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Melissa Alcaraz, Brigham Young University UYCA SUMMER YOUTH COURT CONFERENCE
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Three main points I will cover today are: Why Data Matters Preliminary Research Results Recommendations on Data Collection INTRODUCTION
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WHY COLLECT DATA State mandate In order to maintain certification, peer courts must submit standard data set. Data that is gathered will be used to inform studies and annual reports This will be used to evaluate and improve peer courts
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Data can examine answers to important questions, such as: Who is being referred to peer court most often? Male/female, racial groups, etc. What are the most common offenses among youth offenders? Why? WHY DATA MATTERS
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Data can examine answers to important questions, such as: What are their backgrounds? Income, single parent home, etc. What do those that graduate from peer court have in common? Dispositions, background, etc. WHY DATA MATTERS
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We can discover what is and isn’t working. Ultimately, data matters because the youth that are involved in peer court matter. WHY DATA MATTERS
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Data retrieved from the Salt Lake Peer Court Data was collected during intake. Used JATRAX system and income surveys I inputted the data into the Excel spreadsheet Salt Lake Peer Court 186 Total Referrals 129 Cases NOT active. PRELIMINARY RESULTS
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Graduated Compliant: 41% Race: PRELIMINARY RESULTS Race of Youth Offenders (%) American Indian2.33 % Asian0.78 % Black9.30 % Hispanic/Latino48.84 % Middle Eastern0.78 % Pacific Islander1.55 % White36.43 %
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Gender of those referred Male: 55.5% Female: 44.5% Graduation rate by gender Male: 46.5% Female: 35.1% Most common offense Truancy: 46.5% 31.7% of these offenders came from single-parent households. Average length of time in court 127 days Single-parent Families 32.6% of all youth referred PRELIMINARY RESULTS
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DATA COLLECTION RECOMMENDATIONS
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Creating a code for “Offenses” Make sure every offense has a different number Add the list to the coding guide If a youth offender has multiple offenses, create an additional row(s) and log the additional offenses. DATA COLLECTION RECOMMENDATIONS
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Common error “0” vs. missing Consequences of incorrect data Accurate assumptions cannot be made Incorrect data is equivalent to non-data DATA COLLECTION RECOMMENDATIONS
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QUESTIONS?
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Melissa Alcaraz melissa.alcaraz@live.com CONTACT INFORMATION
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