This document provides methodology details for an analysis of child assessment data from 6,600 children enrolled in 15 California Head Start programs. The analysis examines developmental progress between fall 2008 and spring 2009 assessments using the Desired Results Developmental Profile-Revised (DRDP-R) assessment tool. Child Care Results conducted the analysis to evaluate the effect of Head Start programs on child development and presented results in a 4-page bulletin. This document provides supplemental details on the sample, data, study design, and results.
A program evaluation of alive to the world 2009 william & maryChus
This document provides a program evaluation of Alive to the World (AAQ), a character education program used in Latin America. It assesses the program's potential to promote democratic values based on surveys of students in Mexico, Peru, and Venezuela.
The initial survey results showed statistically significant positive impacts on students' attitudes related to democratic values. However, the survey methodology had limitations that require the results be interpreted cautiously. To address these, a new survey instrument and methodology were developed.
While further testing is needed, evidence supports that AAQ has the potential to positively impact democratic values in Latin America. Environmental factors not related to the program also influence values and must be considered. Overall, the evaluation finds promise for A
First 5 Ventura County administered a parent survey in 2015 to evaluate its programs. The survey aimed to measure progress on access to care, kindergarten readiness, and parent/family knowledge. This report summarizes the survey results and characteristics of families served. It finds that most families served were Hispanic, had low incomes, and children received multiple services on average. The survey responses and intake data suggest that programs have helped increase access to care, school readiness, and parent knowledge of child development. Areas for continued improvement are also discussed.
This document summarizes the results of a statewide survey of 895 registered Latino voters in California conducted in July 2010 about early childhood education and preschool. Key findings include:
1) Latino voters see preschool as important for giving children an advantage in school, but believe California is not doing enough to ensure access to affordable, quality preschool programs.
2) While the state budget is challenging, Latino voters support continued investment in preschool to prepare children for kindergarten and beyond.
3) Latino voters are more likely to support political candidates who want to increase funding for preschool and early learning.
AttendaNCe Counts: What North Carolina School Districts are Doing to Reduce C...Molly Osborne
This document discusses chronic absenteeism in North Carolina school districts. It finds that chronic absenteeism is an issue that affects many North Carolina communities and students, especially students from certain racial/ethnic groups. The document reports on a survey of North Carolina school districts that asked districts to self-assess their attendance policies and practices. The survey looked at four areas: data collection and use, family engagement, strategic planning, and community partnerships. The survey found that while districts feel fairly confident in their data collection, they see more room for improvement in data analysis and family engagement strategies. The document discusses recent state-level efforts to address chronic absenteeism and identifies questions for districts to improve their policies and practices.
Wind power is a renewable and abundant source of energy that can help meet global electricity demand. It has grown significantly in recent decades, with over 80 countries now using wind power commercially and it providing over 2.5% of worldwide electricity usage. Wind turbines convert the kinetic energy of wind into electrical energy to feed into the electric grid. While wind power has many benefits like no emissions, some challenges remain such as its non-dispatchable nature and dependence on wind conditions. Technological developments continue to aim to increase reliability and lower costs.
CPR is an important skill for school-aged children who may engage in risky behaviors or jobs requiring it, and for adults living with others who may need CPR. Hands-Only CPR for adults involves calling 911 and continuous chest compressions without breaths. While training is recommended to learn proper form, Hands-Only CPR can increase bystander response in emergencies compared to conventional CPR. For infants, children, and certain causes of arrest like drowning, conventional CPR including breaths may provide better outcomes than Hands-Only.
A program evaluation of alive to the world 2009 william & maryChus
This document provides a program evaluation of Alive to the World (AAQ), a character education program used in Latin America. It assesses the program's potential to promote democratic values based on surveys of students in Mexico, Peru, and Venezuela.
The initial survey results showed statistically significant positive impacts on students' attitudes related to democratic values. However, the survey methodology had limitations that require the results be interpreted cautiously. To address these, a new survey instrument and methodology were developed.
While further testing is needed, evidence supports that AAQ has the potential to positively impact democratic values in Latin America. Environmental factors not related to the program also influence values and must be considered. Overall, the evaluation finds promise for A
First 5 Ventura County administered a parent survey in 2015 to evaluate its programs. The survey aimed to measure progress on access to care, kindergarten readiness, and parent/family knowledge. This report summarizes the survey results and characteristics of families served. It finds that most families served were Hispanic, had low incomes, and children received multiple services on average. The survey responses and intake data suggest that programs have helped increase access to care, school readiness, and parent knowledge of child development. Areas for continued improvement are also discussed.
This document summarizes the results of a statewide survey of 895 registered Latino voters in California conducted in July 2010 about early childhood education and preschool. Key findings include:
1) Latino voters see preschool as important for giving children an advantage in school, but believe California is not doing enough to ensure access to affordable, quality preschool programs.
2) While the state budget is challenging, Latino voters support continued investment in preschool to prepare children for kindergarten and beyond.
3) Latino voters are more likely to support political candidates who want to increase funding for preschool and early learning.
AttendaNCe Counts: What North Carolina School Districts are Doing to Reduce C...Molly Osborne
This document discusses chronic absenteeism in North Carolina school districts. It finds that chronic absenteeism is an issue that affects many North Carolina communities and students, especially students from certain racial/ethnic groups. The document reports on a survey of North Carolina school districts that asked districts to self-assess their attendance policies and practices. The survey looked at four areas: data collection and use, family engagement, strategic planning, and community partnerships. The survey found that while districts feel fairly confident in their data collection, they see more room for improvement in data analysis and family engagement strategies. The document discusses recent state-level efforts to address chronic absenteeism and identifies questions for districts to improve their policies and practices.
Wind power is a renewable and abundant source of energy that can help meet global electricity demand. It has grown significantly in recent decades, with over 80 countries now using wind power commercially and it providing over 2.5% of worldwide electricity usage. Wind turbines convert the kinetic energy of wind into electrical energy to feed into the electric grid. While wind power has many benefits like no emissions, some challenges remain such as its non-dispatchable nature and dependence on wind conditions. Technological developments continue to aim to increase reliability and lower costs.
CPR is an important skill for school-aged children who may engage in risky behaviors or jobs requiring it, and for adults living with others who may need CPR. Hands-Only CPR for adults involves calling 911 and continuous chest compressions without breaths. While training is recommended to learn proper form, Hands-Only CPR can increase bystander response in emergencies compared to conventional CPR. For infants, children, and certain causes of arrest like drowning, conventional CPR including breaths may provide better outcomes than Hands-Only.
The document discusses CPR basics and certification. It explains the three Cs of CPR: Check for safety, Call for help, and Care for the person until emergency services arrive. Adult, child, and infant CPR techniques are also summarized, noting differences such as using one or two hands for chest compressions. Certification in CPR and first aid is recommended in case emergency response is needed.
Coal to liquid (CTL) is a process that converts coal into synthetic fuels by liquefying it. It was not economically viable when oil prices were low but interest has grown with higher oil prices. CTL is best for countries with large coal reserves but high oil import dependence, like India and China. While it enhances energy security, CTL faces challenges from high costs and environmental concerns unless carbon capture technology is used. Several CTL plants have been proposed or announced internationally but many face technical, economic, and environmental barriers.
Art Noveau was a popular artistic style from 1890-1910 that influenced decoration, painting, fashion, and other areas. It used organic motifs like flowers, nature, and the human body. Secession architecture in the Czech Republic featured ornate details and flat surfaces. Notable Czech artist Alfonz Mucha worked in this style. In Spain, Antoni Gaudi was a pioneer of modernism and his highly original buildings like Casa Batlló used curved shapes inspired by nature. His most famous work, the unfinished Sagrada Familia in Barcelona, will be completed by 2026 using only donations.
The museum was opened in 2008 in a house built in 1905 to connect the Czech Republic and Germany through friendship and love. It aims to show photos so people can find images of deceased relatives, housing a large collection of original negatives in the original home of photographer Franz Seidel, with all furniture and equipment left as it was before his death. The project cost 55 million Czech crowns with support from several partners.
The Belem Tower was built in the 15th century as a defensive tower at the entrance to the Tagus River in Lisbon to protect the city. It was decorated in the Manueline style with symbols of King Manuel I's power. Over time, as defenses improved, it lost its defensive role but took on other uses like customs control, a telegraph station, and lighthouse. It also served briefly as a political prison. It is now a UNESCO World Heritage Site that represents an important part of Portugal's history during the Age of Discovery.
Portuguese sailors were pioneers of European exploration in the 15th-16th centuries, discovering routes around Africa to India and establishing trade with Asia and Japan. Vasco da Gama led the first fleet around Africa to India in 1498, opening an important trade route. Over subsequent decades, the Portuguese explored Southeast Asia and reached Japan, introducing European musical instruments, foods, and concepts like harmony that influenced Asian cultures.
The Portuguese colonial empire was the first global empire in history beginning in the 15th century. Key dates included Henry the Navigator reaching Cape Vert in 1415 and Bartolomeo Diaz reaching the Cape of Good Hope in 1488, unlocking the route to India. Treaties like the Treaty of Tordesillas in 1494 and Treaty of Zaragoza in 1529 divided the world between Portugal and Spain. Portugal's empire grew through explorers like Vasco da Gama and Pedro Álvares Cabral, and victories over other powers seeking control in places like India and Brazil. At its peak, Portugal's wealthy empire included Brazil, Africa, Asia, and small parts of North America, though it began dis
TURAZZA VOGUE
La pubblicazione nasce dalla collaborazione tra allievi dell'Istituto Turazza di Treviso, settore moda, e alcuni insegnanti. La finalità del progetto è quella di promuovere l'attività del CFP pubblicizzando i capolavori realizzati dai ragazzi durante l'anno 2013/2014.
Prof. Tiziano Pavan per la fotografia
Prof. Paola Bortoluzzi per la post produzione video
Prof. Anna Monforte per i testi
Prof. Giuseppina Menoncello, fashion consulting
Si ringrazia tutta la 3 moda
Executive Summary of Strategic Plan for Children's ServicesChelsea Eickert
1) Solid Ground is a nonprofit that aims to end poverty and homelessness. It provides housing and support services to over 300 children annually through its residential programs.
2) The organization created a strategic plan to better serve children by addressing needs identified through an assessment. Key issues included behavioral health, educational challenges, and lack of family/community support.
3) The plan focuses on four areas: health and well-being, education and career pathways, social connections, and out-of-school programming. It outlines goals, best practices, and outcome indicators to guide implementation over four stages from 2015-2020.
Sammet, Moore & Wilson.2013.Measuring Positive Development of Youth in Contex...Kara Sammet
This document describes the creation and validation of the Desired Results Developmental Profile-School Age (DRDP-SA), a strengths-based assessment tool designed to measure the positive development of youth in before- and after-school programs in California. The DRDP-SA assesses development across six domains using embedded observations during program activities. An initial validation study with a representative sample of 705 youth found the tool had good technical properties, with one domain (physical development) needing further refinement. The DRDP-SA provides a unique approach for evaluating programs' impact on youth well-being compared to narrow testing approaches.
This report of activities was submitted to the Michigan Interagency Coordinating Council from Early On Public Awareness. Time period of activities: 9/2/2010 through 10/15/2010.
This document presents research on the quality of childcare and its effects on school readiness and child development outcomes. It discusses different types of childcare (parental, home-based, center-based) and reviews literature showing that higher quality care leads to better school preparation. The authors conducted interviews and surveys of parents, teachers and administrators to examine factors like curriculum, funding, facilities, teacher qualifications, and state laws. The analysis found some correlations between these factors and parent perceptions of quality care. The conclusions call for more research on childcare's impacts to influence policymaking.
The document discusses the opportunities and challenges facing early childhood education systems given recent economic conditions and policy changes. It argues that states must transform their service, information, and management systems to take advantage of new federal funding opportunities while addressing budget cuts. An integrated early childhood data system is needed to evaluate programs, improve outcomes for at-risk children, and inform policy decisions.
This document introduces the Child Status Index (CSI), a tool developed to monitor outcomes for orphaned and vulnerable children (OVC) receiving support programs in sub-Saharan Africa. The CSI was created through participatory research with local communities to assess child well-being across 12 domains, including food, shelter, health, education and psychosocial support. It provides a standardized way for programs to evaluate how services are impacting children and to match children's needs with available support. The CSI is being implemented by various NGOs and national monitoring systems to improve outcomes for OVC.
The Difference You Make: Using Data to Highlight Equity for Allappliedsurveyresearch
The document discusses using data to highlight equity and accountability in social programs. It introduces Results-Based Accountability (RBA) and Collective Impact (CI) frameworks. A case study of a Kindergarten School Readiness Assessment in Santa Clara County is presented. Key concepts in RBA like community results, indicators, and performance measures are defined. The importance of aligning community and program data is emphasized. Early results from applying RBA and CI principles to improve kindergarten readiness in the Alum Rock School District are shared, showing the positive impact of preschool, family engagement, quality programs, and collaboration.
This document provides information from the Maryland State Department of Education's Division of Early Childhood Development newsletter from Winter 2015. It discusses Maryland being awarded a $15 million federal grant to expand access to pre-kindergarten programs. It also discusses the first administration of the Kindergarten Readiness Assessment for over 3,500 public school kindergarten students to measure school readiness. Finally, it introduces new developmental screening requirements for child care programs to assess children ages birth to five years old.
The document discusses CPR basics and certification. It explains the three Cs of CPR: Check for safety, Call for help, and Care for the person until emergency services arrive. Adult, child, and infant CPR techniques are also summarized, noting differences such as using one or two hands for chest compressions. Certification in CPR and first aid is recommended in case emergency response is needed.
Coal to liquid (CTL) is a process that converts coal into synthetic fuels by liquefying it. It was not economically viable when oil prices were low but interest has grown with higher oil prices. CTL is best for countries with large coal reserves but high oil import dependence, like India and China. While it enhances energy security, CTL faces challenges from high costs and environmental concerns unless carbon capture technology is used. Several CTL plants have been proposed or announced internationally but many face technical, economic, and environmental barriers.
Art Noveau was a popular artistic style from 1890-1910 that influenced decoration, painting, fashion, and other areas. It used organic motifs like flowers, nature, and the human body. Secession architecture in the Czech Republic featured ornate details and flat surfaces. Notable Czech artist Alfonz Mucha worked in this style. In Spain, Antoni Gaudi was a pioneer of modernism and his highly original buildings like Casa Batlló used curved shapes inspired by nature. His most famous work, the unfinished Sagrada Familia in Barcelona, will be completed by 2026 using only donations.
The museum was opened in 2008 in a house built in 1905 to connect the Czech Republic and Germany through friendship and love. It aims to show photos so people can find images of deceased relatives, housing a large collection of original negatives in the original home of photographer Franz Seidel, with all furniture and equipment left as it was before his death. The project cost 55 million Czech crowns with support from several partners.
The Belem Tower was built in the 15th century as a defensive tower at the entrance to the Tagus River in Lisbon to protect the city. It was decorated in the Manueline style with symbols of King Manuel I's power. Over time, as defenses improved, it lost its defensive role but took on other uses like customs control, a telegraph station, and lighthouse. It also served briefly as a political prison. It is now a UNESCO World Heritage Site that represents an important part of Portugal's history during the Age of Discovery.
Portuguese sailors were pioneers of European exploration in the 15th-16th centuries, discovering routes around Africa to India and establishing trade with Asia and Japan. Vasco da Gama led the first fleet around Africa to India in 1498, opening an important trade route. Over subsequent decades, the Portuguese explored Southeast Asia and reached Japan, introducing European musical instruments, foods, and concepts like harmony that influenced Asian cultures.
The Portuguese colonial empire was the first global empire in history beginning in the 15th century. Key dates included Henry the Navigator reaching Cape Vert in 1415 and Bartolomeo Diaz reaching the Cape of Good Hope in 1488, unlocking the route to India. Treaties like the Treaty of Tordesillas in 1494 and Treaty of Zaragoza in 1529 divided the world between Portugal and Spain. Portugal's empire grew through explorers like Vasco da Gama and Pedro Álvares Cabral, and victories over other powers seeking control in places like India and Brazil. At its peak, Portugal's wealthy empire included Brazil, Africa, Asia, and small parts of North America, though it began dis
TURAZZA VOGUE
La pubblicazione nasce dalla collaborazione tra allievi dell'Istituto Turazza di Treviso, settore moda, e alcuni insegnanti. La finalità del progetto è quella di promuovere l'attività del CFP pubblicizzando i capolavori realizzati dai ragazzi durante l'anno 2013/2014.
Prof. Tiziano Pavan per la fotografia
Prof. Paola Bortoluzzi per la post produzione video
Prof. Anna Monforte per i testi
Prof. Giuseppina Menoncello, fashion consulting
Si ringrazia tutta la 3 moda
Executive Summary of Strategic Plan for Children's ServicesChelsea Eickert
1) Solid Ground is a nonprofit that aims to end poverty and homelessness. It provides housing and support services to over 300 children annually through its residential programs.
2) The organization created a strategic plan to better serve children by addressing needs identified through an assessment. Key issues included behavioral health, educational challenges, and lack of family/community support.
3) The plan focuses on four areas: health and well-being, education and career pathways, social connections, and out-of-school programming. It outlines goals, best practices, and outcome indicators to guide implementation over four stages from 2015-2020.
Sammet, Moore & Wilson.2013.Measuring Positive Development of Youth in Contex...Kara Sammet
This document describes the creation and validation of the Desired Results Developmental Profile-School Age (DRDP-SA), a strengths-based assessment tool designed to measure the positive development of youth in before- and after-school programs in California. The DRDP-SA assesses development across six domains using embedded observations during program activities. An initial validation study with a representative sample of 705 youth found the tool had good technical properties, with one domain (physical development) needing further refinement. The DRDP-SA provides a unique approach for evaluating programs' impact on youth well-being compared to narrow testing approaches.
This report of activities was submitted to the Michigan Interagency Coordinating Council from Early On Public Awareness. Time period of activities: 9/2/2010 through 10/15/2010.
This document presents research on the quality of childcare and its effects on school readiness and child development outcomes. It discusses different types of childcare (parental, home-based, center-based) and reviews literature showing that higher quality care leads to better school preparation. The authors conducted interviews and surveys of parents, teachers and administrators to examine factors like curriculum, funding, facilities, teacher qualifications, and state laws. The analysis found some correlations between these factors and parent perceptions of quality care. The conclusions call for more research on childcare's impacts to influence policymaking.
The document discusses the opportunities and challenges facing early childhood education systems given recent economic conditions and policy changes. It argues that states must transform their service, information, and management systems to take advantage of new federal funding opportunities while addressing budget cuts. An integrated early childhood data system is needed to evaluate programs, improve outcomes for at-risk children, and inform policy decisions.
This document introduces the Child Status Index (CSI), a tool developed to monitor outcomes for orphaned and vulnerable children (OVC) receiving support programs in sub-Saharan Africa. The CSI was created through participatory research with local communities to assess child well-being across 12 domains, including food, shelter, health, education and psychosocial support. It provides a standardized way for programs to evaluate how services are impacting children and to match children's needs with available support. The CSI is being implemented by various NGOs and national monitoring systems to improve outcomes for OVC.
The Difference You Make: Using Data to Highlight Equity for Allappliedsurveyresearch
The document discusses using data to highlight equity and accountability in social programs. It introduces Results-Based Accountability (RBA) and Collective Impact (CI) frameworks. A case study of a Kindergarten School Readiness Assessment in Santa Clara County is presented. Key concepts in RBA like community results, indicators, and performance measures are defined. The importance of aligning community and program data is emphasized. Early results from applying RBA and CI principles to improve kindergarten readiness in the Alum Rock School District are shared, showing the positive impact of preschool, family engagement, quality programs, and collaboration.
This document provides information from the Maryland State Department of Education's Division of Early Childhood Development newsletter from Winter 2015. It discusses Maryland being awarded a $15 million federal grant to expand access to pre-kindergarten programs. It also discusses the first administration of the Kindergarten Readiness Assessment for over 3,500 public school kindergarten students to measure school readiness. Finally, it introduces new developmental screening requirements for child care programs to assess children ages birth to five years old.
America's Backbone: Education and our YouthSahr Saffa
The document summarizes three programs that aim to reduce high school dropout rates: Communities in Schools, Career Academies, and GradNation. It finds that Career Academies is the best option as it offers comprehensive resources and pathways at the lowest cost of around $159,000 per 200 students. While all three programs have helped reduce dropout rates, the author recommends implementing Career Academies on a national level through the Department of Education due to its combination of impact and affordability.
This document proposes an evaluation plan for the Girls' Empowerment through Education and Health Activity (ASPIRE) project in Malawi. ASPIRE aims to improve education and health outcomes for 125,000 adolescent girls through activities like health education, teacher training, infrastructure improvements, and community sensitization. The evaluation will assess progress toward short-term outcomes after one year and gather feedback to improve program implementation before expanding to more schools. Key evaluation questions focus on outcomes achieved, unintended impacts, participant satisfaction, and implementation challenges encountered.
The document summarizes Smart Start, an early childhood program in North Carolina. It was created in 1993 to address the problem that many children were coming to school unprepared to learn. Smart Start works to improve early education programs, health screenings, and parental support. Evaluations show positive results, like more children in high-quality care and more receiving developmental screenings. Smart Start is funded through state appropriations and private funds raised locally. It aims to give all North Carolina children the opportunity to succeed.
The document summarizes Smart Start, an early childhood program in North Carolina. It was created in 1993 to address the problem that many children were coming to school unprepared to learn. Since then, North Carolina children have shown improved outcomes such as higher SAT scores and graduation rates. However, more work remains as the number of children in poverty has increased. Smart Start funds evidence-based programs to improve early education, health, and parenting support with the goal of giving all children an equal chance at success. Independent evaluations show positive results and a high return on investment.
The document summarizes the Learning Together program implemented across Vermont's parent child centers to help break intergenerational cycles of teen pregnancy, poverty, and other issues. Key findings from the first statewide evaluation show that among 170 participating parents, only two became pregnant again while enrolled. Participation improved academic achievement, work readiness, positive parenting skills, and consistent birth control use. Overall, the Learning Together program helps young parents build skills to prevent problems and achieve their dreams of healthy families.
Bantwana Child Profiling Report SZ FINAL Aug 2010Mavis Vilane
This document is a child profiling report prepared by Ivelina Borisova and Mavis Vilane in September 2009. It summarizes the results of a survey of vulnerable children in South Africa. The survey looked at children's demographic information, basic needs, health, psychosocial well-being, education, risks, and relationship to caregivers. Key findings included issues with food security, healthcare access, and psychosocial well-being for many children. The report provides detailed data on children's challenges and needs to help organizations better assist vulnerable youth.
Achieving Measurable Collective Impact with Results-Based Accountability - Co...Clear Impact
Achieving Measurable Collective Impact with Results-Based Accountability - Common Agenda
Partners from local, state and national initiatives are working together to understand how to meet the conditions of collective impact. Organizations often seek like-minded partners in order to reach common goals. Partnerships are formed. Meetings are held. But to what end? Stakeholders are convened from numerous programs aimed at support community well-being. These partnerships often find themselves continuing to focus on the outcomes for individuals, rather than on the collective impact of aligned partners throughout the community. Over time, meeting attendance falls and partners end up falling short of measurable results. What causes these well-intentioned efforts to flounder?
This workshop series will detail how partners and stakeholders can understand and implement the five conditions of collective impact by implementing the RBA framework. Each webinar will focus on a specific condition, allowing participants to have a deeper understanding of what it takes to practically apply RBA to meet that condition. The series will also include case studies that illustrate how partner organizations can align their efforts to achieve measurable community results with sustainable change. Participants are encouraged register for the full series, as each webinar will build upon the content from previous sessions.
Check out more videos and webinars on our website: https://clearimpact.com/resources/videos/
The document summarizes California's Desired Results program standards for early childhood education centers and preschools. The standards focus on achieving desired results or conditions of well-being for children and families. They address six domains: children's personal/social competence, effectiveness as learners, physical/motor skills, safety/health, and family support/goal achievement. Programs are evaluated based on measures within each domain to ensure they are meeting standards and improving practices.
EDUC – 3003 Week 2Assignment 1
Ashley Ann Abron
Walden University
1)Pages 35-41 of Assessing and Guiding Young Children's Development and Learning outline four general decision-making categories of assessment. Briefly, summarize each of these. Identify when assessment for each category is most likely to be effective.
When simplified teachers use assessment into two categories (1) to use the information to work with their students individually/group and (2) to monitor their progress. To avoid being overwhelmed with information the assessment process follows four general decision-making categories. The first is Assessing to Know Children Individually and as Members of a Group. The one thing that individuals and groups have in common is that they each have their own approaches to what and how they learn. Teachers will have to maneuver working with both and how their attitudes and habits can affect their learning. Knowing the abilities of children individually as well as in a group will help the teacher to aid in their educational development and interest. Assessment will be most effective when a teacher can discern when to assess a child individually and when to asses as a group. In addition a teacher should know the strength of the individual and the group. If an issue is clearly limited to an individual there is no need to assess the whole class. Teachers should also keep in mind what can influence a child's behavior such as the environment, time of day, materials available, and other children.
Another general decision-making category of assessment is Assign Progress Toward Expected Outcomes in Development and Learning. This means that as children progress through their education it is expected that they meet certain requirements. Children are expected to reach certain milestones not only in their growth and development but also in their academic’s studies. To ensure that children are reaching these milestones assessments should be frequent can cover various aspects for them to be the most effective. The third general decision-making categories of assessment are Expected Child Outcomes in Major Development Domains. When assessing student’s teachers should focus on the major domains of child development; physical, social, emotional, and cognitive. Each domain is important to the overall development of growth of a child. For teachers to successfully assess each domain of a child is to record the progress of each, even if it isn't required.
Conclusively Expected Child Outcomes Stated as Standards is the last category of the assessment decision making. Organization in child education from state departments to school districts have written out specific academic and developmental standards for children of every age group. Standards are directed towards content and performance from the general to the specific. In this regard, assessment is most effective when it is flexible and comprehensiv.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
1. January 2010
This PDF document and the supporting four page Child Outcomes Bulletin
2010 were constructed through a creative collaboration of Child Care Results,
the California Head Start Association, and the 15 participating Head Start
programs listed on page four. This analysis of child assessment data on
6,600 children was conducted by Child Care Results in the fall of 2009. The 4
page bulletin can be found www.caheadstart.org/ChildOutcomes2010. An
electronic version of this methodology can be found on-line at:
www.childcareresults.com/ChildOutcomes2010.
The analysis was conducted by Child Care Results alone. Any errors are
solely the responsibility of Child Care Results.
About Child Care Results
Child Care Results is specializes in helping child care organizations get the
most value from their child assessment, community assessment and survey
data.
Through careful analysis, we give decision makers the critical insights they
need to make informed decisions. Our results are always presented in
thoughtful and compelling visual formats to make it easy for everyone to
understand and use the information.
For more about Child Care Results, please visit www.childcareresults.com
About the California Head Start Association
The California Head Start Association is the unified voice providing leadership
and advocacy for the Head Start community. The California Head Start
Association is an important strategic partner representing Head Start interests
in California and the nation.
For more about the California Head Start Association, please visit
www.caheadstart.org
www.childcareresults.com
info@childcareresults.com | (800) 493-8621
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2. Supporting Methodology
Supplement to the Child Outcomes Bulletin 2010
The following methodology explains what we could not fit in the 4 page
bulletin and includes complete regressions results. It also clarifies
applications of and potential issues with the analysis.
1. Sample and Data Descriptions
2. Study Design
3. Policy Context
4. Results
5. Conclusion
1. Sample and Data Descriptions
Data were collected on 14,444 children participating in 15 different Head Start
programs from across the state of California. Children enrolled in Head Start are
typically from families at or below 100% of the Federal Poverty Level. In some
circumstances, a limited number of children from families above the poverty level
can be enrolled.
The children were assessed using the Desired Results Developmental Profile-
Revised (DRDP-R). Among California Head Start programs, the DRDP-R is typically
administered three times during the year: once within sixty days of enrollment,
again after three months, and the third time after another three months.
Generally, these assessments happen in the fall, winter, and spring respectively.
Frequently, however, children enroll at different times during the year resulting in a
slightly different timing for the assessments. For convenience, we will refer to the
first assessment as the fall assessment and the third assessment as the spring
assessment.
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3. About the DRDP-R
The Desired Results Developmental Profile– Revised (DRDP-R) was developed by
the California Department of Education. The DRDP-R assessment for Preschoolers
includes ten indicators. The indicators include fundamental areas of development
(e.g. Math, Literacy, Social and Interpersonal Skills) and have been aligned to the
Head Start outcome framework domains.
Table 1: Desired Results Indicators
Self Awareness & Self Children show self-awareness and a positive self-concept
Concept
Social Interpersonal Skills Children demonstrate effective social and interpersonal skills
Self Regulation Children demonstrate effective self-regulation in their behavior
Language Children show growing abilities in communication and language
Learning Children show interest, motivation, and persistence in their
approaches to learning
Cognitive Competence Children show cognitive competence and problem-solving skills
through play and daily activities
Math Children demonstrate competence in real-life mathematical
concepts
Literacy Children demonstrate emerging literacy skills
Motor Skills Children demonstrate an increased proficiency in motor skills
Safety and Health Children show an emerging awareness and practice of safe and
healthy behavior
There are thirty-nine measures grouped into these ten indicators. Within each
measure, children are assessed at one of four developmental levels or as “Not yet
at first level”.
Developmental Levels (listed from least developed to most developed):
0 – Not Yet at First Level
1 – Exploring
2 – Developing
3 – Building
4 – Integrating
In validating the tool, researchers combined the indicators into six developmental
domains (or indicator groupings). Since the validity of the tool was demonstrated
using scores combined at the level of the domains, the analysis was conducted at
that level and the Child Outcomes Bulletin reports on these six developmental
domains.
Developmental Domains (or Indicator Groupings)
1. Self Concept – Social Interpersonal Skills
2. Self Regulation – Safety and Health
3. Language – Literacy
4. Learning – Cognitive Competence
5. Math
6. Motor Skills
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4. The analysis does not include the English language learners measures, added in the
Head Start version of the DRDP-R. These measures specifically address the
development of language skills for non-English speaking children but were not
included in CDE studies validating the tool.
Research on the DRDP-R demonstrates that most children reach the third
developmental level by the end of preschool. While this is not a research based
indicator of school readiness, it is a useful informal benchmark. The analysis in this
bulletin uses that level of development as a benchmark to evaluate the program
effect of Head Start programs on Child Development. Throughout the bulletin, you
will see the language referring to the “top two developmental levels”. This
indicates that children are at or above the third developmental level (out of four)
within each developmental domain.
About the Data
Data were collected from the fall of 2008 and spring of 2009 assessment periods.
We did not collect data on the winter of 2009 assessments. Only children who were
assessed in both the fall of 2008 and the spring of 2009 were included in the
analysis.
The 15 agencies were geographically spread across the state of California and were
selected based on their use of the DRDP-R and their willingness to contribute data
to the project. Given the varying size of Head Start programs, four of the 15
accounted for 58% of the starting sample, with one program contributing 24% of
the sample. Given the non-random nature of the agency selection, these results
cannot be generalized to California Head Starts as a whole.
Table 2: Participating Agencies
Agency County # of Children % of Total
Community Action Commission of Santa Santa Barbara County 761 5%
Barbara
Community Action Partnership of Kern Kern County 1,575 11%
Community Action Partnership of San San Luis Obispo County 295 2%
Luis Obispo
Center for Community and Family Los Angeles County 988 7%
Services
Child Care Resource Center Los Angeles County 590 4%
Child Development Resources of Ventura Ventura County 821 6%
County
Kidango Santa Clara and Alameda 183 1%
Counties
MAAC Project San Diego County 803 6%
Merced County Office of Education Merced County 870 6%
Neighborhood House Association San Diego County 3,421 24%
Orange County Head Start Orange County 1,889 13%
Placer Community Action Council Placer County 297 2%
Santa Cruz Community Counseling Santa Cruz County 257 2%
Center
Sierra Cascade Family Opportunities Lassen, Modoc, Plumas, 159 1%
& Sierra Counties
Tulare County Office of Education Tulare County 1,535 11%
TOTAL 14,530
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5. Data were collected on all preschool children served by the Head Start programs.
However, the analysis focuses on typically developing children in center based
programs between the ages of 45 and 59 months. Excluding children who do not fit
into these categories or for whom key information was missing, a total of 6,619
were included in the analysis.
• Head Start guidelines mandate that at least 10% of the children served have
a special need. Given the significant impact that a special need can have on
a child’s development, limited data on the nature or severity of the special
need, and the fact that not all special needs children are assessed using the
DRDP-R, we chose to focus the analysis on typically developing children.
This eliminated 1,034 children from the analysis (this number is less than
10% because many special needs children are assessed using the DRDP
Access assessment tool rather than the DRDP-R).
• To maximize flexibility in serving diverse families and communities, Head
Start operates through different structures. Most commonly, children are
served in center-based programs, but children are also cared for through
licensed-family homes or in home-based programs. To simplify the analysis
we focused only on children served through center-based programs. This
criterion eliminated 977 children from the analysis.
• Although data were collected on all preschool age children (3 and 4 years-
old), the analysis only focused on children between the ages of 45 and 59
months at the time of the fall assessment. We focused the analysis on this
age group because they had a reasonable probability of having attended
preschool the prior year. 3,335 children fell out side this age range.
• For a variety of reasons, demographic information was missing on a number
of children. If the demographic information was incomplete for a child, their
data were dropped from the data set. This eliminated an additional 2,565
children – most of whom we did not have information on whether the
children had a special need.
The tables below describe the final sample of 6,619 children by gender,
ethnicity/language, age, and whether the children we enrolled in Head Start during
the previous year.
Table 3: By Gender and Prior Enrollment
Number and Percentage of Children in Final Sample
Prior Enrollment in Head Start
No Yes Total
Gender # % of row # % of row # % of column
Male 1,630 50% 1,605 50% 3,235 49%
Female 1,629 48% 1,755 52% 3,384 51%
Total 3,259 49% 3,360 51% 6,619 100%
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6. Table 4: By Ethnicity-Language and Prior Enrollment
Number and Percentage of Children in Final Sample
Prior Enrollment in Head Start
No Yes Total
% of % of % of
Ethnicity-Language # row # row # column
White-English 247 56% 195 44% 442 7%
African American-English 148 46% 173 54% 321 5%
Latino-English 549 54% 461 46% 1,010 15%
Latino-Spanish 1,796 49% 1,902 51% 3,698 56%
Other Ethnicity-Other Language 114 45% 137 55% 251 4%
Other Ethnicity-English 135 49% 142 51% 277 4%
Asian/Pacific Islander-East Asian
Languages 112 41% 161 59% 273 4%
Other Ethnicity-Spanish 158 46% 189 54% 347 5%
Total 3,259 49% 3,360 51% 6,619 100%
Table 5: By Age and Prior Enrollment
Number and Percentage of Children in Final Sample
Prior Enrollment in Head Start
No Yes Total
Age # % of row # % of row # % of column
45 to 47 months 866 71% 346 29% 1,212 18%
48 to 50 months 745 50% 756 50% 1,501 23%
51 to 53 months 666 46% 793 54% 1,459 22%
54 to 56 months 620 43% 836 57% 1,456 22%
57 to 59 months 362 37% 629 63% 991 15%
2. Study Design
Of the 6,619 children used in the primary analysis (all of whom were enrolled in the
2008-2009 school year), 3,360 (50.8%) were also enrolled in the Head Start
program during the previous school year (2007-2008). The fall 2008 assessment
results (that represent the children’s development level at the beginning of the
school year) were compared between those children new to the Head Start program
this year and those who were enrolled in the program last year. In essence, this
methodology captures the program effect of three year-old preschool. We compare
children entering 4 year-old preschool with and without 3 year-old preschool.
Children who enrolled in Head Start during the previous year act as our
experimental group and children new to the program this year serve as our
comparison group.
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7. Regression analysis controls for the variations in the characteristics of the two
groups and allows us to determine the statistical significance of the difference
between the two groups. To make the final analysis as easy to understand as
possible, we express the results in terms of the probability that a child would be in
the top two developmental levels with and without the previous year of enrollment.
This approach required the use of logit regressions.
To understand the design in a simplistic way, imagine two groups of children. All of
the children are 4 ½ years-old, have the same demographic characteristics, and are
recently enrolled in a Head Start program in the fall of 2008. The only observable
difference between the two groups is that one group was enrolled in the Head Start
program in the previous year – i.e. the fall of 2008 is the beginning of their second
year of Head Start participation. The second group is completely new to the Head
Start program. Comparing the DRDP-R assessment scores of these two groups of
children shortly after they start in the fall of 2008 provides a reasonable estimate of
the program effect of participating in Head Start during the previous year.
There are four potential issues with the analysis:
• Potential selection bias
• Potential inter-rater reliability issues
• Uncertainty of prior enrollment data
• Concerns over data use and accountability
Potential selection bias
The analysis presented below, unambiguously finds that children with prior
participation in Head Start have higher levels of development. However, other
factors besides prior enrollment in Head Start may influence the difference in
development levels between the two groups in the fall of 2008. For example,
children who enroll earlier in Head Start may have more involved parents or more
enriched home environments, which could lead to higher development. We test for
this effect by following both groups of children from the fall of 2008 to the spring of
2009 and find that both have the same rate of growth during the year. While this
would lead us to believe that the difference we see in the groups is the result of the
Head Start program, a limitation of the analysis is that we cannot be sure. A more
structured research design (which is not possible using operational data) would be
required to eliminate the possibility of selection bias.
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8. Table 6: Comparing the Growth of Children With and Without Prior Head
Start Enrollment
Reported in the DRDP-R Scale Score
p-value,
No Prior Prior All two
Domain Score Enrollment1 Enrollment2 Children tailed3
Self Mean of Fall '08 Scores 199.2 209.4 204.4
Awareness – Mean of Spring '09
Social Scores 231.7 238.8 235.3
Interpersonal
Skills Growth 32.4 29.4 30.9 <0.0000
Self Mean of Fall '08 Scores 200.6 211.3 206.0
Regulation- Mean of Spring '09
Safety and Scores 234.0 240.8 237.4
Health Growth 33.4 29.4 31.4 <0.0000
Mean of Fall '08 Scores 201.4 210.9 206.2
Language- Mean of Spring '09
Literacy Scores 231.7 239.0 235.4
Growth 30.3 28.1 29.2 <0.0000
Mean of Fall '08 Scores 200.6 209.8 205.3
Learning- Mean of Spring '09
Cognitive Scores 230.0 236.1 233.1
Growth 29.3 26.4 27.8 <0.0000
Mean of Fall '08 Scores 199.5 209.6 204.6
Mean of Spring '09
Math
Scores 230.4 237.5 234.0
Growth 30.8 28.0 29.3 <0.0000
Mean of Fall '08 Scores 202.3 214.5 208.5
Mean of Spring '09
Motor Skills
Scores 241.3 247.5 244.4
Growth 39.0 33.0 35.9 <0.0000
Note 1: Children not enrolled in Head Start during the 2007-2008 school year.
Note 2: Children enrolled in Head Start during the 2007-2008 school year.
Note 3: Results of a t-test on growth of children with "No Prior Enrollment" compared to children with "Prior
Enrollment" in Head Start. The hypothesized mean difference is 0.
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9. Potential inter-rater reliability issues
The DRDP-R assessment results are based on individual teacher observations and
assessments. Naturally, this raises the question of whether the assessments of two
different teachers can be compared to one another. Research on the DRDP-R tool
shows that it has high inter-rater reliability, between 0.87 and 0.90
(http://www.wested.org/desiredresults/training/questions.htm 11/1/2009). Still, it
may be argued that inter-rater reliability would be lower in less controlled field
settings where there may be variability in training, experience, or effort.
Even if it were possible to demonstrate that there are issues with inter-rater
reliability in our sample, we do not believe that would undermine the clear pattern
of children with prior enrollment having higher levels of enrollment. It may,
however, make the precise magnitude of the differences less reliable.
Uncertainty of prior enrollment data
The classification of children into those with and without prior Head Start
enrollment is based whether the child was enrolled in the same Head Start program
in the 2007-2008 school year. We know this with a high degree of certainty.
Unfortunately, it is possible that a child not enrolled in Head Start in 2007-2008
was enrolled another child development program or possibly even another Head
Start program. The impact on the analysis is that we may be underestimating the
program effect of these Head Start programs. If a significant number children
categorized as not having prior enrollment participated in other child development
programs than our estimates of Head Start’s program effect are too low.
This issue does not cause us any concern from the perspective of using this analysis
to inform public policy. Our ultimate conclusion is that this analysis provides
evidence that Head Starts provide quality child development supports and as such
should be supported. If we are underestimating the benefits of Head Start, that
would in no way undermine this conclusion (in fact it would reinforce the
conclusion).
Concerns over data use and accountability
In the wake of the national No Child Left Behind legislation, many in the early
childhood education (ECE) community fear that assessment data (or DRDP-R data
specifically) will evolve to be used as a tool for accountability. Or more bluntly from
the perspective of many in the ECE field – there is a fear that DRDP-R data will
someday be used to punish programs and teachers. Some may feel that this
analysis is a step in that direction.
We recognize that there is some risk that this type of analysis (using DRDP-R data
to evaluate program effect) may be misused within an accountability framework.
However, there is also tremendous value in giving programs and policy makers a
direct measure of program effect. Ultimately, we do not believe that it makes
sense to ignore the value that can be gleaned from DRDP-R data because of fears
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10. that the data may be misused.
We believe strongly that the DRDP-R tool is not appropriate for use as an
accountability tool. As an observation based tool, the DRDP-R is subjective by
nature. Using incentives or pressure on programs or teachers to meet certain
benchmarks could undermine the integrity of the results and jeopardize the
usefulness of assessment data. The DRDP-R tool and DRDP-R data can be very
useful for guiding program activities and understanding program results. But to
protect that value, DRDP-R outcomes should not be used for accountability. (This
does not apply to making programs accountable for adequate implementation of the
DRDP-R process).
Finally, we should note that this methodology requires very large sample sizes
making it impossible to perform on a class level.
3. Policy Context
Head Start is perhaps the most researched and evaluated federal program in
existence. As a result, there is ample academic literature on the benefits of Head
Start programs. The National Head Start Association has a good overview of the
academic findings on the benefits of Head Start. It can be found on-line at
http://www.nhsa.org/files/static_page_files/399E0881-1D09-3519-
AD56452FC44941C3/BenefitsofHSandEHS.pdf . More broadly, the RAND
Corporation recently did a review of the academic literature on the impact of quality
preschool programs:
“[A] review of the rigorous evaluations of high-quality preschool programs
demonstrates that well-designed programs that serve children one or two years
before kindergarten entry can
• improve measures of school readiness,
• raise performance on academic achievement tests in the early elementary
grades,
• generate sustained effects on academic achievement into the middle-
school years …
• [reduce] special-education use and grade repetition and
• [increase] rates of high-school graduation”
Karoly, Lynn A., Preschool Adequacy and Efficiency in California: Issues, Policy Options, and Recommendations, Santa Monica, Calif.: RAND Corporation,
2009
The California Head Start Child Outcomes Bulletin was not designed as an academic
study nor was it designed to contribute to the academic literature. What is missing
at the policy and program level are adequate operational metrics of program effect.
Quality programs are the key to achieving the benefits identified in the academic
literature. But how can we be assured of quality in a program stretching the
breadth of the largest state in the Union?
Full academic studies cannot reasonably be performed on continuing operations of
significant scale. As a result, policy makers rely on less reliable information as a
matter of course. Sound operational analysis must fill the gap between academic
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11. capabilities to evaluate and the need run a program on a day to day basis. The
limitations of this analysis (as addressed in the Analytical Design section) do reduce
the reliability of the results, but less reliable does not translate into unreliable. The
diagram below presents a reliability continuum with which to put this analysis in a
proper perspective.
Reliability Continuum
Ranking of the Reliability of Various Types of
Information Used to Inform Policy Making
Qualitative or Indirect Quasi Experimental Design Perfectly Reliable
Analysis • Most Preschool Studies • Does not exist
• Measuring program
inputs (e.g. teacher
Anecdotal or Heuristic Pre-Experimental Design Full Experimental Design
education, program
• Politically powerful and regulations) • Our analysis • Academic gold stan-
notoriously unreliable dard
We believe that the most powerful argument in favor of Head Start comes from a
combination of academic research, operational analysis, and qualitative
information. It is important that the Child Outcomes Bulletin not be presented as
the equivalent of academic research, but also that the value of operational analysis
not be discounted.
Despite the inevitable imperfections of operational analysis, these results do
support the conclusion that California Head Starts participating in the bulletin are
having a positive impact on child development. That positive impact is evidence
that these are quality programs and the academic literature shows clear benefits to
children and society resulting from quality child development programs. Therefore,
policy makers should endeavor to support Head Start.
4. Results
Below are detailed logit regression results for each domain. We also show the
calculations for how the results were interpreted. The odds of a child being in the
top two developmental levels are calculated for three scenarios:
1. The Typical Odds for a child in the sample,
2. The No Prior Enrollment Odds for a child who was not enrolled in Head
Start in the previous year.
3. The Prior Enrollment Odds for a child who was enrolled in Head Start in
the previous year.
The difference between the three calculations is in the treatment of the prior
enrollment variable. For the Typical Odds calculation, the percentage of sample is
set to 51%. But for the No Prior Enrollment Odds and the Prior Enrollment Odds
scenario calculations it is set to 0 and 1 respectively. These changes are bolded.
The first step in the calculation is multiplying the estimates from the regression
results (see the first row) by the Percentage of the sample. We show this as
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12. equation A. The calculations where this equation are used are in the orange box in
the “Interpreting the Lang-Lit Logit Regression” table below.
Equation A: [Estimate] X [Percentage of Sample]
Equation B is then used to calculate the Odds in the three scenarios. “e” is the
base of the natural logarithm equal to 2.71828182845904. The use of this
equation is highlighted using the purple circles in the “Interpreting the Lang-Lit
Logit Regression” table below.
Equation B: e raised to the power of the sum of ([Estimate] X [Percentage of
Sample])
Roncek, Dennis W. “Using Logit Coefficients to Obtain the Effects of Independent Variables on Changes in Probabilities” Social Forces; Dec 1991; 70, 2.
Finally, the odds are converted to probabilities using equation C. The use of
equation C is bracketed in green below.
Equation C: Probability = [Odds] / ([Odds] + 1)
The probabilities and percentage point difference in the black box are the numbers
presented in the Child Outcomes Bulletin.
Lang-Lit Logit Regression Results
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
n=6436 Const.B0 Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Results
Estimate -3.375 0.277 -0.827 -0.254 0.061 -0.467 -0.403 -0.293 -0.809 1.103 0.281 0.868 1.259 1.515
Standard Error 0.239 0.087 0.172 0.189 0.232 0.249 0.250 0.244 0.307 0.100 0.195 0.183 0.177 0.183 An indicator was
t(6410) -14.107 3.195 -4.808 -1.338 0.265 -1.873 -1.615 -1.202 -2.639 11.011 1.443 4.754 7.107 8.289 included in the
p-level 0.000 0.001 0.000 0.181 0.791 0.061 0.106 0.229 0.008 0.000 0.149 0.000 0.000 0.000 analysis to
-95%CL -3.844 0.107 -1.165 -0.625 -0.393 -0.956 -0.893 -0.771 -1.410 0.907 -0.101 0.510 0.912 1.157 account for the
+95%CL -2.906 0.448 -0.490 0.118 0.515 0.022 0.086 0.185 -0.208 1.300 0.663 1.226 1.606 1.874 Head Start
program each
Wald's Chi-square 199.014 10.205 23.119 1.790 0.070 3.507 2.607 1.445 6.962 121.252 2.081 22.604 50.506 68.709
child
p-level 0.000 0.001 0.000 0.181 0.791 0.061 0.106 0.229 0.008 0.000 0.149 0.000 0.000 0.000 participated in.
Odds ratio (unit ch) 0.034 1.320 0.437 0.776 1.063 0.627 0.668 0.746 0.445 3.014 1.324 2.382 3.522 4.551 For purposes of
-95%CL 0.021 1.113 0.312 0.535 0.675 0.384 0.409 0.462 0.244 2.476 0.904 1.665 2.489 3.180 confidentiality,
+95%CL 0.055 1.565 0.613 1.125 1.674 1.022 1.090 1.203 0.812 3.668 1.940 3.406 4.985 6.513 these individual
Odds ratio (range) 1.320 0.437 0.776 1.063 0.627 0.668 0.746 0.445 3.014 1.324 2.382 3.522 4.551 results are not
provided.
-95%CL 1.113 0.312 0.535 0.675 0.384 0.409 0.462 0.244 2.476 0.904 1.665 2.489 3.180
+95%CL 1.565 0.613 1.125 1.674 1.022 1.090 1.203 0.812 3.668 1.940 3.406 4.985 6.513
Interpreting the Lang-Lit Logit Regression
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
Constant Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Result
Percentage of Sample 100% 51% 56% 15% 5% 5% 4% 4% 4% 51% 23% 22% 22% 15%
Typical Odds -- Odds
of a typical child being in
the top 2 developmental
levels: Percentage of Sample X Estimate =
0.075 -3.375 0.142 -0.461 -0.039 0.003 -0.024 -0.017 -0.012 -0.031 0.558 0.064 0.191 0.278 0.225 -0.091
No Prior Enrollment
Odds -- Odds of a child
with no prior enrollment
being in the top 2
developmental levels:
0.043 -3.375 0.142 -0.461 -0.039 0.003 -0.024 -0.017 -0.012 -0.031 0.000 0.064 0.191 0.278 0.225 -0.091
Prior Enrollment
Odds -- Odds of a child
with prior enrollment
being in the top 2
developmental levels:
0.129 -3.375 0.142 -0.461 -0.039 0.003 -0.024 -0.017 -0.012 -0.031 1.103 0.064 0.191 0.278 0.225 -0.091
Predicted Actual
Odds Probability Probability
Typical 0.075 7% 10%
No Prior Enrollment
Prior Enrollment
0.043
0.129
4%
11%
5%
15% Calculated using
Equation A
Percentage Point Increase 7%
Ratios 3.014 2.783 3.120
Calculated using
Calculated using Equation C
Equation B
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13. Self-Soc Logit Regression Results
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
n=6500 Const.B0 Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Results
Estimate -2.668 0.273 -0.478 -0.061 0.167 -0.266 -0.673 -0.208 -0.776 1.037 0.173 0.736 0.892 1.239
Standard Error 0.193 0.072 0.149 0.161 0.203 0.212 0.233 0.214 0.278 0.080 0.144 0.136 0.134 0.139 An indicator was
t(6410) -13.809 3.793 -3.213 -0.381 0.821 -1.251 -2.882 -0.972 -2.794 12.984 1.201 5.418 6.646 8.888 included in the
p-level 0.000 0.000 0.001 0.703 0.412 0.211 0.004 0.331 0.005 0.000 0.230 0.000 0.000 0.000 analysis to
-95%CL -3.047 0.132 -0.770 -0.377 -0.231 -0.682 -1.131 -0.627 -1.320 0.881 -0.110 0.470 0.629 0.966 account for the
+95%CL -2.289 0.415 -0.186 0.254 0.565 0.151 -0.215 0.211 -0.231 1.194 0.456 1.003 1.156 1.512 Head Start
program each
Wald's Chi-square 190.683 14.389 10.322 0.145 0.674 1.566 8.308 0.945 7.805 168.593 1.441 29.351 44.167 79.005
child
p-level 0.000 0.000 0.001 0.703 0.412 0.211 0.004 0.331 0.005 0.000 0.230 0.000 0.000 0.000 participated in.
Odds ratio (unit ch) 0.069 1.314 0.620 0.941 1.182 0.767 0.510 0.812 0.460 2.822 1.189 2.088 2.441 3.451 For purposes of
-95%CL 0.048 1.141 0.463 0.686 0.793 0.505 0.323 0.534 0.267 2.413 0.896 1.600 1.876 2.626 confidentiality,
+95%CL 0.101 1.514 0.830 1.289 1.759 1.163 0.806 1.235 0.793 3.300 1.578 2.725 3.176 4.535 these individual
Odds ratio (range) 1.314 0.620 0.941 1.182 0.767 0.510 0.812 0.460 2.822 1.189 2.088 2.441 3.451 results are not
provided.
-95%CL 1.141 0.463 0.686 0.793 0.505 0.323 0.534 0.267 2.413 0.896 1.600 1.876 2.626
+95%CL 1.514 0.830 1.289 1.759 1.163 0.806 1.235 0.793 3.300 1.578 2.725 3.176 4.535
Interpreting the Self-Soc Logit Regression
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
Constant Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Result
Percentage of Sample 100% 51% 56% 15% 5% 5% 4% 4% 4% 51% 23% 22% 22% 15%
Typical Odds -- Odds
of a typical child being in
the top 2 developmental
levels: Percentage of Sample X Estimate =
0.141 -2.668 0.140 -0.267 -0.009 0.008 -0.014 -0.028 -0.009 -0.030 0.527 0.039 0.162 0.197 0.184 -0.189
No Prior Enrollment
Odds -- Odds of a child
with no prior enrollment
being in the top 2
developmental levels:
0.084 -2.668 0.140 -0.267 -0.009 0.008 -0.014 -0.028 -0.009 -0.030 0.000 0.039 0.162 0.197 0.184 -0.189
Prior Enrollment
Odds -- Odds of a child
with prior enrollment
being in the top 2
developmental levels:
0.236 -2.668 0.140 -0.267 -0.009 0.008 -0.014 -0.028 -0.009 -0.030 1.037 0.039 0.162 0.197 0.184 -0.189
Predicted Actual
Odds Probability Probability
Typical 0.141 12% 16%
No Prior Enrollment 0.084 8% 9%
Prior Enrollment 0.236 19% 23%
Percentage Point Increase 11%
Ratios 2.822 2.474 2.673
Reg-Sh Logit Regression Results
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
n=6497 Const.B0 Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Results
Estimate -3.660 0.425 -0.310 0.000 0.045 -0.168 -0.371 -0.255 -0.571 0.871 0.344 0.744 0.870 1.133
Standard Error 0.394 0.065 0.085 0.000 0.164 0.164 0.171 0.174 0.215 0.070 0.122 0.118 0.117 0.124 An indicator was
t(6410) -9.291 6.523 -3.645 -0.051 0.274 -1.029 -2.172 -1.471 -2.660 12.513 2.808 6.286 7.402 9.177 included in the
p-level 0.000 0.000 0.000 0.959 0.784 0.304 0.030 0.141 0.008 0.000 0.005 0.000 0.000 0.000 analysis to
-95%CL -4.432 0.297 -0.476 0.000 -0.277 -0.489 -0.706 -0.595 -0.992 0.734 0.104 0.512 0.639 0.891 account for the
+95%CL -2.888 0.552 -0.143 0.000 0.367 0.152 -0.036 0.085 -0.150 1.007 0.584 0.976 1.100 1.376 Head Start
program each
Wald's Chi-square 86.322 42.553 13.284 0.003 0.075 1.059 4.717 2.163 7.075 156.577 7.885 39.520 54.793 84.214
child
p-level 0.000 0.000 0.000 0.959 0.784 0.304 0.030 0.141 0.008 0.000 0.005 0.000 0.000 0.000 participated in.
Odds ratio (unit ch) 0.026 1.529 0.734 1.000 1.046 0.845 0.690 0.775 0.565 2.388 1.410 2.104 2.386 3.106 For purposes of
-95%CL 0.012 1.346 0.621 1.000 0.758 0.613 0.494 0.551 0.371 2.084 1.109 1.668 1.895 2.438 confidentiality,
+95%CL 0.056 1.738 0.867 1.000 1.444 1.164 0.965 1.089 0.861 2.737 1.793 2.653 3.004 3.957 these individual
Odds ratio (range) 1.529 0.734 1.000 1.046 0.845 0.690 0.775 0.565 2.388 1.410 2.104 2.386 3.106 results are not
provided.
-95%CL 1.346 0.621 1.000 0.758 0.613 0.494 0.551 0.371 2.084 1.109 1.668 1.895 2.438
+95%CL 1.738 0.867 1.000 1.444 1.164 0.965 1.089 0.861 2.737 1.793 2.653 3.004 3.957
Interpreting the Reg-Sh Logit Regression
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
Constant Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Result
Percentage of Sample 100% 51% 56% 15% 5% 5% 4% 4% 4% 51% 23% 22% 22% 15%
Typical Odds -- Odds
of a typical child being in
the top 2 developmental
levels: Percentage of Sample X Estimate =
0.216 -3.660 0.218 -0.173 0.000 0.002 -0.009 -0.015 -0.011 -0.022 0.441 0.078 0.164 0.192 0.169 1.092
No Prior Enrollment
Odds -- Odds of a child
with no prior enrollment
being in the top 2
developmental levels:
0.139 -3.660 0.218 -0.173 0.000 0.002 -0.009 -0.015 -0.011 -0.022 0.000 0.078 0.164 0.192 0.169 1.092
Prior Enrollment
Odds -- Odds of a child
with prior enrollment
being in the top 2
developmental levels:
0.331 -3.660 0.218 -0.173 0.000 0.002 -0.009 -0.015 -0.011 -0.022 0.871 0.078 0.164 0.192 0.169 1.092
Predicted Actual
Odds Probability Probability
Typical 0.216 18% 21%
No Prior Enrollment 0.139 12% 13%
Prior Enrollment 0.331 25% 29%
Percentage Point Increase 13%
Ratios 2.388 2.043 2.241
13 of 16
14. Lrn-Cog Logit Regression Results
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
n=6506 Const.B0 Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Results
Estimate -2.651 0.241 -0.703 -0.201 -0.230 -0.609 -0.829 -0.257 -0.960 1.003 0.278 0.899 1.090 1.299
Standard Error 0.191 0.071 0.146 0.160 0.207 0.209 0.227 0.206 0.269 0.078 0.144 0.136 0.134 0.141 An indicator was
t(6410) -13.911 3.406 -4.818 -1.256 -1.115 -2.909 -3.652 -1.250 -3.567 12.910 1.921 6.592 8.112 9.243 included in the
p-level 0.000 0.001 0.000 0.209 0.265 0.004 0.000 0.211 0.000 0.000 0.055 0.000 0.000 0.000 analysis to
-95%CL -3.024 0.102 -0.988 -0.516 -0.635 -1.019 -1.274 -0.661 -1.487 0.851 -0.006 0.631 0.827 1.024 account for the
+95%CL -2.277 0.379 -0.417 0.113 0.175 -0.198 -0.384 0.146 -0.432 1.156 0.561 1.166 1.353 1.575 Head Start
program each
Wald's Chi-square 193.521 11.603 23.213 1.578 1.243 8.460 13.337 1.562 12.725 166.678 3.691 43.455 65.813 85.435
child
p-level 0.000 0.001 0.000 0.209 0.265 0.004 0.000 0.211 0.000 0.000 0.055 0.000 0.000 0.000 participated in.
Odds ratio (unit ch) 0.071 1.272 0.495 0.818 0.794 0.544 0.436 0.773 0.383 2.728 1.320 2.456 2.974 3.667 For purposes of
-95%CL 0.049 1.108 0.372 0.597 0.530 0.361 0.280 0.517 0.226 2.342 0.994 1.880 2.285 2.784 confidentiality,
+95%CL 0.103 1.461 0.659 1.119 1.191 0.820 0.681 1.157 0.649 3.177 1.752 3.209 3.870 4.830 these individual
Odds ratio (range) 1.272 0.495 0.818 0.794 0.544 0.436 0.773 0.383 2.728 1.320 2.456 2.974 3.667 results are not
provided.
-95%CL 1.108 0.372 0.597 0.530 0.361 0.280 0.517 0.226 2.342 0.994 1.880 2.285 2.784
+95%CL 1.461 0.659 1.119 1.191 0.820 0.681 1.157 0.649 3.177 1.752 3.209 3.870 4.830
Interpreting the Lrn-Cog Logit Regression
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
Constant Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Result
Percentage of Sample 100% 51% 56% 15% 5% 5% 4% 4% 4% 51% 23% 22% 22% 15%
Typical Odds -- Odds
of a typical child being in
the top 2 developmental
levels: Percentage of Sample X Estimate =
0.151 -2.651 0.123 -0.392 -0.031 -0.011 -0.032 -0.034 -0.011 -0.037 0.509 0.063 0.197 0.241 0.193 -0.021
No Prior Enrollment
Odds -- Odds of a child
with no prior enrollment
being in the top 2
developmental levels:
0.091 -2.651 0.123 -0.392 -0.031 -0.011 -0.032 -0.034 -0.011 -0.037 0.000 0.063 0.197 0.241 0.193 -0.021
Prior Enrollment
Odds -- Odds of a child
with prior enrollment
being in the top 2
developmental levels:
0.247 -2.651 0.123 -0.392 -0.031 -0.011 -0.032 -0.034 -0.011 -0.037 1.003 0.063 0.197 0.241 0.193 -0.021
Predicted Actual
Odds Probability Probability
Typical 0.151 13% 17%
No Prior Enrollment 0.091 8% 10%
Prior Enrollment 0.247 20% 24%
Percentage Point Increase 12%
Ratios 2.728 2.386 2.502
Math Logit Regression Results
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
n=6414 Const.B0 Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Results
Estimate -3.065 0.197 -0.937 -0.369 -0.131 -0.869 -0.594 -0.384 -0.847 1.027 0.455 0.791 1.207 1.454
Standard Error 0.230 0.085 0.167 0.184 0.229 0.256 0.250 0.239 0.290 0.097 0.184 0.178 0.172 0.178 An indicator was
t(6410) -13.317 2.328 -5.600 -2.003 -0.574 -3.390 -2.377 -1.606 -2.917 10.641 2.465 4.436 7.010 8.178 included in the
p-level 0.000 0.020 0.000 0.045 0.566 0.001 0.017 0.108 0.004 0.000 0.014 0.000 0.000 0.000 analysis to
-95%CL -3.517 0.031 -1.265 -0.731 -0.581 -1.372 -1.085 -0.852 -1.416 0.838 0.093 0.441 0.870 1.106 account for the
+95%CL -2.614 0.363 -0.609 -0.008 0.318 -0.367 -0.104 0.085 -0.278 1.217 0.816 1.141 1.545 1.803 Head Start
program each
Wald's Chi-square 177.337 5.419 31.360 4.010 0.329 11.491 5.652 2.579 8.507 113.224 6.075 19.676 49.138 66.880
child
p-level 0.000 0.020 0.000 0.045 0.566 0.001 0.017 0.108 0.004 0.000 0.014 0.000 0.000 0.000 participated in.
Odds ratio (unit ch) 0.047 1.218 0.392 0.691 0.877 0.419 0.552 0.681 0.429 2.794 1.575 2.206 3.344 4.282 For purposes of
-95%CL 0.030 1.032 0.282 0.482 0.560 0.254 0.338 0.426 0.243 2.312 1.097 1.555 2.386 3.022 confidentiality,
+95%CL 0.073 1.438 0.544 0.992 1.374 0.693 0.901 1.088 0.758 3.376 2.262 3.129 4.687 6.068 these individual
Odds ratio (range) 1.218 0.392 0.691 0.877 0.419 0.552 0.681 0.429 2.794 1.575 2.206 3.344 4.282 results are not
provided.
-95%CL 1.032 0.282 0.482 0.560 0.254 0.338 0.426 0.243 2.312 1.097 1.555 2.386 3.022
+95%CL 1.438 0.544 0.992 1.374 0.693 0.901 1.088 0.758 3.376 2.262 3.129 4.687 6.068
Interpreting the Math Logit Regression
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
Constant Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Result
Percentage of Sample 100% 51% 56% 15% 5% 5% 4% 4% 4% 51% 23% 22% 22% 15%
Typical Odds -- Odds
of a typical child being in
the top 2 developmental
levels: Percentage of Sample X Estimate =
0.083 -3.065 0.101 -0.524 -0.057 -0.006 -0.045 -0.025 -0.016 -0.033 0.522 0.103 0.174 0.268 0.216 -0.106
No Prior Enrollment
Odds -- Odds of a child
with no prior enrollment
being in the top 2
developmental levels:
0.049 -3.065 0.101 -0.524 -0.057 -0.006 -0.045 -0.025 -0.016 -0.033 0.000 0.103 0.174 0.268 0.216 -0.106
Prior Enrollment
Odds -- Odds of a child
with prior enrollment
being in the top 2
developmental levels:
0.137 -3.065 0.101 -0.524 -0.057 -0.006 -0.045 -0.025 -0.016 -0.033 1.027 0.103 0.174 0.268 0.216 -0.106
Predicted Actual
Odds Probability Probability
Typical 0.083 8% 11%
No Prior Enrollment 0.049 5% 6%
Prior Enrollment 0.137 12% 16%
Percentage Point Increase 7%
Ratios 2.794 2.577 2.818
14 of 16
15. Mot Logit Regression Results
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
n=6538 Const.B0 Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Results
Estimate -0.895 0.044 -0.562 -0.218 -0.054 -0.219 -0.157 -0.273 -0.709 0.792 0.316 0.597 0.735 1.121
Standard Error 0.138 0.054 0.117 0.130 0.165 0.163 0.173 0.168 0.187 0.056 0.088 0.088 0.088 0.098 An indicator was
t(6410) -6.504 0.817 -4.800 -1.674 -0.326 -1.340 -0.907 -1.624 -3.803 14.185 3.590 6.779 8.347 11.490 included in the
p-level 0.000 0.414 0.000 0.094 0.744 0.180 0.364 0.104 0.000 0.000 0.000 0.000 0.000 0.000 analysis to
-95%CL -1.165 -0.062 -0.791 -0.473 -0.377 -0.538 -0.496 -0.603 -1.075 0.683 0.144 0.424 0.562 0.930 account for the
+95%CL -0.625 0.150 -0.332 0.037 0.269 0.101 0.182 0.057 -0.344 0.901 0.489 0.769 0.908 1.312 Head Start
program each
Wald's Chi-square 42.302 0.667 23.036 2.801 0.106 1.797 0.824 2.638 14.461 201.219 12.891 45.961 69.681 132.018
child
p-level 0.000 0.414 0.000 0.094 0.744 0.180 0.364 0.104 0.000 0.000 0.000 0.000 0.000 0.000 participated in.
Odds ratio (unit ch) 0.408 1.045 0.570 0.804 0.948 0.804 0.855 0.761 0.492 2.208 1.372 1.816 2.086 3.068 For purposes of
-95%CL 0.312 0.940 0.453 0.623 0.686 0.584 0.609 0.547 0.341 1.979 1.155 1.528 1.755 2.534 confidentiality,
+95%CL 0.535 1.162 0.717 1.038 1.309 1.106 1.200 1.058 0.709 2.463 1.631 2.158 2.478 3.715 these individual
Odds ratio (range) 1.045 0.570 0.804 0.948 0.804 0.855 0.761 0.492 2.208 1.372 1.816 2.086 3.068 results are not
provided.
-95%CL 0.940 0.453 0.623 0.686 0.584 0.609 0.547 0.341 1.979 1.155 1.528 1.755 2.534
+95%CL 1.162 0.717 1.038 1.309 1.106 1.200 1.058 0.709 2.463 1.631 2.158 2.478 3.715
Interpreting the Mot Logit Regression
Asian/Pacific Other
African Other Islander-East Other Ethnicity-
Latino- Latino- American- Ethnicity- Asian Ethnicity- Other Prior 48 to 50 51 to 53 54 to 56 57 to 59 Individual
Constant Female Spanish English English Spanish Languages English Language Enrollment Months Old Months Old Months Old Months Old Agency Result
Percentage of Sample 100% 51% 56% 15% 5% 5% 4% 4% 4% 51% 23% 22% 22% 15%
Typical Odds -- Odds
of a typical child being in
the top 2 developmental
levels: Percentage of Sample X Estimate =
0.724 -0.895 0.023 -0.314 -0.033 -0.003 -0.011 -0.007 -0.011 -0.027 0.402 0.072 0.132 0.162 0.168 0.022
No Prior Enrollment
Odds -- Odds of a child
with no prior enrollment
being in the top 2
developmental levels:
0.484 -0.895 0.023 -0.314 -0.033 -0.003 -0.011 -0.007 -0.011 -0.027 0.000 0.072 0.132 0.162 0.168 0.022
Prior Enrollment
Odds -- Odds of a child
with prior enrollment
being in the top 2
developmental levels:
1.069 -0.895 0.023 -0.314 -0.033 -0.003 -0.011 -0.007 -0.011 -0.027 0.792 0.072 0.132 0.162 0.168 0.022
Predicted Actual
Odds Probability Probability
Typical 0.724 42% 43%
No Prior Enrollment 0.484 33% 32%
Prior Enrollment 1.069 52% 54%
Percentage Point Increase 19%
Ratios 2.208 1.584 1.666
Effect Size Calculations and Comparisons
To understand the relative impact of these results and to compare them to the
results found in other preschool program, the “effect size” was calculated. The
effect size is the change attributed to the program divided by the standard
deviation of the comparison group. In the example of the Language and Literacy
domain, the change attributed to the program is the 7 percentage point (0.0734)
increase in children in the top two developmental levels. The standard deviation for
the comparison group was 0.2165. The effect size for Language and Literacy is
0.339. The table below shows the effect sizes for all of the DRDP-R domains:
DRDP-R Domain Effect Size
Lang Lit 0.339
Self Soc 0.405
Reg Sh 0.377
Lrn Cog 0.391
Math 0.323
Mot 0.406
Overall the effect size ranges between 0.323 to 0.406. These results are
comparable to those found for other preschool programs. Below are the effect sizes
15 of 16
16. from studies of other preschool programs:
Project Effect Size Length of Preschool
Range
Head Start National Study 0.147 to 0.319 one year of preschool
Tulsa Head Start Program 0.334 to 0.514 one year of preschool
Tulsa Public School Pre-K Program 0.355 to 0.985 one year of preschool
Abecedarian Project 1.08 average three years of preschool
Perry Preschool Study 0.77 to 1.16 two years of preschool
Gormley, Jr., William T.; Phillips, Deborah; Gayer, Ted “Preschool Programs Can Boost School Readiness” Georgetown University.
http://www.crocus.georgetown.edu/reports/scilong.pdf 12/01/09.
5. Conclusion
The California Head Start Child Outcomes Bulletin provides imperfect but
reasonable estimates of the program effect of Head Start programs on child
development. Significant improvements in child development were found to
correspond with participation in Head Start programs. The findings, based on
operational analysis, are not as robust as most academic studies, but are superior
to most of the information available to program administrators and policy makers
on an on-going basis. The observed program impact is evidence that the Head
Starts included in the analysis are providing quality early education comparable to
other quality programs. The benefits of quality early education experiences are well
documented. That body of evidence combined with this analysis provides amble
reason to support California Head Starts.
16 of 16