Head Start Data
The Head Start program was conceived in 1964 to provide equal access to education for low-income families. As part of President Johnson’s War on Poverty, Head Start was intended to level the playing field in education. In addition to supporting families, preparing students for school is one of the primary goals of the program, possible through Head Start data collection.
In 2007, Congress passed the Improving Head Start for School Readiness Act, requiring Head Start and Early Head Start Programs to turn to data-driven decision-making. Of course, the term “data-driven“ begs some definition. What exactly qualifies as acceptable data and how is it used to improve Head Start programs and prepare children for their educational journeys?
Fortunately, the Head Start program has come up with some guidelines for quality data. Let’s look more closely at how Head Start characterizes and identifies quality data.
Guidelines for Data from Project Head Start
Infants and toddlers are undergoing a period of rapid growth and development. As such, Head Start data collection is imperative for early education programs to evolve with the changing needs of children and families. The Head Start program uses a combination of qualitative and quantitative data for program direction and evaluation. But what are the characteristics of qualitative and quantitative data?
- Qualitative Data– Qualitative data refers to data collected through questionnaires, interviews, observations, and focus groups. With qualitative data, there are no numerical values. Instead, data collection occurs in the form of a narrative, and reviewers need to look for common links and themes to evaluate it properly.
- Quantitative Data– Quantitative data uses numerical values to quantify a value. For instance, parents in a Head Start program may be asked to rate specific aspects of their experience on a scale from one to five.
Head Start programs can use the collected data to make decisions about how and where to direct their limited resources for the best outcomes. Although each program collects its own data, Head Start gives the following as examples of data collection methods programs can use:
- Family referrals
- The utilization of community resources by families
- Safety checks
- Finance and budget
- Program self-assessments
- Pregnant mother, child, and family health (includes physical health, mental health, oral health, and nutrition)
- Attendance of children, staff, and families and the length of time in which they stay in the program
- Developmental screenings and ongoing assessments of school readiness and early intervention outcomes for infants and toddlers with disabilities
- Home child visit and group care quality
- Children and family demographics
- Family partnership agreement goals and the progress made toward meeting them
Of course, Head Start data collection needs some guidelines to ensure the results reflect the needs of the community.
Assuring Quality in Head Start Data Collection
Of course, whatever data is collected, the process needs to be approached scientifically for the best potential impact on program development. But how does the program differentiate the quality level of the collected data? Head Start has identified six quality data characteristics:
- Relevant
- Timely
- Accurate
- Complete
- Valid
- Reliable
These are more than mere buzz words; they form the standards by which Project Head Start collects and uses data. As such, the program provides definitions and examples for each of these concepts as summarized in the following chart:
Quality Data Characteristic Definition and Reason for Inclusion Examples Relevant · Relevant data is connected to the reason for which it is being collected, meaning that the reason for data collection must be specifically identified and appropriate to the program’s goals.
· In Head Start programs, data should reflect how well the program supports children and families.
· Programs will need to identify which data are most relevant to determine the program’s effectiveness in the community it serves.· At the child level, if a program wants to evaluate how a child’s receptive and expressive language skills are developing, the data collected should reflect these specific areas of language development.
· At the program level, a grantee wanting to monitor how group education or home visits affect language development would collect data on how adults engage with children during group education and home care visits.Timely · Data collection should happen as quickly as possible after an activity and be made available for program development.
· Keeping data updated lends credibility to an institution’s process of data analysis and implementation.
· By providing continuous updates on a child’s development, staff can tailor their responses to the child’s current needs, interests, and abilities.· A program uses a web-based management information system to provide immediate updates on health information, including shot records and well-child checks. This quick reporting can improve the program’s ability to provide quality health care. Accurate · Presenting accurate data means the data is clear, in adequate detail, and free from errors.
· Accurate data also means timely delivery of data and ongoing updates.· Staff who are working with children provide written notes that are regularly updated. The notes contain only facts and accurately represent the order in which an event occurred. Notes should include details such as what time the event happened, where it occurred, and how long it took to complete. Observations are recorded as close to the actual event as possible. Complete · Data collection needs regular monitoring to ensure that all required elements are regularly captured.
· Missing information can have a detrimental impact on the ability of staff to evaluate the program properly and provide the most appropriate care.
· Although it is challenging to avoid missing data due to family availability or other issues, programs should aim for complete data.· Educators provide regular data regarding each child’s progress towards meeting school-readiness goals.
· Staff files are kept updated with degrees, certifications, and qualifications.Valid and Reliable · Valid data collection means that collected data is accurate to what it intends to measure.
· Reliable means that the data provides consistent and dependable information.· Programs develop measures that reflect the needs of the population and provide results in a consistent manner.
Head Start programs use the above methods to strengthen program offerings and produce improved outcomes for the children and families in their care. With the results of appropriate data collection, programs can offer continuous quality improvement at the child level and support the staff education that promotes such development. In short, Head Start data collection makes program offerings better, which in turn supports the building of a child’s educational foundation.
Incorporating Data Collection into Head Start Programs
It may be difficult to elicit information if staff and families are reluctant to provide it. Data collection takes time, and it is easy to let it slide in the face of caring for our children. With that in mind, the question is, how can Head Start programs support data collection and create a “Data-Informed Culture” in the classroom?
Introducing data-collection is a gradual process, where all parties need to be informed of its importance and committed to continuous improvement. As such, Head Start advocates for the Four “R” approach:
- Responsible: Are you using the data responsibly? Programs need to collect high-quality data. The program defines characteristics of high-quality data as meeting the following criteria:
o It offers an accurate picture of the subject (children, family, staff, program, etc.)
o The program processes and uses data in a timely manner.
o The collection of data maximizes information while minimizing the time taken to collect it.
- It includes information about limitations and the appropriate use of the data.
- Respectful: Are you using the data respectfully? Staff needs to respect the family’s beliefs, values, and culture during data collection. Some things to consider include:
o Are you presenting surveys, questionnaires, and other documents in the family’s language?
o Are you prioritizing family input?
o Are you communicating your understanding of a family’s importance in the life of the child?
o Are you allowing families to tell their stories?
- Relevant: Is the data you are using relevant? Relevant data has the following qualities:
o Your data answers specific questions about program development, including program goals, objectives, and outcomes.
o Your data produces results that are meaningful to both staff and families, supporting their interactions.
o You collect reliable data.
o You collect valid data.
- Your data are equitable, accessible, and culturally and linguistically appropriate.
- Relationship-Based: Are you using data in a relationship-based way? Some things to consider include:
o Are staff encouraging parent leaders to help parents learn about data collection? Is the communication from leaders responsible, respectful, and relevant?
o Is the program using family engagement practices in their data collection?
With these guidelines, the Head Start program strives to collect data that will reflect the needs of the community and support the families it serves.
Systematic Head Start Data Collection
Data is an essential part of Head Start’s Parent Family and Community Engagement Framework. As such, programs must systematically develop their data collection methods. Head Start structures its data collection activities in four distinct steps:
- Systematic preparing and planning (developing useful questions)
- Collecting family-related data
- Aggregating and analyzing collected data
- Using and sharing the results.
Let’s take a closer look at how you can implement data collection using the four steps, according to Head Start guidelines:
| Step | Action Items | Example Details for Action Item |
| Systematic Planning and Preparing | Identify what you want to know and why you need to know it. | Develop questions with the input of staff and families. Questions should help you target and evolve your program to meet current needs. |
| " " | Develop specific questions to elicit the most relevant information. | State questions as clearly and simply as possible. Avoid asking more than one question at a time. |
| " " | Tailor questions to fit the needs of your program. | Questions can collect information either quantitatively or qualitatively. Decide if you need broad or limited information or if you need to measure the program’s efforts (offerings) or overall effects (outcomes). |
| " " | Once developed, check your questions with staff, families, and your community. | Asking for input from families helps you tailor your questions to their needs. In addition, inclusion helps build relationships and shows respect for the families you serve. |
| Collecting Family-Related Data | Head Start programs collect many types of data. | Keep in mind that Head Start programs already collect some of the data you may need for decision-making. |
| " " | There are different methods for data collection. | You can collect data from self-reporting, observation, and parental reporting, depending on the information you need. |
| " " | Programs use many different tools for data collection. | Tools are the instruments you use to collect data. Examples include intake forms, standardized measures, surveys, and questionnaires. Consider which type of tool will best elicit the information you need to collect. |
| " " | Develop a system for recording, organizing, and storing your collected data. | Data is only useful if it is analyzed. Make sure you have a system-wide method for recording data, and everyone is trained to use it. Data collected now may be useful for future analysis, so store it appropriately. |
| Aggregating and Analyzing Collected Results | You can analyze data in different ways. | Decide what methods you will use to analyze the data before it is collected. |
| " " | Methods of data analysis should help answer the original question. | Decide if you are looking for individual or aggregated data. |
| " " | Data analysis can be simple or complex. | Consult professionals as needed, especially for complex analysis. |
| Using and Sharing Results | Develop a plan for sharing data before collection begins. | Determine with whom you will share data, what data you will share, and how you will share it. Consider your goals as you develop your plan. |
| " " | Determine what information you can share and what is confidential. | Do not share data with an audience who does not need to know or is legally prohibited from knowing. |
| " " | When presenting data, be aware of its limitations and convey that to your audience. | Data always has limitations. Keep in mind that your data may point you towards additional questions, which you may then need to develop a plan for answering. |
| " " | Head Start encourages you to share your data and support continuous learning. | Consider sharing data with Head Start even if it was not solicited. Strengthening the Head Start program helps everyone. |
The Head Start Project encourages programs to use these methods toward one or more of their seven defined Family Outcomes, which are:
- Family Well-Being
- Positive Parent–Child Relationships
- Families as Lifelong Educators
- Families as Learners
- Family Engagement in Transitions
- Family Connections to Peers and Community
- Families as Advocates and Leaders
Keeping these seven desirable outcomes in mind, you can develop your approach to Head Start data collection through each of the four steps. Remember also to incorporate the four “R’s” of data collection described above.
Supporting Head Start Educators and Caregivers with Data Collection
When discussing data collection, it is essential to acknowledge the needs of educators and the work they perform. Keeping track of data, especially if it is a new program concept, can be a frustrating and challenging process. How can we ease the process of Head Start data collection in the classroom?
As mentioned above, web-based software can provide at least part of the answer. In fact, some educational software, like Learning Genie, were specifically developed with the needs of Head Start programs in mind. Learning Genie’s Head Start-specific software includes a data-monitoring module with a dashboard and quick report compiling.
Learning Genie software has features that help educators and parents in myriad ways, including supporting communication, portfolio development, and educational tracking. Head Start programs can benefit from the program’s Family Engagement Strategy, which includes language translation software, parental reminders, and tracking in-kind hours. Some other benefits of Learning Genie include:
- The software is available on an app that you can download to a phone or tablet.
- Educators can send pictures, videos, and notes to families throughout the day, allowing families to have insight into the child’s world outside of the home.
- Educators can make books and materials available to parents for continued learning in the home.
- Educators can quickly and easily print portfolios.
- Educators can track data on an easy-to-use dashboard.
- Educators can use voice-to-text for writing and use batch reporting to save time.
- The software can translate 104 different languages.
- Parents can receive parenting tips.
All in all, it is easy to see why educational software in general, and Learning Genie in particular, is a popular choice for early education programs. Educational software streamlines communication, streamlines the data collection process, and helps educators input and access data more efficiently. In a nutshell, it saves our educators precious time and resources, which they can then devote to their little learners.
How Head Start Can Jump Start Infants and Toddlers in Education
Infants and toddlers have a lot to learn in a short amount of time, and all children should have access to quality programming that helps them meet crucial developmental milestones. Head Start programs are one way to help children and families navigate the initial steps of the educational system.
However, to provide the best quality, programs need to respond to the changing needs of staff, children, families, and their communities. With high-quality Head Start data collection and ongoing continuous improvement, the chances of building a supportive environment and affecting the best outcomes increase. And best outcomes mean well-adjusted, happy, and successful children with supportive families.
