The ability to become a data consumer is an art form which transcends professions. A vital aptitude in today’s information age, Google’s Chief Economist Dr. Hal R. Varian commented, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades” (Dykes, 2016).
Step 3 of your Project further develops your ability to understand, process, and extract value from data. Also known as data aggregation, this step prompts you to pull together major findings and data points from multiple evaluation sources and culminate them into one complete programmatic story. This aggregate story will become your program’s source of truth, guiding initiatives, interventions, and teacher/family relationships.
In this Group Discussion Board, you and your group members aggregate the data from Connor Street’s evaluations. You look for comparisons, trends, and causal relationships to draw conclusions about the program’s strengths and opportunities for improvement. You also consider how stakeholders might perceive this information.
Reference: Dykes, B. (2016, March 31). Data storytelling: The essential data science skill everyone needs. Forbes. https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-need
A Measuring What Matters: Exercises in Data Management—Exercise 3: Aggregate and Analyze
MEASURING WHAT
MATTERS: EXERCISES IN
DATA MANAGEMENT
EXERCISE 3:
AGGREGATE AND
ANALYZE
Revised
Acknowledgments
The National Center on Parent, Family, and Community Engagement would like to acknowledge the
leadership of the Harvard Family Research Project, with support from the Brazelton Touchpoints Center,
in developing this resource. These organizations represent diverse roles, expertise, and perspectives;
their input and feedback were essential in creating this resource. We recognize and value the role of
parents and programs in making a difference for children, families, and communities.
This document was originally developed with funds from Grant #90HC0003 and modified with funds from
Grant #90HC0014 for the U.S. Department of Health and Human Services, Administration for Children and Families,
Office of Head Start, and Office of Child Care, by the National Center on Parent, Family, and Community Engagement.
This resource may be duplicated for noncommercial uses without permission.
For more information about this resource,
please contact us: PFCE@ECtta.info | 1-866-763-6481
Suggested citation: U.S. Department of Health and Human Services, Administration for Children
and Families, Office of Head Start, National Center on Parent, Family, and Community Engagement.
(Revised 2019). Measuring What Matters: Exercises in Data
Management—Exercise 3: Aggregate and Analyze.
mailto:PFCE@ECtta.info
Measuring What Matters: Exercises in Data Management—Exercise 3: Aggregate and Analyze
Measuring What Matters
Exercise 3: Aggregate and Analyze
Exercise 3 is about aggregating, disaggregating, and analyzing data.
Analyzing data means examining information you have collected and
making sense of it.
This exercise introduces two ways to analyze your program’s data:
1) aggregation, and 2) disaggregation. “Aggregation” involves combining
and presenting similar data from multiple sources. “Disaggregation” means
taking a summary of data and breaking it into parts. Aggregating and
disaggregating data can help you organize the data you have collected.
Next, you can analyze and use the data.
This exercise presents a scenario about the fictional Hopeful Beginnings
Head Start Program as it analyzes data from its seven sites. You can use
this exercise to:
• Understand how aggregating data can give a whole picture of your
program’s PFCE work.
• Understand how disaggregating data can provide information about
how program sites or subgroups of families are making progress
toward goals.
• Analyze data to help track family and program progress toward goals.
How to Use Exercise 3:
On Your Own
• Read the scenario, Aggregating and Analyzing Data to Build Family
Connections.
• Complete Table 4, using information from your own program.
With a Group
• Share your
The ability to become a data consumer is an art form which transcends professions. A vital aptitude in today’s information age, Google’s Chief Economist Dr. Hal R. Varian commented, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades” (Dykes, 2016).
Step 3 of your Project further develops your ability to understand, process, and extract value from data. Also known as data aggregation, this step prompts you to pull together major findings and data points from multiple evaluation sources and culminate them into one complete programmatic story. This aggregate story will become your program’s source of truth, guiding initiatives, interventions, and teacher/family relationships.
In this Group Discussion Board, you and your group members aggregate the data from Connor Street’s evaluations. You look for comparisons, trends, and causal relationships to draw conclusions about the program’s strengths and opportunities for improvement. You also consider how stakeholders might perceive this information.
Reference: Dykes, B. (2016, March 31). Data storytelling: The essential data science skill everyone needs. Forbes. https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/#63d7604352ad
Examine the “Measuring What Matters” article, which details Step 3: Aggregating Data. Then, revisit the Week 5 and 6 Group Discussion Board in which each group member detailed his or her initial analysis. As you review each analysis, reflect on the key findings and insights that are both similar to and different from your own ideas.
Review the findings of your colleagues and address the following prompts under each member.
1. Explain comparisons and trends that might exist among the key findings.
2. Explain which key findings might be in contrast with each other and why.
3. Explain how and/or why one key finding might be impacting (either positively or negatively) the successfulness of another data set.
4. Explain what might be the greatest strength of the program, as well as what might be the most important area for which the program needs to improve and why.
5. Explain how these major findings might be perceived across stakeholder groups and why.
Revisit this Discussion Board throughout Week 7 to collaborate with colleagues by responding to their posts. Work with colleagues to synthesizing the groups insights into one cohesive evaluation. which details the quality and effectiveness of the program. Note what you have learned and/or any insights you have gained as a result of the comments your colleagues made and the connections you have made with the Learning Resources.
Support your comments with in-text citations and references following the APA style guide.