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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

Group Discussion: Step 3: Aggregate the Data

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 

Part 1

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.

Part 2

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.

Kelli Barnes 

RE: Group B Discussion – Module 3

COLLAPSE

Top of Form

Group Discussion 

Part 1

 Evaluations are put into practice to insure a program or facility’s reliablity, funcitionality, accuracy, and effeciency.  This process is no stranger to the education world or Connor Street Childhood program. For a program to run effectively, suffeciently, and promote academic and overall developmental growth, these evaluations are imperative.   Along with many evaluation tools, we begin to discuss specific ones, their purpose, and their potential challenges or successes.  I have selected Evaluation 4: Program Adminsitration Scale (PAS).  The role of this scale is to assess leadership managesment for early childhood programs along a 7-point scale. In the case of Connor Street Childhood Program, this evaluation will be used to gather information that measures the quality of administrative, management, and leadership practices at Connor Street Childhood Program. 
 Within the major findings of the initial results, there was a postive overall rating.  The scales included 10 catergories when scoring early childhood programs.  Those categories include: human resources development, personnel cost and allocation, center operations, child assessment, fiscal management, program planning and evaluation, family partnerships, marketing and public relations, technology, and staff qualifications. In an early childhood program like Connor Street, it is clear why these catergories would be included to insure effecient instruction, care, and family involvement.  In addition, adminsitrative services would need to be just as competent. Where the findings indicated positive overall rating, there were two sub-scales that received below average rating.  Those were staff qualifications and program planning. Thus indicating that Connor Street needs support in professional development of staff and program planning. 
 While there was an overall positive rating, staff perceptions related to the leadership and management was not reflected.  Feedback is crucial and can most certainly facilitate postiive change, according to Foster-Nelson (2012).  When teachers have a stakeholder position and their feedback is accepted and valued, positive change can be inevitable.   Another aspect of the PAS that can be challenging would be the subjective manner that data is recorded.  With different percspectives recording, data can often be skewed based on observations.  Although these are details that were not inlcuded in the initial major findings, they are important to keep in mind when considering the Program Administration Scale (PAS).  

Reference

Foster-Nelson, A.(2012). The role of distributive leadership in the implementation of Program Administration Scale assessment recommendations. Proquest, LLC. MI  (.The role of     distributive leadership in the implementation of Program Administration Scale assessment recommendations – ProQuest)

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