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Development of a Tool to Measure the Reasons for Chronic Absenteeism

This study aims to develop a comprehensive self-report instrument to measure the reasons for chronic absenteeism among secondary students. The instrument's psychometric properties will be examined, including factor structure, reliability, and relation to student absences.

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Development of a Tool to Measure the Reasons for Chronic Absenteeism

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  1. Development of a Tool to Measure the Reasons for Chronic Absenteeism Amber Brundage, Jose Castillo, Julie Daye, Gary Lam, Rachel Tan University of South Florida

  2. Advance Organizer • Chronic Absenteeism Overview • RCA Instrument Development • National Validation Study • Next Steps & Implications for Practice • Questions

  3. Chronic Absenteeism

  4. Chronic Absenteeism (CA) • No standard definition • Often based on total number of days missed • Does not differentiate reasons for absences • States vary in threshold for number of days (15-21) • Frequently defined as: • Missing 10% or more of instructional days • Florida one of few states that collect data on CA • FL reports students missing 21 or more days per year • Missing 15 or more days of school per year

  5. Prevalence of Chronic Absenteeism • Based on national research, conservative estimates: • 10% of US students miss 21+ days of school per year • 14-15% of US students miss 18+ days of school per year 5-7.5millionstudents each year!! • 13/14 OCR data found 6.5 million students missed 15+ days of school Balfanz & Byrnes, 2012; U.S. Department of Education, Office for Civil Rights, 2016

  6. Chronic Absenteeism Patterns • Rates of chronic absenteeism drop from a high in kindergarten each year through fifth grade and then rise significantly in middle and high school (Balfanz & Byrnes, 2012).

  7. Implications of Chronic Absenteeism Missing 10 percent or more of instructional days has significant impact on student outcomes. Chronic absenteeism is associated with: Decreased on-time graduation rates & post-secondary enrollment Increased dropout rates Decreased reading levels & overall academic performance (Balfanz & Byrnes, 2012; Chang & Romero, 2008)

  8. Proposed Reasons for Chronic Absenteeism Balfanz & Byrnes (2012) Barriers/Can’t Aversions/Won’t Disengagement/Don’t Something prevents them from attending (illness, transportation, child care or family obligations) Avoidance of interactions or events at school (affective or perceptions physical/ psychological safety issues, school climate, stress) Would rather be somewhere else, do not make the effort to attend school and/or do not see the value in school

  9. Instrument Development

  10. Why This Instrument? • In order for educators to develop interventions aimed at reducing absences, they must accurately understand why students are not coming to school. • Currently, there are not comprehensive, yet efficient tools for middle and high schools students that measure students’ self-reported reasons for chronic absenteeism aligned with current research.

  11. Purpose of the Study • Develop a self-report instrument to measure reasons for chronic absenteeism among secondary students • Examine the psychometric properties of a self-report measure of chronic absenteeism (the Reasons for Chronic Absenteeism survey [RCA]) • What is the factor structure of the RCA? • What is the reliability of the resultant factors? • To what extent does the RCA relate to student absences?

  12. RCA Development Steps “Gold standard” survey development procedures recommended by DeVellis (2012) Literature review Item development Expert review panel Cognitive interviews National validation

  13. Literature review Item development Expert review panel Cognitive interviews National validation • Drew from research on truancy, attendance, chronic absenteeism, school refusal, dropout prevention • Building a Grad Nation attendance checklist • Preliminary versions of the survey • Controlled for readability - multiple metrics indicated 3rd-4th grade reading level

  14. Literature review Item development Expert review panel Cognitive interviews National validation • 13 reviewers were asked to rate each item on 0-2 scale for: Relevance, Clarity & Necessity • National, state, district and school-level • Researchers/TA providers, state, district, school administrators • Established 70% agreement threshold in all categories to retain items “as is” • Items not meeting threshold were reviewed/revised by development team

  15. Literature review Item development Expert review panel Cognitive interviews National validation 2 items deleted 4 items split into 2 items each 6 items added

  16. Literature review Item development Expert review panel Cognitive interviews National validation • 8 cognitive interviews/7.5 surveys were conducted • 4 middle school students, 4 high school students • 3 males & 5 females • 3 White, 3 Multi-racial, 2 Hispanic/Latino • 1 ELL • Interviewees verbalized thought process for each item • Provided feedback on: • Confusing words/items • Language, sentence structure, terms • Redundant items • Missing items • Had to move • General suggestions for improvement • Descriptors for response options

  17. Literature review Item development Expert review panel Cognitive interviews National validation • The Reasons for Chronic Absenteeism (RCA) is a 41 item survey for middle and high school students who are chronically absent • Completed online • Taken independently while at school • Approximately 7 min for completion time • Measures the reasons for chronic absenteeism • Designed for use at the aggregate or individual level to inform data-based problem solving and intervention development

  18. Literature review Item development Expert review panel Cognitive interviews National validation • 14 demographic questions: (age, gender, grade, race, SES, primary language, disability status, method of transportation to/from school, current grades, estimation/perception of absences) • 41 reasons for absences questions • Multiple items for each domain: Barriers, Aversions, Disengagement • 3 open-ended questions that ask for: • Any other reasons for absences • Reasons students do come to school • What would help them come to school more often/miss fewer days

  19. Content Domains • Barriers • Health related, transportation, housing or material instability, adult responsibilities, suspensions, court/juvenile justice involvement • Aversions • Bullying/harassment, personal stress, school stress, school climate, safety/conflict • Disengagement • Value of school, substance use

  20. Scoring Rubric For each survey item, students rate the item as: 0 ------------- 1 ------------- 2 ------------- 3 Never Rarely Sometimes Usually This is nevera reason you have missed school. This is not very often a reason you have missed school. This is a reason you have missed school more than 3 times. This is oftenthe reason you have missed school.

  21. National Validation: Data Collection

  22. National Validation Sample • Recruitment: • National Listserves • MTSS • PBIS • Project website & social media • Attendance Works • Promotion at conferences • Emails to district and school contacts • FL regional district PLC sessions

  23. RCA Administration Procedures 1 Participating districts/schools identified primary contact (school or district facilitator) to help identify participant schools and coordinate training 2 Participating schools identified middle and high school students who missed10% or more of instructional days (17-18+ days) during 15/16 school year 3 Key district/school staff participated in 1 hour training on RCA administration via Adobe Connect 4 District facilitator sent SurveyMonkey link to participating schools during survey window: September-December 2016

  24. RCA Administration Procedures 5 District facilitator provided school demographic data for participating schools: • Type of setting(urban, rural, etc.) • Total school enrollment • Percentage of students eligible for free or reduced lunch • Percentage of students receiving special education services • Percentage of students designated as ESOL • Percentage of students by race/ethnicity • Percentage and number of student chronically absent (Missed 10% or more of school days in 2015/2016) • Number of days in 2015/2016 school year

  25. Analyses

  26. Analyses • Descriptive Analyses • Population characteristics • Category distributions • Student absence reports • Exploratory Factor Analysis • Oblique rotation using SAS • Reviewed model fit statistics, item factor loadings, and item/factor interpretability • Minimum factor loading = .30 • Confirmatory Factor Analysis • Nested 4-factor CFA based on EFA results- Mplus • Continuous Variables • Analysis: Type=Complex

  27. Descriptive Analyses

  28. Participant Demographics: Background Characteristics

  29. Participant Demographics: Grade-Level

  30. Participant Demographics: Race/Ethnicity and Transportation

  31. School Data Collection • Schools reported ranges for the percentage and number of chronically absent students: • .04%- 56% (69% in an alternative school) • 1 – 593 students • Schools varied in the percentage of chronically absent students surveyed: • 6%- 200%

  32. Student Response Data Quantitative Responses • Within each category (Heath Related, etc.), the percentage of students who endorsed items as “Sometimes” or “Usually” was totaled: • Overall • By State • By District • By School Qualitative Responses • For each open-ended item, student responses were coded into themes and the instances of a theme were totaled

  33. Student Quantitative Responses by Category

  34. Student Reported Absences: Previous Year 57% of respondents did not accurately recall and/or report true numbers of absences

  35. Student Reported Absences: Previous Month 68% of respondents report absences consistent with chronic absenteeism this year

  36. Student Perception of Absences: Compared to Other Students 55% of respondents perceive their absence amount to be the same or less than other students

  37. Qualitative 1: Other Reasons for Absences - 4430 Responses

  38. Qualitative 2: Reasons for Attendance - 4859 Responses

  39. Qualitative 3: Supports to Improve Attendance - 4525 Responses

  40. Report Example

  41. Explorative Factor Analysis (EFA)

  42. EFA Model Fit Indices

  43. Factor Loadings .30 Significance Threshold

  44. Preliminary Factor Structure

  45. Confirmatory Factor Analysis (CFA)

  46. CFA Models • Original model: 4 factors with 2 items that dually loaded moved secondary factor • Poor model fit • Subsequent models: Reviewed modification indices • Moved items that conceptually acceptable • Controlled for correlated errors • Removed items • Many were items added after expert panel review or those suggested to split into two items

  47. CFA

  48. CFA Factor Loading Ranges All items contributed significantly

  49. CFA Factor Correlations

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