1. Early Childhood Longitudinal Study Min-Jong, Youn Steve Maczuga

2. ECLS Program ECLS-B birth cohort: birth thru K ECLS-K kindergarten cohort: K thru 8 th grade

3. ECLS-K Information on young children Data collections: K-8 th grade More attention to parents and the family (e.g., non-parental care, out of school experiences, parental involvement) Information on children’s cognitive/socioemotional status/health condition

4. Strengths of the ECLS-K Design Children’s assessment at the start of school Fall and spring assessment in kindergarten and first grade Several time points Assessments not limited to children’s academic achievement Information on multiple environments and from multiple sources

5. Focus Area School readiness Transitions into kindergarten and elementary school and from elementary to middle school Relationship between early school experiences and later school performance Growth in cognitive, social, and physical development from childhood through adolescence

6. Core Data Collection Assessments Student questionnaires Parent interviews Teacher questionnaires (A:classroom instruction/B:views on school) School administrator questionnaires Student records abstracts School facilities checklist

7. ECLS-K Components ChildParentTeacherSchool CognitiveParent and child demographics Teacher background School demographics Socioemotional status Child and family health Classroom environment School climate PhysicalFamily characteristics School climateSchool programs PsychomotorParent-child interactions Student profileEducational goals and objectives

8. Data Collection KindergartenFirst GradeThird GradeFifth gradeEighth Grade Fall 1998Fall 1999 (30%) Spring 1999Spring 2000 (refreshed) Spring 2002Spring 2004Spring 2007

9. Sample Sizes Over Time Data CollectionDirect Child Assessment Parent Interview Fall K19,17318,097 Spring K19,96718,950 Fall First5,291(30%)5.071 Spring First16,72715,626 Spring Third14,47013,489 Spring Fifth11,34610,996 Spring Eighth9,2968,755

10. Sample Spring KSpring 1stSpring 3rdSpring 5 th Mean number of children per school 13855 Mean number of children per teacher 6322 *Based on reports from the children’s reading teachers

11. Sample Characteristics Nationally representative of kindergartens, Kindergarteners, and Kindergarten teachers Nationally representative of first-graders Not-representative of 3 rd, 5 th, and 8 th graders Oversampling of private schools and private school children/Asian/Pacific islanders

12. ECLS-K Assessment Cognitive Socioemotional Physical

13. The Assessment Cognitive: Reading (K-8) Math (K-8) General Knowledge (K-1) Science(3,5,8) * All assessment are recalibrated using IRT

14. Reading Assessment K-8K-5K-3K -1Level1: Letter recognition Level2:Beginning sounds Level3: Ending sounds Level4: Sight words Level5:Words in context Level6:Literal inference Level7:Extrapolation Level8:Evaluation Level9:Evaluating Non-fiction Level10:Evaluating complex syntax

15. Math Assessment K-8K-3K-1Level1:Number and shape Level2:Relative size Level3:Ordinality, sequence Level4: Addition and subtraction Level5:Multiplicaiton and division Level6:Place value Level7:Rate and measurement Level8:Fractions Level9:Area and Volume

16. Language Minority Children Those who fail English oral language developmental scale(OLDS) do not take Reading and General knowledge tests But they take Spanish Mathematics, Psychomotor, Height, and Weight

17. Identifying Language Minority Children 1)Students show home language was not English 2)Teachers were asked about child’s language use in and out of the classroom 3)Children were administered the Oral Language Development Scale(OLDS)

18. Indirect Assessment Academic Rating Scale(ARS) -Teacher report on children’s cognitive knowledge and skills Social Rating Scale(SRS) -Teacher and parent report on children’s social skills

19. Indirect Assessment Socioemotional Social skills Approaches to Learning Externalizing and Internalizing problem Self-Control Self-Concept (3,5,8)

20. Physical and Motor Specifications Physical: Height/Weight/Body Mass Index(BMI) Motor (Fall kindergarten only) -Fine Motor: -Copy basic figures/constructs wooden blocks -Gross Motor: Balance on each foot, Hop on one foot, Skip, Walk backward

21. Practical Issues Naming variable Change of school Weighting

22. Naming of Variables Level of variable(assessment, parent, student, teacher, school) + Round of data collection (1-6) Examples C1R3MSCL C2R3MSCL C4R3RSCL C6R3RSCL WKSESL/W1SESL/W3SESL/W5SESL P2HEMPL S4TEST

23. ECLS-K variable naming CodeVariableExp A/BTeacher questionnaire CChild assessmentCombined with R:extrapolation KSchool facility check list PParent interview RChild demographice.g., region SSchool administrator questionnaire TTeacher questionnairestudent scores

24. Timing Round of dataexplanation 1 = fall kindergarten 2 = spring kindergarten 3 = fall first grade 4 = spring first grade 5 = spring third grade 6 = spring fifth grade Number is used to indicate in which round of data collection the variable was obtained K=kindergarten 1=first grade 3=third grade 5=fifth grade Variables beginning with “W” (e.g., wksesl)

25. Composite variables Users may not have all necessary data to create composite e.g., Child characteristics Child care information Parent characteristics Household characteristics Classroom characteristics School characteristics Race, gender, BMI, SES SES=education, household, and income (40% imputed)

26. Example C1R3MSCL C2R3MSCL C4R3RSCL C6R3RSCL WKSESL/W1SESL/W3SESL/W5SESL P2HEMPL S4TEST

27. Changed schools or teachers Variables that identify children who changed schools or teachers Example: R4R2SCHG=changed schools between rounds 2and 4 R4R2TCHG=changed teachers between round 2 and 4 - Drop cases-

28. Missing data values -1 not applicable, including legitimate skips -7 Refused -8 Don’t know -9 Not Ascertained (blank) System missing *Be cautious with “-1” which may not be missing

29. What is the Difference Between Weighted and Unweighted Data With unweighted data, each case is counted equally. Unweighted data represent only those in the sample who provide data. With weighted data, each case is counted relative to its representation in the population. Weights allow analyses that represent the target population.

30. How are Weights Used? Dataset with 5 cases. Value 4 2 1 5 2 Weight 1 2 4 1 2 Sample mean (4+2+1+5+2) = 2.8 Weighted mean (4*1) + (2*2) + (1*4) + (5*1) + (2*2)/sum of weights = (4 + 4 + 4 + 5 + 4)/10 = 2.1

31. Weighting 1)Level of Analysis: child, teacher, or school 2)Round of data: cross-sectional or longitudinal (Choose time period: e.g., k thru 3 rd grade) 3)Source of data: Child assessment, parent interview, and/or teacher questionnaires

32. Weighting The first letter element in a weight variable name indicates the level of analyses School level analyses: “S” Teacher level analyses: “B” Child level analyses (cross-sectional/longitudinal): “C” Except base year child level analyses (longitudinal): “BY” – e.g., BYCOMW0=Child assessment data from fall-AND spring-kindergarten in conjunction with one or more rounds of parent and/or teacher base year data

33. Data round The second element in a weight variable name indicates the round of data Cross sectional data indicates with single number: 123456 Longitudinal analyses include two or more numbers “45”for round 4 and 5 “124” for rounds 1,2, and 4 “1_6F” ‘for rounds 1,2,3,4,5,6 (F=full sample) “1_5S” for rounds 1,2,4,5 (S=subsample)

34. Source of the Data Child assessments (alone or in conjunction with any combination of a limited set of child characteristics, e.g., age, sex, race/ethnicity) have a “C” Parent Interview: “P” Child/parent/teacher have a “CPT” In 5 th grade “CPT” is followed by either “R”,”M”,”S” for teachers

35. Examples: C23PWO C- for the child-level analysis 23-for analysis of data from rounds 2 and 3 P for analysis of parent interview data

36. Example C6CPTM0 “C” for child-level analysis “6 for analysis of data from round 6 “CPTM” for analysis of child, parent, and math teacher

37. Example C1_6FC0 Round 1, 2, 4,5, and 6 assessment data C1_6FP0

38. Strength of ECLS-K 1)Rich information on children and family 2)Health condition 3)Frequent time points 4)Assessment

39. Help Order free CD http://www.edpubs.gov/ProductCatalog.aspx?KeyWordSearch=& TypeofSearch=exact&searchterm=ECLS-K Chapter 7 in the ECLS-K, 5 th Grade User’s Guide has Tables 7- 15 and 7-16 that describe the differences in the public and restricted datasets. The User’s Guide can be found online at: http://sodapop.pop.psu.edu/codebooks/ecls/k5userpart2.pdf http://sodapop.pop.psu.edu/codebooks/ecls/k5userpart2.pdf Weighting: ECLS-K report chapter 9 Assessment: ECLS-K report chapter 3

40. Help Here’s a short explanation from the NCES: http://nces.ed.gov/ecls/kinderfaq.asp?faq=1 http://nces.ed.gov/ecls/kinderfaq.asp?faq=1 Weighting http://help.pop.psu.edu/data-collections/early- childhood-longitudinal-study- ecls/Working%20with%20the%20ECLS- K%20Data.ppt/view

41. Weighting and Complex Sample Design Help Pages http://help.pop.psu.edu/help-by-statistical-method/weighting/working-with-sampling-weights On the above page, the last two links are specific to the E CLS datasets and contain code examples and des criptions of complex sample design.