Effectiveness of an Adapted Behavioral Education Program Targeting Attendance Improvement.
School counselors are well positioned to help address attendance issues because their work often centers on helping students in the academic and social/emotional domains by delivering responsive services connected to student needs (American School Counselor Association [ASCA], 2012). Student absenteeism concerns are connected to many larger issues such as mental health concerns, family conflict, and negative experiences in school (Bimler & Kirkland, 2001; Sugrue, Zuel, & LaLiberte, 2016). Providing services to students who are absent or tardy is considered an appropriate school counseling activity (ASCA, 2012).
At the Southern California elementary school where this study was conducted, the average daily attendance at midyear was above 90%, yet approximately 30 students were identified as absentee. The California Department of Education (2017a) defines chronic absentee as a student missing 10% or more of school days in a year. The 2017-2018 year comprised 175 student attendance days. Prior to this study, attendance interventions in this school centered on parent meetings and truancy notices. No school personnel were responsible for addressing attendance. The school hosted community and parent programs throughout the year, and a study by Sheldon (2007) provided evidence that similar programs positively impacted attendance. General practice was for office staff to send truancy notices and administrators to conduct parent meetings. Teachers also contacted parents of students with multiple absences. Despite these practices, the school staff did not see significant increase in average daily attendance for the identified absentee students.
The school counselor implemented the program in this study after discussions with administrators about attendance concerns. Some of the main concerns for implementing an intervention were cost, practicality of implementation, and tracking outcomes. One promising identified intervention was the Early Truancy Prevention Program, but it required significant teacher involvement and teacher compensation after school (Cook et al., 2017). We chose to use an adaptation of the Behavior Education Program (BEP) because it was a low-cost intervention that could be implemented with fidelity in the school (Hawken, MacLeod, & Rawlings, 2007). It also targeted negative relationships and experiences in the school that were factors contributing to student absences (Bimler & Kirkland, 2001; Mallett, 2016; Sugrue et al., 2016).
As the school counselor, I was the primary researcher and conducted the attendance intervention in collaboration with schoolteachers and administrators. I have been a school counselor for 4 years. I used the BEP for behavioral issues for 2 years and have 2 years of experience adapting BEPs for attendance.
This study was based on the implementation of a BEP model intervention to address chronic absenteeism. The intervention was completed entirely within a school setting and focuses on positive student-adult interactions. Students were selected for the Attendance Check-In Check-Out (CICO) program based on a review of school-wide attendance data. Students participated in daily individual check-ins from March 2018 through May 2018. Check-ins included rapport building, reviewing attendance progress, and goal setting toward reinforcers. The purpose of this research study was to answer the question: Will a school-based attendance intervention program result in an improvement in year-end attendance and absenteeism outcomes for students?
The terms student dropout, truancy, and absenteeism are often used interchangeably in conversations around student attendance. In California, recent absentee rates were 10.8% for 2017, with some student subgroups (African American, Native American) having rates as high as 18-20% (California Department of Education, 2017b). Student truancy is also a national issue. As described earlier, the U.S. Department of Education (2016) reported that more than 6 million students were chronically absent. Vaughn, Maynard, Salas-Wright, Perron, and Abdon (2013) reported that more than 17,000 youth between the ages of 12 and 17 skipped school, as recorded on the 2009 National Survey on Drug Use and Health.
Factors Related to Truancy
Numerous studies have detailed the factors that impact student truancy. Bimler and Kirkland (2001, pp. 90-91) reported 10 reasons that students are truant. These reasons included conflict with school structure, delinquent activity, personal problems, low family prioritization for school, personality problems, personal isolation, association with truant groups, rebellious behavior, living in a home environment not conducive to school, and lack of interest in education.
Sugrue, Zuel, and LaLiberte (2016) identified multiple factors that impact chronic absenteeism for elementary students. Interviews with community agency workers in truancy intervention programs revealed that the following factors impacted elementary attendance: housing, transportation, parental substance abuse and mental health, family size, family conflict, child-teacher relationships, negative school experiences, knowledge of attendance policies, employment, poverty, and cultural conflicts (Sugrue et al., 2016). Several truancy factors overlapped in the Bimler and Kirkland (2001) and Sugrue et al. (2016) studies: mental health concerns, family conflict, and negative conflicts or experiences with school.
The belief that parents impact student attendance is strong, particularly in elementary school. Santiago, Garbacz, Beattie, and Moore (2016) reported that "higher levels of parent trust in school were associated with decreased levels of emotional symptoms, peer problems, and total difficulties" (p. 1019). However, they reported that trust was bidirectional; student behavior may impact teacher trust (Santiago, Garbacz, Beattie, & Moore, 2016). Veenstra, Lindenberg, Tinga, and Ormel (2010) reported that focus on relationships with parents at home and teachers at school was preventative for truancy. A study by Robinson, Lee, Dearing, and Rogers (2018) showed that attendance improved when parents were informed about the number of student absences and absences relative to their classmates.
Attention is increasing to the importance of student-adult connections as a factor for student truancy. Veenstra et al. (2010) provided insight about the importance of social bonds by reporting that low attachment to parents and teachers was an indicator of truancy. They also found that social bonds with classmates had no effect on truancy. Kim and Page (2013) found children with poorer emotional regulation were more likely to exhibit behavior problems and higher risk for truancy. This concept is supported by Anderson, Christenson, Sinclair, and Lehr (2004), who concluded that students' and interventionists' reports of better relationships were associated with improved attendance in school. Mallett (2016) also reported that school attendance increased when students and families were more connected to schools.
Some researchers have focused on trends that impact truancy. For example, Gottfried (2017) found that fall semester absences predicted spring absences. Gottfried also found linkages to higher spring truancy among students in female, English-language learner, free and reduced lunch, and special education groups.
To address the factors that lead to truancy, researchers have implemented programs in settings including school, community, and student/family-based settings. One literature review, searching for studies between 1990 and 2007, found that the 16 reviewed studies lacked universal definitions for truancy (Sutphen, Ford, & Flaherty, 2010). A more recent review compiled a meta-analysis of 16 truancy interventions and found no significant difference in attendance outcomes by setting (Maynard et al., 2013). This review also found no difference in significance between interventions implemented in one or multiple settings. Considering that court, school, and community interventions produced similar results, Maynard, McCrea, Pigott, and Kelly (2013) concluded that practitioners should select programs based on ease of implementation.
The use of positive behavior interventions for attendance has gained attention over punitive measures. Freeman et al. (2016) reported that schools implementing school-wide positive behavior interventions and supports increase their school-wide attendance an average of 0.07% a year. Supported by their findings, Kim and Page (2013) suggested that interventions targeting truant elementary students should include positive social behavior strategies. They found that students with poor emotional regulation lack emotional security with attachment figures and need interventions to develop emotional regulation skills. Anderson et al. (2004) reported on the positive relationship-building intervention Check & Connect and revealed that positive relationships were related to better student attendance. Their discussion emphasized increasing relationship-based attendance interventions in schools (Anderson et al., 2004).
Adapted BEPs in elementary settings have shown evidence of benefits in areas outside of attendance improvement. Mitchell, Adamson, and McKenna (2017) reported on the effects of CICO from 16 published studies, a majority of which were in elementary schools. CICO is a school-based behavioral intervention focusing on frequent feedback. Researchers have reported that CICO has positive and neutral effects for decreased problem behavior and increased academic engagement. Ross and Christian (2015) concluded that an adaptation of CICO with social skills with first through third graders led to increased positive social engagement. Turtura, Anderson, and Boyd (2014) reported that CICO with three students in a middle school resulted in reduced problem behavior and increased work completion and homework accuracy. Hawken, MacLeod, and Rawlings (2007) found that the BEP, an elementary school-based CICO behavioral program, improved behavior and academic performance. Their findings had two notable points. First, students who did not show behavioral improvement required intensive individual interventions. Second, BEP had high fidelity in a school setting, implying that this style of intervention is practical in a school environment.
Although many interventions in the literature address truancy and absentee issues, research targeting interventions exclusively in the primary or elementary setting is beginning to emerge. Robinson et al. (2018) noted, "Despite the well-documented association between attendance in kindergarten and elementary school and positive student outcomes, there is little experimental research on how to reduce student absenteeism" (p.1165).
The following studies have shown evidence of attendance improvement in an elementary setting. In comparing National Network of Partnership Schools (NNPS; schools implementing community and family programming) to non-NNPS schools, Sheldon (2007) found that NNPS schools improved attendance on an average of 0.5%, while non-NPSS schools experienced a slight decline annually. Cook, Dodge, Gifford, and Schulting (2017) reported on the outcomes of the Early Truancy Prevention Program, which was piloted in 20 classrooms of a school system in the southern United States. Their study showed significant decreases in absences for students with frequent absences (six or more). A recent study by Robinson et al. (2018) found a 15% reduction in chronic absenteeism by sending informational mailers about student attendance. Students who were socioeconomically disadvantaged benefited greatly from this intervention, seeing an average absence reduction of 1.02 days compared to 0.42 days for nondisadvantaged students.
In Romania, psychologists conducted a motivational interviewing group to improve attendance outcomes in adolescents. The authors reported that group outcomes were statistically significant, detailing a 61 % decrease in the truancy rate of the experimental group (Enea & Dafinoiu, 2009).
Suggestions From Previous Research
Past studies suggest that absenteeism affects many students on a local and national scale. Numerous factors influence absenteeism for a student, and interventions target community, family, and school levels. Parent trust and awareness of attendance issues show evidence of affecting absenteeism, yet student behavior may influence parent trust. When considering the type of intervention, little continuity exists around terminology and outcomes. Moreover, evidence indicates that the intervention's setting (i.e., school, family, or community) does not have a significant impact on its effectiveness. Previous literature suggests that targeted, school-based counseling interventions have the potential for success with an absentee student population, particularly those focusing on positive relationships and social skill building.
Gaps in the Current Literature
A wealth of information has been published on factors that impact absenteeism (e.g., Bimler & Kirkland, 2001; Sugrue et al., 2016). However, despite increasing research on truancy interventions, results were inconsistent. Findings in some studies suggested that interventions focusing on teacher-student relationship building had a positive impact on attendance (Anderson et al., 2004; Freeman et al., 2016; Kim & Page, 2013; Mallett, 2016). This study investigated the effectiveness of a positive relationship intervention on attendance. I also explored the suggestion from Sutphen et al. (2010) that interventionists working with students can benefit from direct interventions more than from large-scale partnerships.
This Southern California school's attendance problem centered around 30 students who were identified as absentee (average daily attendance = 86.79%). Previous interventions were parent meetings and delivery of truancy letters. We selected the intervention attendance CICO, an adapted BEP, to target positive student-adult relationships and improve attendance.
This action research study addressed attendance issues at the school counselor practitioner researcher's elementary school. The district institutional review board granted permission to conduct this study, and I adhered to ethical research practices. I collected average daily attendance and school data, pre- and postintervention, to determine effectiveness. The setting, participants, design, instruments, procedures, and data analysis are described below.
The attendance CICO intervention was implemented in an urban elementary school setting in Southern California. The school had 680 students from transitional-kindergarten through eighth grade and a population that was 64% Latina/o, 35% Black, and 1% Other, with 88% receiving free and reduced lunch. The gender makeup of the school was approximately 53% female and 47% male. The chronic truancy rate for 2013-2014 was 9% of the total population (GreatSchools, 2017).
Students were selected based on the criteria for being a "chronic absentee." I reviewed attendance data for the 20172018 school year and identified 30 students with attendance under 90% (missing more than 18 days in the 2017-2018 school year). I provided consent forms in English and Spanish, and students and parents agreed to participate in the intervention by completing and returning the form.
Of the 30 students, 19 did not participate in the intervention due to parent or student refusal. Eleven students participated in the intervention; of these, four were second graders, three were third graders, and four were fourth graders. Nine participating students were Latina/o and two were Black. Five participants were male and six were female, and two students received special education services. The preintervention average daily attendance ranged from 81.13% to 89.62%, with the average at 86.79%.
Attendance CICO was adapted from the BEP using the following elements: (a) conducting daily check-ins to build positive student-adult relationships, (b) making data-based decisions, and (c) providing feedback and reinforcers (see Figure 1; Crone, Hawken, & Homer, 2010).
Daily check-ins. Student progress was monitored by reviewing changes in average daily attendance. In the attendance CICO design, students checked in with the school counselor in the morning (before class). This aspect of the intervention was based on the daily features of the BEP, in which students start each day with a positive interaction with an adult (Crone et al., 2010, p. 26). Students earned one attendance point for each check-in with the school counselor. During the check-in, I reviewed the number of points accumulated by the student, talked with the student briefly to build a positive student-adult relationship, and set goals with the student for the next positive reinforcer based on attendance points.
Data monitoring. This was central to the design of attendance CICO. Each month, I monitored daily attendance percentages to select students for the intervention and track student progress. This aspect of the intervention was based on data monitoring in the BEP. Regular monitoring of student data provided information to determine whether interventions should be modified, continued, or ended (Crone et al., 2010, p. 34).
Reinforcements and feedback. I tracked attendance points and reviewed progress with the students to track progress toward earning positive reinforcers. This aspect of the intervention was based in the behavioral principles of the BEP: At-risk students benefited from clear expectations, frequent feedback, consistency, and reinforcement for meeting goals (Crone et al., 2010, p. 15). I marked attendance points on an attendance tracker spreadsheet and reviewed it weekly with the students to track their progress toward positive reinforcers. I collaborated with each student to set reinforcers, which could include extra recess, afternoon game time, or lunchtime with the school counselor. Crone, Hawken, and Homer (2010) noted that the importance of reinforcers was to help the student set goals and see that their behavior is noticed by others.
I created two documents to monitor the attendance CICO intervention. First, a monthly average daily attendance spreadsheet allowed me to monitor changes in average daily attendance for each student. Second, I used an attendance tracker sheet (see Figure 2) daily to mark completed check-ins and review attendance points toward student positive reinforcers.
I collected data on average daily attendance and met individually with identified students. The intervention took place in school, before the start of school instruction.
My initial meetings with each student covered an overview of the attendance CICO intervention, morning check-ins, attendance points, and setting positive reinforcers. Next, I met with each student daily for approximately 1 min. Additional meetings could be scheduled for specific issues brought up in check-ins. The conversation contained the following elements: rapport-building interaction, monitoring of attendance points, and review of progress toward positive reinforcers.
Students received positive reinforcers after earning the first 10 points and for every 20 attendance points thereafter. I met with students in groups during the afternoons to provide reinforcers. To evaluate effectiveness, I reviewed attendance data monthly.
Average daily attendance. The number of present days divided by the total number of school attendance days was the calculation for average daily attendance. Effectiveness of the attendance CICO intervention was measured by comparing pre- and postintervention student attendance data. Preintervention attendance was collected in March 2018 and postintervention data in May 2018. The academic year began in August 2017 and ended in June 2018.
Informal data collection. In attendance CICO check-ins, I collected students' responses to identify factors contributing to their school absences.
Data analysis. I analyzed the average daily attendance by conducting a t test that paired two samples for means. Analysis of the pre- and postintervention data indicated any statistical significance of attendance CICO results. A comparison of days absent among participants also provided additional information on the effectiveness of attendance CICO.
I analyzed the data using statistical functions in Microsoft Excel and compared pre- and postintervention daily average attendance to determine the intervention effectiveness. I conducted a paired-sample t test to determine the difference in pre and postintervention average daily attendance, with a significance level of p < .05. I found a nonsignificant correlation of p = .6347 between the preintervention average daily attendance (M = 86.79%, SD = .031%) and postintervention average daily attendance (M = 86.36%, SD = .052%; see Table 1).
Review of the data in Table 2 showed that average daily attendance decreased for six students, one of whom had a 7.92% decrease, while five students showed positive attendance changes. Overall, the intervention resulted in a slight decrease of 0.43% in participants' average daily attendance. Four students (36.36%) decreased the number of absences from 1 month before the intervention to the first 4 weeks of intervention. Five students (45.45%) decreased the number of absences from the first 4 weeks of intervention to the last 4 weeks of intervention. Among all the students, the mean decrease of absences was 0.18 from the first 4 weeks of intervention to the last 4 weeks of intervention.
According to informal data collection (see Table 3), students attributed 51.47% of absences during the intervention to health issues. Another major factor was family health issues (36.76%); 7.35% were related to vacation, 2.94% to negative school experiences, and 1.47% to school suspension.
Prior research indicated effectiveness for interventions targeting positive student-adult relationships (Cook et al., 2017; Mitchell, Adamson, & McKenna, 2017; Sugrue et al., 2016; Turtura, Anderson, & Boyd, 2014). Positive student-adult relationships were a daily emphasis of this study. Students in this intervention built positive relationships with the school counselor and their peers and averaged a decrease in absences during the intervention. Although not statistically significant, informal exit interviews with students indicated that they enjoyed the group reinforcers and check-ins with the school counselor. For reinforcers, students played games and discussed attendance issues in a group format.
Previous literature attributed absenteeism issues to multiple factors (Bimler & Kirkland, 2001; Sugrue et al., 2016), notably the common factors of mental health concerns, family conflict, and negative conflicts or experiences with school. Attendance CICO specifically targeted negative conflicts or experiences with school, which was more impactful for some participants than others. For example, the family of Student 2 (-7.92% avg. daily attendance change) reported family health issues that led to absences. An intervention addressing health issues might have been more relevant for this student. Student 1 (2.44% avg. daily attendance change) reported that two absences were due to negative school experiences, specifically bullying issues. For Student 1, attendance CICO brought to light and helped resolve his negative experiences in school. For future interventions, a preintervention screening would be helpful to identify students most appropriate for the relationship-building intervention.
The timing of implementing attendance CICO may have affected attendance outcomes. With the intervention beginning in March 2018, the findings support Gottfried (2017) who found that each fall absence was linked to an additional half-day absence in the spring. More than half (54.5%) of participants had a decline in attendance, although the decrease was more than 1 % for only three students. The intervention began in the spring, when participants already maintained a low average daily attendance. Attendance CICO may have prevented a larger decrease in average daily attendance. Eight participants (72.72%) concluded the intervention with the same or a decreased number of absences when comparing the first 4 and last 4 weeks of the intervention. Prior to this study, I completed a pilot using attendance CICO and found that an October-to-May intervention yielded a significant 7.02% increase in average daily attendance. Comparing the average daily attendance of students in the pilot study, attendance initially dropped but remained constant through the year in 2016-2017, while attendance had steadily dropped throughout the prior year, 2015-2016 (see Figure 3). Attendance CICO may also have a stabilizing impact as a year-round attendance intervention. Gottfried (2017) and my findings suggest that school-based attendance interventions should start in the fall to maximize effectiveness.
The goal of this intervention was to explore the effectiveness of a practical, school-based attendance intervention. Some school-based interventions, such as Check & Connect, have efficacy yet require teachers or staff to work outside the school setting (Anderson et al., 2004; Cook et al., 2017). The use of mailers to target parental beliefs on attendance is a low-cost, promising intervention that was published after the present study (Robinson, Lee, Dearing, & Rogers, 2018). Although my findings did not indicate that attendance CICO significantly increased average daily attendance, this intervention did show promise on decreasing student absences. Multiple students in the study showed a decrease in absences during the period of the intervention. Moreover, the mean of student absences 1 month preintervention (3.27) decreased 0.36 compared to the last month of intervention (2.91; see Table 2). Measures of average daily attendance accurately show attendance over the year but do not account for the time outside the intervention (August-March).
I used the findings from this study to consider many changes to attendance CICO and future attendance interventions. First, prescreening could target students impacted by negative conflicts or school experiences. Second, attendance interventions could begin earlier in the year, noting the possible stabilizing effect of attendance CICO. Third, the intervention could include greater efforts to communicate with parents about improving attendance. Recent studies suggested that parent communication may impact absenteeism (Robinson et al., 2018). Finally, many student absences were related to health concerns. Prioritizing collaboration with a health practitioner would be essential to helping students and their families.
Implications for Practice and Research
The results of this study have several implications that may help school counselors identify practical and effective interventions to improve student attendance.
First, school counselors might collaborate with other stakeholders while implementing attendance interventions. In this study, students reported that 51.47% of absences were due to health concerns. This information was valuable for the principal and school nurse to address student needs. For Student 2 (2.44% avg. daily attendance change), attendance CICO revealed that the student was staying home to avoid a bullying issue. I referred the student to a restorative conference with the identified students, resolved the issue, and ultimately improved his attendance. A key function of school counselors is to collaborate with principals and stakeholders to address student needs (ASCA, 2012).
Second, school counselors should continue looking to current literature to find the most appropriate interventions for their setting. For the Southern California school in this study, low cost and practicality were important, leading to our use of attendance CICO. Other schools likely have different needs and funding. A school with more resources may select a program like Check & Connect or the Early Truancy Prevention Program, which involves more teacher and financial commitment (Anderson et al., 2004; Cook et al., 2017). Recent articles about other low-cost interventions, like an attendance mailing program (Robinson et al., 2018), can provide insight on new approaches to addressing attendance issues. School counselors are crucial in recommending the best interventions, knowing the school's needs and resources.
Third, school counselors should focus on parent trust and beliefs when implementing attendance interventions. We selected attendance CICO in part because the school was having little success with parent meetings for absentee students. Issues with parent trust may have affected the success of those meetings. Only 36.66% of parents completed consent forms for their students to participate in the attendance CICO study, indicating that trust may have been a primary factor. Sheldon (2007) found that schools implementing community and family programs had improved attendance, and the programming was taking place in the school. Santiago et al. (2016) found that higher levels of parent trust were associated with child prosocial behavior. Robinson et al. (2018) focused on mailers to change parent attendance beliefs. Parent beliefs may have impacted student absences in the current study, with 44.11% of absences reported by students as family health concerns (36.76%) and vacation (7.35%; see Table 3). Extensive evidence of beneficial parent attendance interventions may be limited, but targeting parent trust and beliefs has promise.
Multiple limitations impacted this study. First, replicating a pilot study with a different population may have resulted in a different outcome. The pilot was implemented in a school with different demographics (99% Black, 1% multiethnic) in Northern Illinois. Second, timing of the intervention may have been a factor in results. Although average daily attendance measures absenteeism, it measured 7 months outside this study, whereas the pilot intervention was implemented over most of the school year. Third, the small sample size of the intervention may prevent drawing of conclusions and generalizing this intervention to other schools and populations. The pilot intervention included 23 students, while this study only included 11. Fourth, the school counselor implemented the study, leading to the possibility of researcher bias.
This study described attendance CICO and its impact on attendance outcomes in a school setting. The school counselor collected average daily attendance data to investigate the impact of the intervention, and the results did not show significant improvement in student attendance. The intervention does show promise with a resulting decrease of student absences during the intervention. Interpreting results requires caution because of limitations described in the research design. More rigorous research design will better demonstrate the impact student-adult relationships have on attendance. Increasing school-based research will inform more practical and effective strategies for school counselors, specifically in an elementary setting.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Theodore Stripling [ID] https://orcid.org/0000-0002-9957-8579 References
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Theodore Stripling is a high school counselor with the University of Chicago Laboratory Schools in Chicago, IL.
Theodore Stripling  [ID]
 University of Chicago Laboratory Schools, Chicago, IL, USA
Theodore Stripling, University of Chicago Laboratory Schools, 1152 N Kedzie Ave, Apt 302, Chicago, IL 60651, USA.
Caption: Figure 1. Overview of attendance check-in check-out design.
Caption: Figure 2. Attendance tracker.
Caption: Figure 3. Attendance attendance check-in check-out pilot study: Average daily attendance comparison.
Table 1. Data Analysis of Average Daily Attendance (Pre- and Postintervention) t Test: Paired Two Sample for Means. Data Elements Preintervention Mean 0.867918182 Variance 0.000978552 Standard deviation 0.03128 Observations 11 Pearson's correlation 0.874836858 Hypothesized mean difference 0 df 10 t Stat 0.490083208 p (T [less than or equal to] t) one-tail 0.317324411 t Critical one-tail 1.812461123 p (T [less than or equal to] t) two-tail 0.634648822 t Critical two-tail 2.228138852 Data Elements Postintervention Mean 0.863627273 Variance 0.00271834 Standard deviation 0.05213 Observations 11 Pearson's correlation Hypothesized mean difference df t Stat p (T [less than or equal to] t) one-tail t Critical one-tail p (T [less than or equal to] t) two-tail t Critical two-tail Table 2. Pre- and Postintervention Student Data. Student Preintervention (%) Postintervention (%) 1 82.08 84.52 2 81.13 73.21 3 83.96 83.33 4 89.62 88.69 5 88.68 88.10 6 88.68 91.07 7 86.79 85.12 8 85.85 85.12 9 89.62 89.88 10 88.68 88.69 11 89.62 92.26 Mean 86.79 86.36 Attendance Absences 4 Weeks Student Change (%) Before Intervention 1 2.44 3 2 -7.92 7 3 -0.63 1 4 -0.93 1 5 -0.58 1 6 2.39 2 7 -1.67 4 8 -0.73 4 9 0.26 6 10 0.01 4 11 2.64 3 Mean -0.43 3.27 Absences First 4 Weeks Absences Last 4 Weeks Student Intervention Intervention 1 4 1 2 8 9 3 2 4 4 3 2 5 3 2 6 2 1 7 5 5 8 3 3 9 2 2 10 1 3 11 1 0 Mean 3.09 2.91 Table 3. Student Response for Factors Leading to School Absence. Attendance Factor Absences Percentage Health 35 51.47 Family health 25 36.76 Vacation 5 7.35 Negative school experiences 2 2.94 Suspension 1 1.47
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|Title Annotation:||Practitioner Research|
|Publication:||Professional School Counseling|
|Date:||Sep 1, 2018|
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