Audited Financial Statements Patton Fuller Community Hospita

Audited Financial Statementspatton Fuller Community Hospitalstatemen

Audited Financial Statements Patton - Fuller Community Hospital Statement of Revenue and Expense 2009 and 2008 (In thousands) (Audited) Revenues Net Patient Revenue $459,900 $418,509 Other Revenue $3,082 $2,805 Total Revenues $462,982 $421,314 Expenses Salaries and benefits $220,752 $214,129 Supplies $74,584 $71,346 Physician and professional fees $110,376 $107,065 Utilities $1,200 $1,164 Other $1,840 $1,785 Depreciation & Amorization ("non-cash" expenses) $36,036 $24,955 Interest $3,708 $3,597 Provision for doubtful accounts $14,797 $13,383 Total Expenses $463,293 $437,424 Operating Income ($311) ($16,110) Non-operating income (loss) Investment income ($62) $264 Net Income ($373) ($15,); includes adjustment due to audit &R&Z&F

Paper For Above instruction

The evaluation of a new after-school math club's effectiveness requires a comprehensive plan utilizing both quantitative and qualitative methods. This approach ensures a thorough understanding of the program's impact on students’ enjoyment of math and their standardized test scores, aligning with the objectives outlined by the teacher and addressing the principal's concerns on accountability and effectiveness.

Data Collection and Rationale

To measure the "raise scores" objective, the primary quantitative data will involve standardized math test scores administered before and after students participate in the math club. These scores will be collected through formal assessments to evaluate learning gains attributable to the club. Additionally, comparing these scores with those of a similar control group of students who did not participate in the club can offer insights into the relative effectiveness of the intervention. The data's appropriateness lies in its ability to directly measure academic achievement, providing concrete evidence of progress or lack thereof.

For the "enjoy math" objective, qualitative data collection methods will include surveys and reflective journals from students, teachers, and parents. These will gather subjective experiences and attitudes toward mathematics and the club activities. Focus groups with students and interviews with teachers and parents will enhance understanding of emotional engagement, motivation, and perceptions of the club's influence on students. Observations during club meetings and classroom activities will provide contextual insights into student behavior and enthusiasm.

Research Hypotheses and Design Classification

Regarding the "raise scores" objective, the hypothesis posits: "Participation in the after-school math club will lead to a significant increase in students’ standardized math test scores." For quantitative validation, a pretest-posttest design involving the same group of students (one-group design) will be employed, where scores before and after participation are compared using a dependent t-test. Alternatively, comparing test score gains between club participants and non-participants through an independent t-test can offer comparative evidence.

If the design incorporates two groups, the hypothesis for this would be: "Students who participate in the math club will demonstrate greater improvement in math scores than students who do not participate." The analysis will focus on difference scores (posttest minus pretest) across groups to assess the club's impact statistically.

The qualitative component aims to explore attitudes toward math, perceived enjoyment, and motivation. Data from surveys and interviews will be analyzed through thematic content analysis, identifying patterns and themes related to engagement and attitudes. Coding responses for recurring topics like enthusiasm, confidence, and peer support will provide a nuanced understanding of affective changes resulting from the club.

Data Analysis and Addressing Principal’s Concerns

For the quantitative data, dependent t-tests will compare pretest and posttest scores within the same group to detect statistically significant improvements in math achievement, directly addressing the "raise scores" objective. For the comparison between groups, independent t-tests on gain scores will reveal whether participation correlates with greater score improvements. Such analyses provide objective evidence of the program’s efficacy, satisfying accountability needs.

The qualitative data will be examined for recurring themes and sentiments, indicating whether students and teachers perceive increased enjoyment and motivation for math. Consistent positive themes documented across interviews, surveys, and journals will support the conclusion that the club has effectively enhanced students’ attitudes toward mathematics, thereby addressing the "enjoy math" objective.

Overall, integration of these methods ensures a comprehensive evaluation. Quantitative results will confirm or disprove statistically whether the club influences academic achievement, while qualitative insights will elucidate changes in students’ affective engagement and motivation. Together, these data sources will enable an informed judgment about the program's success and areas for improvement, fulfilling the principal’s requirements for accountability and evidence-based decision-making in educational interventions.

References

• Creswell, J. W. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE Publications.
• Gay, L. R., Mills, G. E., & Airasian, P. (2012). Educational Research: Competencies for Analysis and Applications (10th ed.). Pearson.
• Fraenkel, J. R., Wallen, N. E., & Hyun, A. S. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill Education.
• Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
• Babbie, E. (2016). The Practice of Social Research (14th ed.). Cengage Learning.
• Rubin, H. J., & Rubin, I. S. (2011). Qualitative Interviewing: The Art of Hearing Data (3rd ed.). SAGE Publications.
• Maxwell, J. A. (2013). Qualitative Research Design: An Interactive Approach (3rd ed.). SAGE Publications.
• Gordon, R. A., & Nelson, G. (2014). The Use of Mixed Methods in Education Research. Journal of Educational Research, 107(4), 237-245.
• Fitzgerald, T. (2016). Data Analysis in Education Research. Routledge.