent I've been in both and they both have positive and negative qualities. It; up to the teacher to make the
environment workable
permanent larger
permanent larger
permanent larger, has windows, more storage space
permanent larger, more storage space
permanent larger, sink available
permanent less echo sounding/better continuous ventilation and air flow (heat or cool
permanent less mildew
permanent less noise, more room, smell portables at our school have a bad smell
permanent less noise, more built-in storage, sinks, safer in an earthquake, close to center of school
permanent less odor more ventilation
permanent less sterile looking more in the school mainstream
permanent lots of cupboard space not matter what
permanent more built-in storage sink more charm
permanent more closets etc
permanent more convenient, running water, centrally located , more attractive, more quiet
permanent more light, wall space, open feeling of it
permanent more solid, don't leak, don't smell like artificial-allergenic materials, larger plus cabinets and plumbing
permanent more space sink and drinking fountain, students need to be able to wash their hands without running to the
restroom at all times
permanent more storage and windows
permanent more storage, but like portables because they are closed.
permanent more windows, better view larger. Also there have been complaints about allergy problems in portables from
teachers and students
permanent more windows, lighter
permanent more windows, sink, more storage, wiring
permanent newer portables are fine, but some older ones leak and have musty odors
permanent no mildew, better HVAC, noise from walking on portable floor is annoying
permanent noise and ventilation
permanent noise level lower, more storage, sink in classroom, bulletin boards, nicer atmosphere
permanent obvious
permanent part of the building
permanent permanent classrooms provide a sturdier, quieter more spacious environment - better insulated, no noisy
ramps or noise from neighboring portables, also the long hallway environment does not make efficient use of
space and deal with real classroom needs.
permanent permanent for a sink, no mold or fungus that portables get, new portable great for hanging student work
permanent portable - we only received Sparkletts recently - no sink - little coverage during rainy day, far from restroom
permanent portable classrooms tend to give students with allergies more problems. Many do not have running water or
appropriate storage.
permanent portable is far away from main facilities, no attached workroom, noises echo and air conditioning make it
difficult to hear, some are too small, not enough cabinets
permanent portable lack storage, water, adequate natural light, often have stronger odors from industrial glues, easier to
break into, located on perimeter of the school
permanent portable smell musty no sink & the floor makes too much noise when kids move around
permanent portables are poorly constructed, floor and ramp noisy, no storage, no sinks (water

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permanent portables are too far away from facilities
permanent portables don't provide running water, they're far away for emergencies, restrooms, copy machines, no
storage for supplies compared to building with pods
permanent portables stink
permanent portables tend to take on a musty smell, have few windows, they're cramped; no sinks
permanent possible built in cabinets/solid foundation
permanent provides a greater sense of stability and has tech and sink/water
permanent quiet
permanent quieter
permanent quieter, usually larger, access to center workroom
permanent ramps are too noisy for portables not enough windows
permanent reasons in question 19
permanent right now I'm in 1/3 of a portable and it is very crowded. However, it looks like next year, I'll be in a larger
permanent classroom
permanent roomier and aesthetically more appealing
permanent running water, students aren't drenched on rainy days, better accessibility to other teachers
permanent safer for 1st graders to be in the building, sink in classrooms close to bathrooms, two times larger than my
portable, available storage, cleaner air, printers nearby, more stable in an earthquake
permanent seems safer, less allergy troubles
permanent sink - water
permanent sink and built in cabinets and drawers
permanent sink and storage
permanent sink, closer to supplies, closer to bathroom, closer to peers
permanent sink/accessible to water for cleanup
permanent size is larger and more natural light
permanent size of room availability of a sink/water, and built in cabinets for storage of classroom materials, location to
office
permanent size, accessibility, central location to services
permanent size, smell , water
permanent size, storage space, ventilation
permanent size, ventilation
permanent smell, dampness
permanent solid, less noisy
permanent sometimes portables have an unpleasant odor
permanent space, availability of sink and fountain in class, storage, proximity to colleagues
permanent space, not portables because of space, noise and air quality
permanent space, sink, storage
permanent storage and access to main bldg.
permanent storage behind boards, sink area, wall of windows
permanent storage, magnetic whiteboards, soundproof(floors, walls etc.
permanent storage, sinks
permanent storage, space, proximity to office
permanent storage/building access- bathroom, teacher's lounge/pod access/cleaner and nicer rooms- our portables are
dirty and ugly dust causes allergies
permanent the classroom should have running water, space in which to move for collaborative groups and fresh air or air
conditioning we have no drinking fountain or sink
permanent the issues listed above
permanent The older portables have a distinct odor, are rarely cleaned, carpets are dirty and swept about once per
week, no indoor water, no center work area, no small group area for parent helpers
permanent The permanent classrooms have better build-in cabinets, pods, for working space and proximity to office,
restrooms, running water/sinks and team members. Also the permanent classrooms have better computers/
printer equipment.
permanent the portables now are not a full size portables long rectangular in shape that do not allow for flexible
movement
permanent the room is brighter, storage space and cleaner
permanent there is usually a lot more storage space and it is more secure, inside or attached to a building
permanent they are larger and they have sinks w/air. Storage is much better
permanent they are usually on the central part of the campus
permanent they usually have two exits and running water as well as larger size
permanent They're larger, have more storage and are usually closer to teacher workroom and restrooms
permanent usually the size and shape of permanent, at least at the schools I've been
permanent view
permanent want a sink-water-windows that open (lots of windows)
permanent water availability, proximity to office
permanent less distractions, usually have a sink, closer to main school, not as dirty
permanent water
permanent water and phone available in the classroom
permanent water supply, safety and health issues(portables have too many formaldehyde fumes and molds
permanent water, built in storage
permanent water, closeness to necessities for young children

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permanent water, hallways cover
permanent water, sink - part of community
permanent you have more contact with other colleagues
portable 3 times, no comments
portable air conditioning, control of my thermostat
portable at my school the permanent classrooms have very small or no windows, none of which can be opened. The
doors open up to a central atrium where students eat lunch and it is very noisy. Also I like having my own
thermostat to control climate not central air.
portable At this school site only portable classrooms can be closed off from other classrooms and noises, however as
a teacher you sacrifice necessary items, 1, use of sinks and water for projects hygiene and fluids that are
much needed. Releasing or waiting for 30 students (sometimes 90 students after PE periods to get drinks
causes huge losses of time. we have only 2 drinking fountains for all students outside. Our temperatures are
usually warm and students require water to drink. 2. Use of built in storage so room is cluttered and creates a
maze. 3 proximity to main office, student and teacher bathroom facilities, work space areas and telephones.
All of these are a problem and eat away at precious time for both students and teachers. On a more positive
side, cold temperatures esp. air conditioning are very uncomfortable for my body. I like being able to adjust
the a/c. heat and air ventilation. The down side of this is the a/c. unit makes lots of noise and makes hearing
students and teacher more difficult so you have to raise voice, ask for repeats or be very stuffy and
uncomfortable during oral reading or discussions, reports etc.
portable because at our school the walls are not permanent
portable because climate can be controlled by teacher
portable because it is enclosed and quiet, otherwise I would prefer a permanent classroom where rooms are not open
portable because o the noise situation in the building, this used to be an open school. Now thin portable walls
separate the rooms
portable because of the noise factor in building
portable because quiet, self contained
portable because the portable classrooms are usually larger
portable bigger
portable bulletin boards
portable depends on set up of school, open school environment
portable due to 20-1 the inside classrooms were reduced in size substantially
portable has air conditioning
portable I know who's making the noise, My class, not my neighboring teacher's. Also I can control the temperature
portable I like being control of my own noise instead of an open area
portable in this school permanent rooms have some open walls
portable Its much larger than the permanent classroom
portable larger, less distractions
portable lends for flexibility (walls are often not sound proof in a permanent building - some are simply sight barriers
portable more room for reading groups and older kids upper grade
portable more space
portable more space for 32 students
portable much quieter, more room to move around especially [my school]. When I taught at O[my previous school] I
liked the permanent rooms
portable my portable is in better condition than the permanent classrooms at my school. I have air conditioning and
bulletin board walls
portable noise doesn't drift between rooms
portable only because our portables have air conditioning for our year round classes through the end of July
portable permanent classrooms open into each other, no doors/or privacy
portable prefer if provided with adequate storage, they are larger and generally brighter than permanent rooms
portable quieter room environment than the open classroom building
portable the classrooms inside the building are open-space classrooms
portable the rest of [my school] is an open school and it can be very noisy
portable The state portables are the largest on campus, The small ones are ridiculous for anyone to teach in

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7.2.3 Additional Comments
Answers to Question 18 (a.k.a. Voices from the trenches)
I use an overhead projector everyday, I wish my ac was quieter
Does mold and other bacteria grow under portables? Air conditioner, which is necessary, is too noisy.
Noise is a huge problem, it sometimes seems louder in adjoining classrooms than in the room creating the noise. My
classroom is in a pod adjoining 3 other open classes
We need walls & doors to function. Permanent classrooms are so poorly designed, noisy, with inability to turn off lights
since they are also used by other classrooms.
My portable is right next to the playground. I can not open my door for fresh air between 9-12:45 then the afternoon PE
begins. The blower on the AC is very loud making it hard to teach over. My window and door are on the same side so I
get no cross ventilation
The best schools I've seen were old ones - with banks of windows to open ( on both sides of the room, for cross
ventilation) and a sink with water. I know [my school] won architectural awards but its not a good building for a school.
Note: 19 - Windows so we're not closed in
As long as the school as an open environment allowing noise to travel to other classrooms I vote for a portable. I believe I
do a better teaching job
Note question 18, reduced class sizes in lower grades forced upper grades with higher class sizes into portables question
21, I tried using portable fans because they were quieter but they were not able to move the air around enough.
Comments, I have read that natural lighting has made a significant difference in student achievement as well as starting
times.
My air conditioning unit is very loud it interferes with teaching, also we face a busy street - street noise
Darken room only when we're watching an educational video (not every day but every time we watch a video.
I can only open the windows in the back of the portables. If I open the front windows it is too noisy. I face the playground
and recess occurs all day long (each grade level is on a different recess schedule) The noise was problematic during
testing from recess. curtains would be a good idea for the portables facing the playground like mine. The students get
distracted with watching other students playing
Please do not give this to teachers and expect to get it back immediately. A little professional courtesy (especially at the
end of the year) would be appreciated.
I close the skylight to darken the room when watching a video.
Having internet access is wonderful, but printing to printer in the bldg. Is very inconvenient. It is also very difficult not
having water in the classroom.
Mold in portables under ground problem for allergies. Portables need 2 exit doors. Please help Calif. Get more square
footage per child. It's crazy. Especially with computers taking more space. No phones in our portables, no link to office,
no water.
Carpeting is not cleaned enough or rather not replaced often enough! It is disgusting Give me linoleum floors with an area
rug any day.
My room is either too hot or too cold. Air circulation and proper temperature almost a impossible feat to obtain
Tremendous construction has been going on for the last two years, really bad during STAR testing this year. Jack
hammer one day and ground pounder and earth movers
Lights will be out when I use the overhead projector
I have no door, I must leave through another teacher's classroom, I have no access tot he lights. We are a middle room.
The circuits are in the outside rooms, it is unsafe residing in the middle room. We have enough land to build permanent
wings on our school site. Please help us to improve instruction by increasing classroom size.
I have no secondary window to use for cross ventilation and the air conditioning unit has to be shut off for students to hear
instruction. They tried to adjust it but it is still too loud. If I had a second window, I probably wouldn't need to use it at all
which would save a lot of money and energy
I would love to be able to close off some of the noise around me
Our school is open space so its impossible to control the noise level around us.
Several of the lights that are controlled by the switch in my classroom are located in the classroom next door. I feel it is
important to have a quiet working environment. Although we have an 'open' school environment, it would be helpful to
install walls and doors between the classrooms

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I do not have any control of my lights in fact sometimes partial lights are turned off because of other classrooms. As of this
month May my air conditioner has begun to work and has been broken since Sept. I would love to have a door and
complete walls so sounds couldn't come in my class, which hinders learning when children can't hear.
Storage space in my school and room could easily be converted as described above. I have asked for a Formica
countertop to replace the hardboard top for the past eight years with no success. Why were the same type drapes used
for replacement when they have been very unsatisfactory all these years. Why not tinting or shatter proof windows (tinted)
used for replacement
Orientation is important my room's wing runs NE to SW/ very little direct sun enters. Porch covers help
I use the overhead projector often with the lights off
Our heating systems it either is on full blast or it's off. It can't be adjusted. Rooms are very dirty, not cleaned until the end
of year or I clean it all the time myself
I don't adjust the thermostat but I do turn on the heater in the morning to warm up the room. I close the door when
children walk by who are coming back from upper grade recess
Very unhandy to close curtains- portable fan is mine, very hot in Sept/Oct. no air cond. Window provide no ventilation
I do like our cathedral ceiling and whiteboards, The drapes are horrible. They are almost impossible to close so we do
without closing them sometimes even when darkness is desired. Irony; drapes were just replaced with the same difficult
set. Vertical blinds would be nice. Fan blows papers around. Heater is extremely noisy.
Question 21 teacher also answered N/A to last 7 responses with memo: I share a large room with a wall that was built to
divide it in half, we share lights, thermostat, etc.
In a shared classroom with a drywall separating the two our lights were the same, so we could not turn them off during
teaching time. We also shared a phone and a sink
Note question 18, we did a drawing out of a hat, we agreed on that.
Lights work on 1 switch I turn them off for the overhead, computers, and TV screens. Note question 19, unnumbered
answers, storage, bulletin boards
The classroom has an electrical problem and at times throughout the year a group of lights has been out
My classroom is about 18' wide and 30' long. Way too narrow to adequately teach 7-8 yr. Olds.
All classrooms should have windows that open. This school doesn't and kids (myself included) are always suffering
congestion, headaches, sneezing. Our school is old and is in desperate need of the vents replaced and proper a/c. It's
hard to learn when you're sneezing all day and suffering from headaches
Fresh air and ventilation are very important in keeping students alert and the classroom light and airy for maximum
learning
This is an old school, the black soot coming out of the vents is frightening. Since we were built on an open structure
basis, and then changed to open the a/c ventilation system is very substandard. I'm quite sure it contributes to germ
infestation.
The best thing about my portable is the control over the heat and air conditioning. The permanent bldg. Doesn't have
individual control. It's always a problem
I have no windows in my room and I must walk through another class to get to an outside door, I have no light switch, it is
in another classroom
I have been in several rooms in different districts and feel that windows or lack of in my classroom has had a major impact
on my teaching and the students learning
I consider the physical environment to be an extremely important factor in student parent and teacher attitudes and feeling
about school
I'd love to open windows but noise is always a problem. The lights in the classroom seem insufficient, but we get use to
them. The light is grayish.
I do not have blinds to block out light
If the permanent classroom had a door access to the adjoining classrooms, I would find this more ideal. I enjoy the open
environment, but find it inconvenient when it comes to testing situations and quiet time
I have an open classroom in a pod situation. While it is conducive to team building and support from fellow teachers, it is
noisier and distracting to my students, I would prefer doors
I think children perform better in a closed classroom with few interruptions
Temp. control is a major problem in my room which is colder than the others on my system. Noise level is high from male
teacher next door (panels separate rooms) whose voice booms

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My windows won't open properly or I would open them regularly, My classroom has poor lighting. As a wearer of glasses
this provides difficulty for me. I feel eye strain on overcast days. I also receive weekly complaints from students regarding
poor lighting. I truly feel that this problem at [my school] has been made known yet nothing has been done about it.
Permanent classrooms tend to be bigger, The children need space to move around. I love my classroom and would not
be happy in a portable.
Many portable classrooms are in need of repair or replacement, especially on older campus grounds
Although we have been told that our windows have been tinted for sun glare this proves to be ineffective. Teachers still
have to construct devices that cover the windows to reduce glare and darken the room
Did CUSD hire you to do this survey?
I have also taught in an open classroom where students can hear and see what's going on in all classrooms. This is the
most ill conceived structure for learning, note question 21 windows don't open
You can't give me a survey and expect that I drop everything I do so can fill it out and return it to the office the same day.
Please respect my teaching responsibilities next time
I think upper graders should get priority on the bigger classrooms, It doesn't make sense that a class of 20 smaller
students has a bigger room than a class of 29 bigger students
I currently enjoy my classroom very much. It is what I consider large: have sinks, air conditioning and storage. I am not
against portables but against not been given a full size portable. I believe it is good for myself and students to breathe in
some fresh air, helps us all think
I like a lot of white board room in front, back, and the sides
Because kindergarten is considered 20-1 but it's not considering we still have 34 bodies to accommodate I doubt the extra
fixtures (, closet for back packs) will ever be addressed
Can improvement be made on the upkeep of our buildings? The floors that bounce when there's movement in our room or
next door, record players skip and overhead projection jumps on the screen
We'd like more space! Tiny portables for tiny people don't offer room for the extra movement that happens ALL DAY
Teaching pre-school without running water makes me feel like it's the 1900s. We carry pails of water! Also we share
inadequate bathroom facilities with the rest of the school, The floor is often wet and slippery
20-1 is great, but when classroom size is so greatly reduced stress is increased noise increased no room for centers
My class is part of a large classroom, I have no access to thermostat, intercoms are shared
Teachers need lots of storage space
I love my room but I would like to have more light (natural)
I loved my previous room because it had a large skylight with adjustable blinds. I wish my current room had more
windows instead of narrow slits
I would love to have a window and door. If they put a door up we wouldn't get ventilation
After teaching [at my school] for five years I have come to really appreciate the effects of natural light and closed
classrooms. The only thing I would change about my classroom is not having a glass slider between my room and room
#15 and having direct access to the work room. With 30+ 4th graders it would also be nice to have extra square footage.
I turn off ac before propping door open, I miss having windows to open, I try hard to conserve energy by not using all
lights all time
Our rooms are open, without windows, it can be distracting due to noise of other classes
I helped open the school, and we had choice of classroom since then I feel that being in a room gives me priority for it,
unless I change grade levels. Teach w/all lights out only when doing projects requiring it(science) or when using an
overhead. Wind and noise keep me from opening my door more often. Adjusting my thermostat doesn't do much good.
My classroom has very poor air circulation due to construction of walls to form additional classes. No windows open, open
door is too noisy
I was happy with my location and still am
Lights in my room are also controlled by other rooms I can't turn off all the lights for a movie because it would effect
surrounding classrooms
Wish I could open a window, very noisy only crack back window. Open door for ventilation except at others recess and
lunch noise
The portable I am in is way too small for 21 bodies. The size absolutely affects learning in my classroom. It makes an
already challenging job that much more difficult. Small rooms like mine are not shaped or equipped (heard of

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70
painting/cleaning/washing hands without water for 20 six year olds) for children this age. These tiny rooms are far from
the best interest for a learning environment for young children.
I have a great room. We always keep our doors shut to keep it cool inside and it is noisy in the lunch area right outside
our door, We darken the room to watch movies. As a ritual I turn out some lights when I read to the class.
The light provided by the skylight make teaching with an overhead projector excellent
A room with theatre-like wall for projection would be great if we had projection devices
My thermostat runs on extremes - My class gets way hot/stuffy or chilly w/quite a breeze depending on fluctuation
between 1 or 2 degrees (i.e. 71 hot 70 cold
No window to open, Superintendent's instructions do not allow for open doors in air-conditioned rooms, I can and do close
the louvers in the sky light at times
The so-called double wide portables are too small! I am in the middle room and the students do not have enough space to
move around. Most large projects are eliminated because of lack of space and no access to water. The room is so small
that we use the ramp outside to set up centers. The door is always open because the poor circulation in the room gets us
sick, since we have no water to wash our hands after sneezing and coughing all over them, we get sick more often and
pass colds, flu etc to each other because of our close proximity
Have custodian adjust heat/cool up or down. Sometimes lights off when I do a read aloud
My room is bright/clean/with air conditioning. If it were larger, it would be a perfect learning environment, P.S. I have a
great view.
Magnet white boards portable classrooms don't have them
Note from section a question 6- not here anymore, it was a portable which was put on our campus in Nov. and removed
during the summer. Before that I was in a very old portable that ended up being re-roofed, carpeted etc. I got very sick in
that old portable. Our classroom portable numbers change yearly, depending on how many portables we have ---question
22 comments I'm so glad you are looking at this. I'd love to help you more. I've been at 5 schools in my district in the past
21 years feel free to contact me again
I hate my classroom this year! I am in a portable without a sink, removed from campus, and it takes us 10 min each way
to the playground. To make matters worse I am next door to adult special ed ( they make a lot of noise) and next to a
school that is operating out of a church (noise) Plus we face a busy street with construction going on all year. The noise
and constant traffic drives me crazy. Oops almost forgot to mention the room is infested with mice. Although traps have
been set, the mice no longer enter them, and because of the children, poison cannot be put out.
Student performance on tests is primarily based on 3 factors, educational level of parent, student work habits and the test
itself and correlation to curriculum
I realize that 20-1 has created the need for portables, but they're highly inconvenient for both students and teachers.
Could teachers and grade levels rotate in the building
AC comes on freezing - nice to be able to control ac but noisy and can hear recess if windows open we are treated as if
not as important as those inside the main bldg. No restroom, no workroom. 98-99 we had to fight to get white boards that
erased. ( additional comments from this teacher, question 19- My primary concern is having 40 kids in the room for a rainy
day lunch w/1/2 noon supervisor. Also, no soap we would like Purell or wetwipes supplied for us. Question 20, have to
push in TV up the ramp many of us share this TV. question 21-97-98 I was housed in the YMCA rm. I could not use the
room after school and was treated very rudely. The Y presently uses our picnic tables and leaves much garbage daily
I'm really a long distance from bathrooms and the teacher's workroom It's frustrating to be crowded
Portable ventilation is poor, either it is freezing or stuffy. Students are encouraged to dress in layers for this situation.
Proximity to restrooms and team members truly increases our ability to effective and efficient. Also we are concerned with
hygiene due to lack of running water and sinks. Teachers are purchasing Purell or wetwipes with own money for
students. Thank you for looking at this very important issue.
Door opens to lunch tables, we have 4 different lunch times and the noise makes it impossible to leave the door open, I
have a skylight and keep it open (automatic shutters) all the time for the natural sunlight. A plus for portables is that the
door and windows open up to the outside, natural light & fresh air, I work inside the building
I would add a window particularly with a view, to my choices in #19, I have a few slits for windows I find better than
nothing. I would hate to teach inside a building with no natural light
Please do not disregard the cleaning, dust, mold, etc of the rooms. Also, portables need to have running water and sinks
for drainage
I love my room, just not the location

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71
7.3 Bias Analysis Models
New Model Change Old Model
Capistrano, Teacher Bias Analysis - Reading Daylight new-old Capistrano, Original Analysis Reading Daylight
28-2 (Original population) R^2 C17-rd
Model R^2 0.248 0.002 Model R^2 0.246
B Std. Error p (Signif) BB Std. Error p (Signif)
(Constant) 3.009 0.303 0.000 (Constant) 3.025 0.298 0.000
Classroom characteristics Classroom characteristics
Daylight code 0.475 0.086 0.000 0.011 Daylight code 0.464 0.085 0.000
Operable windows 0.650 0.212 0.002 0.007 Operable windows 0.643 0.212 0.002
Teacher Characteristics
Teacher 3 -0.917 0.288 0.001
Teacher 5 -1.335 0.388 0.001
Log yrs teaching 0.221 0.090 0.014
Student characteristics Student characteristics
Grade 2 10.823 0.251 0.000 -0.037 Grade 2 10.860 0.251 0.000
Grade 3 4.368 0.255 0.000 0.069 Grade 3 4.298 0.254 0.000
Grade 4 0.944 0.252 0.000 0.008 Grade 4 0.937 0.252 0.000
GATE program -1.432 0.257 0.000 0.020 GATE program -1.452 0.257 0.000
LANG program 0.827 0.239 0.001 -0.011 LANG program 0.838 0.239 0.000
School sites School sites
Sch 61 2.173 0.371 0.000 -0.022 SCH 61 2.195 0.370 0.000
Sch 62 1.634 0.485 0.001 0.049 SCH 62 1.584 0.477 0.001
Sch 64 2.536 0.638 0.000 0.019 SCH 64 2.517 0.638 0.000
Sch 67 1.296 0.418 0.002 -0.062 SCH 67 1.359 0.416 0.001
Sch 72 -1.486 0.378 0.000 -0.027 SCH 72 -1.460 0.376 0.000
Sch 77 0.826 0.429 0.054 -0.036 SCH 77 0.863 0.428 0.044
Sch 81 0.822 0.433 0.058 -0.168 SCH 81 0.990 0.431 0.022
Sch 82 1.664 0.450 0.000 -0.004 SCH 82 1.668 0.449 0.000
Sch 85 -1.316 0.389 0.001 -0.062 SCH 85 -1.254 0.388 0.001
Sch 73 1.574 0.515 0.002 0.047 SCH 73 1.528 0.516 0.003
Outliers Outliers
O 82 39.693 7.910 0.000 0.043 O 82 39.650 7.916 0.000
O 71 40.741 7.918 0.000 0.061 O 71 40.680 7.925 0.000
O 17 42.271 7.921 0.000 0.923 O 17 41.348 7.922 0.000
O 58 35.509 7.916 0.000 -0.055 O 58 35.564 7.923 0.000
O 50 36.757 7.911 0.000 0.214 O 50 36.543 7.915 0.000
O 28 -37.307 7.921 0.000 0.163 O 28 -37.470 7.926 0.000
Dependent Variable: Reading Delta (sp98-fa97) Dependent Variable: Reading Delta (sp98-fa97)
Figure 22 - Capistrano Reading Models, Original Population, with and without
Teacher Variables

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
72
New Model Change Old Model
Capistrano, T eacher Analysis - Math Daylight new-old Capi strano, Origi nal Analysis Math Daylight
28-2 (Original population) R^2 C17-md
Model R^2 0.259 0.003 Model R^2 0.256
B Std. Error p (Signif) BB Std. Error p (Signif)
(Constant) 9.045 0.464 0.000 (Constant) 8.026 0.407 0.000
Classroom characteristics Classroom characteristics
Daylight code 0.430 0.072 0.000 -0.075 Daylight code 0.504 0.067 0.000
Teacher characteristics
Teacher 3 -0.933 0.248 0.000
Teacher 5 -0.688 0.335 0.040
Log yrs teaching 0.373 0.077 0.000
Student characteristics Student characteristics
Grade 2 9.624 0.216 0.000 -0.088 Grade 2 9.711 0.215 0.000
Grade 3 5.949 0.220 0.000 0.018 Grade 3 5.931 0.219 0.000
Grade 4 1.802 0.216 0.000 -0.011 Grade 4 1.813 0.216 0.000
Absences unverified -0.263 0.123 0.033 0.000 Absences unverified -0.263 0.123 0.032
Absences unexecused -0.029 0.014 0.043 -0.003 Absences unexecused -0.026 0.014 0.069
GATE program -1.191 0.222 0.000 0.045 GATE program -1.236 0.223 0.000
Language program 0.488 0.205 0.017 -0.001 Language program 0.490 0.205 0.017
School characteristics School characteristics
School Pop-per 500 -0.995 0.000 0.000 -0.483 School Pop-per 500 -0.512 0.000 0.010
School sites School sites
SCH 59 -1.356 0.435 0.002 -0.267 SCH 59 -1.089 0.435 0.012
SCH 60 -1.044 0.397 0.009
SCH 61 0.808 0.321 0.012 -0.091 SCH 61 0.898 0.313 0.004
SCH 62 0.992 0.403 0.014 -0.457 SCH 62 1.448 0.395 0.000
SCH 66 1.172 0.514 0.023
SCH 67 0.838 0.355 0.018
SCH 71 0.803 0.429 0.061
SCH 72 -1.538 0.330 0.000 0.075 SCH 72 -1.613 0.321 0.000
SCH 74 -0.887 0.392 0.024
SCH 77 0.963 0.366 0.009 -0.204 SCH 77 1.167 0.365 0.001
SCH 81 -0.678 0.356 0.056
SCH 82 1.046 0.381 0.006 -0.152 SCH 82 1.198 0.379 0.002
Outliers Outliers
O 33 34.151 6.827 0.000 0.089 O 33 34.062 6.838 0.000
O 18 35.754 6.820 0.000 0.639 O 18 35.115 6.837 0.000
O 32 61.994 6.824 0.000 -0.461 O 32 62.456 6.835 0.000
O 48 -45.808 6.822 0.000 0.614 O 48 -46.422 6.831 0.000
O 45 -40.193 6.819 0.000 0.117 O 45 -40.310 6.830 0.000
O 02 -33.568 6.828 0.000 0.898 O 02 -34.466 6.830 0.000
Dependent Variable: MATHDELT Dependent Variable: MATHDELT
Figure 23 - Capistrano Math Models, Original Population, with and without
Teacher Variables

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73
With Teacher Info Change No Teacher Info
Capistrano, Teacher Bias Analysis Reading Daylight new-old Capistrano, Teacher Bias Analysis Reading Daylight
TS2 Teacher Survey Population R^2 TS2 Teacher Survey P opulation
Model R^2 0.243 0.004 Model R^2 0.239
B Std. Error p (Signif) B B Std. Error p (Signif)
(Constant) 3.277 0.520 0.000 (Constant) 3.905 0.500 0.000
Classroom characteristics Classroom characteristics
Daylight code 0.463 0.107 0.000 0.030 Daylight code 0.434 0.107 0.000
Operable windows -0.599 0.296 0.043 -0.066 Operable windows -0.533 0.296 0.072
Teacher characteristics
Teacher 2 1.097 0.282 0.000
Teacher 6 0.741 0.321 0.021
Student characteristics Student characteristics
Grade 2 10.710 0.395 0.000 0.077 Grade 2 10.634 0.394 0.000
Grade 3 4.083 0.398 0.000 0.160 Grade 3 3.924 0.397 0.000
Grade 4 0.881 0.403 0.029 0.092 Grade 4 0.789 0.400 0.049
GATE program -1.439 0.396 0.000 -0.006 GATE program -1.434 0.396 0.000
Ethnic 3 0.816 0.394 0.038 -0.008 Ethnic 3 0.824 0.395 0.037
School characteristics School characteristics
Vintage 0.034 0.013 0.006 0.001 Vintage 0.034 0.012 0.007
School site
Sch 61 2.269 0.606 0.000 -0.088 Sch 61 2.357 0.607 0.000
Sch 72 -2.225 0.656 0.001 0.007 Sch 72 -2.232 0.656 0.001
Sch 74 -1.568 0.634 0.013 -0.189 Sch 74 -1.379 0.634 0.030
Sch 82 1.916 0.796 0.016 -0.173 Sch 82 2.089 0.796 0.009
Sch 84 -1.417 0.826 0.086 -0.202 Sch 84 -1.216 0.823 0.140
Sch 85 -1.212 0.614 0.048 -0.225 Sch 85 -0.987 0.609 0.105
Outliers Outliers
O 28 -36.805 8.211 0.000 0.539 O28 -37.344 8.227 0.000
O 69 -32.407 8.217 0.000 0.365 O69 -32.772 8.235 0.000
O 17 41.258 8.222 0.000 0.628 O17 40.630 8.238 0.000
Figure 24 - Capistrano Reading Model, Teacher Survey Population, with and
without Teacher Variables

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With Teacher Info Change No Teacher Info
Capistrano, Teacher Bias Analysis Math Daylight new-old Capistrano, Teacher Bias Analysis Math Daylight
TS2 Teacher Survey Population R^2 TS2 Teacher Survey Population
Model R^2 0.277 0.003 Model R^2 0.274
B Std. Error p (Signif) B B Std. Error p (Signif)
(Constant) 5.115 0.661 0.000 (Constant) 6.302 0.481 0.000
Classroom characteristics
Daylight code 0.497 0.105 0.000 -0.048 DAY_REV 0.544 0.104 0.000
OPERWIN 0.801 0.301 0.008 -0.031 OPERWIN 0.831 0.297 0.005
Teacher characteristics
Teacher 3 -0.625 0.236 0.008
Teacher 7 0.430 0.256 0.092
Log yrs teaching 0.464 0.197 0.019
Student characteristics Student characteristics
Grade 2 10.409 0.332 0.000 0.148 Grade 2 10.261 0.328 0.000
Grade 3 6.165 0.343 0.000 0.223 Grade 3 5.941 0.338 0.000
Grade 4 1.942 0.338 0.000 0.041 Grade 4 1.901 0.338 0.000
GATE program -1.226 0.335 0.000 -0.026 GATE program -1.200 0.335 0.000
Ethnic 4 4.348 2.617 0.097 0.116 Ethnic 4 4.232 2.620 0.106
Ethnic 2 1.767 1.049 0.092 -0.024 Ethnic 2 1.792 1.051 0.088
School Characteristics School Characteristics
Vintage 0.020 0.012 0.084 0.006 Vintage 0.014 0.011 0.222
School sites School sites
Sch 59 -1.758 0.727 0.016 0.003 Sch 59 -1.760 0.725 0.015
Sch 60 -1.311 0.569 0.021 -0.152 Sch 60 -1.159 0.564 0.040
Sch 62 1.065 0.566 0.060 -0.241 Sch 62 1.306 0.551 0.018
Sch 67 0.887 0.530 0.095 -0.182 Sch 67 1.069 0.528 0.043
Sch 71 3.948 1.834 0.031 -0.182 Sch 71 4.130 1.830 0.024
Sch 72 -1.496 0.592 0.012 0.558 Sch 72 -2.054 0.575 0.000
Sch 77 1.424 0.684 0.038 0.190 Sch 77 1.235 0.678 0.069
Sch 82 2.577 0.692 0.000 0.146 Sch 82 2.431 0.690 0.000
Sch 83 0.986 0.526 0.061 0.112 Sch 83 0.874 0.525 0.096
Sch 84 -1.622 0.711 0.023 -0.044 Sch 84 -1.578 0.710 0.026
Sch 85 1.100 0.563 0.051 0.498 Sch 85 0.603 0.541 0.265
Sch 173 2.036 0.659 0.002 0.109 Sch 173 1.927 0.657 0.003
Outliers Outliers
O 48 -47.476 6.930 0.000 0.637 O 48 -48.114 6.939 0.000
O 32 62.531 6.927 0.000 -0.243 O 32 62.774 6.938 0.000
a. Dependent Variable: MATHDELT
Figure 25 - Capistrano Math Model, Teacher Survey Population, with and without
Teacher Variables

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With Teacher Info Change No Teacher Info
Capistrano Teacher Bias Analysis Reading Daylight new-old Capistrano, Original Analysis Reading Daylight
27-4 (expanded population) R^2 27-4 (expanded population)
Model R^2 0.240 -0.006 Model R^2 0.246
B Std. Error p (Signif) B Std. Error p (Signif)
(Constant) 3.083 0.320 0.000 (Constant) 3.161 0.319 0.000
Classroom characteristics Classroom characteristics
Daylight code 0.418 0.077 0.000 0.002 Daylight code 0.416 0.076 0.000
Teachers characteristics
Teacher 1 -1.649 0.551 0.003
Teacher 3 -1.321 0.595 0.026
Teacher 2 1.210 0.344 0.000
Teacher 6 0.842 0.306 0.006
Log yrs teaching 0.398 0.208 0.056
Student characteristics Student characteristics
Grade 2 10.574 0.238 0.000 0.085 Grade 2 10.489 0.236 0.000
Grade 3 4.372 0.241 0.000 0.119 Grade 3 4.253 0.240 0.000
Grade 4 0.953 0.237 0.000 0.060 Grade 4 0.893 0.236 0.000
Gender -0.298 0.165 0.070 0.010 Gender -0.308 0.165 0.062
Ethnic 6 1.323 0.754 0.079 Ethnic 6 1.353 0.755 0.073
GATE program -1.539 0.242 0.000 -0.018 GATE program -1.521 0.242 0.000
Lang program 0.703 0.252 0.005 0.005 Lang program 0.698 0.252 0.006
Econ 3 -3.060 0.996 0.002 Econ 3 -2.798 0.990 0.005
Building characteristics
Vintage 0.048 0.010 0.000 Vintage 0.049 0.010 0.000
School site School site
SCH 61 2.328 0.461 0.000 0.007 SCH 61 2.321 0.460 0.000
SCH 62 1.229 0.470 0.009 -0.012 SCH 62 1.242 0.463 0.007
SCH 64 3.086 0.916 0.001 0.345 SCH 64 2.742 0.904 0.002
SCH 67 1.068 0.424 0.012 0.016 SCH 67 1.051 0.420 0.012
SCH 70 1.803 0.893 0.043 SCH70 1.615 0.883 0.067
SCH 71 0.990 0.493 0.045 SCH71 0.968 0.490 0.048
SCH 72 -1.089 0.387 0.005 0.078 SCH 72 -1.167 0.386 0.002
SCH 77 0.908 0.412 0.028 -0.083 SCH 77 0.991 0.412 0.016
SCH 79 1.030 0.531 0.052 SCH 79 0.921 0.529 0.082
SCH 81 2.202 0.475 0.000 0.124 SCH 81 2.078 0.464 0.000
SCH 82 2.370 0.481 0.000 0.044 SCH 82 2.325 0.480 0.000
SCH 93 1.388 0.491 0.005 0.051 SCH 93 1.337 0.491 0.006
Outliers Outliers
O 82 38.594 7.884 0.000 0.078 O 82 38.517 7.892 0.000
O 71 41.114 7.882 0.000 0.080 O 71 41.034 7.891 0.000
O 17 42.753 7.885 0.000 0.913 O 17 41.841 7.890 0.000
O 28 -37.450 7.886 0.000 0.033 O 28 -37.483 7.892 0.000
O 80 -36.638 7.877 0.000 O 58 -36.746 7.886 0.000
O 69 -32.099 7.884 0.000 O 50 -32.825 7.889 0.000
Dependent Variable: READDELT Dependent Variable: Reading Delta (sp98-fa97)
Figure 26 - Capistrano Reading Model, Expanded Population, with and without
Teacher Variables

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With Teacher Info Change No Teacher Info
Capistrano Teacher Bias Analysis Math Daylight new-old Capistrano, Original Analysis Math Daylight
27-4 (expanded polulation) R^2 27-4 (expanded population)
Model R^2 0.2 52 0.002 Model R^2 0.250
B Std. Error p (Signif) B Std. Error p (Signif)
(Constant) 7.505 0.291 0.000 (Constant) 7.558 0.291 0.000
Classroom characteristics Classroom characteristics
Daylight code 0.301 0.066 0.000 -0.051 Daylight code 0.351 0.064 0.000
Teacher characteristics Teacher characteristics
Teacher 3 -0.834 0.244 0.001
Teacher 6 -0.846 0.357 0.018
Log yrs teaching 0.389 0.076 0.000
Student characteristics Student characteristics
Grade 2 9.442 0.205 0.000 -0.053 Grade 2 9.495 0.205 0.000
Grade 3 5.806 0.209 0.000 0.022 Grade 3 5.784 0.209 0.0 00
Grade 4 1.754 0.206 0.000 -0.007 Grade 4 1.761 0.206 0.000
Abscences unverified -0.162 0.131 0.216 0.009 Abscences unverified -0.172 0.131 0.1 91
Abscences unexecused -0.029 0.014 0.037 -0.002 Abscences unexecused -0.027 0.014 0.049
Gender 0.258 0.144 0.072 0.002 Gender 0.256 0.144 0.075
GATE program -1.341 0.211 0.000 0.015 GATE program -1.356 0.211 0.000
Lang program 0.611 0.217 0.005 -0.004 Lang program 0.615 0.217 0.0 05
Econ 3 -2.236 0.538 0.000 -0 .008 Econ 3 -2.228 0.5 36 0.000
School characteristics School characteristics
Vintage 0.034 0.008 0.000 -0.001 Vintage 0.035 0.008 0.000
School site School site
SCH 59 -1.607 0.391 0.000 0.018 SCH 59 -1.625 0.391 0.000
SCH 60 -1.434 0.408 0.000 -0.086 SCH 60 -1.348 0.408 0.001
SCH 62 0.670 0.389 0.085 -0.242 SCH 62 0.912 0.384 0.017
SCH 69 -0.886 0.336 0.008 -0.097 SCH 69 -0.788 0.336 0.019
SCH 72 -2.206 0.337 0.000 0.087 SCH 72 -2.293 0.337 0.000
SCH 74 -0.963 0.418 0.021 -0.268 SCH 74 -0.695 0.416 0.094
SCH 77 0.890 0.367 0.015 -0.024 SCH 77 0.914 0.367 0.013
SCH 78 -0.824 0.356 0.021 0.001 SCH 78 -0.825 0.353 0.020
SCH 79 0.848 0.470 0.071 0.049 SCH79 0.799 0.470 0.089
SCH 82 1.264 0.424 0.003 -0.006 SCH82 1.270 0.424 0.003
SCH 84 -0.663 0.410 0.106 -0.001 SCH 84 -0.662 0.410 0.107
Outliers Outliers
O 33 34.133 6.868 0.000 0.102 O 33 34.031 6.877 0.000
O 18 34.905 6.861 0.000 0.061 O 18 34.844 6.870 0.000
O 32 62.516 6.866 0.000 -0.514 O 32 63.030 6.874 0.000
O 48 (46.018) 6.864 0.000 0.497 O 48 -46.516 6.870 0.000
O 45 (40.246) 6.860 0.000 0.275 O 45 -40.521 6.868 0.000
O 77 (36.783) 6.861 0.000 0.140 O 77 -36.924 6.870 0.000
O 02 (33.621) 6.869 0.000 0.287 O 02 -33.908 6.877 0.000
Dependent Variable: MATHDELT Dependent Variable: MATHDELT
Figure 27 - Capistrano Math Model, Expanded Population, with and without
Teacher Variables

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Descriptive Statistics Capistrano Original Population
N Minimum Maximum Mean Std. Dev.
Daylight Code 8268 0.000 5.000 2.029 1.241
Window Code 8268 0.000 5.000 1.364 1.093
Skylight Type A 8268 0.000 1.000 0.060 0.237
Skylight Type AA 8268 0.000 1.000 0.034 0.181
Skylight Type D 8268 0.000 1.000 0.013 0.113
Skylight Type C 8268 0.000 1.000 0.042 0.201
Skylight Type B 8268 0.000 1.000 0.041 0.197
Operable Windows 8268 0.000 1.000 0.607 0.488
Teacher 1 8268 0.000 1.000 0.295 0.456
Teacher 2 8268 0.000 1.000 0.175 0.380
Teacher 3 8268 0.000 1.000 0.182 0.386
Teacher 4 8268 0.000 1.000 0.054 0.226
Teacher 6 8268 0.000 1.000 0.101 0.301
Teacher 5 8268 0.000 1.000 0.067 0.251
Teacher 7 8268 0.000 1.000 0.179 0.384
Log yrs teaching 8268 0.000 42.000 6.641 9.190
School Pop-per 500 8268 404.000 1518.000 879.430 201.472
Classroom Pop 8268 5.000 44.000 23.896 5.886
Grade 2 8268 0.000 1.000 0.268 0.443
Grade 3 8268 0.000 1.000 0.245 0.430
Grade 4 8268 0.000 1.000 0.250 0.433
Vintage 8268 2.000 64.000 17.666 13.295
Absences Unverified - per 10 8268 0.000 12.000 0.107 0.622
Absences Unexcused -per 10 8268 0.000 60.000 5.325 5.361
Tardies 8268 0.000 105.000 4.740 8.540
Gender 8268 0.000 1.000 0.509 0.500
Ethnic 4 8268 0.000 1.000 0.003 0.050
Ethnic 1 8268 0.000 1.000 0.050 0.218
Ethnic 6 8268 0.000 1.000 0.013 0.111
Ethnic 3 8268 0.000 1.000 0.147 0.354
Ethnic 2 8268 0.000 1.000 0.015 0.121
Ethnic 7 8268 0.000 1.000 0.002 0.040
GATE program 8268 0.000 1.000 0.135 0.342
Lang program 8268 0.000 1.000 0.172 0.377
Econ 3 8268 0.000 1.000 0.147 0.203
Econ 8268 0.000 1.000 0.087 0.282
Sch 59 8268 0.000 1.000 0.032 0.176
Sch 60 8268 0.000 1.000 0.041 0.198
Sch 61 8268 0.000 1.000 0.067 0.251
Sch 62 8268 0.000 1.000 0.043 0.204
Sch 64 8268 0.000 1.000 0.020 0.142
Sch 66 8268 0.000 1.000 0.032 0.176
Sch 67 8268 0.000 1.000 0.053 0.224
Sch 69 8268 0.000 1.000 0.064 0.245
Sch 70 8268 0.000 1.000 0.035 0.185
Sch 71 8268 0.000 1.000 0.034 0.180
Sch 72 8268 0.000 1.000 0.066 0.248
Sch 74 8268 0.000 1.000 0.043 0.202
Sch 76 8268 0.000 1.000 0.046 0.210
Sch 77 8268 0.000 1.000 0.050 0.218
Sch 78 8268 0.000 1.000 0.043 0.203
Sch 79 8268 0.000 1.000 0.041 0.198
Sch 81 8268 0.000 1.000 0.056 0.229
Sch 82 8268 0.000 1.000 0.043 0.203
Sch 84 8268 0.000 1.000 0.029 0.169
Sch 85 8268 0.000 1.000 0.062 0.241
Sch 173 8268 0.000 1.000 0.031 0.172
Sch 273 8268 0.000 1.000 0.024 0.152
Valid N (listwise) 8268
Figure 28 - Descriptive Statistics, Capistrano Original Population

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
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Descriptive Statistics Capistrano Teacher Survey Population
N Minimum Maximum Mean
Std.
Deviation
Math Delta 3889 -29.000 79.000 13.128 8.091
Reading Delta 3899 -22.000 59.000 9.251 9.399
Daylight code 3949 0.000 5.000 2.222 1.329
Operable windows 3949 0.000 1.000 0.551 0.498
School Pop-per 500 3949 404.000 1518.000 896.234 204.224
Classroom Pop 3949 11.000 34.000 23.838 5.766
Vintage 3949 2.000 64.000 18.112 13.796
Grade 2 3949 0.000 1.000 0.294 0.456
Grade 3 3949 0.000 1.000 0.243 0.429
Grade 4 3949 0.000 1.000 0.243 0.429
Absences Unverified 3949 0.000 11.000 0.070 0.517
Absences Unexcused 3949 0.000 60.000 5.043 5.502
Tardies 3949 0.000 73.000 4.707 8.503
Gender 3949 0.000 1.000 0.514 0.500
Ethnic 4 3949 0.000 1.000 0.002 0.042
Ethnic 1 3949 0.000 1.000 0.051 0.221
Ethnic 6 3949 0.000 1.000 0.011 0.106
Ethnic 3 3949 0.000 1.000 0.150 0.357
Ethnic 2 3949 0.000 1.000 0.011 0.106
Ethnic 7 3949 0.000 1.000 0.002 0.039
GATE program 3949 0.000 1.000 0.130 0.336
Lang program 3949 0.000 1.000 0.174 0.380
Econ 3 3949 0.000 0.960 0.165 0.212
Log yrs teaching 3949 0.693 3.738 2.462 0.663
Teacher 1 3949 0.000 1.000 0.241 0.428
Teacher 2 3949 0.000 1.000 0.343 0.475
Teacher 3 3949 0.000 1.000 0.290 0.454
Teacher 4 3949 0.000 1.000 0.126 0.332
Teacher 6 3949 0.000 1.000 0.232 0.422
Teacher 7 3949 0.000 1.000 0.399 0.490
Sch 59 3949 0.000 1.000 0.028 0.165
Sch 60 3949 0.000 1.000 0.047 0.211
Sch 61 3949 0.000 1.000 0.060 0.238
Sch 62 3949 0.000 1.000 0.064 0.244
Sch 64 3949 0.000 1.000 0.022 0.145
Sch 65 3949 0.000 1.000 0.046 0.209
Sch 66 3949 0.000 1.000 0.039 0.194
Sch 67 3949 0.000 1.000 0.058 0.234
Sch 68 3949 0.000 1.000 0.045 0.207
Sch 69 3949 0.000 1.000 0.041 0.197
Sch 70 3949 0.000 1.000 0.004 0.062
Sch 72 3949 0.000 1.000 0.049 0.215
Sch 74 3949 0.000 1.000 0.046 0.210
Sch 76 3949 0.000 1.000 0.033 0.178
Sch 77 3949 0.000 1.000 0.036 0.186
Sch 78 3949 0.000 1.000 0.059 0.236
Sch 79 3949 0.000 1.000 0.020 0.139
Sch 81 3949 0.000 1.000 0.065 0.247
Sch 82 3949 0.000 1.000 0.030 0.171
Sch 83 3949 0.000 1.000 0.063 0.244
Sch 84 3949 0.000 1.000 0.031 0.172
Sch 85 3949 0.000 1.000 0.059 0.236
Sch 93 3949 0.000 1.000 0.032 0.176
Sch 94 3949 0.000 1.000 0.025 0.155
Valid N (listwise) 3862
Figure 29 - Descriptive Statistics, Capistrano Teacher Survey Population

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
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Descriptive Statistics Capistrano Expanded Population
N Minimum Maximum Mean
Std.
Deviation
Math Delta 9186 -29.000 79.000 12.565 7.914
Reading Delta 9195 -22.000 59.000 8.771 9.010
Daylight Code 9302 0.000 5.000 1.977 1.240
Operable Windows 9302 0.000 1.000 0.574 0.495
School Pop-per 500 9302 404.000 1518.000 886.693 190.423
Classroom Pop 9302 5.000 44.000 23.880 5.885
Grade 2 9302 0.000 1.000 0.273 0.446
Grade 3 9302 0.000 1.000 0.244 0.429
Grade 4 9302 0.000 1.000 0.248 0.432
Absences Unverified 9302 0.000 12.000 0.094 0.584
Absences Unexcused 9302 0.000 60.000 4.672 5.324
Tardies 9302 0.000 105.000 4.143 8.146
Gender 9302 0.000 1.000 0.508 0.500
Vintage 9302 2.000 64.000 16.844 13.157
Ethnic 4 9302 0.000 1.000 0.002 0.047
Ethnic 1 9302 0.000 1.000 0.052 0.222
Ethnic 6 9302 0.000 1.000 0.012 0.110
Ethnic 3 9302 0.000 1.000 0.139 0.346
Ethnic 2 9302 0.000 1.000 0.014 0.117
Ethnic 7 9302 0.000 1.000 0.002 0.041
GATE program 9302 0.000 1.000 0.138 0.345
Lang program 9302 0.000 1.000 0.164 0.371
Econ 3 9302 0.000 1.000 0.153 0.199
Teacher 1 9302 0.000 1.000 0.248 0.432
Teacher 3 9302 0.000 1.000 0.177 0.381
Teacher 2 9302 0.000 1.000 0.146 0.353
Teacher 4 9302 0.000 1.000 0.053 0.225
Teacher 5 9302 0.000 1.000 0.052 0.222
Teacher 6 9302 0.000 1.000 0.098 0.298
Teacher 7 9302 0.000 1.000 0.170 0.375
Log yrs teaching 9302 0.000 3.738 1.045 1.291
Sch 59 9302 0.000 1.000 0.038 0.191
Sch 60 9302 0.000 1.000 0.038 0.191
Sch 61 9302 0.000 1.000 0.048 0.213
Sch 62 9302 0.000 1.000 0.042 0.200
Sch 64 9302 0.000 1.000 0.018 0.134
Sch 66 9302 0.000 1.000 0.028 0.164
Sch 67 9302 0.000 1.000 0.046 0.209
Sch 68 9302 0.000 1.000 0.033 0.179
Sch 69 9302 0.000 1.000 0.055 0.228
Sch 70 9302 0.000 1.000 0.032 0.177
Sch 71 9302 0.000 1.000 0.031 0.172
Sch 72 9302 0.000 1.000 0.053 0.225
Sch 74 9302 0.000 1.000 0.033 0.179
Sch 76 9302 0.000 1.000 0.043 0.203
Sch 77 9302 0.000 1.000 0.047 0.211
Sch 78 9302 0.000 1.000 0.048 0.214
Sch 79 9302 0.000 1.000 0.026 0.159
Sch 80 9302 0.000 1.000 0.045 0.207
Sch 81 9302 0.000 1.000 0.043 0.203
Sch 82 9302 0.000 1.000 0.035 0.183
Sch 83 9302 0.000 1.000 0.045 0.207
Sch 84 9302 0.000 1.000 0.040 0.196
Sch 85 9302 0.000 1.000 0.051 0.219
Sch 93 9302 0.000 1.000 0.030 0.171
Sch 94 9302 0.000 1.000 0.021 0.142
Valid N (listwise) 9123
Figure 30 - Descriptive Statistics, Capistrano Expanded Population

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
80

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
81
7.4 Grade Level Models
New Model Change Old Model
Capistrano Grade Level Interaction Reading Daylight new-old Capistrano, Original Analysis Reading Daylight
CGL6-rd R^2 C17-rd
Model R^2 0.239 -0.007 Model R^2 0.246
B Std. Error p (Signif) B B Std. Error p (Signif)
(Constant) 2.774 0.399 0.000 (Constant) 3.025 0.298 0.000
Classroom Characteristics Classroom Characteristics
Daylight Code 0.396 0.080 0.000 -0.068 Daylight code 0.464 0.085 0.000
Operable Window 0.643 0.212 0.002
Teacher Characteristics
Teacher 1 -1.148 0.493 0.020
Teacher 2 1.134 0.344 0.001
Teacher 6 0.625 0.308 0.043
Student Characteristics Student Characteristics
Grade 2 12.478 1.041 0.000 1.618 Grade 2 10.860 0.251 0.000
Grade 3 5.819 1.432 0.000 1.521 Grade 3 4.298 0.254 0.000
-0.937 Grade 4 0.937 0.252 0.000
Ethnic 6 1.306 0.746 0.080
GATE Program 1.086 0.485 0.025 2.537 GATE Program -1.452 0.257 0.000
Lang Prog 0.441 0.525 0.400 -0.397 Lang Prog 0.838 0.239 0.000
Econ 3 -4.077 1.307 0.002
School Characteristics School Characteristics
Vintage 0.054 0.011 0.000
School Site School Site
Sch 61 1.888 0.472 0.000 -0.307 Sch 61 2.195 0.370 0.000
Sch 62 0.986 0.478 0.039 -0.598 Sch 62 1.584 0.477 0.001
Sch 64 3.207 0.933 0.001 0.690 Sch 64 2.517 0.638 0.000
Sch 67 0.827 0.436 0.058 -0.532 Sch 67 1.359 0.416 0.001
Sch 70 2.277 0.923 0.014
Sch 72 -1.262 0.402 0.002 0.198 Sch 72 -1.460 0.376 0.000
Sch 77 0.792 0.423 0.061 -0.070 Sch 77 0.863 0.428 0.044
Sch 79 1.078 0.542 0.047
Sch 81 2.261 0.477 0.000 1.271 Sch 81 0.990 0.431 0.022
Sch 82 2.179 0.492 0.000 0.511 Sch 82 1.668 0.449 0.000
Sch 85 -1.254 0.388 0.001
Sch 73 1.518 0.490 0.002 -0.009 Sch 73 1.528 0.516 0.003
Outliers Outliers
O82 37.789 7.800 0.000 -3.559 O82 39.650 7.916 0.000
O71 40.147 7.798 0.000 -0.533 O71 40.680 7.925 0.000
O17 40.288 7.803 0.000 0.638 O17 41.348 7.922 0.000
O28 -36.386 7.807 0.000 1.084 O28 -37.470 7.926 0.000
O80 -38.527 7.798 0.000 O58 35.564 7.923 0.000
O69 -31.246 7.806 0.000 O50 36.543 7.915 0.000
Interaction Variables De
p
endent Variable: Readin
g
Delta
(
s
p
98-fa97
)
OPWIN_2 0.659 0.341 0.053
CLSPOP_4 0.122 0.060 0.041
ABUNVE_4 0.656 0.305 0.031
ABUNEX_2 0.061 0.031 0.048
Gender 2 -1.234 0.460 0.007
GATE_2 -6.856 0.691 0.000
GATE_3 -3.016 0.671 0.000
LANGPR_2 -1.296 0.719 0.072
LANGPR_3 1.350 0.715 0.059
Econ 3-2 3.411 1.622 0.036
Teach 1-2 1.722 0.567 0.002
Teach 3-2 -2.351 0.650 0.000
De
p
endent Variable: READDELT
Figure 31- Capistrano Grade Level Interaction, Reading Daylight

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New Model Change Old Model
Capistrano Grade Level Interaction Math Daylight new-old Capistrano, Original Analysis Math Daylight
CGL6-md R^2 C17-md
Model R^2 0.261 0.005 Model R^2 0.256
B Std. Error p (Signif) B B Std. Error p (Signif)
(Constant) 7.787 0.481 0.000 (Constant) 8.026 0.407 0.000
Classroom Characteristics Classroom Characteristics
Daylight Code 0.275 0.154 0.073 -0.229 Daylight Code 0.504 0.067 0.000
Da
y
li
g
ht Code*2nd Grad
e
0.320 0.190 0.093
Teacher Characteristics Teacher Characteristics
none significant
Student Characteristics Student Characteristics
Grade 2 11.506 0.871 0.000 1.794 SECOND 9.711 0.215 0.000
Grade 3 3.227 0.893 0.000 -2.704 THIRD 5.931 0.219 0.000
Grade 4 2.451 0.922 0.008 0.637 FOURTH 1.813 0.216 0.000
Gender 0.277 0.143 0.053
GATE program (1.352) 0.211 0.000 -0.115 GATE progam -1.236 0.223 0.000
LANG program 0.566 0.216 0.009 0.077 LANG program 0.490 0.205 0.017
Econ 3 (2.390) 0.907 0.008
Absen Unver -0.263 0.123 0.032
Absen Unexc (0.030) 0.014 0.034 -0.004 Absen Unexc -0.026 0.014 0.069
School Characteristics
Vintage 0.038 0.014 0.008 0.038
School Site School Site
SCH59 (1.818) 0.390 0.000 -0.728 SCH59 -1.089 0.435 0.012
SCH60 (1.390) 0.411 0.001
-0.898 SCH61 0.898 0.313 0.004
SCH62 0.644 0.387 0.096 -0.804 SCH62 1.448 0.395 0.000
SCH67 0.838 0.355 0.018
SCH69 (0.748) 0.341 0.028
-0.803 SCH71 0.803 0.429 0.061
SCH72 (2.815) 0.359 0.000 -1.201 SCH72 -1.613 0.321 0.000
SCH74 (0.936) 0.421 0.026
SCH77 0.797 0.364 0.029 -0.370 SCH77 1.167 0.365 0.001
SCH78 (0.930) 0.362 0.010
SCH82 0.944 0.427 0.027 -0.255 SCH82 1.198 0.379 0.002
SCH84 (0.932) 0.401 0.020
Outliers Outliers
O33 34.480 6.836 0.000 0.418 O33 34.062 6.838 0.000
O18 33.983 6.831 0.000 -1.132 O18 35.115 6.837 0.000
O32 61.652 6.837 0.000 -0.803 O32 62.456 6.835 0.000
O48 (46.429) 6.829 0.000 -0.007 O48 -46.422 6.831 0.000
O45 (40.698) 6.828 0.000 -0.388 O45 -40.310 6.830 0.000
O77 (35.628) 6.832 0.000
O02 (32.938) 6.840 0.000 1.529 O02 -34.466 6.830 0.000
Interaction Variables Dependent Variable: MATHDELT
Vintage 2 (0.046) 0.020 0.021
Vintage 3 0.057 0.019 0.003
Vintage 4 (0.063) 0.020 0.001
School Pop 2 (0.003) 0.001 0.000
School Pop 3 0.002 0.001 0.003
Tardies 2 (0.030) 0.017 0.078
Tardies 3 0.047 0.017 0.006
Econ 3-3 (3.135) 1.190 0.008
a Econ 3-4 3.387 1.258 0.007
Teach 1-2 2.140 0.322 0.000
Teach 4-4 2.914 1.292 0.024
De
p
endent Variable: MATHDELT
Figure 32- Capistrano Grade Level Interaction, Math Daylight

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83
New Model Change Old Model
Seattle Grade Level Interaction Reading Daylight new-old Seattle, orginal analysis Reading Daylight
GL2-rd R^2 S9-rd
Model R^2 0.337 0.040 Model R^2 0.297
B Std. Error p (Signif) B B Std. Error p (Signif)
(Constant) 52.107 2.196 0.000 (Constant) 54.667 1.726 0.000
Classroom Characteristics Classroom Characteristics
Daylight Code 2.533 0.373 0.000 0.650 Daylight Code 1.883 0.342 0.000
Portable -2.123 1.121 0.058
Gifted room (70%+) 16.153 1.563 0.000 0.812 Gifted room
(
70%+
)
15.342 0.894 0.000
0.002 Class SF -0.002 0.000 0.001
Students per Class 0.157 0.024 0.000 0.020 Students per Class 0.137 0.025 0.000
Student Characteristics Student Characteristics
Grade 2 15.056 2.491 0.000 8.098 Grade 2 6.957 0.596 0.000
2.074 Grade 3 -2.074 0.523 0.000
-0.949 Grade 4 0.949 0.529 0.073
Ethnic 2 -9.870 0.891 0.000 -1.409 Ethnic 2 -8.461 0.522 0.000
Ethnic 4 -11.016 0.550 0.000 0.152 Ethnic 4 -11.168 0.557 0.000
Ethnic 1 -8.534 1.293 0.000 -0.768 Ethnic 1 -7.766 0.797 0.000
Ethnic 3 -6.165 1.349 0.000 0.394 Ethnic 3 -6.559 1.336 0.000
-0.912 Gender 0.912 0.380 0.016
Econ 2 -10.939 0.446 0.000 -2.264 Econ 2 -8.675 0.475 0.000
Socio 1 -3.311 1.095 0.003 1.169 Socio 1 -4.481 1.131 0.000
Socio 3 -1.616 0.452 0.000 1.001 Socio 3 -2.618 0.480 0.000
Socio 2 -1.949 0.976 0.046 1.233 Socio 2 -3.182 1.011 0.002
School Characteristics School Characteristics
School Pop - per 500 5.574 3.215 0.083 -1.088 School Po
p
-
p
er 500 6.662 1.762 0.000
Outliers Outliers
O26 -63.880 16.619 0.000 1.534 O26 -65.414 16.407 0.000
O64 -66.614 16.613 0.000 1.313 O64 -67.927 16.409 0.000
O07 -68.420 16.626 0.000 1.812 O07 -70.231 16.408 0.000
O73 -72.856 16.612 0.000 -1.715 O73 -71.141 16.408 0.000
O21 -64.758 16.617 0.000 0.457 O21 -65.215 16.413 0.000
Interaction Variables Dependent Variable: Reading NCE 98
VINT_2ND -0.089 0.017 0.000
SCSZ_2ND -0.038 0.010 0.000
SCSZ_4TH 0.017 0.009 0.070
Gen_2ND 4.345 1.046 0.000
Gen_3RD 1.858 0.994 0.062
SQFT_3RD -0.002 0.001 0.003
SQFT_4TH -0.001 0.001 0.071
Eth2_3RD -2.191 1.173 0.062
Eth2_4TH -3.055 1.216 0.012
Eth1_3RD -5.227 1.916 0.006
De
p
endent Variable: Readin
g
NCE 98
Figure 33- Seattle Grade Level Interaction, Reading Daylight

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New Model Change Old Model
Seattle Grade Level Interaction Math Daylight new-old Seattle, original anlysis Math Daylight
SGL2-md R^2 S9-md
Model R^2 0.257 -0.001 Model R^2 0.258 Sig.
B Std. Error p (Signif) B B Std. Error p (Signif)
(Constant) 49.134 2.073 0.000 (Constant) 55.653 1.841 0.000
Classroom Characteristics Classroom Characteristics
Daylight Code 1.585 0.43 8 0.000 0.194 Daylight Code 1.391 0.436 0.001
Open room 3.485 1.650 0.035 -0.022 Open room 3.506 1.579 0.026
Portable -2.496 1.174 0.033 0.562 Portable -3.058 1.171 0.009
Gifted room (70%+) 16.312 0.931 0.000 -0.082 Gifted room (70%+) 16.394 0.931 0.000
Class SF -0.003 0.001 0.003 -0.002 Class SF -0.001 0.001 0.063
Students per Class 0.185 0.054 0.001 0.119 Students per Class 0.066 0.033 0.044
Student Characteristics Student Characteristics
Grade 2 22.935 2.612 0.000 16.832 Grade 2 6.104 0.577 0.000
Grade 3 5.013 2.336 0.032 8.401 Grade 3 -3.388 0.477 0.000
Ethnic 4 -11.440 0.537 0.000 0.011 Ethnic 4 -11.452 0.538 0.000
Ethnic 1 -5.564 0.800 0.000 -0.087 Ethnic 1 -5.477 0.803 0.000
Ethnic 3 -6.974 1.376 0.000 0.004 Ethnic 3 -6.978 1.381 0.000
Gender -2.957 0.390 0.000 0.060 Gender -3.017 0.392 0.000
Econ 2 -5.756 0.474 0.000 0.035 Econ 2 -5.790 0.475 0.000
Socio 1 -4.408 1.163 0.000 -0.069 Socio 1 -4.339 1.167 0.000
Socio 3 -3.525 0.835 0.000 -0.418 Socio 3 -3.107 0.494 0.000
Socio 2 -4.769 1.053 0.000 -0.078 Socio 2 -4.691 1.057 0.000
School Characteristics School Characteristics
Vintage 0.053 0.015 0.001 0.036 Vintage 0.017 0.010 0.098
School Pop-per 500 21.459 3.081 0.000 9.937 School Pop-per 500 11.522 2.065 0.000
Outliers Outliers
O10143 -61.856 16.787 0.000 3.117 O10143 -64.973 16.814 0.000
O9223 57.790 16.748 0.001 -0.259 O9223 58.049 16.824 0.001
O13206 49.760 16.751 0.003 -4.640 O13206 54.400 16.802 0.001
Interaction Variables Dependent Variable: Math NCE 98
SCSZ_2ND -0.061 0.010 0.000
SCSZ_3RD -0.035 0.009 0.000
VINT_2ND -0.121 0.022 0.000
VINT_3RD -0.037 0.021 0.077
CLSZ_4TH -0.205 0.067 0.002
SQFT_2ND 0.002 0.001 0.079
SQFT_4TH 0.003 0.001 0.013
De
p
endent Variable: Math NCE 98
Figure 34 - Seattle Grade Level Interaction, Math Daylight

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
85
Descriptive Statistics Capistrano Grade Level, Reading and Math
N Minimum Maximum Mean Std. Dev.
READDELT 9195 -22.000 59.000 8.771 9.010
MATHDELT 9186 -29.000 79.000 12.565 7.914
Daylight Code 9302 0.000 5.000 1.977 1.240
Teacher 1 9302 0.000 1.000 0.248 0.432
Teacher 3 9302 0.000 1.000 0.177 0.381
Teacher 2 9302 0.000 1.000 0.146 0.353
Teacher 6 9302 0.000 1.000 0.098 0.298
Log yrs teaching 9302 0.000 3.738 1.045 1.291
Grade 2 9302 0.000 1.000 0.273 0.446
Grade 3 9302 0.000 1.000 0.244 0.429
Grade 4 9302 0.000 1.000 0.248 0.432
Vintage 9302 2.000 64.000 16.844 13.157
Gender 9302 0.000 1.000 0.508 0.500
Ethnic 6 9302 0.000 1.000 0.012 0.110
GATE program 9302 0.000 1.000 0.138 0.345
Lang program 9302 0.000 1.000 0.164 0.371
Econ 3 9302 0.000 1.000 0.153 0.199
Sch 61 9302 0.000 1.000 0.048 0.213
Sch 62 9302 0.000 1.000 0.042 0.200
Sch 64 9302 0.000 1.000 0.018 0.134
Sch 67 9302 0.000 1.000 0.046 0.209
Sch 70 9302 0.000 1.000 0.032 0.177
Sch 71 9302 0.000 1.000 0.031 0.172
Sch 72 9302 0.000 1.000 0.053 0.225
Sch 77 9302 0.000 1.000 0.047 0.211
Sch 79 9302 0.000 1.000 0.026 0.159
Sch 81 9302 0.000 1.000 0.043 0.203
Sch 82 9302 0.000 1.000 0.035 0.183
Sch 173 9302 0.000 1.000 0.030 0.171
Valid N (listwise) 9123
Figure 35- Descriptive statistics, Capistrano Grade Level, Reading and Math

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
86
Descriptive Statistics
N Minimum Maximum Mean Std. Dev.
Reading NCE 98 7538 1.000 99.000 57.350 19.518
Daylight Code 7590 1.000 5.000 3.053 0.752
Portable 7617 0.000 1.000 0.030 0.171
Class SF 7617 638.000 3616.000 1110.707 688.906
Gifted room (70%+) 7617 0.000 1.000 0.049 0.216
Students per Class 7600 5.000 80.000 24.025 13.238
Students per School 7617 44.000 308.000 190.663 57.653
Grade 2 7617 0.000 1.000 0.214 0.410
Grade 3 7617 0.000 1.000 0.270 0.444
Grade 4 7617 0.000 1.000 0.249 0.432
Ethnic 2 7617 0.000 1.000 0.214 0.410
Ethnic 4 7617 0.000 1.000 0.227 0.419
Ethnic 1 7617 0.000 1.000 0.066 0.249
Ethnic 3 7617 0.000 1.000 0.021 0.144
Gender 7614 0.000 1.000 0.512 0.500
Econ 2 7617 0.000 1.000 0.405 0.491
Socio 1 7617 0.000 1.000 0.030 0.172
Socio 3 7617 0.000 1.000 0.288 0.453
Socio 2 7617 0.000 1.000 0.043 0.202
Seattle Grade Level, Reading
Figure 36- Descriptive statistics, Seattle Grade Level, Reading

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
87
Descriptive Statistics
N Minimum Maximum Mean Std. Dev.
Math NCE 98 7422 1.000 99.000 58.820 19.467
Daylight code 7590 1.000 5.000 3.053 0.752
Open room 7617 0.000 1.000 0.104 0.306
Portable 7617 0.000 1.000 0.030 0.171
Gifted room (70%+) 7617 0.000 1.000 0.049 0.216
Vintage 7617 7.000 92.000 39.812 26.370
Class SF 7617 638.000 3616.000 1110.707 688.906
Students per Class 7600 5.000 80.000 24.025 13.238
Students per School 7617 44.000 308.000 190.663 57.653
Grade 2 7617 0.000 1.000 0.214 0.410
Grade 3 7617 0.000 1.000 0.270 0.444
Ethnic 4 7617 0.000 1.000 0.227 0.419
Ethnic 1 7617 0.000 1.000 0.066 0.249
Ethnic 3 7617 0.000 1.000 0.021 0.144
Gender 7614 0.000 1.000 0.512 0.500
Econ 2 7617 0.000 1.000 0.405 0.491
Socio 1 7617 0.000 1.000 0.030 0.172
Socio 3 7617 0.000 1.000 0.288 0.453
Socio 2 7617 0.000 1.000 0.043 0.202
Valid N (listwise) 7379
Seattle Grade Level, Math
Figure 37- Descriptive statistics, Seattle Grade Level, Math

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
88

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
89
7.5 Absenteeism Models
Capistrano Absenteeism Analysis
ABS 3 LN
Model R^2 0.049
Std. Erro
r
Beta t p (Signif)
1.651 0.061 27.050 0.000
1 (Constant)
Classroom characteristics -0.059 0.029 -0.026 -2.025 0.043
Semi-open classroom
Student characteristics -0.056 0.022 -0.029 -2.564 0.010
Grade 3 -0.042 0.021 -0.022 -1.975 0.048
Grade 4 0.035 0.017 0.021 2.038 0.042
Gender -0.470 0.042 -0.122 -11.217 0.000
Ethnic 1 -0.144 0.079 -0.019 -1.823 0.068
Ethnic 6 -0.223 0.073 -0.032 -3.038 0.002
Ethnic 2 -0.396 0.198 -0.021 -1.997 0.046
Ethnic 7 -0.100 0.027 -0.040 -3.777 0.000
GATE program -0.154 0.027 -0.073 -5.676 0.000
Lang program 0.213 0.105 0.059 2.026 0.043
Econ 3
School characteristics 0.006 0.001 0.093 4.377 0.000
Vintage 0.000 0.000 0.029 2.004 0.045
School Pop-per 500
School sites -0.105 0.047 -0.025 -2.260 0.024
Sch 59 -0.150 0.050 -0.036 -3.017 0.003
Sch 60 0.112 0.047 0.028 2.386 0.017
Sch 62 -0.454 0.081 -0.094 -5.585 0.000
Sch 64 -0.105 0.044 -0.028 -2.414 0.016
Sch 67 -0.256 0.085 -0.066 -3.020 0.003
Sch 70 -0.151 0.052 -0.034 -2.909 0.004
Sch 74 0.130 0.060 0.026 2.173 0.030
Sch 79 0.092 0.049 0.023 1.867 0.062
Sch 81 0.291 0.051 0.067 5.703 0.000
Sch 82 0.094 0.047 0.024 1.991 0.047
Sch 84 0.182 0.056 0.039 3.244 0.001
Sch 173
Outliers 2.528 0.815 0.032 3.102 0.002
O 49
De
p
endent Variable: Lo
g
of Absence da
y
s
Figure 38 - Capistrano Absenteeism Model

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
90
Capistrano Tardiness Model
ABS 3 LN
Model R^2 0.097
BStd. Erro
r
Beta t p (Signif)
1 (Constant) 1.305 0.096 13.623 0.000
Classroom characteristics
Daylight code -0.046 0.012 -0.050 -3.945 0.000
No air conditioning 0.113 0.053 0.029 2.144 0.032
Portable classroom 0.054 0.026 0.024 2.087 0.037
Teacher characteristics
Teacher 1 0.199 0.039 0.080 5.172 0.000
Teacher 3 0.238 0.045 0.084 5.236 0.000
Teacher 7 -0.081 0.036 -0.028 -2.236 0.025
Log yrs teaching -0.006 0.002 -0.054 -3.065 0.002
Student characteristics
Grade 2 0.050 0.025 0.021 2.021 0.043
Ethnic 4 0.545 0.217 0.026 2.515 0.012
Ethnic 1 -0.197 0.052 -0.039 -3.803 0.000
Ethnic 3 0.160 0.037 0.055 4.327 0.000
Ethnic 2 0.424 0.093 0.046 4.541 0.000
GATE program -0.231 0.034 -0.071 -6.839 0.000
Econ 3 0.586 0.126 0.125 4.663 0.000
School characterisics
School Pop-per 500 0.000 0.000 -0.053 -3.189 0.001
School sites
Sch 59 -0.393 0.060 -0.072 -6.552 0.000
Sch 60 -0.102 0.061 -0.019 -1.670 0.095
Sch 61 0.261 0.058 0.054 4.498 0.000
Sch 64 0.455 0.106 0.072 4.294 0.000
Sch 67 -0.183 0.053 -0.038 -3.434 0.001
Sch 70 -0.582 0.115 -0.114 -5.069 0.000
Sch 71 0.140 0.069 0.023 2.028 0.043
Sch 72 -0.219 0.053 -0.048 -4.163 0.000
Sch 74 -0.488 0.067 -0.084 -7.255 0.000
Sch 76 -0.183 0.058 -0.035 -3.165 0.002
Sch 84 0.161 0.055 0.031 2.901 0.004
Sch 173 0.337 0.074 0.055 4.529 0.000
Sch 273 0.207 0.092 0.028 2.253 0.024
Dependent Variable: LNYI_T
Figure 39 - Capistrano Tardiness Model

DAYLIGHTING IN SCHOOLS, RE-ANALYSIS REPORT APPENDICES
91
Absenteeism/Tardiness Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Daylight code 8808 0.000 5.000 1.983 1.197
No Air conditioning 8808 0.000 1.000 0.087 0.282
Semi-open classroom 8808 0.000 1.000 0.162 0.369
Portable classroom 8808 0.000 1.000 0.403 0.491
Modular classroom 8808 0.000 1.000 0.101 0.302
Teacher 1 8808 0.000 1.000 0.260 0.439
Teacher 2 8808 0.000 1.000 0.152 0.359
Teacher 3 8808 0.000 1.000 0.180 0.384
Teacher 4 8808 0.000 1.000 0.052 0.222
Teacher 6 8808 0.000 1.000 0.099 0.299
Teacher 5 8808 0.000 1.000 0.057 0.232
Teacher 7 8808 0.000 1.000 0.165 0.371
Log yrs teaching 8808 0.000 42.000 6.315 9.219
School Pop-per 500 8808 404.000 1518.000 882.632 201.494
Classroom Pop 8808 6.000 34.000 23.422 5.934
Grade 2 8808 0.000 1.000 0.285 0.451
Grade 3 8808 0.000 1.000 0.237 0.425
Grade 4 8808 0.000 1.000 0.241 0.428
Vintage 8808 2.000 64.000 18.518 14.090
Gender 8808 0.000 1.000 0.509 0.500
Ethnic 4 8808 0.000 1.000 0.003 0.051
Ethnic 1 8808 0.000 1.000 0.049 0.216
Ethnic 6 8808 0.000 1.000 0.012 0.111
Ethnic 3 8808 0.000 1.000 0.168 0.374
Ethnic 2 8808 0.000 1.000 0.014 0.119
Ethnic 7 8808 0.000 1.000 0.002 0.044
GATE program 8808 0.000 1.000 0.130 0.336
Lang program 8808 0.000 1.000 0.190 0.392
Econ 3 8808 0.000 1.000 0.178 0.232
Sch 59 8808 0.000 1.000 0.041 0.199
Sch 60 8808 0.000 1.000 0.042 0.201
Sch 61 8808 0.000 1.000 0.054 0.226
Sch 62 8808 0.000 1.000 0.047 0.211
Sch 64 8808 0.000 1.000 0.031 0.173
Sch 67 8808 0.000 1.000 0.053 0.224
Sch 69 8808 0.000 1.000 0.061 0.240
Sch 70 8808 0.000 1.000 0.048 0.214
Sch 71 8808 0.000 1.000 0.034 0.181
Sch 72 8808 0.000 1.000 0.059 0.236
Sch 74 8808 0.000 1.000 0.036 0.187
Sch 85 8808 0.000 1.000 0.046 0.210
Sch 86 8808 0.000 1.000 0.052 0.221
Sch 87 8808 0.000 1.000 0.053 0.224
Sch 88 8808 0.000 1.000 0.028 0.166
Sch 81 8808 0.000 1.000 0.048 0.213
Sch 82 8808 0.000 1.000 0.038 0.191
Sch 84 8808 0.000 1.000 0.047 0.212
Sch 173 8808 0.000 1.000 0.033 0.179
Sch 273 8808 0.000 1.000 0.022 0.148
O 16 8808 0.000 1.000 0.000 0.011
O 17 8808 0.000 1.000 0.000 0.011
O 15 8808 0.000 1.000 0.000 0.011
O 50 8808 0.000 1.000 0.000 0.011
Valid N (listwise) 8808
Figure 40 - Capistrano Absenteeism/Tardiness Descriptive Statistics