Analysis of data is as important as collection. Once you have the baseline data, you will want to understand what has contributed to this picture. To truly understand the current conditions in communities, you need to understand what happened in the past and what is happening now. Every community is the product of numerous experiences that influence and shape the conditions and opportunities available to residents. These experiences, both positive and negative, have an underlying story that may not initially be clear. This kind of analysis is called finding the “story behind the data” (sometimes more formally called a “segmentation analysis”). You’ll want to ask why the baseline looks the way it does and whether and why different groups of families and individuals in your neighborhood are experiencing similar or different results.
To answer these questions, you have to identify the root causes of the current conditions in your
neighborhood. In other words, what is the primary source or origin of the issue/problem? What seems to be contributing to good results and bad? In trying to understand what the root causes are, you will have to investigate:
• The characteristics of those experiencing different kinds of results (e.g. age, gender, race or ethnic group)
• Any location in the community where good or bad outcomes seem more concentrated
• Risk factors that contribute to poor outcomes
• Promotive/protective factors that contribute to good results
• Attitudes and beliefs of community members
• Systemic or institutional practices that are correlated with good or bad outcomes for different populations
Make sure that you discuss your data findings with community partners, particularly residents. Ask them what they think the numbers mean and find out which root causes seem to be particularly relevant for the community and why. You may find gaps in perceptions between certain groups in the community, like youth and adults. Or you may find a lot of passion about an issue in your conversations after the data has been collected that was not as visible before.
DEFINITION
RISK FACTORS conditions that put children/and or families at risk and increase the likelihood of negative outcomes, e.g. inadequate health care, poverty, substance abuse.
PROMOTIVE/PROTECTIVE FACTORS conditions that help children and families develop resilience to avoid or overcome risks, e.g. a family life with a high degree of love and support, a caring and encouraging environment or strong ties to a faith community.
As you analyze the data, keep the following considerations in mind:
Look for alignment between facts/figures and perceptions/beliefs. Compare facts, figures and statistics to what families living in the area have to say about their needs, assets and dreams. When all sources of information—whether statistics, surveys, interviews or focus groups—point to the same issue, then alignment and agreement exists. Sometimes, however, the data are not in alignment. For example, certain demographic groups may experience something that is not present in the larger community—like a focus group of senior citizens bringing up concerns that are not experienced by the larger population. Don’t ignore an issue just because alignment does not exist. Sometimes alignment doesn’t exist because you need to clarify or gather additional information. Ultimately, the community needs to work together to understand how the data relates to the results you all want to achieve.
Pay attention to connections. During the data-collection process, some interesting information may come up that will connect issues and/or reveal unintended consequences of policies and programs that are supposed to improve results for families. For example, a workforce development council may report an increase in single mothers gaining employment while schools may report an increase in student tardiness and absences. Perhaps this indicates that previously unemployed mothers are now leaving the house earlier than their school-age children, leading to more school tardiness and absences. As information and perspectives are gathered, pay attention to their interconnectedness. These are opportunities to develop interventions across issues, not just within single issues.
Use comparative data. Comparative data are often important to truly understand how critical an issue is to the community agenda. For example, if the infant mortality rate in a particular community is 15.5 per 1,000 live births, what does this mean? Does it mean that this community has a serious level of infant mortality? To fully understand this, the information would need to be compared with population statistics for other similar communities, the state average or national statistics.
Beware of the downside of data. Just as information helps us understand an issue, data can also misrepresent or obscure the real meaning of an issue. Keep in mind certain red flags. For example, the sample group or the numbers reviewed may be too small to draw a solid conclusion about the information gathered. Or you may find that the data you are reviewing was manipulated to emphasize a particular point for political or personal reasons. Data-collection instruments may also vary in terms of how a question is posed and that can skew responses so that, even on the same topic, results cannot be fairly compared. Be sure to confirm and validate data with the community.
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UNCOVERING THE STORY BEHIND THE DATA