Recognized ASCA Model Program (RAMP) and Student Outcomes in Elementary and Middle Schools.
Although reports on comprehensive school counseling programs have been primarily positive, Lapan (2012) argued that critical challenges remain in providing all students the benefits of such programs due to the substantial implementation gaps in counseling programs across schools. For example, Lapan highlighted findings from the 2010 Public Agenda report, which noted that more than 50% of students report not utilizing counseling services. Furthermore, most studies have measured the comprehensiveness of a school counseling program through teacher (Lapan et al., 2001) or school counselor perceptions--most recently utilizing the Survey of Comprehensive School Counseling Programs (Lapan, Gysbers, & Kayson, 2006) or the School Counseling Program Implementation Survey (Clemens et al., 2010). The structured surveys are useful, but self-report is often influenced by social desirability bias or varied interpretation of questions. Along with implementation and measurement, questions remain about the actual quality of comprehensive programs. Martin, Carey, and DeCoster (2009) illustrated this point: Although 43 states have implemented comprehensive school counseling programs, only 10 states have evaluation systems for these programs and even fewer are designated as rigorous evaluations.
Given the questions around implementation, self-report, and the quality of school counseling programming, further inquiry is needed. Wilkerson, Perusse, and Hughes (2013) recommended that researchers assess outcomes for the Recognized ASCA Model Program (RAMP) designation from the American School Counselor Association (ASCA). RAMP is a recognition awarded to schools that have successfully demonstrated and documented their implementation of a comprehensive school counseling program based on the ASCA National Model. Since the designation of RAMP status schools began in 2003, we are aware of only a handful of studies that have examined the relationship of RAMP to student outcomes. Despite this limited outcome evidence, schools continue to obtain RAMP status: As of 2019, more than 1,100 schools in 41 states had earned the RAMP recognition (ASCA, 2019b). In theory, the RAMP designation offers an opportunity to externally measure both implementation and quality to enable more substantial inquiry on the impact of comprehensive programs.
ASCA National Model and RAMP
The ASCA National Model (2019a) serves as the most recent iteration in a 50-year charge toward developing comprehensive school counseling programs in K-12 educational settings. By establishing a set of developmental student competencies (e.g., mind-sets and behaviors) and a common framework for developing, implementing, and evaluating counseling programs (Scarborough & Luke, 2008), the ASCA National Model helps school counselors implement comprehensive, data-driven school counseling programs and has been used as a measure of accountability for key stakeholders (ASCA, 2019a). Following the results-based models of accountability espoused by Johnson and Johnson (2003), the ASCA National Model focuses not only on what services should be implemented but also on the impact of those services on students.
After establishing the ASCA National Model, ASCA introduced the RAMP designation for "school counseling programs that have demonstrated advanced implementation of comprehensive, results-based, developmental programs" (ASCA, 2012, p. xi). The RAMP designation of schools provides an opportunity to address the limitations of research, implementation, and evaluation raised by Burkhard, Gillen, Martinez, and Skytte (2012), Lapan (2012), and Wilkerson et al. (2013). Schools that implement RAMP must complete a detailed application that substantiates the school's efforts to develop and implement a comprehensive, data-driven, accountable school counseling program guided by specific, identified needs (Wilkerson, Perusse, & Hughes, 2013). With external review to substantiate comprehensiveness and quality, RAMP provides an ability to audit school counseling programs and to examine the quality of specific components or interventions.
In contrast to the broader scholarship around comprehensive school counseling programs, research on RAMP and its outcomes has been sparse. Over the last decade, a few dissertations have explored teacher perceptions of counselors in RAMP schools (Vacchio, 2012), RAMP's relationship to job satisfaction (Bentley, 2015; Slaughter, 2016), and school counselors' experiences with RAMP (Duquette, 2019; Fitzgerald, 2019). Another dissertation (Ward, 2009) considered the associations between RAMP and student attendance and achievement outcomes. Analyzing administrative and survey data, Ward (2009) found positive relationships between RAMP status and elementary school students' attendance and reading achievement. Similarly, in her dissertation, Nava (2018) found positive associations between RAMP implementation and student achievement outcomes, especially for Black students in one high school in California.
To date, a small number of published journal studies have investigated RAMP. Goodman-Scott and Grothaus (2017a, 2017b) examined the congruence between RAMP and positive behavioral intervention and supports (PBIS) in the roles school counselors play and the goals of PBIS programming. Published studies examining associations between RAMP and student outcomes have generally returned statistically insignificant results. For example, Wilkerson and colleagues (2013) found no significant school-level achievement differences in RAMP middle and high schools, albeit a positive relationship in elementary schools. Likewise, in a two-state investigation of elementary, middle, and high schools, Goodman-Scott et al. (2019) found no significant relationships between RAMP status and school-level student outcomes (e.g., achievement, suspension rates, and attendance). Both Wilkerson et al. (2013) and Goodman-Scott et al. (2019) used outcome data aggregated to the school level.
We continued this inquiry on RAMP and student outcomes. In this study, we estimated the associations between RAMP and the achievement and attendance outcomes of elementary and middle school students. One advantage of our study is access to student-level achievement and attendance outcomes (rather than school-level data). Furthermore, each of these previous studies compared outcomes for RAMP schools and non-RAMP schools--using stratified sampling (Wilkerson et al., 2013) or a more rigorous propensity score matching method (Goodman-Scott et al., 2019). Our analyses took a different approach by assessing how student achievement and attendance changed after schools became RAMP-designated.
1. Do students attending RAMP-designated elementary and middle schools have higher adjusted average achievement on statewide assessments?
2. Do students attending RAMP-designated elementary and middle schools have fewer school absences?
3. Do these results vary based on students' race/ethnicity or economically disadvantaged status?
To assess outcomes for students attending RAMP-designated schools, we focused on elementary and middle schools in Wake County, NC, during the 2008-2009 through 2014-2015 school years. Although Wake County has several RAMP-designated high schools, we restricted our focus to elementary and middle schools given data access and differences in achievement testing in high school grades. Wake County is the largest school district in North Carolina--enrolling nearly 160,000 students in 171 schools--and is the 15th largest school system in the United States. The district is a longtime proponent of comprehensive school counseling and has many schools that have successfully completed the RAMP recognition process.
Table 1 displays descriptive data for RAMP and non-RAMP elementary and middle schools in Wake County. In this table and for all of our analyses, RAMP is a time-varying indicator equal to 1 for schools that are RAMP-designated in a given school year and 0 if the school is not RAMP-designated. As shown in the bottom panel of Table 1, during our study period, 14 elementary schools and 14 middle schools held the RAMP designation. Specifically, we observed 12 elementary schools and 11 middle schools switch from non-RAMP to RAMP status during our 7-year study period. Two elementary schools and three middle schools held the RAMP designation throughout our entire study period--meaning the schools became RAMP-designated before 2009 and re-earned the RAMP designation without a time lapse. Of the schools studied, 95 elementary and 20 middle schools never held the RAMP designation during our study period.
RAMP elementary and middle schools generally enroll fewer racial/ethnic minority and economically disadvantaged students than non-RAMP schools (see Table 1). These differences are especially pronounced in elementary schools. For example, 29% of the students in RAMP-designated elementary schools are economically disadvantaged versus 43% in non-RAMP elementary schools. Likewise, RAMP schools have slightly higher performance composites (i.e., the percentage of state tests passed) and lower short-term suspension rates. Data on school expenditures are mixed--RAMP elementary schools spend more per pupil, whereas non-RAMP middle schools spend more in that category than their RAMP counterparts. Finally, teacher credential data show that RAMP schools employ a higher percentage of nationally board-certified teachers and a lower percentage of novice teachers.
Our analyses focused on two student academic outcomes: exam scores on statewide achievement tests and absences from school. We focused on these academic measures for several reasons: (a) student achievement is a strong predictor of educational attainment and other academic outcomes, (b) school attendance is a measure of engagement with school and predicts student achievement (Gottfried, 2010), and (c) prior work shows that school counselors influence both of these student outcomes (Lapan, Gysbers, Bragg, & Pierce, 2012; Reback, 2010).
For student achievement, we analyzed students' mathematics and reading test scores from end-of-grade (EOG) exams in Grades 3-8. Each spring (May/June), approximately 650,000 North Carolina public school students take these EOG exams in elementary and middle grades (3-8). We standardized these exam scores by year, grade, and subject area across all students in North Carolina public schools and estimated separate models in elementary grades mathematics and reading and in middle grades mathematics and reading. With these data, we were able to assess whether students in RAMP-designated schools had higher levels of adjusted average achievement.
For absences, we analyzed the total number of days absent for students in Grades 3-8 during a given school year (i.e., days the student did not attend school). These absences could be for excused and unexcused reasons. As an extension of this continuous measure, we created a dichotomous indicator for chronically absent students. We defined chronically absent students as those who missed more than 10% of school days in a given academic year. We estimated separate absence models for elementary grades students (Grades 3-5) and middle grades students (Grades 6-8). These absence measures enabled us to assess whether students in RAMP-designated schools have higher levels of attendance.
In these analyses, we aimed to isolate the associations between a school's RAMP status and student achievement and attendance. This was challenging due to selection concerns. Schools decide to pursue the RAMP credential; therefore, differences across schools rather than RAMP itself may explain differences in student outcomes. That is, if well-organized, high-achieving, or otherwise relatively advantaged schools are more likely to earn a RAMP designation, then students attending RAMP schools may have better outcomes than students attending non-RAMP schools even if the RAMP program did nothing to benefit students. Some evidence for this concern is illustrated by the observable differences between RAMP and non-RAMP elementary and middle schools in Table 1.
To date, Goodman-Scott and colleagues (2019) have performed the most empirically rigorous analysis of RAMP. They used propensity score analyses to match RAMP schools to observationally similar schools that did not hold the RAMP designation. This strategy addresses some concerns about selection into the RAMP program because it compares observationally similar schools with and without RAMP. However, this approach does not explicitly adjust for unobservable characteristics (e.g., school culture, organizational structure) and does not test whether RAMP actually improves student outcomes.
Given these concerns, our preferred analytical approach was a regression model with a school fixed effect. Rather than comparing student achievement and attendance at RAMP versus non-RAMP schools, this approach makes within-school comparisons. This means that we assessed whether student achievement and attendance improved at schools after they earned the RAMP credential relative to achievement and attendance before becoming a RAMP school. Although a school fixed effect is a more rigorous way to isolate the associations between RAMP and student outcomes, this modeling strategy does have limitations. Results from a school fixed effect model may fail to show RAMP effects if the benefits of RAMP accrue while a school is completing application materials (i.e., before it is officially a RAMP school). In analyses, we coded a school as RAMP beginning in the first school year in which it was officially RAMP-designated. A school fixed effect may also have generalizability concerns, meaning that the estimated effect would not be replicated in a different set of schools. We acknowledge these limitations but still consider school fixed effect models as the most rigorous way to isolate the relationships between RAMP and student outcomes.
To address our first two research questions, we estimated regression models for student achievement and absences with all Wake County elementary and middle school students in the 2009 through 2015 school years. For our third research question--whether results differ based on student demographic characteristics--we estimated separate models for Black, Hispanic, and White students. We also estimated separate models for students who have ever been classified as economically disadvantaged (qualifying for subsidized school meals) and for those who have never been classified as economically disadvantaged. In all of these analyses, our RAMP estimates reflected the changes in achievement and attendance for students attending schools that switched from non-RAMP to RAMP during the study period. As detailed below, all of our analyses controlled for select student and time-varying school characteristics. Overall, our basic analysis framework is depicted by Equation 1:
[Outcome.sub.ist] = [beta] [RAMP.sub.st] + [alpha] [Student_Char.sub.ist] + [gamma] [School_Char.sub.st] + [[sigma].sub.s] + [[epsilon].sub.ist]. (1)
Here, [Outcome.sub.ist] is the standardized EOG test score, number of days absent, or an indicator for being chronically absent for student i in school s at time t. [RAMP.sub.st] is an indicator equal to 1 if the school is RAMP-designated in a given school year and 0 if it is not. [Student_Char.sub.ist] includes the following student characteristics: race/ethnicity, gender, economically disadvantaged, limited English proficient, special education status, and mobility (switching schools between years). In student achievement models, [Student_Char.sub.ist] also includes students' prior test scores in mathematics and reading (standardized). In additional student absence models, we also controlled for students' number of absences from the previous school year. [School_Char.sub.st] includes controls for the percentage of minority and economically disadvantaged students at the school. [[sigma].sub.s] is a school fixed effect, allowing us to assess how changes in a school's RAMP designation were associated with changes in student outcomes, and [[epsilon].sub.ist] is an error term for unexplained variance. All models also include grade-level and year fixed effects.
Table 2 displays achievement results for elementary and middle school students in Wake County and separate achievement results by race/ethnicity and economically disadvantaged status. All Wake County elementary and middle school students (in 2009-2015) were part of our analysis sample. However, with a school fixed effect, the RAMP estimates are based on students attending schools that switched from non-RAMP to RAMP during the study period. The top panel of Table 2 reveals no significant associations between RAMP and student achievement in mathematics and reading. On average, student achievement on EOG exams in mathematics and reading is no different after a school became RAMP-designated than before it was RAMP-designated. However, these overall results mask some findings for student subgroups. In elementary grades reading, students who are White and never economically disadvantaged scored higher after a school became RAMP-designated. For example, adjusted average achievement for White students is 2% of a standard deviation higher after the school earned the RAMP credential. In middle grades mathematics, we see a similar result for Hispanic students--their adjusted average achievement was nearly 5% of a standard deviation higher after the school became RAMP-credentialed. Although not statistically significant at the 0.05 level (p value < .10), these data suggest that RAMP can positively influence student achievement. Conversely, one statistically significant negative result occurred in middle grades reading: Black students had adjusted average achievement more than 4% of a standard deviation lower after a school became RAMP-designated. To put these results into perspective, we note that 5% of a standard deviation equates to approximately one-half point on EOG exams.
Turning to student absences, estimates from the top panel of Table 3 indicate that student attendance did not change after an elementary school becomes RAMP-designated. For middle schools, however, student attendance improved at a statistically significant level after a middle school became RAMP-designated. On average, middle school students were absent nearly one third of a day less after their school earned the RAMP credential. To put this result into perspective, during our study period, the average number of days absent in a school year for Wake County middle school students was 6.72. Estimates from additional analyses that control for students' prior year absences are statistically insignificant in elementary and middle schools. However, the magnitude of these RAMP estimates is very similar to those in the top panel of Table 3. This suggests that the larger standard errors (and reduced sample size) explain the loss of statistical significance when controlling for prior year absences.
The bottom panel of Table 3 presents absence results for student subgroups. Here, the trends for students who have ever been classified as economically disadvantaged provide optimism. After an elementary school became RAMP-credentialed, students who were ever economically disadvantaged were absent one third of a day less and were 1.2 percentage points less likely to be chronically absent. Likewise, after a middle school became RAMP-credentialed, students who were ever economically disadvantaged were absent nearly one-half day less and were 1.7 percentage points less likely to be chronically absent. To put these results into perspective, approximately 4% of elementary school students and 7.5% of middle school students on average are chronically absent. We also found several middle school student subgroups who benefited at a statistically significant level. After a middle school became RAMP-designated, (a) Hispanic students were absent one-half day less and were two percentage points less likely to be chronically absent; (b) White students were absent two fifths of a day less; and (c) students who were never economically disadvantaged were absent one third of a day less. Estimates from our student subgroup models were very similar in magnitude when we controlled for students' prior year absences.
To put our RAMP results in context, we restate our study limitations. First, a school fixed effect is methodologically rigorous--allowing us to assess how outcomes change after a school earns the RAMP credential--but not necessarily causal. Our study could not fully isolate the mechanisms leading to increased student attendance after a school became RAMP-designated. Second, it is possible that the benefits of RAMP accrue before a school is officially RAMP-designated--that is, as a result of preparing the application materials and implementing the program. These improvements would not be detected with our school fixed effect since the school would not yet have switched from non-RAMP to RAMP-designated. Third, another possibility is that measurable benefits of a comprehensive school counseling program accrue the longer such a program is in place (Sink et al., 2008) or with sustained school counselor continuity. As such, extended time in a RAMP program by a consistent school counseling team may mean stronger associations between RAMP and student outcomes.
These results add to the literature on comprehensive school counseling programs and the emerging outcome research on RAMP. Previous investigations of comprehensive programs relied on self-reports and mostly focused on implementation without a metric of quality. Utilizing RAMP as the independent variable provided an external metric of implementation and quality review. Although RAMP is a more rigorous measure of a school counseling program, we find, with a few exceptions, that student achievement is no different in elementary and middle schools after a school earns RAMP designation. These findings are consistent with Goodman-Scott et al. (2019) and mirror most of the findings of Wilkerson and colleagues (2013).
Although Goodman-Scott et al. (2019) did not find differences in attendance outcomes, we saw significant results that suggest attendance outcomes do improve in RAMP schools, especially for middle schools and more vulnerable students (e.g., Hispanic and economically disadvantaged). Because school counselors do not provide direct classroom instruction in tested subject areas, the nature of school counseling may be more conducive to influencing attendance rather than achievement. Attendance is an indicator of school engagement and may be more malleable through a comprehensive program, particularly as students transition into middle school.
Implications for Practice, Preparation, and Future Research
Although some faith in comprehensive school counseling programs is warranted, given studies showing benefits to students and schools, it is too early to make declarative recommendations about student outcomes and the impact of RAMP. The mixed findings on attendance and largely insignificant results, to date, for RAMP and student achievement raise questions regarding the broader contribution of the RAMP designation. Even so, practitioners may still find RAMP useful in program building (e.g., data collection and integration) or process factors (e.g., improved professionalism, increased teacher and administration support) noted by recent dissertations on RAMP, or in congruence with other school-wide endeavors like PBIS (Goodman-Scott & Grothaus, 2017a). Further, we believe RAMP extends the promise of the programmatic approach to school counseling because it signals a possible measure of quality that extends beyond the school counseling program due to the external review process.
These results require school counseling faculty to be clear about the benefits of RAMP in the preparation of new school counselors. Instead of broad claims about the impact on academic achievement, faculty should discuss the mixed outcome results, possible process benefits, and the overarching issue of professional identity as it relates to RAMP. Current accreditation standards do not specify the ASCA National Model as a requirement, and contemporary counselor preparation programs have considerable variation in program structure and foci (Akos & Scarborough, 2004; Perusse, Goodnough, & Noel, 2001). The utility of the ASCA National Model for preparation consistency, even amid mixed results of RAMP on student outcomes, may provide value to the profession.
Continued research is needed to provide more precise recommendations to the school counseling field. Like any developing research area, variance in sample, measurement, analyses, and outcomes may lead to different findings. Furthermore, improving student achievement may take years of comprehensive school counseling programming.
In particular, future research needs to replicate existing studies in new environments, examine additional outcome measures, and determine whether better measures of intervention and counseling program quality may exist. Researchers must also consider the variation in program implementation within a RAMP school. That is, given variation in student needs and school cultures, quality RAMP programs might employ significantly different interventions within their programmatic approach. A robust research base addresses interventions that school counselors use (e.g., Second Step), but these interventions are very prescriptive, even manualized as compared to the overarching framework provided by the ASCA National Model (ASCA, 2019a). Only limited information is available about external reviewer qualifications or selection (ASCA, 2019b). Furthermore, as noted by Lapan (2012), the "dosage" of counseling that students receive may vary considerably within schools (i.e., not all students may see a school counselor or one student may see the counselor extensively). Measuring the quantity, type, and quality of students' interactions with school counselors as possible moderators of RAMP effects is important for future analyses.
Although the RAMP designation represents one strategy for recognizing program quality, what may matter more is the quality of the school counselors in a program. Individual quality measures, such as indicators for Nationally Certified School Counselors or evaluation ratings of school counselors' performance, may have stronger associations with student outcomes. Evaluation of the quality of school counselors is itself precarious because of the varied and perhaps inadequate measures or experience of administrators to access accurately. However, the competence of individual school counselors in delivering classroom instruction and individual/group counseling remains a core part of a quality school counseling program.
Finally, although specific school counselor interventions like Student Success Skills have documented impact on academic achievement (Villares et al., 2012), we acknowledge that test scores and attendance are only a few of the important student outcomes to consider. For example, Ward (2009) suggests that RAMP contributed to an increase in students' conflict resolution skills, ability to understand and manage feelings, studying and test-taking skills, and knowledge of career and postsecondary educational opportunities. Future studies might consider perception data as additive and should examine outcome measures that are more proximal or perhaps more malleable (e.g., [Free Application for Federal Student Aid (FAFSA[R])]. completion, perceptions of school climate) to school counselor programming. Also probable is that other outcomes--for example, socioemotional development, relationships with peers and teachers, student behavior and engagement with school, mental health, and school safety--might be more appropriate measures for a comprehensive school counseling program.
Like previous analyses, we find mixed results regarding the impact of RAMP on student outcomes. We believe our analyses provide optimism about the relationship between RAMP and student attendance in middle school. We also believe inquiry on elementary and middle schools adds to the more extensive outcome research on comprehensive school counseling programs in high schools. Moving forward, continued analyses are needed to more fully understand RAMP's potential impact on school and student success.
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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project is financed by American School Counselor Association (2017 ASCA Research Grant).
Patrick Akos [ID] https://orcid.org/0000-0002-6710-5902
Akos, P., & Scarborough, J. (2004). An examination of the clinical preparation of school counselors. Counselor Education and Supervision, 44, 96-107.
American School Counselor Association. (2012). ASCA national model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.
American School Counselor Association. (2019a). ASCA national model: A framework for school counseling programs (4th ed.). Alexandria, VA: Author.
American School Counselor Association. (2019b). Recognized ASCA model program (RAMP). Retrieved from https://www.schoolcoun selor.org/school-counselors/recognized-asca-model-program-(ramp)
Bentley, T. S. (2015). An examination of the relationship between RAMP (recognized ASCA model program) and job satisfaction among school counselors. Dissertation Abstracts International: Section A. Humanities and Social Sciences. ProQuest Information & Learning. Retrieved from https://auth.lib.unc.edu/ezproxy_auth. php?url=http://search.ebscohost.com/login.aspx?direct=true& db=psyh&AN=2015-99050-247&site=ehost-live&scope=site
Burkhard, A. W., Gillen, M., Martinez, M. J., & Skytte, S. (2012). Implementation challenges and training needs for comprehensive school counseling programs in Wisconsin high schools. Professional School Counseling, 16, 136-145. doi:10.1177/ 2156759X0001600201
Carey, J., Harrington, K., Martin, I., & Hoffman, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA national model school counseling programs in rural and suburban Nebraska. Professional School Counseling, 16, 100-107. doi:10.1177/2156759X0001600202
Clemens, E. V., Carey, J. C., & Harrington, K. M. (2010). The school counseling program implementation survey: Initial instrument development and exploratory factor analysis. Professional School Counseling, 14, 125-133. doi:10.1177/2156759X1001400201
Duquette, K. (2019). A narrative inquiry into elementary school counselors' perceptions of important considerations during the process of gaining recognized ASCA model program (RAMP) certification. Dissertation Abstracts International: Section A. Humanities and Social Sciences. ProQuest Information & Learning. Retrieved from https://auth.lib.unc.edu/ezproxy_auth.php?url=http://search. ebscohost.com/login.aspx?direct=true&db=psyh&AN=2019-00351-003&site=ehost-live&scope=site
Fitzgerald, A. R. (2019). An examination of current and past recognized ASCA model program (RAMP) recipients and their experiences with pursuing the re-RAMP designation. Dissertation Abstracts International: Section A. Humanities and Social Sciences. ProQuest Information & Learning. Retrieved from https://auth.lib.unc.edu/ezproxy_auth.php?url=http://search.ebsco host.com/login.as.px?direct=true&db=psyh&AN=2019-23491-073&site=ehost-live&scope=site
Goodman-Scott, E., & Grothaus, T. (2017a). RAMP and PBIS: "They definitely support one another": The results of a phenomenological study (part one). Professional School Counseling, 21. doi:10.5330/ 1096-2409-21.1.119
Goodman-Scott, E., & Grothaus, T. (2017b). School counselors' roles in RAMP and PBIS. Professional School Counseling, 21. doi: 10.5330/1096-2409-21.1.130
Goodman-Scott, E., Taylor, J., Kalkbrenner, M., Darsie, J., Barbosa, R., Walsh, K., & Worth, A. (2019). A multivariate analyses of variance investigation of school-level RAMP status across two states. Counseling Outcome Research and Evaluation. doi:10.1080/ 21501378.2019.1575696
Gottfried, M. A. (2010). Evaluating the relationship between student attendance and achievement in urban elementary and middle schools: An instrumental variables approach. American Educational Research Journal, 47, 434-465. doi:10.3102/000283120 9350494
Johnson, S., & Johnson, C. D. (2003). Results-based guidance: A systems approach to student support programs. Professional School Counseling, 6, 180-184.
Lapan, R. T. (2012). Comprehensive school counseling programs: In some schools for some students but not in all schools for all students. Professional School Counseling, 16, 84-88. doi:10.1177/ 2156759X1201600201
Lapan, R. T., Gysbers, N. C., Bragg, S., & Pierce, M. E. (2012). Missouri professional school counselors: Ratios matter, especially in high-poverty schools. Professional School Counseling, 16, 108-116. doi:10.1177/2156759X0001600207
Lapan, R. T., Gysbers, N. C., & Kayson, M. (2006). The relationship between the implementation of the Missouri Comprehensive Guidance Program and student academic achievement. Columbia: University of Missouri.
Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A statewide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling and Development, 79, 320-330.
Macdonald, G., & Sink, C. A. (1999). A qualitative developmental analysis of comprehensive guidance programmes in schools in the United States. British Journal of Guidance & Counseling, 27, 415-430.
Martin, I., Carey, J., & DeCoster, K. (2009). A national study of the current status of state school counseling models. Professional School Counseling, 12, 378-386. doi:10.1177/ 2156759X0901200506
Nava, Y. (2018, January 1). The impact of a comprehensive school counseling program based on the American School Counselor Association's recognized model program on student achievement and college/career readiness (Doctoral dissertation). ProQuest LLC. Retrieved from https://auth.lib.unc.edu/ezproxy_auth.php? url=http://search.ebscohost.com/login.aspx?direct=true&db=e ric&AN=ED588299&site=ehost-live&scope=site
Perusse, R., Goodnough, G., & Noel, C. (2001). A national survey of school counselor preparation programs: Screening methods, faculty experiences, curricular content, and fieldwork requirements. Counselor Education & Supervision, 40, 252-263.
Public Agenda. (2010). Can I get a little advice here? Retrieved from https://www.publicagenda.org/pages/can-i-get-a-little-advice-here
Reback, R. (2010). Schools' mental health services and young children's emotions, behavior, and learning. Journal of Policy Analysis and Management, 29, 698-727. doi:10.1002/pam.20528
Scarborough, J. L., & Luke, M. (2008). School counselors walking the walk and talking the talk: A grounded theory of effective program implementation. Professional School Counseling, 11, 404-416. doi:10.1177/2156759X0801100607
Sink, C., Akos, P., Turnbull, R., & Mvududu, N. (2008). An investigation of comprehensive school counseling programs and academic achievement in Washington state middle schools. Professional School Counseling, 12, 43-53. doi:10.1177/2156759X0801200105
Slaughter, R. A. (2016). Job satisfaction among high school counselors in RAMP-designated schools. Dissertation Abstracts International: Section A. Humanities and Social Sciences. ProQuest Information & Learning. Retrieved from https://auth.lib.unc.edu/ ezproxy_auth.php?url=http://search.ebscohost.com/login.aspx? direct=true&db=psyh&AN=2016-21246-163&site=ehost-live& scope=site
Vacchio, C. B. (2012, January 1). A phenomenological study examining classroom teachers' perceptions of the roles of professional school counselors and school counseling programs in RAMP designated schools (Doctoral dissertation). ProQuest LLC. Retrieved from https://auth.lib.unc.edu/ezproxy_auth.php?url=http://search. ebscohost.com/login.aspx?direct=true&db=eric&AN=ED545 861&site=ehost-live&scope=site
Villares, E., Frain, M., Brigman, G., Webb, L., & Peluso, P. (2012). The impact of student success skills on standardized test scores: A meta-analysis. Counseling Outcome Research and Evaluation, 3, 3-16. doi:10.1177/2150137811434041
Ward, C. A., (2009). An examination of the impact of the ASCA National Model on student achievement at recognized ASCA model program (RAMP) elementary schools. Dissertation Abstracts International. ProQuest LLC. UMI #3401416. Corpus Christi: Texas A&M University.
Whiston, S. C. (2007). Outcomes research on school counseling interventions and programs. In B. T. Erford (Ed.), Transforming the school counseling profession (pp. 38-50). Upper Saddle River, NJ: Pearson Education.
Wilkerson, K., Perusse, R., & Hughes, A. (2013). Comprehensive school counseling programs and student achievement outcomes: A comparative analysis of RAMP versus non-RAMP schools. Professional School Counseling, 16, 172-184. doi:10.1177/ 2156759X1701600302
Young, A., & Kaffenberger, C. (2011). The beliefs and practices of school counselors who use data to implement comprehensive school counseling programs. Professional School Counseling, 15, 67-76. doi:10.1177/2156759X1101500204
Patrick Akos, PhD, is a professor of school counseling at the University of North Carolina at Chapel Hill. Email: email@example.com
Kevin C. Bastian, PhD, is a senior research associate in the Department of Public Policy at UNC Chapel Hill and is the director of research at the Education Policy Initiative at Carolina (EPIC).
Thurston Domina, PhD, is a professor of educational policy and sociology at the University of North Carolina at Chapel Hill.
Lucia Mock Munoz de Luna is a doctoral student in cultural studies and literacies at the University of North Carolina at Chapel Hill.
Patrick Akos  [ID], Kevin C. Bastian , Thurston Domina , and Lucia Mock Munoz de Luna 
 University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Patrick Akos, PhD, University of North Carolina at Chapel Hill, Box 3500, Chapel Hill, NC 27599, USA.
Table 1. Characteristics of RAMP and Non-RAMP Schools (2009-2015). Elementary Schools Non- RAMP RAMP School size 738.39 664.18 Minority percentage 42.54 55.25 Economically disadvantaged 28.59 43.31 percentage Performance composite 76.75 71.06 Short-term suspension rates 3.50 4.11 (per 100 students) Violent acts rates 3.94 2.95 Total per-pupil expenditures 8,905.02 8,199.55 Student services 506.63 332.66 expenditures NBC percentage 19.84 15.41 Novice teacher percentage 15.44 19.85 Unique school count 14 95 School-by-year count 62 605 Middle Schools Non- RAMP RAMP School size 1,088.93 950.74 Minority percentage 52.50 56.41 Economically disadvantaged 38.58 41.84 percentage Performance composite 69.08 68.91 Short-term suspension rates 18.96 26.46 (per 100 students) Violent acts rates 10.12 13.84 Total per-pupil expenditures 7,419.31 8,657.28 Student services 363.86 533.56 expenditures NBC percentage 18.84 15.10 Novice teacher percentage 14.96 19.13 Unique school count 14 20 School-by-year count 57 125 Note. This table displays mean school-level characteristics for RAMP and non-RAMP elementary and middle schools in Wake County, North Carolina. RAMP indicates that the school was RAMP-identified in a given school year. Non-RAMP indicates that the school was never RAMP-designated during the study period. School-level data come from the 2008-2009 through 2014-2015 years. RAMP = Recognized ASCA Model Program; ASCA = American School Counselor Association. Table 2. RAMP Student Achievement Results (2009-2015). Elementary Mathematics All student analyses RAMP school -0.001 (0.026) Observations 103,261 Student subgroup analyses Black students at RAMP schools 0.021 (0.037) Observations 23,283 Hispanic students at RAMP schools 0.031 (0.044) Observations 14,593 White students at RAMP schools -0.011 (0.024) Observations 53,167 Ever economically disadvantaged 0.026 (0.038) students at RAMP schools Observations 38,624 Never economically disadvantaged -0.010 (0.029) students at RAMP schools Observations 64,637 Elementary Reading All student analyses RAMP school 0.030 (0.020) Observations 103,935 Student subgroup analyses Black students at RAMP schools 0.035 (0.041) Observations 23,617 Hispanic students at RAMP schools 0.013 (0.034) Observations 14,725 White students at RAMP schools 0.020 (+) (0.012) Observations 53,335 Ever economically disadvantaged 0.020 (0.024) students at RAMP schools Observations 39,124 Never economically disadvantaged 0.028 (+) (0.017) students at RAMP schools Observations 64,811 Middle Mathematics All student analyses RAMP school 0.012 (0.029) Observations 158,749 Student subgroup analyses Black students at RAMP schools -0.018 (0.030) Observations 39,094 Hispanic students at RAMP schools 0.049 (+) (0.028) Observations 20,111 White students at RAMP schools 0.006 (0.019) Observations 81,951 Ever economically disadvantaged -0.007 (0.032) students at RAMP schools Observations 59,284 Never economically disadvantaged 0.024 (0.019) students at RAMP schools Observations 99,465 Middle Reading All student analyses RAMP school -0.004 (0.009) Observations 159,381 Student subgroup analyses Black students at RAMP schools -0.044 ** (0.018) Observations 39,417 Hispanic students at RAMP schools 0.010 (0.018) Observations 20,272 White students at RAMP schools 0.001 (0.008) Observations 82,071 Ever economically disadvantaged -0.014 (0.016) students at RAMP schools Observations 59,795 Never economically disadvantaged -0.008 (0.008) students at RAMP schools Observations 99,586 Note. This table displays regression results and standard errors (in parentheses) from models focused on elementary and middle schools in Wake County. RAMP indicates that the school was RAMP-identified in a given school year. All models include student demographics, prior student achievement, school characteristics, and a school fixed effect. Observations are the number of student-level records in the analysis. (+), *, and ** indicate statistical significance at the .10, .05, and .01 levels, respectively. RAMP = Recognized ASCA Model Program; ASCA = American School Counselor Association. Table 3. RAMP Student Absence Results (2009-2015). Elementary Schools Days Absent All student analyses RAMP school -0.112 (0.108) Observations 234,692 Student subgroup analyses Black students at RAMP schools -0.256 (0.236) Observations 56,051 Hispanic students at RAMP schools 0.053 (0.276) Observations 36,285 White students at RAMP schools -0.047 (0.101) Observations 115,195 Ever economically disadvantaged -0.337 (+) (0.190) students at RAMP schools Observations 95,008 Never economically disadvantaged -0.027 (0.095) students at RAMP schools Observations 139,684 Elementary Schools Chronically Absent All student analyses RAMP school -0.001 (0.006) Observations 234,692 Student subgroup analyses Black students at RAMP schools -0.008 (0.007) Observations 56,051 Hispanic students at RAMP schools -0.002 (0.010) Observations 36,285 White students at RAMP schools 0.003 (0.003) Observations 115,195 Ever economically disadvantaged -0.012 (+) (0.007) students at RAMP schools Observations 95,008 Never economically disadvantaged 0.003 (0.003) students at RAMP schools Observations 139,684 Middle Schools Days Absent All student analyses RAMP school -0.325 * (0.159) Observations 211,513 Student subgroup analyses Black students at RAMP schools -0.400 (0.259) Observations 56,660 Hispanic students at RAMP schools -0.536 * (0.244) Observations 29,449 White students at RAMP schools -0.411 * (0.163) Observations 101,938 Ever economically disadvantaged -0.464 (+) (0.229) students at RAMP schools Observations 85,159 Never economically disadvantaged -0.362 * (0.141) students at RAMP schools Observations 126,354 Middle Schools Chronically Absent All student analyses RAMP school -0.005 (0.004) Observations 211,513 Student subgroup analyses Black students at RAMP schools -0.009 (0.008) Observations 56,660 Hispanic students at RAMP schools -0.019 * (0.007) Observations 29,449 White students at RAMP schools -0.004 (0.004) Observations 101,938 Ever economically disadvantaged -0.017 * (0.007) students at RAMP schools Observations 85,159 Never economically disadvantaged -0.002 (0.003) students at RAMP schools Observations 126,354 Note. This table displays regression results and standard errors (in parentheses) from models focused on elementary and middle schools in Wake County. RAMP indicates that the school was RAMP-identified in a given school year. Days absent is a continuous measure; chronic absences is a dichotomous indicator for being absent more than 10% of the school days. All models include student demographics, school characteristics, and a school fixed effect. Observations are the number of student-level records in the analysis. (+), *, and ** indicate statistical significance at the .10, .05, and .01 levels, respectively. RAMP = Recognized ASCA Model Program; ASCA = American School Counselor Association.
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Featured Research|
|Author:||Akos, Patrick; Bastian, Kevin C.; Domina, Thurston; de Luna, Lucia Mock Munoz|
|Publication:||Professional School Counseling|
|Date:||Sep 1, 2018|
|Previous Article:||Effectiveness of an Adapted Behavioral Education Program Targeting Attendance Improvement.|
|Next Article:||Experiences of Elementary School Counselors and Students in Using Reality Art Therapy to Address Chronic Conditions.|