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Davis shapa


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Davis shapa

  1. 1. The Collaborative for the Analyses of Pathways from Childhood to Adulthood: The Journey of Collaboration Pamela Davis-Kean University of Michigan The Center for the Analysis of Pathways from Childhood to Adulthood Funded by NSF Grant # 0322356 & 0818478
  2. 2. Goals of the Collaborative <ul><ul><li>A relatively large set of established, similar, and committed longitudinal projects involving scholars from a range of behavioral science disciplines. </li></ul></ul><ul><ul><li>Examining the full span of life-course development, from childhood to adolescence to adulthood and across generations. </li></ul></ul><ul><ul><li>New understanding of human development in the many contexts in which it occurs, including families, the peer group, neighborhoods, schools, and communities. </li></ul></ul><ul><ul><li>Utilizing the most advanced statistical analysis techniques. </li></ul></ul><ul><ul><li>Having findings that will have implications for understanding and creating effective interventions. </li></ul></ul><ul><ul><li>Dissemination of both the substantive and methodological knowledge derived from the collaborative. </li></ul></ul>
  3. 3. Additional Goals <ul><li>Replicate research instead of relying on single studies to dominate our understanding of a phenomena </li></ul><ul><li>Use the information to inform our science on what are strong findings and weak. </li></ul><ul><li>We need data sharing and collaborative work. </li></ul>
  4. 4. Members of CAPCA John Bates Lars Bergman Paul Boxer Andrew Collins Danielle Crosby Robert Crosnoe Pamela Davis-Kean Eric Dubow Greg Duncan Jacque Eccles Michelle Englund Leon Feinstein Elizabeth Gershoff Clyde Hertzman Rowell Huesmann Richard Gonzalez Aletha Huston Rainer Silbereisen Daniel Keating Katja Kokko Jennifer Lansford Jennifer Maggs Katherine Magnuson Chandra Muller Sheryl Olson Greg Pettit Lea Pulkkinen Arnold Sameroff Barbara Schneider Ingrid Schoon John Schulenberg Katariina Salmela-Aro
  5. 5. Structure of Center <ul><li>Data Center at University of Michigan </li></ul><ul><ul><li>Staffed with 2 full-time data analyst with advanced degrees in psychology and epidemiology </li></ul></ul><ul><ul><li>3 meetings a year for subgroups to meet and for coordination of the Center activities </li></ul></ul><ul><ul><li>Use of conference calls and internet resources between meetings to keep groups moving toward their goals. </li></ul></ul><ul><ul><li>Facilities for members and their students to work together with CAPCA analyst </li></ul></ul><ul><ul><li>One workshop a year that concentrates on longitudinal statistics. Open to all members but we encourage our members to send grad students and post-docs. </li></ul></ul>
  6. 6. Using 6 longitudinal data sets, the authors estimate links between three key elements of school readiness--school-entry academic, attention, and socioemotional skills--and later school reading and math achievement. In an effort to isolate the effects of these school-entry skills, the authors ensured that most of their regression models control for cognitive, attention, and socioemotional skills measured prior to school entry, as well as a host of family background measures. Across all 6 studies, the strongest predictors of later achievement are school-entry math, reading, and attention skills. A meta-analysis of the results shows that early math skills have the greatest predictive power, followed by reading and then attention skills. By contrast, measures of socioemotional behaviors, including internalizing and externalizing problems and social skills, were generally insignificant predictors of later academic performance, even among children with relatively high levels of problem behavior. Patterns of association were similar for boys and girls and for children from high and low socioeconomic backgrounds. School readiness and later achievement. Duncan, Greg J.; Dowsett, Chantelle J.; Claessens, Amy; Magnuson, Katherine; Huston, Aletha C.; Klebanov, Pamela; Pagani, Linda S.; Feinstein, Leon; Engel, Mimi; Brooks-Gunn, Jeanne; Sexton, Holly; Duckworth, Kathryn; Japel, Crista Developmental Psychology, Vol 43(6), Nov 2007, 1428-1446
  7. 7. Changes in the Relation of Self-Efficacy Beliefs and Behaviors Across Development Pamela E. Davis-Kean 1,* , L. Rowell Huesmann 1 Justin Jager 1 , W. Andrew Collins 2 , John E. Bates 3 , Jennifer E. Lansford 4 Child Development Volume 79, Issue 5, pages 1257–1269, September/October 2008 <ul><li>Many social science theories that examine the connection between beliefs and behaviors assume that belief constructs will predict behaviors similarly across development. Converging research implies that this assumption may not be tenable across all ages or all belief constructs. Thus, to test this implication, the relation between behavior and beliefs about the self was examined in 2 independent data sets with 2 different constructs: aggression and achievement. The respondents were 6–18 years of age and predominately Caucasian. Results using quasi-simplex structural equation models suggest that self-beliefs become more strongly related to behavior as children grow older independent of the reliability of the measures used. Possible limitations in the use of self-report methodology with young children are discussed. </li></ul>
  8. 8. Other questions asked and replicated <ul><li>Do fraction skills predict to algebra skills </li></ul><ul><ul><li>Yes, in both US and UK </li></ul></ul><ul><li>Is spanking only negative for children of European descent or for race groups. </li></ul><ul><ul><ul><li>Negative for all race groups </li></ul></ul></ul><ul><li>Is alcohol use/abuse just an adolescent issue? </li></ul><ul><ul><li>No, stronger prediction from childhood measures than adolescent measures </li></ul></ul>
  9. 9. NICHD Child Care Study <ul><li>Many press releases about child care </li></ul><ul><li>Long-term effect of time in child care to problem behaviors </li></ul><ul><li>NEVER replicated </li></ul><ul><li>Selected, non-representative sample </li></ul><ul><li>ECLS-B research is suggesting a selection effect of parents who find a child difficult going into child care. </li></ul>
  10. 10. Issues and Problems <ul><li>Authorship on publications continues to be a challenge. </li></ul><ul><li>Many of the data sets represented in this Center have a long history with a certain group of researchers. </li></ul><ul><li>Each subgroup has made its own determination on how authorship should be established for papers. Some groups have opted to coauthor papers and others have found ways to get authorship credit for individual contributions to a larger collected work (i.e., the Monograph). </li></ul><ul><li>Special issues of journals also have been suggested as a way to receive individual credit through a larger set of joint publications. </li></ul><ul><li>Intellectual property and ownership of data and ideas </li></ul>
  11. 11. What have we learned? <ul><li>A mixture of junior and senior researchers is the best combination for getting work done </li></ul><ul><li>Researchers will work for free if they are invested in the question and can work with other motivated researchers. </li></ul><ul><li>Our statistics are still not perfect for everything we need to deal with regarding context and individual development (e.g. correlated relations) </li></ul><ul><li>Secondary, longitudinal data analyses is time consuming </li></ul>
  12. 12. Final Thoughts <ul><li>The administrators need to be trusted by all who are participating. The success of the bigger project versus individual success. </li></ul><ul><ul><li>This can be in direct opposition to tenure! </li></ul></ul><ul><ul><li>How do you promote the value of collaborative data? </li></ul></ul><ul><li>The value of listening and negotiating is a major asset. </li></ul><ul><li>Times are changing and our science and how we do science needs to change as well </li></ul>
  13. 13. THANK YOU FOR YOUR ATTENTION [email_address]