Relationships between the middle school concept and student demographics
| Pages | 265-281 |
| DOI | https://doi.org/10.1108/JEA-04-2019-0071 |
| Date | 08 March 2020 |
| Published date | 08 March 2020 |
| Author | Scott Christopher Woods,Jennifer Grace Cromley,Donald Gene Hackmann |
| Subject Matter | Education,Administration & policy in education,School administration/policy,Educational administration,Leadership in education |
Relationships between the middle
school concept and
student demographics
Scott Christopher Woods
University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
Jennifer Grace Cromley
Department of Educational Psychology, University of Illinois at Urbana-Champaign,
Champaign, Illinois, USA, and
Donald Gene Hackmann
Department of Policy, Organization and Leadership,
University of Illinois at Urbana-Champaign, Champaign, Illinois, USA and
School of Education, Iowa State University, Ames, Iowa, USA
Abstract
Purpose –This study explored implementation of the middle school concept (MSC) in Illinois middle-level
schools, examining relationships between MSC implementation and schools’relative wealth, racial/ethnic
composition, and achievement levels.
Design/methodology/approach –This quantitative study utilized a sample of 137 Illinois middle-level
schools, defined as containing any combination of grades 5–9, including at least two consecutive grade levels
and grade 7. Principals completed an online survey, identifying levels of implementation of advisory, teaming
with common planning time (CPT), and a composite of both advisory and teaming with CPT.
Findings –Schools with high advisory implementation had significantly higher rates of Latinx enrollments.
Schools with lower operating expenditures per pupil were significantly less likely to implement advisory or
advisory and teaming. Teaming had a significant relationship with composite PARCC test scores, but there
was no significant effect for advisory and no significant interaction of advisory and teaming together.
Practical implications –MSC is more expensive to implement, and affluent districts may have the financial
means to absorb these costs. Although teaming facilitated improved state test scores, advisory programming
did not result in significantly improved scores.
Social implications –Lack of access to MSC programming in less affluent communities presents an equity
issue for low-income students and students of color.
Originality/value –This study contributes to researchexamining underlying issues of race and poverty and
their effects on academic achievement and the effectiveness of the MSC.
Keywords Advisory, Middle school, Common planning time, Interdisciplinary teaming, Middle grades,
Middle school concept
Paper type Research paper
Introduction
Organizational structures of middle-level schools that support the education of young
adolescentsvary greatly across theUnited States and have evolved overtime, and the research
related to these structures is limited (Ellerbrock et al., 2018). Currently, the most common
arrangement contains grades 6–8(Lounsbury, 2013). According to the National Center for
Education Statistics (Snyder et al., 2016), there was a 462 percent increase in middle-level
schools (schoolsbeginning with grade 4, 5, or 6 and ending with grade6, 7, or 8) in the United
States from 1970 to 2000, and by 2010 middle-level schools totaled about 13,000. As grade
configurations shifted, principals promoted changing organizational and programmatic
features to support young adolescents’developmental needs, referred to as the middle school
concept(MSC; Lounsbury, 2013;Roney etal.,2008).Key MSC features includeinterdisciplinary
teaming, common planning time for teamsof teachers, and advisory programing.
Implementation
of middle
school concept
265
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0957-8234.htm
Received 25 April 2019
Revised 8 September 2019
17 October 2019
10 December 2019
Accepted 12 December 2019
Journal of Educational
Administration
Vol. 58 No. 3, 2020
pp. 265-281
© Emerald Publishing Limited
0957-8234
DOI 10.1108/JEA-04-2019-0071
Although researchers have examined MSC implementation levels (McEwin and Greene,
2010,2011) and MSC’s relationship with school effectiveness (Olofson and Knight, 2018), a
problem exists: the role that demographics of students and schools play in implementation
practices has not been fully explored. If MSC practices are essential to address young
adolescents’needs (National Middle School Association [NSMA], 2012;Roney et al., 2008), it is
important to discern the extent to which these practices are implemented and consider
whether all students, regardless of race or family/community income status, are afforded
equitable access. Principals and teachers should make programmatic decisions based upon
factors identified as effective in supporting students’academic progress. This study
investigated MSC implementation in Illinois public middle-level schools, addressing five
research questions (Table I).
Review of the literature
Schools implementing MSC provide organizational and programmatic structures
intentionally designed to meet young adolescents’cognitive, social, physical, and
emotional needs. Specific to MSC are key systems of support that include advisory
programming, grouping students and teachers onto interdisciplinary teams, and providing
team teachers with common planning time (Flowers and Mertens, 2013). Although affective
Research question Analytical strategy
RQ1. How are Illinois middle-level schools clustered
with regard to MSC implementation levels?
State-wide survey of middle-grade school principals;
statistical analysis resulting in grouping of schools
into four clusters of MSC implementation
One-way ANOVAs—Are the clusters distinct on the
clustering variables?
IV: Cluster membership—categorical
DVs: Teaming score and Advisory score—
continuous
RQ2. What is the relationship between schools’
relative wealth and MSC implementation levels?
One-way ANOVAs
IV: Cluster membership—categorical
DVs
a
: Relative wealth (both rates of qualification for
free and reduced-price lunch and school district
operational expenditure per pupil were used)—
continuous
RQ3. What is the relationship between schools’
student racial/ethnic composition and MSC
implementation levels?
One-way ANOVAs
IV: Cluster membership—categorical
DVs: Percent of school population belonging to a
specific racial/ethnic group—continuous
RQ4. What is the relationship between schools’
academic achievement levels and MSC
implementation levels?
Two-way ANOVA
IVs: high-Advisory cluster, high-Teaming cluster,
and high Advisory cluster 3high-Teaming cluster—
categorical
DV: Composite PARCC score
b
—continuous
RQ5. What is the relationship between schools’
academic achievement and MSC implementation
levels, after accounting for school demographics and
funding?
Two-way ANCOVA
IVs: high-Advisory cluster, high-Teaming cluster,
and high Advisory cluster 3high-Teaming cluster—
categorical
COVs: Relative wealth AND Race/ethnicity
%—continuous
DV: Composite PARCC score—continuous
Note(s):
a
Separate analyses were conducted for each DV;
b
The composite PARCC score represents the
aggregate percentage of students scoring proficient or higher (4 or 5) in a given setting
Table I.
Research questions
with analytical
strategies
JEA
58,3
266
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