Effective Assessment and Evaluation of School, Family and Community Partnerships

1. Effective Assessment and Evaluation of School, Family and Community Partnerships TAPE 2008 Education Partnerships Conference January 28-29, 2008

2. What we know?

3. What we know? (cont.)

4. Epstein�s Action Team for Partnerships Model

5. Epstein�s Six Types of Involvement Parenting Communicating Volunteering Learning at Home Decision Making Collaborating with the Community

6. Parenting: Assist families with parenting and child-rearing skills, understanding child and adolescent development, and setting home conditions that support children as students at each age and grade level. Assist schools in understanding families. Parent Resource Room or Resource Booth Financial Aid Workshop for Parents and Students Adult-Only Math Tutoring

7. Communicating: Communicate with families about school programs and student progress through effective school-to-home and home-to-school communications. �Now for the Good News� Communication Spanish for Educators High School Parent Survey

8. Volunteering:

9. Learning at Home:

10. Decision Making:

11. Collaborating with the Community:

12. What is an Action Team for Partnerships? Members of the Action Team for Partnerships (ATP) work together to: Review school goals Select, design and implement partnership activities Evaluate and improve and partnership practices

13. Action Team for Partnerships (Cont.)

14. Sample: One year action plan to reach results in Math.

15. Sample: One year action plan for Transitions.

16. Sample: One year action plan for Postsecondary  Planning.

17. Characteristics of "Good" Action Plans QUESTIONSQUESTIONS

18. Sample: Principal Survey

19. Sample: Year End Evaluation (Type 1 � Parenting)

20. Sample: Year End Evaluation (Type 2 � Communicating)

21. Major Considerations for Data Collection Conceptualization The model (Epstein) should guide the inquiry Measurement The research objectives should be clearly stated The variables should be clearly defined Time parameters for data collection outlined Design Statistical concerns addressed (e.g. unit of analysis) Is the model data-driven? Does it address the needs/requirements of the District Plan, State and Federal Requirements. Is the model data-driven? Does it address the needs/requirements of the District Plan, State and Federal Requirements.

22. FWISD Research Objectives (Collecting Ingredients) How extensive is parent liason involvement? To what degree are community partnerships utilized? Parent Liason Logs What type of perceptions do parents have about the school�s efforts toward parent engagement? Survey (Ritblatt et al., 2002). Are any of the above related to student achievement? Hocus Pocus!

26. Parent Perception Survey (Ritblatt et al., 2002) 14 Likert Scale Questions (5 point Scale) Some questions reverse-scored English and Spanish Translations Examples My child's school does not welcome parents I feel that the neighborhood around my child's school is unsafe I cannot participate in parent activities unless transportation is provided

27. Single Level of Analysis Problem Traditional analytic strategies such as ANOVA, Correlation and Simple Regression are often based on aggregated school-level data.

28. What�s the strength of the relationship between Schools O,X and l ?

29. And now�

30. Single Level of Analysis Problem Using Single Level Analysis leads to� Unit of analysis problem (e.g. child vs. school) Aggregation bias (child SES vs. school SES) When sample sizes vary, statistical power is reduced Incorrectly estimated precision/standard errors

31. Multiple Levels Data in educational settings are hierarchical. Students are nested within classrooms, within schools, within neighborhoods. This creates a lack of independence between observations. In other words, there is a natural co-variation within the school between the students living in the same neighborhood, attending the same school, and being taught by the same teacher. We still want to examine these factors, but we want to control for the lack of independence between students in a particular school (capture the within-subject variance).

32. Hierarchical Linear Modeling: Partitioning the Variance Two-level cross-sectional (clustered) data: Schools assigned at random to treatments with students nested within schools Studying social or ethinic inequality: contributions of student background and school segregation Two-level models for repeated measures: Children�s vocabulary growth during the second year of life Items nested within students Repeated measures on subjects under different experimental conditions

33. HLM (cont�d) Three-level models: Students nested within classrooms within schools Repeated measures within students within schools Cross-classified models: Students nested within neighborhoods (regions) and schools Repeated measures on students crossed by teachers

34. A Work in Progress Data Entry/Collection Parent-Student Links Teacher-Student Links Return Rates

35. References Epstein, J. et al. (2002). School, Family, and Community Partnerships: Your Handbook for Action. Corwin Press:CA. Raudenbush, S., and Bryk, A. Hierarchical Linear Models: Applications and Data Analysis Methods. Sage:CA. Ritblatt, S., Beatty, J., Cronan, T., & Ochoa, A. (2002). Relationships among Perceptions of Parent Involvement, Time Allocation, and Demographic Characteristics: Implication for Policy Formation. Journal of Community Psychology, 30 (5). 519-549.

36. Thank you� Dave Guzman Director, FWISD Parent Engagement dave.guzman@fwisd.org 817-871-2454 Trey Asbury Research Analyst, FWISD trey.asbury@fwisd.org 817-871-2424