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School absence and student background factors: A multilevel analysis

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School absence and student background factors: A multilevel analysis
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   International Education Journal Vol 2, No 1, 2001 http://iej.cjb.net 59 School absence and student background factors: A multilevel analysis Sheldon Rothman Massachusetts Department of Education srothman@doe.mass.edu As part of regular collections, South Australian government schools provide data onstudents, including individual student absences during one full term (usually 10 weeks).These data were analysed to understand how student absence is affected by student background and school contexts. A multilevel statistical model of student absence wasdeveloped using data collected in 1997, and repeated for 1999. This paper presents the findings for students in primary schools, showing that absence rates for indigenousstudents, while higher than the rates for non-indigenous students, are affected by school factors such as the concentration of indigenous students in the school and schoolsocioeconomic status. student attendance, student absence, multilevel models, socioeconomic status, indigenous students Introduction Regular attendance is an important factor in school success. Students who are chronic non-attenders receive fewer hours of instruction; they often leave education early and are more likelyto become long term unemployed, homeless, caught in the poverty trap, dependent on welfare,and involved in the justice system (House of Representatives 1996, p. 3). High rates of studentabsenteeism are believed to affect regular attenders as well, because teachers must accommodatenon-attenders in the same class. It has been suggested that chronic absenteeism is not a cause of academic failure and departure from formal education, but rather one of many symptoms of alienation from school. Chronic absenteeism, truancy and academic failure may be evidence of adysfunctional relationship between student and school, suggesting that schools need to be morestudent-centred and supportive of students with different needs. This argument is supported byresearch that highlights significant associations between student background factors, poor attendance, and early school leaving (Altenbaugh, et al. 1995; Bryk & Thum 1989; Fernandez &Velez 1989).Previous research has concentrated on students who are “chronic” or “persistent” non-attenders,examining family, academic and social background factors related to the student. Other researchhas concentrated on schools with high absence rates, examining student composition, school“climate” and other organisational factors associated with these rates. What has been missing is acombination of these two approaches, because the computational technology has not beenavailable.A European perspective on student absences was provided in a study of absenteeism in 36 highschools in four Dutch cities. Bos, Ruijters and Visscher (1992) examined aspects of absences for individual classes over three school days, a Monday, Wednesday and Friday, covering a total of 8,990 lessons. They differentiated between truancy (disallowed absence, one “without a reasonthat is considered valid by the school”) and allowed absences (one “regarded as valid by theschool”). They found variation by school in the determination of a truancy, but calculated overallabsence rates of 9.1 per cent, comprising a 4.4 per cent truancy rate and a 4.7 per cent allowed absence rate. Truancy rates were lower in pre-university tracks than vocational education tracks,  60School absence and student background factors: A multilevel analysis highest on Fridays, and tended to be higher later in the school day. Whole-day truancy occurred more frequently on Mondays. The proportion of “non-Dutch” students in the school accounted for 42 per cent of the variance in school truancy rate. The authors used schools’ administrativedata to get a snapshot of truancy, reporting valuable information about truancy and absenteeism ingeneral.DeJung and Duckworth (1986) reported on a study of absences in two cities in the westernUnited States. Examining data from six high schools on class absences rather than whole-dayabsences, they calculated absence rates of 15 per cent for the larger of the two districts, and 10 per cent for the smaller. When using whole-day absences only, rates were 4.4 per cent for the larger district and 2.8 per cent for the smaller. The researchers also asked students why they were absentfrom individual class periods. Of the 1,200 students in the sample, 20 per cent of students stated that they had “other things to do,” rather than attend school for a day; illness and personal problems accounted for less than 10 per cent of absences. Students with very high absence ratesidentified parties, drugs and a general dislike of school for most of their absences.Throughout the 1970s, American high school principals consistently identified poor attendance asthe major problem facing secondary school administrators. But rather than define poor attendance,studies concentrated on examining factors associated with it. Wright (1978) analysed secondaryschool-level data in Virginia, surveying schools on their attendance rates and aspects of thecurriculum, organisation and staff. He found statistically significant differences by location: urbanschools had the lowest attendance rates, then suburban schools; schools in other areas had thehighest attendance rates. Within these geographical groupings, different factors were related toattendance rates, including subject offerings (electives), work programs for school credit, and ageof the teaching staff.Reid (1982), using data from an urban comprehensive school in a disadvantaged area of Wales,examined social background factors and self-concept in “persistent” absentees, whom he defined as students with absence rates of 65 per cent of every school term, and control groups of matched students, who were “good attenders, usually making 100 per cent attendance during an averageterm.” He found differences in family structure, father’s occupation, mother’s employment and occupation, and eligibility for free school meals. Of the three groups in the study, persistentabsentees also scored lowest on the Brookover scale of academic self-concept, and lowest on theCoopersmith scale of self-esteem, with no differences between male and female absentees.Two high schools in Ontario, Canada, contributed data on 54 students to a study to determine theinfluence of personal, family and school factors on absenteeism. Corville-Smith, Ryan, Adams and Dalicandro (1998) used discriminant analysis to identify which factors could identify truants.Perceptions of school and parental discipline and control were found to be significant factors, aswere students’ perceptions of family conflict, academic self-concept and social competence inclass. Unfortunately, their sample was severely restricted by selection bias: only 27 of a possible295 volunteered to participate, and more than two-thirds were female.Some researchers have attempted to examine the influence of attendance on academic achievement.In 1923, Odell (1923) reported small, non-significant correlations between attendance and either academic achievement or intellectual development, but significant correlations between attendanceand grades awarded by teachers for class work. Finch and Nemzek (1935) reported that schoolgrades were related to student attendance for the 1934 graduating class at one high school inMinneapolis, Minnesota. Kersting (1967) compared attendance records for the 100 highestachieving and 100 lowest achieving students in the junior high school where he was teaching.Comparing these extreme groups, he found significant differences in attendance. These studies   Rothman61 show that while there may be a relationship between attendance and achievement, it is very poor attenders whose achievement is low, but no threshold absence rate is defined.Research on student attendance points to some groups of students whose attendance record, as agroup, is relatively poor, such as the “non-Dutch” students reported by Bos, Ruijters and Visscher (1992). For most collections of student attendance data in Australia, however, suchinformation has not been available. Most education departments limit their annual end-of-year collections to absences at the school level, with no differentiation by any student factors. In 1997,South Australia began an annual collection of data on student absences during one ten-week term.This paper provides an analysis of these data, supplementing a summary report provided toschools and education department officials (Rothman 1999). Data In South Australia, government schools have the capacity to monitor student attendanceelectronically using computers and software. This software, called EDSAS, allows schools torecord the date, type and reason for each student non-attendance. 1  Four types of non-attendancecan be recorded: whole-day, morning, afternoon, and late. Sixteen reasons can be recorded, nine of which count as absences. The others, such as sport excursions and work experience, are acceptablereasons for which the student is considered present. This information can then be matched withstudent information to provide a rich picture of attendance and non-attendance patterns. Availablestudent information, as provided by the school as unit records during the midyear census, includesgrade (year level), date of birth, sex, indigenous status, socioeconomic status, and special need. 2 The data in this paper were collected from schools that use EDSAS to monitor student attendance.For this paper, only whole-day absences for full-time students were used. When absence rates arediscussed, the sample was limited to those students who were enrolled at one school for the entireterm. The number of students and schools included each year are contained in Table 1.Comparative enrolment data are from the midyear census, conducted each year on the first Fridayon or after 1 August and reported in the National Schools Statistics Collection (Australian Bureauof Statistics 1998).In 1997 and 1998, Term 2 began after the Anzac Day holiday and was ten weeks long. There weretwo Monday holidays—Adelaide Cup Day (Week 4) and Queen’s Birthday (Week 7)—bringingthe total number of school days to 48. Term 2 started one week earlier in 1999; with Mondayholidays for Anzac Day (Week 2), Adelaide Cup Day (Week 5) and Queen’s Birthday (Week 9),there were 52 school days.The data contained in this paper are from the 1997 and 1999 collections of individual studentabsences. To ensure consistency for the analysis, the files were trimmed to include only primarylevel full-time students who attended a single school for the entire term, resulting in 67,732students in 304 schools in 1997, and 84,820 students in 411 schools in 1999.Because the data are based on administrative collections, there are limits to the student-level and school-level variables that are included. Student-level variables include sex (SEX, male=0,female=1), indigenous background (ABOR, indigenous=1), SES (CARD, low SES=1), and gradelevel (Reception to Year 7). School-level variables include location (LOCATION, metropolitan=0,country=1), size (SIZE), per cent indigenous students (PCTABOR), per cent low SES students(PCTCARD), and per cent female students (PCTFEM). Other school indicators (CommonwealthLiteracy Program or Country Areas Program school) were eliminated because of their similarity toother school-level variables. Grade level was eliminated because there was little variation by gradelevel across schools. Frequencies and summary statistics for the files are listed in Table 1.  62School absence and student background factors: A multilevel analysis Table 1.Summary statistics of variables, 1997 and 1999 1997 (48 days)1999 (52 days) Student-level variables nPer cent of sample Absencesper studentnPer cent of sample Absencesper student Sample total 67,732 100.0 2.9 84,820 100.0 3.3 Grade level Reception6,92410.23.59,06510.73.8Year 18,26012.23.110,48512.43.4Year 28,46012.52.810,78412.73.2Year 38,85313.12.710,67212.63.0Year 48,62812.72.810,99413.03.0Year 58,81413.02.710,99013.03.0Year 68,98113.32.910,88612.83.3Year 78,81213.03.210,94412.93.5 Sex Male34,98151.62.943,82151.73.2Female32,75148.43.040,99948.33.3 Indigenous background Non-indigenous65,75597.12.882,32097.13.1Indigenous1,9772.97.42,5002.97.3 Socioeconomic background Middle/upper SES41,47061.22.654,42164.22.9Lower SES26,26238.83.430,39935.84.0 Location a Country25,80038.13.328,97034.23.5Metropolitan41,93261.92.755,85065.83.1 School-level variables MeanMinimumMaximumMeanMinimumMaximum  Absences per student b 3.00.611.13.30.211.7Size (Students R-7)222.812757206.411812Per cent female47.929.666.748.030.070.8Per cent indigenous3.20.084.03.60.0100.0Per cent low SES40.80.388.537.34.290.5 Number of country schools 16153.0%20249.1% Number of metropolitan schools 14347.0%20950.9% a  Location was used as a school-level variable only. b  Absences per student is an unweighted measure. For the weighted average, see the rate for student-level variables. Methodology It was assumed that individual student absences were influenced by student characteristics, suchas sex, indigenous background and low socioeconomic status, in the context of the school thestudent attended. The relationship among the variables can be denoted asTOTABS = β 0  j  + β 1  j (SEX) + β 2  j (ABOR) + β 3  j (CARD) + ε i  j at the student level, and  β 0  j = γ  00  + γ  01 (LOCATION) + γ  02 (PCTABOR) + γ  03 (PCTCARD) + γ  04 (SIZE) + u 0  j β 1  j = γ  10  + γ  11 (LOCATION) + γ  12 (PCTABOR) + γ  13 (PCTCARD) + γ  14 (SIZE) + u 1  j β 2  j = γ  20  + γ  21 (LOCATION) + γ  22 (PCTABOR) + γ  23 (PCTCARD) + γ  24 (SIZE) + u 2  j β 3  j = γ  30  + γ  31 (LOCATION) + γ  32 (PCTABOR) + γ  33 (PCTCARD) + γ  34 (SIZE) + u 3  j at the school level. Preliminary analysis showed that because the percentage of students by sex isgenerally within a narrow range for primary schools, the variable PCTFEM could be excluded    Rothman63 from the model. Each of the student-level variables was grand-centred around the mean, so that theintercept term would represent the estimated mean number of days absent for schools, assumingthat each school enrolled students with all the same student-level characteristics. Figure 1.Distribution of number of days absent per student, 1997 (top) and 1999 (bottom) Analysis of absences across all students in all schools showed that the data did not fit a normaldistribution. In 1997, 27.7 per cent of students had no absences all term; in 1999, 24.4 per centhad no absences. The high number of zeros in the data (see Figure 1) meant that a standard transformation could not be used to approximate a normal distribution. HLM offerscomputational options for a dependent variable that represents counts. The Poisson option inHLM results in a nonlinear analysis using a hierarchical generalised linear model (Bryk,Raudenbush and Congdon 1996, ch. 5). The analysis proceeds adding variables in three stages,with adjustments at each stage to include only significant variables. The final model shows howeach of the variables influences a school’s absence rate. The analysis was first done using the 1997data, with 1999 used as a replication. The following discussion considers the 1997 analysis;results for 1999 are contained in the tables. The steps in the analysis are similar to those followed  by Rumberger (1995) in his analysis of middle-school dropouts.
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