The document summarizes a technical report that uses hierarchical linear modeling to replicate a previous study examining the influence of district size, school size, and socioeconomic status on student achievement in Washington state. The study analyzes 4th and 7th grade test score data and finds that:
1) Large district size is detrimental to achievement as it strengthens the negative relationship between school poverty and student achievement.
2) The negative relationship between school poverty and achievement is stronger in larger districts.
3) Small schools appear to have the greatest equity effects, with the effect of school-level poverty on achievement being smallest when both the district and school are small.
Dr. William Allan Kritsonis, Editor-in-Chief, NATIONAL FORUM JOURNALS (Founded 1982). Dr. Kritsonis has served as an elementary school teacher, elementary and middle school principal, superintendent of schools, director of student teaching and field experiences, professor, author, consultant, and journal editor. Dr. Kritsonis has considerable experience in chairing PhD dissertations and master thesis and has supervised practicums for teacher candidates, curriculum supervisors, central office personnel, principals, and superintendents. He also has experience in teaching in doctoral and masters programs in elementary and secondary education as well as educational leadership and supervision. He has earned the rank as professor at three universities in two states, including successful post-tenure reviews.
Factors Correlated with Educational Attainment
Applied Analysis has been asked by the Las Vegas Chamber of Commerce to examine various aspects of Nevada’s system of elementary and secondary education in public schools (“K-12”). One such aspect is the extent to which student achievement is related or unrelated to socio-economic factors and/or measures of school operations, including, without limitation, financial resources. This briefing examines the most commonly cited factors, analyzing each against student performance on standardized exams and graduation rates in all 50 states and the District of Columbia.
The study finds that decreasing the size of school districts has a substantial and statistically significant positive effect on graduation rates. Conversely, consolidation of school districts into larger units leads to more students dropping out of high school. The results of the analysis indicate that decreasing the average size of a state's school districts by 200 square miles leads to an increase of about 1.7 percentage points in its graduation rate. This finding is particularly important for states with very large school districts.
Dr. William Allan Kritsonis, Editor-in-Chief, NATIONAL FORUM JOURNALS (Founded 1982). Dr. Kritsonis has served as an elementary school teacher, elementary and middle school principal, superintendent of schools, director of student teaching and field experiences, professor, author, consultant, and journal editor. Dr. Kritsonis has considerable experience in chairing PhD dissertations and master thesis and has supervised practicums for teacher candidates, curriculum supervisors, central office personnel, principals, and superintendents. He also has experience in teaching in doctoral and masters programs in elementary and secondary education as well as educational leadership and supervision. He has earned the rank as professor at three universities in two states, including successful post-tenure reviews.
Factors Correlated with Educational Attainment
Applied Analysis has been asked by the Las Vegas Chamber of Commerce to examine various aspects of Nevada’s system of elementary and secondary education in public schools (“K-12”). One such aspect is the extent to which student achievement is related or unrelated to socio-economic factors and/or measures of school operations, including, without limitation, financial resources. This briefing examines the most commonly cited factors, analyzing each against student performance on standardized exams and graduation rates in all 50 states and the District of Columbia.
The study finds that decreasing the size of school districts has a substantial and statistically significant positive effect on graduation rates. Conversely, consolidation of school districts into larger units leads to more students dropping out of high school. The results of the analysis indicate that decreasing the average size of a state's school districts by 200 square miles leads to an increase of about 1.7 percentage points in its graduation rate. This finding is particularly important for states with very large school districts.
Influence of Home and School Based Factors on Pupils Academic Performance at ...ijtsrd
"The aim of primary education is to provide education at the basic level of all ongoing primary school pupils. This study was carried out to investigate influence of home and school based factors on pupil's academic performers at Kenya certificate of primary education in Makadara sub county, Nairobi County. The study adopted the ex post facto design which involved the studies that investigate possible causes and effects by observing an existing condition and searching back in time for possible causal factors. It involved testing out possible antecedents of events that had happened and cannot be manipulated by the investigator. The study sampled 240 teachers, 39 Parents Association members and 150 pupils from class 6 and 7. The data collection instruments comprised of questionnaires and interview guide. Data collected was categorized, coded, analyzed then tabulated. The analysis was done using Statistical Package for Social Sciences SPSS . The analysis was both qualitative and quantitative. Quantitative analysis considered use of frequency counts and distribution, tabulation totals and calculation of percentages aimed at generating the data collected into meaningful groups and frequency tables for further analysis. Qualitative analysis involved the conclusions from the respondents' opinions. The study established that most parents had a college educational level, majority of the teachers were female whereas majority of the students were males. It also established that parental level of income influenced pupils' performance in KCPE at 60 s. Physical facilities and teaching and learning resources were also cited as factors that highly influence performances. The researcher recommended that the parents should provide a conducive learning environment at home to give the pupils ample time and space to study. Parents ought to strive to provide the basic required learning materials that are vital for a good performance in the KCPE exam irrespective of their level of income. The government should endeavor to allocate funds to be used for improving on the existing teaching and learning resources in public primary schools while adding more. The government should allocate enough funds that will enable provision of key physical learning facilities. Prof. Lewis Ngesu | Awuonda Faith Atieno ""Influence of Home and School Based Factors on Pupils Academic Performance at Kenya Certificate of Primary Education in Makadara Sub-County, Nairobi County"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21607.pdf
Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/sociology/21607/influence-of-home-and-school-based-factors-on-pupils-academic-performance-at-kenya-certificate-of-primary-education-in-makadara-sub-county-nairobi-county/prof-lewis-ngesu"
Across the country schools face a multitude of challenges related to student discipline and school climate that potentially impact social and academic outcomes for students. Schools are continually changing and the demands that students face daily have increased at a rapid rate. When students are ill-equipped to face such demands, and traditional reactive approaches to discipline are employed, there is an increased likelihood that they will drop out, or will face punitive measures that do not ultimately improve behaviors (Morrissey et al., 2010). Choosing to dropout of high school may cause serious repercussions for students, their communities and families. Although many interventions currently used to decrease the number of dropouts do not have strong evidence to support their effectiveness (Freeman et al., 2015), several studies conducted in the past 20 years indicate that improved outcomes for students graduating high school have occurred through various interventions. School of Life (SOLF) is a intervention offered as an alternative to in school detention and suspensions. Although other dropout prevention programs have been evaluated, SOLF is a time and resource efficient method for targeting dropout and students who have participated in this intervention over the past three years have seen positive results, including higher rates of graduation (Baggaley, 2015). The purpose of the current study was to answer the following three research questions: 1. What is the effect of the SOLF on grade advancement/dropout rates? 2. What is the effect of SOLF on attendance? 3. What is the effect of SOLF on school connectedness and student motivation?
Dr. William Allan Kritsonis & Steven Norfleetguestfa49ec
Dr. William Allan Kritsonis & Steven Norfleet
In 2004, Dr. William Allan Kritsonis was recognized as the Central Washington University Alumni Association Distinguished Alumnus for the College of Education and Professional Studies. Dr. Kritsonis was nominated by alumni, former students, friends, faculty, and staff. Final selection was made by the Alumni Association Board of Directors. Recipients are CWU graduates of 20 years or more and are recognized for achievement in their professional field and have made a positive contribution to society. For the second consecutive year, U.S. News and World Report placed Central Washington University among the top elite public institutions in the west. CWU was 12th on the list in the 2006 On-Line Education of “America’s Best Colleges.”
Influence of Financial Support Services on Academic Performance of Secondary ...QUESTJOURNAL
ABSTRACT: It’s noteworthy that whereas various mechanisms have been rolled out to mitigate runaway cost of schooling in Kenya, financing education especially in secondary schools remains out of reach for many parents in Kenya. The purpose of this study was to address this gap by examining financial support services on offer within schools and their influence on academic performance of protestant and catholic sponsored secondary schools in Trans-Nzoia County, Kenya. The study was guided by structural functional theory and adopted a cross-sectional descriptive survey research design under a mixed research design paradigm. A blend of sampling techniques that involved multiphase and stratified sampling was used to select schools while purposive sampling was used to select school management staff. Out of a sample frame of 192 schools, 92 were religious sponsored hence targeted for study. A sample size of 45 schools that had been in existence for 4years and above were selected and the 45 head teachers of these schools were principal respondents. Descriptive statistics used involved use of cross tabulations, frequencies and percentages while inferential statistics involved use of chi-square to test association between financial support services and academic performance of schools. The findings indicated that there is a significant relationship between some aspects of financial support services and students’ academic performance among religious sponsored schools. It was also established that the cost of education is still beyond the reach of ordinary students and there is need for the sponsor churches to deliberately target such needy students with specific financial support services that impact on the learner’s wellbeing and therefore academic performance. The study recommends that schools should be encouraged to have specific sponsor programmes packaged with specific financial support services either in cash or in- kind to cushion needy students.
National STEM Resources: A 2014 BibliographyJulia Cothron
Julia Cothron maintains an on-going bibliography of national STEM resources as she works with strategic planning and advocacy in Virginia. This 2014 bibliography reflects recent work with STEM advocacy, assessments and accountability systems, science and literacy skills, mathematics and science curricula, teacher education and workforce skills.
Ziyanak, sebahattin the effectiveness of survey instruments nfaerj v29 n3 2016William Kritsonis
This article examines how sociological imagination of the individuals living in southeastern Turkey is constructed through Movie, The Bliss. Traditional and modern forms of life are symbolically constructed in this movie. The framework of “honor killing,” “masculinity in southeastern Turkey," “cultural deficiency,” and “othering” will be analyzed to explicate how stereotypical southeastern characters are reproduced. Content analysis technique is applied to interpret apparent and latent contents, contexts, aspects and so forth. Developed categories are revisited through Ibn Khaldun's Typology, cultural deficiency theory, Tonnies’ theory, Durkheim’s view on society, and Goffman’s framing process.
William Allan Kritsonis, PhD - Editor-in-Chief, NATIONAL FORUM JOURNALS (Established 1982)
Virginia STEM Resources: A 2014 BibliographyJulia Cothron
As part of her advocacy for strong STEM education in Virginia, Dr. Julia Cothron maintains an on-going bibliography. This 2014 listing of resources for STEM-related advocacy in Virginia will enable users to a) research the general laws and regulations governing Virginia's K-12 schools, b) find policy makers, c) understand Virginia's curriculum standards, d) know the regulations governing teacher preparation and licensure, e) quickly find key professional organizations, and f) secure workforce information.
Influence of Home and School Based Factors on Pupils Academic Performance at ...ijtsrd
"The aim of primary education is to provide education at the basic level of all ongoing primary school pupils. This study was carried out to investigate influence of home and school based factors on pupil's academic performers at Kenya certificate of primary education in Makadara sub county, Nairobi County. The study adopted the ex post facto design which involved the studies that investigate possible causes and effects by observing an existing condition and searching back in time for possible causal factors. It involved testing out possible antecedents of events that had happened and cannot be manipulated by the investigator. The study sampled 240 teachers, 39 Parents Association members and 150 pupils from class 6 and 7. The data collection instruments comprised of questionnaires and interview guide. Data collected was categorized, coded, analyzed then tabulated. The analysis was done using Statistical Package for Social Sciences SPSS . The analysis was both qualitative and quantitative. Quantitative analysis considered use of frequency counts and distribution, tabulation totals and calculation of percentages aimed at generating the data collected into meaningful groups and frequency tables for further analysis. Qualitative analysis involved the conclusions from the respondents' opinions. The study established that most parents had a college educational level, majority of the teachers were female whereas majority of the students were males. It also established that parental level of income influenced pupils' performance in KCPE at 60 s. Physical facilities and teaching and learning resources were also cited as factors that highly influence performances. The researcher recommended that the parents should provide a conducive learning environment at home to give the pupils ample time and space to study. Parents ought to strive to provide the basic required learning materials that are vital for a good performance in the KCPE exam irrespective of their level of income. The government should endeavor to allocate funds to be used for improving on the existing teaching and learning resources in public primary schools while adding more. The government should allocate enough funds that will enable provision of key physical learning facilities. Prof. Lewis Ngesu | Awuonda Faith Atieno ""Influence of Home and School Based Factors on Pupils Academic Performance at Kenya Certificate of Primary Education in Makadara Sub-County, Nairobi County"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21607.pdf
Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/sociology/21607/influence-of-home-and-school-based-factors-on-pupils-academic-performance-at-kenya-certificate-of-primary-education-in-makadara-sub-county-nairobi-county/prof-lewis-ngesu"
Across the country schools face a multitude of challenges related to student discipline and school climate that potentially impact social and academic outcomes for students. Schools are continually changing and the demands that students face daily have increased at a rapid rate. When students are ill-equipped to face such demands, and traditional reactive approaches to discipline are employed, there is an increased likelihood that they will drop out, or will face punitive measures that do not ultimately improve behaviors (Morrissey et al., 2010). Choosing to dropout of high school may cause serious repercussions for students, their communities and families. Although many interventions currently used to decrease the number of dropouts do not have strong evidence to support their effectiveness (Freeman et al., 2015), several studies conducted in the past 20 years indicate that improved outcomes for students graduating high school have occurred through various interventions. School of Life (SOLF) is a intervention offered as an alternative to in school detention and suspensions. Although other dropout prevention programs have been evaluated, SOLF is a time and resource efficient method for targeting dropout and students who have participated in this intervention over the past three years have seen positive results, including higher rates of graduation (Baggaley, 2015). The purpose of the current study was to answer the following three research questions: 1. What is the effect of the SOLF on grade advancement/dropout rates? 2. What is the effect of SOLF on attendance? 3. What is the effect of SOLF on school connectedness and student motivation?
Dr. William Allan Kritsonis & Steven Norfleetguestfa49ec
Dr. William Allan Kritsonis & Steven Norfleet
In 2004, Dr. William Allan Kritsonis was recognized as the Central Washington University Alumni Association Distinguished Alumnus for the College of Education and Professional Studies. Dr. Kritsonis was nominated by alumni, former students, friends, faculty, and staff. Final selection was made by the Alumni Association Board of Directors. Recipients are CWU graduates of 20 years or more and are recognized for achievement in their professional field and have made a positive contribution to society. For the second consecutive year, U.S. News and World Report placed Central Washington University among the top elite public institutions in the west. CWU was 12th on the list in the 2006 On-Line Education of “America’s Best Colleges.”
Influence of Financial Support Services on Academic Performance of Secondary ...QUESTJOURNAL
ABSTRACT: It’s noteworthy that whereas various mechanisms have been rolled out to mitigate runaway cost of schooling in Kenya, financing education especially in secondary schools remains out of reach for many parents in Kenya. The purpose of this study was to address this gap by examining financial support services on offer within schools and their influence on academic performance of protestant and catholic sponsored secondary schools in Trans-Nzoia County, Kenya. The study was guided by structural functional theory and adopted a cross-sectional descriptive survey research design under a mixed research design paradigm. A blend of sampling techniques that involved multiphase and stratified sampling was used to select schools while purposive sampling was used to select school management staff. Out of a sample frame of 192 schools, 92 were religious sponsored hence targeted for study. A sample size of 45 schools that had been in existence for 4years and above were selected and the 45 head teachers of these schools were principal respondents. Descriptive statistics used involved use of cross tabulations, frequencies and percentages while inferential statistics involved use of chi-square to test association between financial support services and academic performance of schools. The findings indicated that there is a significant relationship between some aspects of financial support services and students’ academic performance among religious sponsored schools. It was also established that the cost of education is still beyond the reach of ordinary students and there is need for the sponsor churches to deliberately target such needy students with specific financial support services that impact on the learner’s wellbeing and therefore academic performance. The study recommends that schools should be encouraged to have specific sponsor programmes packaged with specific financial support services either in cash or in- kind to cushion needy students.
National STEM Resources: A 2014 BibliographyJulia Cothron
Julia Cothron maintains an on-going bibliography of national STEM resources as she works with strategic planning and advocacy in Virginia. This 2014 bibliography reflects recent work with STEM advocacy, assessments and accountability systems, science and literacy skills, mathematics and science curricula, teacher education and workforce skills.
Ziyanak, sebahattin the effectiveness of survey instruments nfaerj v29 n3 2016William Kritsonis
This article examines how sociological imagination of the individuals living in southeastern Turkey is constructed through Movie, The Bliss. Traditional and modern forms of life are symbolically constructed in this movie. The framework of “honor killing,” “masculinity in southeastern Turkey," “cultural deficiency,” and “othering” will be analyzed to explicate how stereotypical southeastern characters are reproduced. Content analysis technique is applied to interpret apparent and latent contents, contexts, aspects and so forth. Developed categories are revisited through Ibn Khaldun's Typology, cultural deficiency theory, Tonnies’ theory, Durkheim’s view on society, and Goffman’s framing process.
William Allan Kritsonis, PhD - Editor-in-Chief, NATIONAL FORUM JOURNALS (Established 1982)
Virginia STEM Resources: A 2014 BibliographyJulia Cothron
As part of her advocacy for strong STEM education in Virginia, Dr. Julia Cothron maintains an on-going bibliography. This 2014 listing of resources for STEM-related advocacy in Virginia will enable users to a) research the general laws and regulations governing Virginia's K-12 schools, b) find policy makers, c) understand Virginia's curriculum standards, d) know the regulations governing teacher preparation and licensure, e) quickly find key professional organizations, and f) secure workforce information.
In five SlideShares, Restoring the Pulse of Nature in Euclid presents two goals for stormwater Integrated Planning in Euclid, Ohio: a) Revive the natural regulation of stormwater at relatively low cost and high community benefit. b) Reconnect fragmented natural habitat areas as a means to build local biodiversity and natural capital.
SS#3, Integrated Planning, shows how the City of Euclid can develop an EPA-sanctioned Integrated Plan (IP). With an IP, stormwater becomes a resource in potential benefit to the larger community. Bioretention is the primary means to rebuild ‘natural capital’ under an Integrated Plan. Euclid’s IP is based upon restoring ghost water features. The evolution of Euclid watersheds is described.
The five SlideShares:
1) Streams into Sewers: http://www.slideshare.net/roylarick/150307-1-streams-into-sewers
2) Initial Green Solutions: http://www.slideshare.net/roylarick/euclid-initial-green-solutions
3) Integrated Planning: http://www.slideshare.net/roylarick/euclid-integrated-green-plan
4) Eco-Greenways: http://www.slideshare.net/roylarick/euclid-bioretention-greenways
5) Euclid Ecology Unit: http://www.slideshare.net/roylarick/150324-euclid-ecology-unit
INTER-ORGANIZATIONAL TIES AND TOTAL CUSTOMER SOLUTION STRATEGIC POSITIONING F...Mateus Cozer
O ponto básico do processo de gerenciamento estratégico é determinar como as
empresas adquirem e sustentam uma vantagem competitiva. Neste trabalho, objetiva-
se analisar a relação entre o posicionamento competitivo de uma empresa e os
laços inter-organizacionais criados com seus clientes como uma forma de adquirir
vantagem competitiva sustentável. O foco do estudo é entender o processo competitivo
tendo por referência o Projeto Delta, de Hax e Wilde II, o qual propõe
três alternativas de posicionamento estratégico.
Partial transcript of interview with PK Chan, CEO of Editgrid, a Hong Kong born & bread Web 2.0 start-up that provides collaborative online spreadsheets.
SharePoint Workflow für die Erstellung von ArbeitszeugnissenIOZ AG
Monatlich werden im Spital SRO ca. 100 Zeugnisse erstellt. Damit die Arbeitszeugnisse nicht mehr mühsam über Word erstellt werden müssen, haben wir mit Hilfe eines Workflows die Arbeitszeugniserstellung in SharePoint automatisiert.
Exploratory Mobile Testing Webinar_XBOSoft_jean_annharrisonXBOSoft
To Automate or not to Automate your Mobile Testing.
In mobile testing just poking at the GUI will leave bugs hiding. So different tests and a variety of testers are needed. Context is also important; there is no one test set or test approach that will work all the time.
JeanAnn Harrison has years of experience with mobile testing and is a well-known figure in the QA and software testing community. She regularly speaks at conferences and publishes in software testing magazines.
In these slides JeanAnn discusses mobile testing strategies that deliver the right results.
You will learn:
- Types of Mobile Testing
- When and when not to automate your mobile testing
- Mobile exploratory testing strategies and guidelines
- Lesson learned
Dissertation Chair Dr. William Allan Kritsonis & Steven Norfleetguestfa49ec
Dr. William Allan Kritsonis & Steven Norfleet
In 2004, Dr. William Allan Kritsonis was recognized as the Central Washington University Alumni Association Distinguished Alumnus for the College of Education and Professional Studies. Dr. Kritsonis was nominated by alumni, former students, friends, faculty, and staff. Final selection was made by the Alumni Association Board of Directors. Recipients are CWU graduates of 20 years or more and are recognized for achievement in their professional field and have made a positive contribution to society. For the second consecutive year, U.S. News and World Report placed Central Washington University among the top elite public institutions in the west. CWU was 12th on the list in the 2006 On-Line Education of “America’s Best Colleges.”
Jones fayettevvile principals and counselors perceptions of freshmen academy ...William Kritsonis
NATIONAL FORUM JOURNALS are a group of national and international refereed, blind-reviewed academic journals. NFJ publishes articles academic intellectual diversity, multicultural issues, management, business, administration, issues focusing on colleges, universities, and schools, all aspects of schooling, special education, counseling and addiction, international issues of education, organizational behavior, theory and development, and much more. DR. WILLIAM ALLAN KRITSONIS is Editor-in-Chief (Since 1982). See: www.nationalforum.com
Running Head HOMESCHOOLS MORE BENEFICIAL 1HOMESCHOOLS MORE B.docxcowinhelen
Running Head: HOMESCHOOLS MORE BENEFICIAL 1
HOMESCHOOLS MORE BENEFICIAL 9
Are Homeschools more beneficial than Public Schools?
2/14/2017
Prospectus
Summary
Should kids be homeschooled, or are they fine in public schools? Not many parents ask themselves this question. However, the number of students who are being homeschooled has been growing significantly within the last several years. The main idea of this paper is why parents, in general, believe public schools are good. Do parents believe public schools are better simply because they don't have the choice to homeschool their children?
Description
This paper will focus on the overall result of homeschooling and public schools. The reasons as to why some parents prefer home schools over public schools will also be explored. Individuals have not invested much of their time to look at the benefits accruing from schooling. People are sending their kids to public schools, but they do not agree completely with everything presented in those schools. The increasing number of parents who are thinking of homeschooling their own children instead of sending them to a public schools indicates a disagreement on the policies and methods of teaching in public schools. One of the controversies revolves around the amount of time and attention that the children need in order to succeed. Others involve the environment with which the student interacts with on a daily basis, which some argue that is more safe and controlled in homeschools.
Research Question
Does homeschooling tend to produce more successful children in the future?
Guiding Questions
Does the amount of attention given to students affect their overall success?
Does the studying and playing environment in school affect the children positively or negatively?
How can parents provide the best education for their children?
Annotated bibliographyBouwer, C., Schalkwyk, L, V. (2011). Homeschooling: Heeding the voices of learners. Education as Change, 15(2), 179-190.
In this paper, Bouwer unusually seeks the feedback from the students in homeschools. He performs this case study by conducting interviews with parents and their children to ask them about their views on their own homeschools. He also takes a closer look at the feedback from both the parents, as well as their children and compares them in order to find any dissimilarities. The article explores the conflicting feedback from the children, which will provide a strong counterargument for my essay. The article comes from a journal article which gives a high credibility to rely on.
Brain, D, R. (2011). 2.04 Million Homeschool students in the United States in 2010. Salem, OR: National Home Education Research Institute.
The report follows previous research concerning the number of students who are homeschooled. Brain utilizes previous research records, and data from federal agencies and states in order to estimate the current number of homeschooled students. The article ...
While research is mixed on whether increases in school spending le.docxphilipnelson29183
While research is mixed on whether increases in school spending lead to better results for students, a study suggests that influxes of dollars from court decisions lead to higher graduation rates and earnings, especially for low-income students.
By
John Higgins
Seattle Times education reporter
In its 2012 McCleary decision, the state Supreme Court was clear Washington’s lawmakers must devote more tax dollars to our public schools to meet their constitutional responsibility.
How much more? The justices didn’t say.
But the case presumes that more money will lead to a better education — and thus better college and life prospects — for every student in the state.
Does the research on school spending warrant that optimism?
It’s a surprisingly difficult question to answer.
While many wealthy parents don’t question whether money matters when they shell out big bucks for private schools, researchers have debated the role of money in public education for a half-century.
Many studies have failed to find a consistent relationship between increased spending and improved test scores, which has led some policymakers to conclude that money doesn’t matter — even in states like Washington, where the investment in education, compared with other states, has been average or below for many years.
But a new study, recently published in a leading economics journal, used a fresh approach that found strong ties between spending and results, and may also explain why past studies failed to find a strong relationship between the two.
That study has direct bearing on what’s happening here because it focuses on what happened in school districts after state supreme courts ordered higher spending.
In short, the researchers found that students in districts with bigger windfalls did better, on average, than students from other districts in the same state that got less. They spent more time in school, for example, and had higher wages as adults.
The study, published in The Quarterly Journal of Economics, is the first to show the long-term effects of school spending.
Lead author Kirabo Jackson of Northwestern University and his co-authors, Rucker Johnson at the University of California, Berkeley, and Claudia Persico at Northwestern, don’t claim to have the last word on spending and achievement.
But Jackson says their study is important because it demonstrates long-term results and uncovers flaws in many past studies.
“If you have people going out there testifying to legislators that money does not matter and there’s no evidence out there that money matters, then it’s germane to the conversation,” Jackson said.
National report
The debate over the benefits of school funding began about 50 years ago with a report ordered by Congress to look at the effect of racial segregation on students.
Named after its lead researcher, James Coleman, of Johns Hopkins University, the two-year national study reached several conclusions about the power of schools to change stude.
Communicating Community Environment of Junior High School Students in the Fir...ijtsrd
The study investigates the community environment, particularly the learning and social communities of junior high school students in the first congressional district of Northern Samar, Philippines. The research design employed descriptive research. The sample consisted of 388 junior high school students enrolled during the Academic Year 2019 2020. The research findings revealed that while the learning community was moderately favorable, the social community was highly favorable. To sum up, the community environment was moderately favorable. It was also indicated that a communication task force should be instituted in schools. In the same manner, the schools should forge for sustainable school students community relations. Veronica A. Piczon | Leah A. De Asis | Brenfred N. Romero "Communicating Community Environment of Junior High School Students in the First Congressional District of Northern Samar, Philippines: Inputs to School-Students-Community Relations" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49272.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/49272/communicating-community-environment-of-junior-high-school-students-in-the-first-congressional-district-of-northern-samar-philippines-inputs-to-schoolstudentscommunity-relations/veronica-a-piczon
“We found that large district size is
detrimental to achievement in Washington 4th and 7th grades in that it strengthens
the negative relationship between school poverty and student achievement.”
Further, they state, “the negative relationship between school poverty and
achievement is stronger in larger districts,” and “small schools appear to have the
greatest equity effects.” In other words, when school poverty is high, children
ii
perform better in small districts, and the effect of school level poverty on
achievement is smallest when both the district and school are small.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Wsrc hlm district size final 10 2-02
1. Technical Report #3 – November 2002
The Influence of District Size, School Size and
Socioeconomic Status on Student Achievement
in Washington: A Replication Study Using
Hierarchical Linear Modeling
Martin L. Abbott, Ph.D.
Jeff Joireman, Ph.D.
Heather R. Stroh, M.ED.
2. The Washington School Research Center (WSRC) is an independent research and data analysis
center within Seattle Pacific University. The Center began in July 2000, funded through a gift from
the Bill and Melinda Gates Foundation. Our mission is to conduct sound and objective research on
student learning in the public schools, and to make the research findings available for educators,
policy makers, and the general public for use in the improvement of schools. We believe that sound
data and appropriate data analysis are vital components for the identification of school and
classroom practices related to increased student academic achievement.
Washington School Research Center
3500 188th St. S.W., Suite 328
Lynnwood, WA 98037
Phone: 425.744.0992
Fax: 425.744-0821
Web: www.spu.edu/wsrc
Jeffrey T. Fouts, Ed.D. Martin L. Abbott, Ph.D. Duane B. Baker, Ed.D.
Executive Director Senior Researcher Director -
Professor of Education Professor of Sociology School Information Services
Copyright 2002 by the Washington School Research Center, Seattle Pacific University. All rights
reserved. Additional copies of this report may be downloaded in pdf format free of charge at
www.spu.edu/wsrc.
3. The Influence of District Size, School Size and
Socioeconomic Status on Student
Achievement in Washington: A Replication
Study Using Hierarchical Linear Modeling
A Technical Report For
The Washington School Research Center
4. Foreword
In recent years there has been a growing interest in the role that school size
plays in creating effective learning environments for students. Serious questions
have been raised about the “bigger is better” approach to schools, and policy
makers are asking researchers if there are research findings on this important
topic. In fact, there have been numerous studies, both quantitative and
qualitative, strongly suggesting students generally do better in smaller schools
than larger schools.
Such a study published in the year 2000 on education in the state of Georgia
caught the interest of the Urban Issues Committee of the Washington State
School Directors’ Association (WSSDA). Recognizing that such research
findings have direct policy implications, the Association approached the
Washington School Research Center (WSRC) about replicating the study in the
state of Washington. Through the joint sponsorship of WSSDA and WSRC, this
technical report is a replication of that study using achievement, poverty, school
and district size data from Washington State.
The research findings on school size show that the question is a complex one,
and that there are numerous factors that might interact with school size to
account for variation in student and school performance. Using a statistical
procedure called Hierarchical Linear Modeling, WSRC researchers Abbott,
Joireman, and Stroh attempt to identify the ways in which district size, school
size, and family income level interact to effect student achievement.
Replication research across states is difficult because of differing state tests,
grade structures, data bases, and other factors. And, in fact, while this study
replicates the general approach used in the Georgia study, it does differ in some
significant ways. First, WASL scores were used for this study rather than the
ITBS; second, because of questions about the reliability of the high school
poverty data, 4th and 7th grade data were analyzed, while the Georgia study
analyzed 8th and 11th grade data. Still, the results presented here complement
the original findings and add to the body of research that strongly suggests that
school size and district size do matter.
The WSRC researchers conclude: “We found that large district size is
detrimental to achievement in Washington 4th and 7th grades in that it strengthens
the negative relationship between school poverty and student achievement.”
Further, they state, “the negative relationship between school poverty and
achievement is stronger in larger districts,” and “small schools appear to have the
greatest equity effects.” In other words, when school poverty is high, children
i
5. perform better in small districts, and the effect of school level poverty on
achievement is smallest when both the district and school are small.
Jeffrey T. Fouts
Executive Director
Washington School Research Center
ii
6. Table of Contents
Introduction ........................................................................................................... 1
Literature Review .................................................................................................. 2
A Replication Study ................................................................................................ 4
Nature of the Data ................................................................................................. 4
Joint Effects of School and District Variables Using Hierarchical Linear Modeling . 5
Fourth and Seventh Grade Equity Effects............................................................... 14
Conclusions ........................................................................................................... 16
References ........................................................................................................... 18
iii
7. The Influence of District Size, School Size and Socioeconomic Status
on Student Achievement in Washington: A Replication Study using
Hierarchical Linear Modeling
Introduction
New interest in the effects of school size on academic achievement has grown in
recent years as nationwide school reform efforts have gained momentum. Policy
makers and practitioners have suggested new models of schools based on the
idea that smaller is better. As these models have gained currency, there is also
a call for research to investigate the likely effects of creating smaller schools.
Are smaller schools needed as a strategy to improve student academic
performance?
Several studies have examined this question by employing single-level
regression models to examine the influence of size (district and school) and low-
income on academic achievement. A recent article by Robert Bickel and Craig
Howley (2000), however, advanced the research in this area by introducing a
multi-level approach to their analysis examining the joint influence of district and
school size on academic performance in Georgia. Their research focuses on the
“cross-level interaction” of district and school socioeconomic status and size
through a single-equation relative-effects model.
The results of the Bickel and Howley study particularly highlight the sizeable
influences of two such cross-level (i.e., district and school level) interactions: the
product of district size and school socioeconomic status (SES), and the product
of school size and district SES (especially at the 8th grade level). School-level
academic performance is negatively affected both by the district poverty-school
size cross-level interaction, and the school poverty-district size cross level
interaction. (These findings are not as consistent at the 11th grade level.) Single
level regression models cannot identify these important cross-level dynamics.
Another important contribution of the Bickel and Howley study is their “equity”
analyses. According to the authors, “equity” refers to non-equivalence in the
socioeconomic status-academic performance relationship as a function of size.
In effect, this asks whether “the amount of variance in achievement associated
with SES is substantially reduced in smaller units” (p. 5). The analysis is
accomplished by examining the variance in school performance associated with
low-income in four different configurations of school and district size (i.e., large
school-large district, large school-small district, small school-large district, and
small school-small district).
1
8. Literature Review
The issue of school and district size effects on student achievement is not new.
Beginning in the 1920s schools and school districts grew larger as districts
attempted to consolidate both administration and curriculum and instruction.
Ellwood Cubberly, a former urban superintendent, advocated for the creation of
large schools as immigrant populations in major cities grew. Meanwhile, Joseph
Kennedy, dean of the school of education at North Dakota State University, was
concerned about losing the participation and identity of local communities as
rural schools consolidated (Robertson, 2001; Howley, 1996). The press for
larger schools continued through the 1950s as U.S. educators felt the pressure of
the “space race” and the need to provide a wider, more academically rigorous
curriculum for future scientists.
Large schools, however, are not without fault. Bracey (2001) noted:
…large schools, especially large high schools, produce their own set of
problems, which a growing number of researchers and policy makers think
can be solved by returning to small schools. Advocates for small schools
have argued that they can
• raise student achievement, especially for minority and low-income
students;
• reduce incidents of violence and disruptive behavior;
• combat anonymity and isolation and, conversely, increase the sense of
belonging;
• increase attendance and graduation rates;
• elevate teacher satisfaction;
• improve school climate;
• operate most cost-effectively;
• increase parents and community involvement; and
• reduce the amount of graffiti on school buildings. (p. 413)
Such arguments by advocates for small schools bear some merit, as research
indicates that school (Lee & Loeb, 2000) and district (Bickel & Howley, 2000;
Johnson, Howley, & Howley, 2002) size can impact student achievement. Lee
and Loeb examined 264 Chicago elementary schools and found that school size
influenced student achievement both directly and indirectly. They reported that
teachers in small schools (less than 400 students) take more responsibility for
students’ academic and social development, and that this in turn enhances
student achievement. They noted that small schools facilitate more intimate and
personal relationships among both teachers and students, and that it is these
relationships that impact student learning. Another study of Chicago’s public
schools found that small schools increase attendance, student persistence,
2
9. performance, graduation rates, grades, course completion, and parent, teacher,
student, and community satisfaction (Walsey, Fine, Gladden, Holland, King,
Mosak, & Powell, 2000).
Other research on school size has found socioeconomic status to be the
confounding variable in the size/achievement equation, noting that as school size
increases, achievement levels for schools with less advantaged students
decreases (Bickel, 1999; Howley & Bickel, 2000). Bickel, Howley, Williams, and
Glascock (2000) confirmed these findings while controlling for ethnicity,
language, size, cost, and curricular composition factors. Interestingly, Howley,
Strange, and Bickel (2000) noted that “size exert[s] a negative influence on
achievement in impoverished schools, but a positive influence on achievement in
affluent schools. That is, all else being equal, larger school size benefits
achievement in affluent communities, but it is detrimental in impoverished
communities” (p. 4). Additionally, the authors’ results indicated that “the
relationship between achievement and SES is substantially weaker in the smaller
schools than in the larger schools” (p. 5).
The authors also noted, however, that small size doesn’t necessarily guarantee
student success. “Small size is a necessary but insufficient condition for school
improvement. . . . It is important to avoid seeing small schools as the sole
solution to all that ails education. Rather, we would suggest that it is a key
ingredient in a comprehensive plan to improve education” (p. 66).
District size may also moderate the effects of school size. Bickel and Howley
(2000) found an interesting interaction between district and school size. The
authors explained:
Larger schools in larger districts seem to propagate inequality of outcomes
by comparison to smaller schools and smaller districts. In fact, smaller
schools in larger districts demonstrate a useful equity effect, as well. For
large schools in smaller districts, however, the improvements in equity
might be so slight as to be called negligible. (p. 21)
The authors (Bickel & Howley, 2000) found that in communities with high rates of
poverty, small schools in small districts increase student achievement. Overall,
“smaller districts and smaller schools demonstrate greater achievement equity”
(Howley, 2000, p. 7).
Simply stated, current research indicates that the amount of impact
socioeconomic status has on student achievement is dependent upon several
factors, including the size of the school and the size of the district in which the
school functions.
3
10. A Replication Study
The present study is a replication of the Bickel and Howley (2000) method to the
Washington state academic performance of 4th and 7th grade students. While
Bickel and Howley focused on the 8th grade Iowa Test of Basic Skills (ITBS) and
the 11th grade Georgia High School Graduation Test, this study examines 4th and
7th grade academic performance on the Washington Assessment of Student
Learning (WASL)1 test that is mandated across the state. A great deal of
attention in Washington has focused on the use of the WASL, which has
replaced other tests (e.g., ITBS) for assessing statewide standards-based
learning objectives.
Bickel and Howley’s method is accomplished in this study by the use of
Hierarchical Linear Modeling through the HLM software program (Raudenbush,
S., Bryk, A., and Congdon, R., 2000). The two approaches attempt to specify the
joint relationships and cross-level interactions of two structural levels (district and
school) on school academic performance.
Nature of the Data
The data used for this replication study were provided by Washington State’s
Office of the Superintendent of Public Instruction, and are from the testing year
2001. The data consist of all 4th and 7th grade student WASL scale scores in
reading and mathematics, aggregated to the school level.2 Schools with less than
ten students were excluded from the study, and, because there was a concern
over the unique characteristics of some “types” of schools, those labeled
“Alternative,” “Institutional,” and other “Unclassified” types were not included.
Table 1 indicates the descriptive data for the variables used in this study.
Percent Free and Reduced (F/R) Lunch is used on both school and district levels
as a measure of low socioeconomic status3. Spansize is the number of students
per grade level and is used as our measure of school size, as it was by Bickel
and Howley (2000). Enrollment is the total number of students per district.
1
For more information on administration of the WASL and ITBS in Washington, visit
www.k12.wa.us/assessment/WASLintro.asp. For more technical information on the WASL, visit
www.k12.wa.us/assessment/qawasl.asp. For more technical information on the ITBS, visit
www.riverpub.com/products/group/itbs.htm.
2
Grade 10 WASL scores were not used since, in our experience, the % Free/Reduced Lunch
data are less reliable at that level.
3
Bickel and Howley (2000) refer to free or reduced price meals as SES.
4
11. Table 1
Descriptive Statistics for 4th and 7th Grades
4th Grade
School-Level Mean SD N
Percent F/R Lunch 37.86 23.62 1035
Spansize 69.67 28.29 1035
Math Scale Score 392.83 14.79 1035
Reading Scale Score 405.45 7.07 1035
District-Level
Percent F/R Lunch 37.92 18.84 251
Enrollment 3903.34 6089.11 250
7th Grade
School-Level Mean SD N
Percent F/R Lunch 33.27 21.33 417
Spansize 177.87 112.74 417
Math Scale Score 366.29 17.81 417
Reading Scale Score 393.79 6.81 417
District-Level
Percent F/R Lunch 37.92 18.75 255
Enrollment 3924.40 6277.40 255
Joint Effects of School and District Variables Using Hierarchical Linear
Modeling
Major Goal
As noted earlier, our major goal was to determine whether the cross-level
interactions (school x district level) between size and socioeconomic status
reported by Bickel and Howley (2000) would replicate within Washington. More
specifically, we were interested in whether the data in Washington would
replicate two major patterns reported by Bickel and Howley: (1) larger schools
are beneficial within affluent communities, whereas smaller schools are more
beneficial within less affluent districts; and (2) the “achievement cost” associated
with less affluent schools is greater in large districts (i.e., the negative
5
12. relationship between school-level poverty and achievement is stronger in larger
districts). The first pattern would require an interaction between school size and
district-level SES; the second pattern would require an interaction school-level
SES and district size.
Hierarchical Linear Modeling (HLM)
To determine whether these two patterns (interactions) were present within the
current Washington State data, we conducted a series of analyses using
hierarchical linear modeling (HLM). HLM is a statistical technique that is
appropriate for analyzing multi-level data (e.g., schools nested within districts).
HLM has a number of advantages over other approaches to multi-level data,
such as ignoring the nested nature of the data (which violates the assumption of
non-independent errors) or pooling the data by, for example, aggregating across
schools within a district (which results in a loss of data, and eliminates the
possibility of examining cross-level interactions).4 In each of the analyses, school
level variables were group centered (district means) and district level variables
were grand mean centered.
Overview of Models 1 and 2
Tables 2 and 3 summarize the results of two different models, respectively.
Model 1 examines the influence of school size and district poverty (on math and
reading at both the 4th and 7th grades), whereas Model 2 examines the influence
of school poverty and district size (on math and reading at both the 4th and 7th
grades).
Model 1 Summary – School Size and District Poverty
We begin by interpreting the results for Model 1, using the 4th grade, shown in
the top half of Table 2. As can be seen, results for math and reading were quite
similar. First, scores in math and reading each showed a highly significant
negative relationship with district poverty (Bs = -47.10 and -24.10, ps < .0001).
Second, math and reading showed no significant relationship with school size (ps
> .35). Third, results did not reveal interactions between school size and district
poverty, findings that were not consistent with Bickel and Howley’s (2000)
findings.
While the interactions were not significant, they were in the expected direction.
To further examine the nature of the interactions, we plotted the relationship
between school size and achievement for districts in the 25th (thin lines) and 75th
(bold lines) percentiles on poverty, as shown in Figure 1. While the interaction
was not significant in either case, there was a tendency for larger schools to be
somewhat more beneficial in more affluent districts (25th percentile, thin line) than
in less affluent districts (75th percentile, bold line).
4
For more information on Hierarchical Linear Modeling, see Raudenbush and Bryk (2002).
6
13. We now turn to the results for the 7th grade, summarized in the bottom half of
Table 2. As can be seen, the 7th grade results replicated the 4th grade results.
First, both math and reading showed a significant negative relationship with
district poverty (Bs = -61.05 and -23.41, ps < .0001). Second, math and reading
showed no significant relationship with school size (ps > .28). Last, results at the
7th grade failed to reveal a significant interaction between school size and district
poverty on either math or reading (ps > .27). For comparison with the 4th grade
results, we present graphs of the relationship between school size and
achievement at the 25th and 75th percentile on district poverty for the 7th grade
(Figure 2). As can be seen, while the interaction was not significant in either
case, there was a tendency for larger schools to be somewhat more beneficial in
more affluent districts (25th percentile, thin line) than in less affluent districts (75th
percentile, bold line).
Taken as a set, results for Model 1 do not replicate Bickel and Howley’s (2000)
findings concerning the interaction between school size and district poverty. As
we explain in the next section, results did replicate Bickel and Howley’s finding
concerning the interaction between school poverty and district size.
Model 2 Summary – School Poverty and District Size
We now turn to the results for Model 2, in which we examine the influence of
school poverty and district size on math and reading in the 4th and 7th grade, as
summarized in Table 3. First, math and reading showed significant negative
relationships with school level poverty in both grades (ps < .001). Second, math
and reading showed a significant positive relationship with district size in the 4th
grade (ps < .004), but showed no significant relationship with district size in the
7th grade (ps > .09). Finally, in each case, school poverty and district size
showed a significant interaction (ps < .001). As we will explain, the nature of this
interaction was consistent with Bickel and Howley’s (2000) findings (for their 8th
grade only – comparable 11th grade results were not significant).
To further examine the nature of this interaction, we plotted the relationship
between school poverty and achievement for small districts (25th percentile, thin
line) and large districts (75th percentile, bold line) at both the 4th and 7th grade.
Figure 3 presents the results for 4th grade; Figure 4 presents the results for the
7th grade. As can be seen, in every case, the negative relationship between
school poverty and achievement is stronger in large districts, thus replicating
Bickel and Howley’s (2000) findings (for the 8th grade only).
7
14. Table 2
Summary of HLM Runs – Model 1 – Effects of School Size and District Poverty
on Math and Reading Achievement in 4th and 7th Grades.
4th Grade (Figure 1)
Math B SE t df p-value
Intercept 389.99776 0.519294 751.015 246 0.000
School Size 0.03 0.031156 0.925 246 0.355
District Poverty -47.10 2.843856 -16.562 246 0.000
SS x DP -0.16 0.143109 -1.127 246 0.260
Reading B SE t df p-value
Intercept 404.00 0.242452 1666.317 246 0.000
School Size 0.01 0.014902 0.574 246 0.566
District Poverty -24.10 1.377889 -17.492 246 0.000
SS x DP -0.087 0.073228 -1.191 246 0.234
7th Grade (Figure 2)
Math B SE t df p-value
Intercept 364.36 0.708944 513.95 253 0.000
School Size 0.04 0.038757 0.95 253 0.343
District Poverty -61.05 4.394758 -13.89 253 0.000
SS x DP -0.25 0.229240 -1.09 253 0.275
Reading B SE t df p-value
Intercept 393.02 0.277042 1418.64 253 0.000
School Size 0.01 0.011056 1.06 253 0.289
District Poverty -23.41 1.776674 -13.18 253 0.000
SS x DP -0.05 0.068546 -0.76 253 0.445
Note. DP = District Poverty (% of students in district on free or reduced lunch);
SS = School Size (spansize).
8
15. Table 3
Summary of HLM Runs – Model 2 – Effects of School Poverty and District Size
on Math and Reading Achievement in 4th and 7th Grades.
4th Grade (Figure 3)
Math B SE t df p-value
Intercept 390.21 0.806115 484.062 247 0.000
School Poverty -0.24 0.038737 -6.228 247 0.000
District Size .000287 0.000107 2.682 247 0.008
SP x DS -0.00001 0.000003 -3.809 247 0.000
Reading B SE t df p-value
Intercept 404.075 0.397746 1015.914 247 0.000
School Poverty -0.14 0.017709 -7.719 247 0.000
District Size .000151 0.000052 2.907 247 0.004
SP x DS -0.000005 0.000001 -4.497 247 0.000
7th Grade (Figure 4)
Math B SE t df p-value
Intercept 364.83 1.020967 357.34 253 0.000
School Poverty -0.40 0.120292 -3.29 253 0.001
District Size .000250 0.000149 1.68 253 0.093
SP x DS -0.00002 0.000006 -3.51 253 0.001
Reading B SE t df p-value
Intercept 393.24 0.401891 978.48 253 0.000
School Poverty -0.18 0.051623 -3.42 253 0.001
District Size .000084 0.000052 1.60 253 0.108
SP x DS -0.000008 0.000002 -3.51 253 0.001
Note. DS = District Size; SP = School Poverty (% students on free or reduced
lunch).
9
16. Figure 1
Math and Reading Achievement as a Function of School Size (Spansize) and
District Poverty (25th and 75th Percentiles) – 4th Grade.
4th WASL
409.7
DISTFR = -0.137913
DISTFR = 0.113489
M 407.1
a
t 404.6
h
402.0
A
399.4
c
h
396.9
i
e 394.3
v
e 391.7
m
e 389.1
n
t 386.6
384.0
-60.94 2.94 66.83 130.71 194.60 258.48
School Size
4th WASL
412.6
R DISTFR = -0.137913
e 411.5
DISTFR = 0.113489
a
d 410.3
i
n 409.1
g
408.0
A
c 406.8
h
i 405.6
e
v 404.4
e
m 403.3
e
402.1
n
t
400.9
-60.94 2.94 66.83 130.71 194.60 258.48
School Size
10
17. Figure 2
Math and Reading Achievement as a Function of School Size (Spansize) and
District Poverty (25th and 75th Percentiles) -- 7th Grade.
7th Grade
398.3
DISTFR = -0.146881
DISTFR = 0.114901
M 394.1
a
t 389.9
h
385.6
A
381.4
c
h
377.2
i
e 372.9
v
e 368.7
m
e 364.5
n
t 360.2
356.0
-168.95 -67.15 34.65 136.45 238.24 340.04
School Size
7th Grade
403.2
R DISTFR = -0.146881
e 401.8
DISTFR = 0.114901
a
d 400.4
i
n 399.0
g
397.6
A
c 396.3
h
i 394.9
e
v 393.5
e
m 392.1
e
390.7
n
t
389.4
-168.95 -66.13 36.68 139.50 242.32 345.13
School Size
11
18. Figure 3
Math and Reading Achievement as a Function of School Poverty and District
Size (25th and 75th Percentile) – 4th Grade
4th WASL
399.7
DISTENRL = -3357.289063
DISTENRL = 558.710938
M 397.3
a
t 394.9
h
392.4
A
390.0
c
h
387.6
i
e 385.1
v
e 382.7
m
e 380.3
n
t 377.8
375.4
-37.93 -18.20 1.52 21.24 40.96 60.69
School Poverty
4th WASL
409.4
R DISTENRL = -3357.289063
e 408.1
DISTENRL = 558.710938
a
d 406.7
i
n 405.3
g
403.9
A
c 402.6
h
i 401.2
e
v 399.8
e
m 398.5
e
397.1
n
t
395.7
-37.93 -18.20 1.52 21.24 40.96 60.69
School Poverty
12
19. Figure 4
Reading Achievement as a Function of School Poverty and District Size (25th and
75th Percentile) for 4th and 7th Grade.
7th Grade
378.4
DISTENR = -3406.395996
DISTENR = 443.604004
M 374.7
a
t 370.9
h
367.2
A
363.4
c
h
359.7
i
e 355.9
v
e 352.2
m
e 348.4
n
t 344.7
340.9
-33.27 -14.76 3.76 22.27 40.79 59.30
School Poverty
7th Grade
399.3
R DISTENR = -3406.395996
e 397.6
DISTENR = 443.604004
a
d 395.9
i
n 394.2
g
392.5
A
c 390.9
h
i 389.2
e
v 387.5
e
m 385.8
e
384.1
n
t
382.5
-33.27 -14.57 4.13 22.83 41.53 60.23
School Poverty
13
20. Fourth and Seventh Grade Equity Effects
In order to measure equity effects, Bickel and Howley (2000) used squared zero-
order correlation values (r2) between SES and achievement within four
categories of district and school size (i.e., large school-large district, large school-
small district, small school-large district, and small school-small district). They
found that,
the predicted equity effect of reducing district size but not school
size would be practically significant; the predicted equity effect of
reducing school size but not district size would also be practically
significant and perhaps somewhat larger; and the combined
strategy of reducing both school and district size would be
predicted to yield substantial equity and excellence effects . . .
(p. 20)
This study replicates Bickel and Howley’s method by creating four categories of
district and school size based on median split values: large school-large district,
large school-small district, small school-large district, and small school-small
district. Within each of these categories are the r2 values between F/R lunch %
and achievement (math and reading WASL scores) for 4th and 7th grades. Table
4 lists the results of these analyses.
Both 4th and 7th grade results reflect Bickel and Howley’s (2000) results in that
the data in this study show the small district-small school category shows the
smallest proportion of variance in achievement associated with school poverty.
Apart from this however, the results are different from Bickel and Howley in at
least one major instance: the large district-large school category did not result in
the greatest amount of variance explained across grade levels and subjects.
The overall results of both 4th and 7th grades reflect the school poverty – district
size interaction results reported in Table 3: that the negative relationship
between school poverty and achievement is stronger in larger districts. Small
schools in small districts explain the least amount of variance (13% to 24% of the
variance in achievement associated with poverty), but the largest amount of
variance explained is in large districts irrespective of school size (41% to 54%).
This finding is consistent across grade levels and subjects despite large
differences between 4th and 7th grade spansizes. The 4th grade spansize is
approximately 2 ½ times smaller than the 7th grade spansize while the mean
district enrollments are approximately equal. Therefore, small schools appear to
have the greatest equity effects, while large districts are the most detrimental.
14
21. Table 4
Variance in Achievement Explained by School Poverty as a Function of
District and School Size: Washington Multi-Level Equity Effects1
Large and Small Districts and Schools—4th, 7th WASL Scores
4th Grade Math Scale Scores
Districts2
Large Small
2 2
r n r n
Grade Span Large 0.41 487 0.27 30
Size3 Small 0.45 416 0.17 101
4th Grade Reading Scale Scores
Districts2
Large Small
2 2
r n r n
Grade Span Large 0.47 487 0.46 30
Size 3 Small 0.54 416 0.13 101
7th Grade Math Scale Scores
Districts4
Large Small
2 2
r n r n
Grade Span Large 0.48 208 - 0
Size 5 Small 0.46 80 0.24 129
7th Grade Reading Scale Scores
Districts4
Large Small
2 2
r n r n
Grade Span Large 0.53 208 - 0
Size 5 Small 0.51 80 0.20 129
1
Variance (r2) in Scale Scores attributable to % Free/Reduced Lunch
2
Median Split = 1,514.5
3
Median Split = 68.9
4
Median Split = 1,411
5
Median Split = 186
15
22. Conclusions
Our study replicated the method of Bickel and Howley (2000) for understanding
the influence of district size, school size and socioeconomic status on student
achievement in Washington. We found that large district size is detrimental to
achievement in Washington 4th and 7th grades in that it strengthens the negative
relationship between school poverty and student achievement. This finding
replicated that of the Bickel and Howley (2000) study.
We did not replicate another of Bickel and Howley’s (2000) findings, however. In
our study, district affluence did not have a significant impact over the school size
– student achievement relationship. The tendency for larger schools to be
somewhat more beneficial in more affluent districts (and, equivalently, for smaller
schools to be more beneficial in less affluent districts) is shown in the analyses,
but was not found to be statistically significant.
The nature and configuration of Washington schools may partially explain the
discrepancy between the findings of the two studies with respect to district
affluence. First, the majority of districts (for both 4th and 7th grades) in
Washington are single-school districts that tend to be smaller, poorer, and more
rural than multi-school districts.5 Second, our study used the WASL as the
measure of student achievement (both 4th and 7th grades) in contrast to Bickel
and Howley’s use of the ITBS (8th grade) and the Georgia High School
Graduation Test (11th grade). Preliminary analyses in Washington indicate that
the WASL and the ITBS have different correlations with school poverty,
especially in math.6 Third, there are a number of other variables not addressed
in either study that may exert important influences on student achievement.
Taken together, the method used by Bickel and Howley (2000) and that of this
study were useful for explaining relationships in multi-level data that could not be
explained by more traditional (single-level) analyses. School and district size are
often assumed to be primary, independent influences on student achievement.
In fact, this is a commonly expressed sentiment even among practitioners and
policy makers. However, based on this study, it appears that size is a more
complex matter, and needs to be viewed in the context of other influences in
order to determine its contribution to school-level achievement.
Certainly, the multi-level findings of our study argue against the simplistic
conclusion that reducing school and/or district size will automatically improve
student achievement, or be more equitable. We are in complete agreement with
Bickel and Howley (2000) in their comment that, “the conclusions of this study
would seem to require rather wide debate and reconsideration of the size issue,
5
We use “single-school districts” to indicate districts with a school containing one (4th and 7th)
grade level.
6
For a comparison of the WASL and ITBS in Washington, see Joireman and Abbott (2001)
16
23. across the spectrum of poverty and wealth, and not just in the case of
impoverished communities” (p. 21).
17
24. References
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Georgia replication of inquiry in education. Huntington, WV: Marshall
University.
Bickel, R., & Howley, C. (2000). The influence of scale on student performance:
A multi-level extension of the Matthew Principle. Education Policy Analysis
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Bickel, R., Howley, C., Williams, T., & Glascock, C. (2000). High school size,
achievement equity, and cost: Robust interaction effects and tentative
results. Washington, D.C.: Rural School and Community Trust.
Bracey, G. (2001). Small schools, great strides. Phi Delta Kappan, 82(5), 413-
414.
Howley, C. (1996). Ongoing dilemmas of school size: A short story. Retrieved
June 4, 2002 from
http://www.aasa.org/issues_and_insights/district_organization/howley_dile
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School and Community Trust.
Howley, C., Strange, M., & Bickel, R. (2000). Research about school size and
school performance in impoverished communities [ERIC Digest].
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Joireman, J., & Abbott, M. (2001) The relationships between the Iowa Test of
Basic Skills and the Washington Assessment of Student Learning in the
State of Washingon [ERIC Document TM033 308]. Lynwood, WA:
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Johnson, J. D., Howley, C. B., Howley, A. A. (2002). Size, excellence, and equity:
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Lee, V., & Loeb, S. (2000). School size in Chicago elementary schools: Effects
on teachers’ attitudes and students’ achievement. American Educational
Research Journal, 37(1), 3-31.
18
25. Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and
data analysis methods (2nd edition). Thousand Oaks: Sage Publications.
Raudenbush, S., Bryk, A., and Congdon, R. (2000) HLM (Version 5) [Computer
Software]. Lincolnwood, IL: Scientific Software International.
Robertson, S. (2001). The great size debate [A CEFPI Brief on Educational
Facility Issues]. Scottsdale, AZ: Council of Educational Facility Planners,
International.
Walsey, P. A., Fine, M., Gladden, M., Holland, N. E., King, S. P., Mosak, E., &
Powell, L. C. (2000). Small schools: Great strides. A study of new small
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19