Middle School Program Coherence and Student Performance

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Table of Contents

Introduction
Methods
Expected results
Discussion
References

Introduction

While middle schools proceed searching for means of adolescent students’ needs addressment, the necessity to conform to test mandates on national and local levels is ever so pressing. The terms of program coherence were coined more than a decade ago but its principles seem sustained enough to improve disintegrated instructional frameworks and create a comprehensible learning atmosphere (Newmann, Smith, & Bryk, 2001).

Research finds a correlation between coherent programs and the overall improvement of middle schools in terms of work conditions, resource allocation, teacher and student engagement (Russell & Bray, 2013; Malen et al., 2015). The nationwide research shows that schools with coherent programs demonstrate more significant improvements in student performance as opposed to those with non-coherent instruction (Goddard, Goddard, Kim & Miller, 2015; Jenkins & Cho, 2014). The proposed quantitative study, therefore, will be devoted to determining if “high growth” schools in Fort Lauderdale, FL, show statistically significant compliance with program coherence model.

Methods

The statistical analysis will be conducted using the data from the surveys of teachers and principals at public schools in Fort Lauderdale, as well as Accountability Reports on student performance in Florida schools.

Participants

The site selection will include 3 schools that are rated “good” by the local community and can be classified as following growth standards, namely: New River Middle School, William Dandy Middle School, and Sunrise Middle School. There will be a total of 34 participants (3 principals and 31 teachers: 12 from New River, 3 from William Dandy, and 16 from Sunrise). The stratified random sampling strategy is chosen because it will possibly countervail the low response rates.

To further increase the response, the survey can be web-based, and the forms can be submitted via Qualtrics software. The one demographics that the participants share is location as they are supposed to be employed in one of the schools surveyed. The education and work experience that they have is relevant but only as a variable. Other demographics (including ethnicity, gender, age, etc.) are of no significance to the present study.

Procedures

The surveys will be delivered via e-mail after the permission has been obtained. Both the teacher survey (reliability of 0.82) and the principal survey (93% success rate) will gauge the teaching personnel’s and the leadership’s intussusceptions of their institutions’ program coherence (Newmann et al., 2001). The latter is going to be taken as the predictor, or independent variable (interval measurement scale), as well as the teachers’ and principals’ experience – another interval variable because the values can be grouped (Howell, 2012).

These variables will be measured through surveys. Student performance, being an outcome of interest to the present study, will be the criterion, or dependent variable, measured with ratio scale. Given the presence of more than two continuous variables (both interval and ratio) and the relationships between the variables is supposedly linear, the SPSS package can be used to analyze bivariate correlation (Huck, 2008; Pace, 2012).

Expected results

The reason for choosing SPSS Statistics lies in the problem statement. The research will be aimed at establishing whether there is a linear relationship between program coherence in schools and the students’ performance in standardized tests. A bivariate Pearson test can show if such relationship is present, establish its statistical significance and direction (Pace, 2013).

As a result of the survey stage of the project, a body of descriptive data is expected to be availed for further analysis. The program coherence indicators will be scaled by 4 points, and after the response data will be transferred from Qualtrics to SPSS, a frequency report will be produced on how the coherence is perceived by the staff and the principals. Pearson bivariate formula will then be used to examine if there is a correlation between Fort Lauderdale schools’ program coherence to student academic achievement variables. This information will be used to answer the question if the schools adherent to the instructional coherence principles demonstrate higher student performance on standard testing.

Discussion

Correlational research is an important utility of social and educational studies. At that, the major strong points of bivariate research are that it can reveal both the presence of the correlation and its strength (or direction). Still, the most obvious limitation of bivariate research is that it can only consider two variables at a time, although some extensions can be conducted later, if needed. A small sample can potentially result in bias, which may be leveraged by random stratification design and unified questionnaire forms. At any rate, the bivariate test perfectly suits the purpose of the proposed research, which is, again, to establish if program coherence compliant middle schools in Fort Lauderdale exhibit high student performance.

The hypothesis that schools adherent to program coherence will have significant correlation between the perceived coherence level and academic achievements, therefore, can be either supported or disproved by the results. If the hypothesis is proved, the implications of the results will mainly concern teachers and principals. The proved hypothesis will present them some points to consider restructuring the program to create a framework to guide the curricular and extracurricular activities, assessment, and learning environment.

References

Goddard, R., Goddard, Y., Kim, E. S., & Miller, R. (2015). A Theoretical and Empirical Analysis of the Roles of Instructional Leadership, Teacher Collaboration, and Collective Efficacy Beliefs in Support of Student Learning. American Journal of Education, 121(4), 501-530.

Huck, S. W. (2008). Reading statistics and research (5th ed.). Boston, MA: Allyn and Bacon.

Howell, D. C. (2012). Statistical methods for psychology (7th ed.). Belmont, CA: Wadsworth.

Jenkins, D. & Cho, S. (2014). Get With the Program… and Finish It: Building Guided Pathways to Accelerate Student Completion. New Directions for Community Colleges, 2013(164), 27-35.

Malen, B., Rice, J. K., Matlach, L. K. B., Bowsher, A., Hoyer, K. M., Mulvaney, K., & Hyde, L. H. (2015). Developing Organizational Capacity for Implementing Complex Education Reform Initiatives: Insights From a Multiyear Study of a Teacher Incentive Fund Program. Educational Administration Quarterly, 51(1), 133-176.

Newmann, F. M., Smith, B., & Bryk, A. S. (2001). Instructional Program Coherence: What It Is and Why It Should Guide School Improvement Policy. Educational Evaluation and Policy Analysis, 23(4), 297-321.

Pace, L. A. (2013). Choosing a statistical test. Web.

Pace, L. A. (2012). Point and click! A guide to SPSS for Windows (5th ed.). Anderson, SC: TwoPaces.com.

Russell, J. L. & Bray, L. E. (2013). Crafting Coherence from Complex Policy Messages: Educators’ Perceptions of Special Education and Standards-Based Accountability Policies. Education Policy Analysis Archives, 21(12), 1-25.

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