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:
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:
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:
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.