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Why Data?

Why Data?. Dr. Laura Tanner-McBrien Coordinator Department of Prevention and Intervention Fresno Unified School District Fresno, California. Objectives. Participants will gain an understanding of how data can be gathered for homeless education and other district programs.

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Why Data?

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  1. Why Data? Dr. Laura Tanner-McBrien Coordinator Department of Prevention and Intervention Fresno Unified School District Fresno, California

  2. Objectives • Participants will gain an understanding of how data can be gathered for homeless education and other district programs. • Participants will understand the importance of a data-driven program for students in achieving academic success. • Participants will understand the financial benefit of having a strong data component. • Participants will gather information to assist them in their own program implementation.

  3. Why Code Students? • For Identification • For Delivering Services • For Monitoring Academic and Behavioral Success • To Track Student Success • To Report Out the Success of a Program

  4. Financial Benefits • Grants • District Funds • District Support • Community Donations or Support

  5. Coding of Students in FUSD Codes in ATLAS Project ACCESS codes can be found under the Student Services tab. Four options for services qualify under Project ACCESS. The codes are entered by Project ACCESS Staff. • Project ACCESS – Homeless • Project ACCESS – Neglected and Delinquent • Project ACCESS – Foster Youth – Out of County Placement • Project ACCESS – Foster Youth – Fresno County Placement A weekly update from the Department of Children and Family Services automatically changes the foster codes. The homeless codes are updated as parents or schools inform Project ACCESS staff of any changes.

  6. Coding of Homeless Youth Project ACCESS – Homeless Codes A AWAITING FOSTER CARE D LIVING IN A DOUBLED-UP SITUATION F FORMERLY HOMELESS – Do Not Qualify for Services M LIVING IN A MOTEL O OTHER, HOMELESS ACCORDING TO HSS R RUNAWAY, POSSIBLY STAYED AT THE SANCTUARY S LIVING IN A SHELTER T TRANSIENT (many moves) U UNACCOMPANIED YOUTH (Caregiver Affidavits)

  7. Coding of Foster Youth Project ACCESS – Foster Care Codes Foster Family Agency 11 Relative Home 21 Guardian Home 22 Tribe Specified Home 23 Foster Family Home 31 Foster Family Agency Certified Home 32 Small Family Home 41 County Shelter/Receiving Home 51 Group Home 52 Court Specified Home 53

  8. Purpose of a Data Base • History or Pattern of Services • Gather Information About a Family • Track Services Provided to a Family • Evaluate Services Provided to Families • For Program Evaluation

  9. FUSD Data Base • MARS Data Base • Communicates With Student Information System • Two Data Bases; One for Homeless, and One for Foster Youth • Contact Information: • David K. Meyers • MARS Group • dmeyers@mars-group.com • 559-261-2220

  10. ATLAS

  11. Data Collection Data Fields Meanings • ID Identification Number • Last Name Last Name • First Name First Name • School School Number • Grade Grade Level • Gender Male or Female • Ethnic Ethnicity • DOB Date of Birth • Speced Special Education Code 61, 66, 91 • Migrant Migrant Program • Gate Gate Code • Lang Home Language Spoken • ELD English Language Development Level • AVID Advancement Via Individual Determination

  12. Data Collection Cont. Program Fields Meaning • Program Program Title • Beginning Date Date Began Program • Level of Service Active or Not • Ending Date Date Services Ended

  13. Data Collection Cont. Academic Data Meaning • AGPA Academic Grade Point Average • Addrcnt Number of addresses in a school year • Enrcnt Number of enrollments in a school year • Credearn Number of credits earned in Semester • Pctattn Percent Attendance • CSTeps CST English Proficiency Score • CSTess CST English Standard Score • CSTmps CST Math Proficiency Score • CSTmss CST Math Standard Score • CAHSEE M Math CAHSEE Score • CAHSEE LA Language Arts CAHSEE score

  14. Data Collection Cont. Behavioral Data Meaning • Behavior Behavior log data • Supensions Number of suspensions • Expulsions Number of expulsions

  15. Data Reporting • Data Share • Graphs and Charts • Formal Evaluations • Special Projects • Dissertation

  16. Quantitative Results

  17. Quantitative Results Cont.

  18. Quantitative Results Cont.

  19. Quantitative Results Cont.

  20. Quantitative Results Cont. Suspensions • 24% of Foster Youth had at least one suspension • 184 Foster Youth • N = 778 • 20% of Homeless Youth had at least one suspension • 433 Homeless Youth • N = 2,194

  21. Quantitative Results Cont.

  22. Quantitative Results Cont.

  23. Qualitative Results Survey Results for Tutorial • 80% responded they attended for credit retrieval • 50% responded they attended for homework • 50% rated the tutorial the top score of “10”; all rated the tutorial as a “5” or better • 65% of the youth indicated they had a great chance of graduating high school due to the help given. • 40% rated the tutoring as a way they earned higher grades and more credits • 40% responded that they would feel comfortable going to their tutorial teacher with a question or problem

  24. Dissertation Results Impact of School Mobility on Academic Achievement for Homeless, Foster, and Housed Students Dissertation, 2009 CSU Fresno UC Davis

  25. Purpose of Study To explore the ramifications of school mobility on academic achievement for homeless and foster youth

  26. MethodologyStudy Groups • 7th – 12th Grade Homeless Students • 7th – 12th Grade Foster Youth • 7th – 12th Grade Non-Mobile or Housed Comparison Group • 6th Grade Students were included in the 2006-2007 data for comparison with 7th Grade 2007-2008 data

  27. Variables Dependent Variables • GPAs • Math CST Scores • LA CST Scores • % Attendance • Credits Earned • Suspensions Independent Variables • School Moves • Address Moves

  28. Specific Research Questions Specifically, the following research questions were addressed: 1. Are there differences in California Standards Test scores between homeless, foster youth, and non-mobile students? 2. Are attendance rates, grade point averages, credits earned, and suspensions different for homeless and foster youth than for housed youth?

  29. Research Questions Cont. 3. Does the number of schools a student attends correlate with their grade point average? 4. Do student behaviors (ie. suspensions) correlate with school mobility? 5. Is there a relationship between academic variables and mobility variables?

  30. Statistical Analysis Descriptive Statistics Means, SD Series of 11 Multivariate One-Way ANOVAs ELA and Math CST scores by grade and year Series of four 3 x 2 Way Repeated Measures ANOVAs Academic variables by group and year Correlation Coefficients Canonical Correlation Academics with mobility

  31. Findings Research Question 1: Are there differences in California Standards Test scores between homeless, foster youth, and non-mobile or housed students? 11 Multivariate One-Way ANOVAs • Homeless and foster youth were more similar than different • Scores for homeless and foster youth were statistically different from housed students • CST scores in 9th – 11th grades were inconsistent

  32. Findings Continued Research Question 2: . Are attendance rates, grade point averages, credits earned, and suspensions different for homeless and foster youth than for housed youth? Four 3 x 2 Repeated Measures ANOVAs • Homeless and foster youth were more similar than different • Scores for homeless and foster youth were statistically different from housed students

  33. Findings Continued Figure 1. Plot of academic GPA by year for housing status

  34. Findings Continued Figure 2. Plot of percent attendance by year for housing status

  35. Findings Continued Figure 3. Plot of number of suspensions by year for housing status

  36. Findings Continued Figure 4. Plot of credits earned by year for housing status

  37. Findings Continued Research Question 3: Does the number of schools a student attends correlate with their grade point average? Research Question 4: Do student behaviors (ie. suspensions) correlate with school mobility? Correlation Coefficients • Found statistically significant correlations between mobility variables and academic variables

  38. Findings Continued Research Question 5: Is there a relationship between academic variables and mobility variables? Canonical Correlation • Housing and School moves accounted for 21% of the variance between academic variables in 2006-2007 and 20% of the variance between academic variables in 2007-2008

  39. Limitations • Reasons for School Moves are Not Known • Pre-mobility Issues are not Considered • Two Years of Data • Missing Data

  40. Implications for Further Research • Qualitative Study Component • Interviews with youth • Housing Situation Comparison • Foster Care Placement Comparison • Transportation Services as a Factor

  41. Questions Why Data?

  42. Contact Information Laura Tanner-McBrien, Ed.D. 1350 M. St., Building B Fresno, CA 92721 Phone: 559-457-3359 Fax: 559-457-3372 laura.mcbrien@fresnounified.org

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