




































    

 American Journal of Medical and Physical Education 
Vol.6, Issue 2; March-April 2021; 

1252 Columbia Rd NW, Washington DC, United States 

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DEMOGRAPHIC DISPARITIES IN QUALITY PHYSICAL EDUCATION 

 

Karen S. Kulik, Hannah M. Brewer and Jonathan S. Baker 

University of California, Los Angeles 

 

Abstract: Physical activity is essential for children's health, but many do not get enough. Schools are a key setting 

for physical activity, but there are challenges to implementing quality physical education programs. This paper 

reviews the factors that affect schools' ability to implement quality physical education programs, and discusses 

strategies for overcoming these challenges. 

Keywords: Physical activity, Children's health, Schools Physical education, Quality Challenges Strategies 

 

 

1. Introduction 

Physical activity has been shown as an effective way to improve the weight status of children and adolescents 

(McDaniel et al., 2014).  Participation in regular physical activity has also been shown to help control body 

weight, maintain healthy bones and reduce the risk for developing chronic diseases such as cardiovascular disease, 

hypertension, and diabetes mellitus (American Heart Association, 2013; Luke et al., 2004; U.S. Department of 

Health and Human Services, 2010). The current public health recommendation is for children to participate in 60 

minutes of daily physical activity (U.S. Department of Health and Human Services, 2008).   Schools have been 

identified as a key setting for children to engage in physical activity because children spend more time in school 

than any other place outside the home (Quitério, 2012).  Although schools may not allow students the opportunity 

to accumulate all of the recommended minutes of physical activity, they do offer opportunities for children and 

adolescents to become active.     

Within the school environment, physical education has been targeted as the most effective way to increase 

physical activity among students (Story et al., 2006; US Department of Health and Human Services, 2010).  For 

students to benefit from the increased amounts of physical activity, schools need to offer quality physical 

education programs that provide opportunities for students to meet the recommended daily amount of physical 

activity.  To help schools develop quality physical education programs, the Society of Health and Physical 

Educators (SHAPE), formerly known as the National Association for Sport and Physical Education (NASPE), 

has developed a set of guidelines to provide schools with specific criteria to help increase opportunities for 

students to meet the recommended daily amount of 225 minutes per week of physical education. The guidelines 

include ensuring that the individuals delivering the physical education are qualified, and that there is appropriate 

safe space for physical education to be administered (National Association for Sport and Physical Education, 

2011). 

Although the benefits of physical activity are clear (US Department of Health and Human Services, 2010) and 

guidelines have been established to help schools deliver quality physical education, many children and 

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adolescents do not participate in regular physical activity.Based on the results from the 2011 National Youth Risk 

Behavior Survey, only 28.7% of high school students meet the recommended level of physical activity (Centers 

for Disease Control and Prevention, 2012).  Moreover, the percentage of students participating in 60 minutes of 

daily physical activity decreases as they progresses across the 9th (30.8%), 10th (30.8%), 11th (27.3%) and 12th 

grades (25.1%). Very few studies have examined the reasons for the lack of physical activity in school.  A critical 

step in the design of quality physical education programs that deliver increased opportunities for physical 

activity,is understanding the factors that may affect the schools ability to implement such a program (Barroso et 

al., 2005).  

A study conducted by Barroso et al. (2005) described the barriers toa quality physical education curriculum as 

reported by elementary physical education specialists trained as part of the Child and Adolescent Trial for 

Cardiovascular Health (CATCH)program.  The data wasobtained from four consecutive annual surveys (2000-

2004) that were collected from 157 teachers.  The combined results from these surveys revealed that significant 

barriers exist to providing quality physical education.   The teachers identified factors including inadequate indoor 

and/or outdoor facilities and insufficient numbers of physical education specialists were among the major 

obstacles (Barroso et al., 2005).  Other reported barriers included low priority of physical education compared to 

other academic subjects and limited financial resources. There is research to suggest that the factors identified 

from this study may influence a schools ability to deliver quality physical education.  

To investigate the effect of location on physical activity, Springer et al. (2009) conducted a study to examine the 

prevalence of physical activity and sedentary behaviors in a probability sample of students in 4th, 8th, and 11th 

grades by urban, suburban and rural locations.  Data was collected from the 2004-2005 School Physical Activity 

and Nutrition (SPAN) study.  The results of the study revealed that urban 8th and 11th grade students reported the 

lowest prevalence of physical activity.  Students in suburban or rural schools were significantly more likely than 

urban students to report higher school-based team sport participation in 8th graders (p=0.001), higher vigorous 

physical activity (p=0.01) and strength training exercises (p=0.01) in 11th grade boys.  Attendance in physical 

education was also higher among urban 4th grade (p<0.01) and urban 11th grade (p=0.05) students.  Participation 

in sports teams (p=0.04) and other organized physical activity (p=0.04) was higher in urban 4th grade girls.  

Participation in vigorous physical activity was higher in urban 8th grade boys (p=0.04) when compared to the 

other students.  The results of this study suggest significant differences in participation in physical activity by 

location status (Springer et al., 2009).  

Similar to Springer, Butcher et al. (2008) conducted a study to assess the rates and correlates of adolescents’ 

compliance with guidelines for physical activity.  The variables examined included: race/ethnicity, income level, 

geographic region, and parental education level.  A phone survey was used to gather self-reported physical activity 

data from 1625 adolescents ages 14-17.  The parents of each adolescent also participated in the phone survey to 

answer demographic questions.  Results of the survey revealed that compliance among adolescents who lived in 

a household with a higher household income (above $60,000) was significantly associated with compliance of 

the physical activity guidelines (p = 0.03), although there was not a significant relationship between compliance 

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and low or middle income.  This suggests that a relationship may exist between socioeconomic status and 

participation in physical activity levels (Butcher et al., 2008).  

In support the research of Springer and Butcher, Jones et al. (2003) conducted a study to examine the effectof 

factors such as location, schools size and school type have on schools in the United States implementing health 

promoting policies, programs, and facilities.  The data used for the study was collected from the School Health 

Policies and Programs Study 2000.  The following variables were used to group the schools: school type (public, 

private, or Catholic), urbanicity (urban, suburban, or rural), and school enrollment size.  The results of this study 

revealed that public schools (vs. private and Catholic schools), urban schools (vs. rural and suburban schools) and 

schools with larger enrollments (vs. smaller schools) had more health-promoting policies, programs and facilities 

in place.  These results are significant because they suggest that students who attend these schools may have 

access to higher quality programs including physical education than students who attend schools with fewer 

resources. 

Therefore, there is evidence to support factors such as school location, school size and socioeconomic status may 

influence physical activity levels among children.  However, what is unclear is how these factors affect a schools 

ability to achieve SHAPE’s recommendationsfor quality physical.  This information may be valuable in 

understanding how to improve the quality of physical education and as a result, improvethe health benefits of 

children and adolescents associated with increased physical activitylevels.  

 The primary focus of this studytherefore,was to determine whether a sample of high schools in Southwestern 

Pennsylvania, USA met the SHAPE guidelines including instruction time for physical education, teacher 

qualifications and facilities available for physical education and to determine the factors that may influence their 

ability to meet these guidelines. Further study aims were to (1) determine if school size influences a schools ability 

to meet the SHAPE guidelines for instruction time for physical education, teacher qualifications and facilities, (2) 

determine if school location influences a schools ability to meet the SHAPE guidelines and (3) determine if 

socioeconomic status influences a schools ability to meet the SHAPE guidelines.   

2. Methods   

2.1 Sample  

There are 91 public high schools in Southwestern Pennsylvania.  The physical education department chairperson 

or designated high school physical education teacher for each of the 91 schools was invited to participate in the 

study via an electronic letter that provided an overview of the study aims and directions on how to complete the 

electronic survey. The email addresses for the physical education department chairperson or designated high 

school physical education teacher were obtained directly from each school’s website.  

2.2 Survey Validity and Reliability  

The survey instrument was developed by the researcher based on an existing questionnaire used by SHAPE to 

evaluate quality physical education programs.  Survey validity was reviewed by a panel of 12 experts in the field 

of physical education and curriculum design.  The survey was distributed to this panel twice during the 

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development process and feedback from each review was incorporated to determine the final version of the 

survey.  

To examine the survey question clarity, general question format, and to provide data on reliability of the survey 

questions, a pilot study was conducted.  The survey was sent to 18 randomly selected schools that were similar in 

demographics (i.e. school size, locale and SES status) to the schools in the target population.  Atest of internal 

consistency was conducted using Cronbach’s Alpha on the pilot study data. 

2.3 Statistics   

The data were analyzed using the statistical program SPSS 16.0.  Descriptive statistics were used to describe the 

study schools, respondents of the survey, instruction time provided by each school and the facilities available for 

physical education.  A chi-square test of independence was calculated to determine if there was a significant 

difference in between schools that responded to the survey and schools that did not respond to the survey.  To 

determine the study schools’ ability to meet the SHAPE recommendations, a within-subjects repeated measures 

of analysis of variance (ANOVA) was used. To determine the effect of demographic factors (school size, location 

and SES) on the study schools’ ability to meet the SHAPE recommendations, repeated measures ANOVA was 

used.  A chi-square test of independence was used to compare the availability of specific type of facilities available 

for physical education and demographic factors.  

3. Results  

3.1 Reliability of the Study Survey Instrument 

The survey instrument specific for this study was developed, and a pilot study was conducted to assess the 

reliability of this survey instrument.  Eighteen schools were selected at random to participate in this pilot study.  

Fifteen of the 18 schools (83.3%) completed the survey.  Cronbach’s alpha was used to measure the internal 

consistency of the survey and this analysis revealed a score of .707.  The test/retest correlation was .979.  Based 

on these results it was determined that the survey met acceptable criteria for reliability to proceed with the 

recruitment of additional schools for this study.  

3.2 Recruitment of Schools  

The target population for this study included 91 public high schools from Southwestern Pennsylvania.  Initial 

outreach to these schools was to identify a Physical Education Department Chairperson or other designated 

physical education teacher to contact regarding participation in this study. Contact information was obtained for 

83 out of the 91 schools (91.2%).     

The invitation letter and electronic survey were sent to the Physical Education Department Chairperson or other 

designated physical education teacher at these 83 schools, with 39 schools partially or fully completing the survey, 

for a response of 46.98%.  When considered based on the sample of 91 possible schools, the response was 42.9%.  

3.3 School Characteristics  

Table 1 summarizes the demographic information including school size, locale and socioeconomic status (SES) 

of the schools that responded to the survey and the schools that did not respond to the survey.  School size was 

determined based on the Pennsylvania Interscholastic Athletic Association (PIAA) ranking system which is 

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established based on senior (12th grade) enrollment.  Rankings include A, AA, AAA, and AAAA with “A” 

representing schools with low enrollment and “AAAA” representing schools with high enrollment.  School locale 

was determined based on the National Center for Educational Statistics (NCES) classification system to describe 

a school’s location in proximity to an urbanized area ranging from “large city” to “rural.”  The SES status of each 

school was determined based on the number of students eligible for a free or reduced lunch and were classified 

as low, middle or high as reported by the Pennsylvania Department of Education (PDE).   

A chi-square test of independence was calculated to determine if there was a significant difference in school size, 

locale or SES status between respondents and non-respondents of the survey.  No significant difference was found 

between respondents and non-respondents for school size (x2 (3) =5.99, P>.05), locale (x2 (5) =6.33, P>.05), or 

SES status (x2 (5) =3.55, P>.05).  

Table 1:  Demographic Information for the Respondent Schools and Non-Respondent Schools  

 
    Respondent  Non-Respondent  

 (N=39)       (N=52)  

    Frequency   Percent   Frequency   Percent   

School Size  by 

PIAA Class   

A   

AA   

9   

7   

23   

18   

14   

17   

27   

33   

 AAA   10   26   14   27   

 AAAA   13   33   7   13   

School Locale   Large city   3   8   7   14   

  Urban fringe of a large city   24   61   34   65   

  Urban fringe of a mid-size city   0   0   1   2   

  Small Town   0   0   1   2   

  Rural, outside CBSAa   5   13   1   2   

  Rural, inside CBSAa   7   18   8   15   

SES Status   Low (1-14.79%)b   15   39   11   21   

  Middle (14.8-32.29%) b   11   28   16   31   

  High (32.3-100%) b   13   33   25   48   

      a = CBSA = Core Based Statistical Area       b = Indicates based on tertiles the percent of students within a 

school receiving a free and reduced price lunch.  

3.4 Achievement of the SHAPERecommendations  

3.4.1Instruction Time.  The instruction time for physical education within each of the study schools was 

collected for each grade level (10, 11, and 12).  The number of days per week that physical education was offered 

in 10th, 11thand 12th grade was 2.8±1.4, 2.7±1.4, and 2.5±1.4 days per week per year, respectively.    Results of a 

withinsubjects repeated measures analysis of variance showed no difference between the number of physical 

education classes offered per week for the entire school year for grades 10, 11 and 12 (P=.135).  The number of 

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minutes per week of physical education was offered in 10th, 11th and 12th grade was 104.9±54.3 min/wk, 

100.1±55.1 min/wk, and 92.3±53.4 min/wk.  A within-subjects repeated measures ANOVA revealed no 

significant difference for minutes of physical education instruction per week between grades 10, 11 and 12 

(p=0.23).  

3.4.2 Academic Training of Physical Education Teachers.Data was collected from each school regarding the 

number of teachers with an undergraduate and/or graduate degree in physical education.  The percent of schools 

reported having 5 or more teachers with an undergraduate degree in physical education was 34.3, whereas 28.6% 

of schools reported that none of their teachers have a graduate degree in physical education.  The mean percentage 

of teachers within a school having an undergraduate degree in physical education was 96.9±14.6% and the mean 

percentage of teachers within each school with a graduate degree in physical education was 65.0±34.8%.  

3.4.3 Facilities Available for Physical Education.Schools reported information regarding the number and type 

of facilities available for physical education.  The percent of schools reported having indoor spaces available for 

physical education was 100% and 94.3% of these schools reported that they also have outdoor grass spaces 

available for physical education.    The majority of schools also reported having access to a track (71.4%), fitness 

center (68.8%), and outdoor concrete spaces (62.9%).  The climbing wall (34.3%) and swimming pool (42.9%) 

were identified as the least available facilities for physical education.  

3.5 Demographic Factors Influencing Achievement of the NASPE Recommendations   

3.5.1 School Size  

To determine the effect of school size on physical education instruction time, the categories of minutes per week 

that physical education was offered were recoded to 29 min/wk for the <30 minute category, the midpoints of the 

range were used for the 30-59, 60-89, 90-119, 120-149, and 150-179 minute categories, and 180 min/wk was used 

for the >180 minute category.  A two-factor (Grade X School Size) repeated measures ANOVA was performed 

and revealed no significant Grade Effect, School Size Effect, or Grade X School Size Interaction Effect for 

minutes of physical education offered.  These data are shown in Table 2.  In addition to the parametric tests, non-

parametric tests were also used.  Results of the Kruskal-Wallis H Test revealed no significant difference between 

school size and physical education instruction time offered in10th grade (H (3) = .577, p=.902), 11th grade (H (3) 

= .245, p=.970), or 12th grade (H (3) = 3.464, p=.325). 

Table 2.  Repeated Measures Analysis of Variance (ANOVA) to Compare Minutes of Physical Education 

Per Week by Grade Level Between Classifications of School Size.  

 
  School Size Categories  p-values  

Grade 

Level   

A   

(N=9)   

AA   

(N=6)   

AAA  (N=10   AAAA   

(N=12)   Grade   
School 

Size   

Grade X  

School  

Size   

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10th 

grade   
111.3±65.3   101.9±47.0   104.5±49.7   95.8±56.2   0.213   0.938   0.115   

  

11th  

grade   

  

97.8±62.7   101.9±47.0   106.1±55.7   95.8±59.7         

12th  

grade   

66.0±45.2   101.9±47.0   106.1±55.7   95.8±59.1         

  

A one-way ANOVA was computed to compare the differences in teacher qualifications as determined by the 

number of teachers with an undergraduate degree or graduate degree in physical education within each school 

and the percent of teachers with an undergraduate degree or graduate degree in physical education within each 

school and school size.  As shown in Table 3, a significant difference was found for the number of teachers with 

an undergraduate degree within each school (F (3, 31) =2.923, p=.049).  Post-hoc comparisons using the LSD test 

indicated that for the number of teachers with an undergraduate degree for PIAA class A schools (3.8±1.1) was 

significantly lower when compared to PIAA class AAAA schools (5.3±1.5).  However, there was no significant 

difference between the percent of teachers with an undergraduate degree (F (3, 31) =.522, p=.671) between school 

size classifications.  Moreover, there was no significant differences found for the number of teachers with a 

graduate degree (F (3, 31) =.877, p=.464) or the percent of teachers with a graduate degree (F (3, 31) = .149, 

p=.929) between school size classifications.  

Table 3.  One Way Analysis of Variance (ANOVA) to Compare Physical Education Teacher Qualifications 

within Each School by Classifications of School Size.  

  

  

Teacher  

Qualifications   

School Size 

Categories 

A   

(N=9)   

  

AA   

(N=6)   AAA   

(N=8)   

AAAA   

(N=12)   

  

F 

value   

p-value   

Number  of        

Teachers 

 with  

Undergraduate  

Degree   

  

Percent  of  

3.8 ±1.1 a   4.0 ±1.1   4.8 ±1.0   5.3 ±1.5a   2.923   0.049   

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Teachers 

 with  

Undergraduate  

Degree   

  

100.0±.0   95.8±10.2   100.0±.0   93.1±24.1   0.522   0.671   

Number  of        

Teachers  

Graduate  

Degree   

with  2.3 ±1.9   3.0 ±2.3   3.3 ±2.1   3.8±2.2   0.877   0.464   

  

Percent  of  

      

Teachers  

Graduate  

Degree   

with  58.2±28.7   66.7±36.8   67.1±38.5   68.1±39.1   0.149   0.929   

               Note:  Values with the same superscript within each grade level are significantly different at p<0.05.  

To compare the number of facilities including the total number of facilities, number of indoor spaces, number of 

outdoor grass spaces and the number of outdoor concrete spaces available within each school to support physical 

education and school size, a one-way ANOVA was computed.  As shown in Table 4, a significant difference was 

found for the number of available indoor spaces between schools based on PIAA classification (F (3, 31) =3.519, 

P=.026).  Post-hoc comparisons using the Tukey test indicated that for the number of available indoor spaces to 

support physical education within each school for PIAA class A schools (1.8±0.8) was significantly lower than 

for PIAA class AAAA schools (3.7±1.5).  No significant difference was found for the total number of facilities 

(F (3, 31) = 1.286, p=.297), the number of available of outdoor grass spaces (F (3, 31) = .409, p=.748), or the 

number of outdoor concrete spaces (F (3, 31) = .1.359, p=.274).  

In addition to the total number of facilities available to support physical education, data was also collected to 

determine the specific types of facilities available within the study schools.   The chi-square test of independence 

was used to compare the availability of facilities including a swimming pool, track, rock climbing wall and a 

fitness center within each school and school size.  A significant relationship was found for the availability of a 

climbing wall by school size with a higher number of small schools having a climbing wall available for physical 

education (x2(3) = 8.185, p=.042).  No significant relationship was found between school size and the availability 

of a swimming pool (x2(3) = 4.318, p=.229), the availability of a track (x2(3) = .700, p=.873), or the availability 

of a fitness center (x2(3) = 1.488, p=.685).   

Table 4.  One Way Analysis of Variance (ANOVA) to Compare the Facilities Available to Support Physical 

Education between Classifications of School Size.  

  

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Facilities   

School 

Size 

Categories 

A   

(N=9)   

  

AA   

(N=6)   

AAA   

(N=8)   

AAAA   

(N=12)   

  

F Value   

p-value   

Total  

Facilities   
7.6±2.1   9.8±4.8   9.4±3.4   10.7±4.0   1.286   0.297   

  

Indoor  

Spaces   

1.8±0.8a   3.5±2.1   3.8±1.7   3.7±1.5a   3.519   0.026   

  

Outdoor  

Grass  

Spaces   

2.6±1.2   2.8±2.4   3.2±2.3   3.4±1.8   0.409   0.748   

  

Outdoor  

Concrete  

Spaces   

0.9±0.8   0.8±0.8   0.6±0.7   1.4±1.2   1.359   0.274   

                Note:  Values with the same superscript within each grade level are significantly different at p<0.05.  

  

3.5.2School Location    

To determine the effect of school location on physical education instruction time, a two-factor (Grade X  

School Size) repeated measures ANOVA was performed and revealed no significant Grade Effect, School Locale 

Effect, or Grade X School Locale Interaction Effect for minutes of physical education offered.  These data are 

shown in Table 5.  Results of the non-parametric test, Kruskal-Wallis H Test, revealed a non-significant difference 

between physical education instruction time and school locale.   

Table 5.  Repeated Measures Analysis of Variance (ANOVA) to Compare Minutes of Physical Education 

Per Week by Grade Level Between School Locales. 

  

Grade  

Level   

School 

Locale 

Categories 

Large City   

(N=3)   

  

Urban  

Fringe of a  

Large City   

(N=22)   

Rural  

Outside  

CBSA   

(N=5)   

Rural Inside  

CBSA   

(N=7)   

p-

values   

Grade   

School 

Locale   

Grade  

X  

School  

Locale   

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10th 

grade   
119.3±78.2   103.8±47.8   80.5±62.7   108.9±63.1   0.145   0.381   0.100   

  

11th  

grade   

  

119.3±78.2   104.5±52.2   56.3±32.7   108.9±63.1         

12th  

grade   

119.3±78.2   104.5±52.2   59.4±30.1   65.8±47.4         

  

A one-way ANOVA was computed to compare the differences in school locale and teacher qualifications within 

each school.  As shown in Table 6, a significant difference was found for the number of teachers with an 

undergraduate degree within each school (F (3, 31) =4.795, p=.007).  Post-hoc comparisons using the Tukey test 

indicated that the number of teachers with an undergraduate degree for schools classified as “urban fringe of a 

large city” (5.1±1.0) was significantly higher than schools classified as “rural, outside CBSA” (3.2±0.4).  No 

significant differences were found for the number of teachers with a graduate degree (F (3, 31) =.1.040, p=.389), 

the percent of teachers with an undergraduate degree (F (3, 31) = 1.377, p=.286), or the percent of teachers with 

a graduate degree  

(F (3, 31) = .658, p=.584)  

Table 6.  One Way Analysis of Variance (ANOVA) to Compare Teacher Qualifications within Each School 

by Between School Locales.  

 
  School Locale Categories    

Teacher  Large City  Urban  Rural  Rural  

Qualifications  (N=3)  Fringe of a Outside  Inside  

F value  p-value  

Large City  CBSA  CBSA  

(N=22)  (N=5)  (N=7)  

 
Number  of  

Teachers  with  

Undergraduate  

Degree  

Number  of  

4.0±0.0  5.1±1.0  a  3.2±0.5 a  3.8±1.9  4.795  0.007  

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Teachers 

 with  

Graduate  

Degree  

  

Percent  of  

3.5±0.7  3.2±2.1  1.8±0.8  4.0±3.0  1.040  0.389  

Teachers 

 with  

Undergraduate  

Degree  

  

Percent  of  

100.0± 0.0  98.9±5.3  100.0± 0.0  86.1±34.0  1.377  0.268  

Teachers 

 with  

87.5±17.7  62.2± 36.7  55.0±20.1  76.4±41.0  0.658  0.584  

Graduate Degree  

 
Note:  Values with the same superscript within each grade level are significantly different at p<0.05.   

A one-way ANOVA was computed to compare the number of facilities including the total number of facilities, 

number of indoor spaces, number of outdoor grass spaces and the number of outdoor concrete spaces available 

within each school to support physical education and school locale.  As shown in Table 7, no significant 

differences were found between school locale and the total number of facilities available within each school to 

support physical education (F (3, 31) = 1.146, p=.346), the number of indoor spaces available (F (3, 31) =1.199, 

p=.326), the number of outdoor grass spaces available (F (3, 31) = 1.361, p=.273), or the number of outdoor 

concrete spaces available (F (3, 31) = .459, p=.731). 

The chi-square test of independence was used to compare the availability of facilities including a swimming pool, 

track, rock climbing wall and a fitness center within each school and school locale.  A significant relationship 

was found between school size and the availability of a climbing wall (x2(3) = 8.827, p=.032).  No significant 

relationship was found between school size and the availability of a swimming pool (x2(3) = 1.294, p=.731), the 

availability of a track (x2(3) = 1.160, p=.763), or the availability of a fitness center (x2(3) = 1.099, p=.777).    

Table 7.  One Way Analysis of Variance (ANOVA) to Compare the Facilities Available to Support Physical 

Education between School Locales.  

  

  

Facilities   

School 

Locale 

Categories 

Large City   

  

Urban  

Fringe of a  

Large City   

Rural  

Outside  

CBSA   

  

Rural  

Inside  

CBSA   

  

  

F Value   

p-value   

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Total  

Facilities   
7.0±4.2   10.0±3.6   7.2±1.5   10.2±5.0   1.146   0.346   

  

Indoor  

Spaces   

Outdoor   

3.0±1.4   3.5±1.7   2.0±1.0   3.0±2.1   1.199   0.326   

  

Grass  

Spaces   

1.0±1.4   3.2±1.9   2.4±1.1   3.7±2.1   1.361   0.273   

  

Outdoor  

Concrete  

Spaces   

0.5±0.7   1.1±1.0   0.8±0.8   0.8±0.8   0.459   0.713   

               Note:  Values with the same superscript within each grade level are significantly different at p<0.05.  

  

3.5.3 Socioeconomic Status (SES)   

To determine the effect of SES on physical education instruction time, a two-factor (Grade X School SES Status) 

repeated measures ANOVA was performed and revealed a non-significant Grade Effect, School SES Status 

Effect, and Grade X School SES Status Interaction Effect for minutes of physical education offered.  These data 

are shown in Table 8.  Results of the non-parametric test, Kruskal-Wallis H Test, revealed a non-significant 

difference between physical education instruction time and school SES status.  

Table 8.  Repeated Measures Analysis of Variance (ANOVA) to Compare Minutes of Physical Education 

Per Week by Grade Level Between School SES Status.  

School SES Status     p-

values   

   

Lowest 

 Tertile  

(1% to <14.8%)   

(N=13)   

Middle 

Tertile   

(14.8%  

<32.3%)   

(N=11)   

to  Highest Tertile   

(32.3% to 100%)   

(N=13)   Grade   

School  

SES  

Status   

Grade  X  

Locale   

School  

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81.3±44.7   96.3±47.3   
 

130.0±58.5   0.248   0.089   0.698   
 

76.7±45.2   103.2±53.6   
 

120.7±60.1         
 

76.7±45.2   89.5±51.3    110.3±60.7          

  

A one-way ANOVA was computed to compare the differences in teacher qualifications within each school and 

SES status.  As shown in Table 9, SES status of the school did not appear to affect the number of teachers with 

an undergraduate degree (F (2, 32) = .776, p=.469) or the number of teachers with a graduate degree (F (2, 32) = 

2.065, p=.143),the percent of teachers with an undergraduate degree (F (2, 32) = 1.560, p=.226), or the percent of 

teachers with a graduate degree (F (2, 32) = .567, p=.573).  

Table 9.  One-Way Analysis of Variance (ANOVA) to Compare Teacher Qualifications within Each School 

by School SES Status.  

  

  

Teacher  

Qualifications   

School SES 

Status  

Lowest Tertile   

(1% to 

<14.8%)   

(N=13)   

  

Middle 

Tertile 

(14.8%  

<32.3%)   

(N=11)   

 to  

Highest Tertile   

(32.3% to 100%)   

(N=13)   

  

F value   

p-

value   

Number  of        

Teachers 

 with  

Undergraduate  

Degree   

  

4.8 ±1.6   4.6 ±1.4    4.2 ±1.0   0.776   0.469   

Number  of        

Teachers  

Graduate  

Degree   

with  3.9 ±2.2   3.4 ±2.6    2.3 ±1.1   2.065   0.143   

  

Percent  of  

      

Teachers 

 with  

Undergraduate  

Degree   

91.0±24.5   100.0±.00    100.0± 0.0   1.560   0.226   

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Percent  of  

Teachers 

 with  

Graduate  

Degree   

72.4± 38.6   65.8±37.3    57.1± 29.3   0.567   0.573   

             Note:  Values with the same superscript within each grade level are significantly different at p<0.05.  

A one-way ANOVA was computed to compare the number of facilities including the total number of facilities, 

number of indoor spaces, number of outdoor grass spaces and the number of outdoor concrete spaces available 

within each school to support physical education and school SES status.  As shown in Table 10, no significant 

differences were found between school SES status and the total number of facilities available within each school 

to support physical education (F (2, 32) = .303, p=.741), the number of indoor spaces available (F (2, 32) =2.248, 

p=.122), the number of outdoor grass spaces available (F (2, 32) = .251, p=.780), or the number of outdoor 

concrete spaces available (F (2, 32) = 1.161, p=.326).   

The chi-square test of independence was used to compare the availability of facilities including a swimming pool, 

track, rock climbing wall and a fitness center within each school and school locale.  No significant relationship 

was found between school SES status and the availability of a swimming pool (x2(2) = .725, p=.696), the 

availability of a track (x2(2) = .217, p=.897), the availability of a climbing wall (x2(2) = 2.553, p=.279), or the 

availability of a fitness center (x2(2) = .375, p=.829).  

Table 10.  One-Way Analysis of Variance (ANOVA) to Compare the Facilities Available to Support 

Physical Education between School SES Status.  

  

  

Facilities   

School SES 

Status   

Lowest 

 Tertile  

(1% to <14.8%)   

(N=13)   

Middle 

Tertile   

(14.8%  

<32.3%)   

(N=11)   

to  

Highest Tertile   

(32.3% to 100%)   

(N=13)   

  

F value   

  

p-value   

Total  

Facilities   
10.1±3.2   9.3±4.0   

 
8.9±4.0   0.303   0.741   

  

Indoor  

Spaces   

  

4.0±1.5   2.9±1.9    2.7±1.5   2.248   0.122   

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Outdoor  

Grass   

Spaces   

3.2±1.9   3.3±1.7    2.8±2.1   0.251   0.780   

  

Outdoor  

Concrete  

Spaces   

0.8±1.1   0.8±0.8    1.3±0.9   1.161   0.326   

             Note:  Values with the same superscript within each grade level are significantly different at p<0.05.  

4. Discussion  

Improving the quality of physical education is necessary to achieve potential health-benefits in children and 

adolescents (Carrel et al., 2005; Dietz, 1997;Pate et al., 2006; and Sallis et al., 1997).   To help guide the 

development of quality physical education programs, the Society of Health and Physical Educators (SHAPE) has 

proposed guidelines for physical education instruction time, physical education teacher qualifications, and 

availability of physical education facilities.  This study focused on describing these components in high schools 

(10th, 11th, 12th grades) located in southwestern Pennsylvania, and examined whether there are demographic 

characteristics of the school (locale, size, SES status) that affect these factors.  This information may be valuable 

in understanding how to improve the quality of physical education across schools with diverse characteristics, 

and may lead to the development of interventions and policies to improve the quality of physical education in 

high schools.     

Results of this study indicates there is no significant relationship between schools size and physical education 

instruction time, teacher qualifications or facilities.  These results are in contrast to the current literature, as 

reported by Jones et al. (2005), which suggests that larger schools may have more health promoting policies and 

facilities than smaller schools thus offering students increased opportunities physical activity.  Although this 

research suggests that school size may affect opportunities for physical activity, the current study measured 

instruction time for physical education and not participation in physical activity which may account for the lack 

of association. The results of this study suggest that the locale of the school has limited impact on the physical 

education variables examined.    

For example, while this study showed a significant difference in the number of physical education teachers with 

an undergraduate degree based on school locale, this finding may simply reflect differences in school size, because 

there was no significant difference for the percent of physical education teachers with an undergraduate or 

graduate degree when compared school locale categories.  Moreover, aside from access to a rock climbing wall, 

access to facilities to support physical education instruction appears to be unaffected by the locale of the school.  

However, these findings that suggest no effect of school locale of physical education instruction variables may 

be inconsistent with the current literature.  Springer et al. (2009) conducted a study to examine physical activity 

levels of students in 4th, 8th, and 11th grades by urban, suburban and rural locations, and found that urban students 

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reported lower levels of physical activity compared to students in suburban and rural locations.  These results 

suggests that locale may affect participation in physical activity in children and adolescents; however, the current 

study assessed instruction time rather than time spent engaged in physical activity which may account for the 

differences in findings between these studies.  

Thus, it may be necessary to further examine the effect of school locale on components of physical education 

instruction, which may provide insight into how these potential differences affect the quality of physical education 

programs in these geographical areas.  

The results of this study suggest that there was no significant relationship between school SES status, instruction 

time, teacher qualifications or facilities.  These results are in contrast with the current literature.  Butcher et al. 

(2008) examined if demographic factors such as race/ethnicity, education level, and SES status affected physical 

activity participation in adolescents and found that adolescents living a household of higher SES status 

participated in higher levels of physical activity when compared to middle and low SES households.  Although 

this research suggests that SES status may affect physical activity participation, the current study measured 

instruction time in physical education, which may account for the lack of association in the results.   

4.1 Limitations 

This study is not without limitations which could impact the application of the observed results. First,the survey 

used in this study assessed the days per week and the number of minutes that physical education is offered to high 

school students in Southwestern Pennsylvania.  However, this may not reflect the time that students are actually 

engaged in physical activity during physical education class.  Also, this survey did not include participation in 

physical activity outside of the physical education class.  The second limitation of this study was the small sample 

size and limited geographic region.  Physical education curriculum components reported for schools in 

Southwestern Pennsylvania are not generalizable to schools in other regions.  Lastly, the study was conducted in 

2009 and the data may not reflect what is currently being implemented in these schools. Although this study was 

not without limitations, it is the first study to describe the ability of schools in Southwestern Pennsylvania to 

achieve SHAPE’s components for quality physical education and to investigate the effect of demographic factors 

such as school size, school locale and SES status on the ability to achieve these components.  

4.2 Conclusion 

Improving the quality of physical education is necessary to achieve potential health-benefits in children and 

adolescents (American Heart Association, 2013; Carrel et al., 2005; Luke et al., 2004; and Allis et al., 1997).   To 

help guide the development of quality physical education programs, the Society of Health and Physical Educators 

(SHAPE) has proposed guidelines for physical education instruction time, physical education teacher 

qualifications, and availability of physical education facilities.  This study focused on describing these 

components in high schools (10th, 11th, 12th grades) located in southwestern Pennsylvania, and examined whether 

there are demographic characteristics of the school (locale, size, SES status) that affect these factors.  This 

information may be valuable in understanding how to improve the quality of physical education across schools 

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with diverse characteristics, and may lead to the development of interventions and policies to improve the quality 

of physical education in high schools.  

The results of the current study suggest that teacher qualifications, facilities and demographic factors such as 

school size, locale and SES status do not influence physical education instruction time and consequently, do not 

impact the quality of physical education.  Quality physical education, however, may be necessary for improving 

health outcomes in children.  Thus, it becomes important to identify other factors or barriers to implementing 

quality physical education. Results of a study conducted by Barroso et al (2005) revealed that physical education 

teachers identified factors such as large class sizes, low priority compared to other academic subjects and 

inadequate financial resources as the top three barriers to implementing quality physical education programs. 

Future research is needed to indentify strategies for improving the quality of physical education despite these 

barriers.  

Although the present study was not without limitations, it is the first study to investigate the ability of schools in 

Southwestern Pennsylvania to achieve SHAPE’s components for quality physical education and to determine 

whether there are demographic factors that affect the ability of the schools to implement these guidelines.  While 

this study examined the effect of these factors on physical education instruction time, it is important to 

differentiate instruction time from measured physical activity within the period of physical education instruction 

and participation in physical activity during the school day outside of physical education.  In fact, the current 

literature investigating the influence of demographic factors on physical education has focused on physical 

activity rather than instruction time (Butcher et al., 2008, Haug et al., 2008; McKenzie et al., 1996; and Springer 

et al., 2009).  However, the SHAPE guidelines for quality physical education focus on instruction time.  Therefore, 

it may be necessary to revise the guidelines for a quality physical education to include participation in physical 

activity rather than duration of instruction time.  

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Barroso C.S., McCullum-Gomez C., Hoelscher D.M., et al. (2005). Self-reported barriers to quality physical 

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