Do you want to build credibility with funders, partners and the general public? Do you want to increase engagement with your audience as you seek to accomplish your agency’s mission? Learn from Westchester Children’s Association’s experience using data to get their message across. In this presentation, we discuss how to: identify data with maximum impact, transform that data into something approachable and appealing, and use that information to mobilize and engage child advocates. We’ll share insights into particular data sources you can explore, online, free tools you can use to present data effectively and strategies for sharing your message with measurable results.
25. Other Sources
Child Data nyskwic.org
datacenter.kidscount.org
Education data.nysed.gov
ocrdata.ed.gov
Health health.westchestergov.com/statistics
health.ny.gov/statistics/
Economy bls.gov
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26. S O U R C E
EVERYTHING
& build credibility.
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27. DATA TRAPS
THERE ARE TWO KINDS OF STATISTICS,
THE KIND YOU LOOK UP & THE KIND YOU MAKE UP.
-REX STOUT, DEATH OF A DOXY
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28. Source: Opinions Dynamic via Fox News
2012 Presidential Election – GOP Candidates
70%
63%
60%
Back Palin Back Huckabee Back Romney
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32. The Problem with Surveys
•Leading questions
•People like to say they’re good
•Majority of surveys are based on samples
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33. What’s a Sample?
•A sample is a subset of a population.
•It is ideally a representative of the population.
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34. Children Under Age 5 in Poverty
in Greenburgh Town
Guess
Male ???
Female ???
One Year
Estimates
0
170
Five Year
Estimates
99
92
2013 One Year Estimates
2009-13 Five Year Estimates
American Community Survey, Table B17001
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35. Margin of Error
the amount by which you expect your results to vary, if a different sample
were taken.
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36. One Year
Estimates
Margin of Error
Male 0 ±193
Female 170 ±233
Five Year
Estimates
Margin of Error
Male 99 ±71
Female 92 ±78
Children Under Age 5 in Poverty
in Greenburgh Town
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2013 One Year Estimates
2009-13 Five Year Estimates
American Community Survey, Table B17001
39. Smaller Samples
LARGER MARGIN OF ERROR
-193
-63
193
403
0
170
-300
-200
-100
0
100
200
300
400
500
Male Female
One Year Estimates
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40. Smaller Margin of Error
LARGER SAMPLES
28 14
99 92
-300
-200
-100
0
100
200
300
400
500
Male Female
Five Year Estimates
170 170
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41. Less “confident” estimates
•Communities with smaller populations
•Subgroups (ex. Gender, Race, Age)
•Surveys that sample across shorter time periods
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52. 2 Presenting Data with Punch
@WCA4KIDS #NFPSUMMIT@WCA4KIDS #NFPSUMMIT
3 Driving Advocacy with Data
1 Finding Data with Impact
53. Presenting Data That Is:
1.ACCURATE
2.APPROACHABLE
3.APPEALING
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54. Accurate
RACE AND HISPANIC OR LATINO
ORIGIN
Percent Margin of
Error
White 60.40% +/-1.7
Black or African American 13.90% +/-0.6
American Indian and Alaska Native 0.30% +/-0.3
Asian 5.00% +/-0.5
Native Hawaiian and Other Pacific
Islander
0.00% +/-0.1
Some other race 15.60% +/-1.6
Two or more races 4.80% +/-1.0
Hispanic or Latino origin (of any race)29.70% +/-0.1
White alone, not Hispanic or Latino 48.10% +/-0.4
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90. Presenting Data Effectively
APPROACHABLE
•Focus on the
main point.
•Provide context.
•Avoid jargon.
•Explain the data.
APPEALING
•Use a simple color
scheme.
•Use color to
support your
message.
•Clear the clutter.
•Use simple (and
free!) tools.
ACCURATE
•Be consistent.
•Be clear and
specific.
•Use notes when
necessary.
•Cite your sources.
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91. 3 Driving Advocacy with Data
2 Presenting Data with Punch
1 Finding Data with Impact