gsbiostat - Angeles

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1 GRADUATE SCHOOL OF NURSING

HOLY ANGEL UNIVERSITY

Master of Science in Nursing

GSBIOSTAT – Biostatistics

I. VISION STATEMENT

The Graduate School envisions itself to be one of the centers of excellence in graduate education and research in the Asia Pacific Region that will produce graduates who are role model catalysts for countryside development in the fields of business, education, information technology, engineering, and nursing.

II. MISSION STATEMENT

The Graduate School offers accessible quality graduate education and research experiences that transform professionals into persons of conscience,

competence and compassion.

III. CORE VALUES

The core values of the Graduate School are Christ-centeredness, integrity, excellence, community, and societal responsibility.

IV. MSN PROGRAM EDUCATIONAL OBJECTIVES (PEOs) The MSN graduates are expected to:

1. Utilize their understanding of the context of nursing practice in initiating actions or interventions and in planning and evaluating programs and policies for specific work settings/foci: client care; nursing education;

nursing research; and nursing leadership, governance, and management;

2. Demonstrate critical thinking and effective communication in nursing practice: client care; nursing education; nursing research; and nursing leadership, governance, and management;

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2 3. Demonstrate leadership and competence in working with clients in

addressing their health needs and problems, and in collaborating with other members of the health team;

4. Integrate theories (both nursing and ‘borrowed’) and research findings in their performance of different roles and functions related to client care; nursing education; nursing research; and nursing leadership, governance, and management;

5. Practice nursing in accordance with professional standards, ethical principles and relevant laws that affect client care; nursing education;

nursing research; and nursing leadership, governance, and management;

6. Conduct research that enhances the performance of functions related to client care; nursing education; nursing leadership, governance, and management;

7. Maintain and enhance competence as professionals by continuously engaging in self-improvement activities and participating in continuing professional development programs; and,

8. Initiate programs, projects, and activities for staff development.

V. MSN STUDENT LEARNING OUTCOMES (SLOs)

An Angelite MSN student is able to demonstrate and master the ability to:

1. listen, comprehend, speak, write and convey ideas clearly and effectively, in person and through electronic media to all audiences (Communication);

2. recognize different value systems, including his or her own (Value and Ethical Reasoning);

3. appreciate the moral dimensions of his or her decisions and accept responsibility for them (Value and Ethical Reasoning);

4. use experience, knowledge, reason and belief to form carefully considered judgments (Critical and Creative Thinking);

5. determine what is wrong and how to fix it, working alone or in groups (Critical and Creative Thinking);

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3 6. combine or synthesize existing ideas, images, or expertise in original

ways and the experience of thinking, reacting, and working in an imaginative way characterized by a high degree of innovation,

divergent thinking, and risk taking (Critical and Creative Thinking);

7. act with an informed awareness of issues and participate in civic life through volunteer activities and leadership (Civic and Global Learning);

8. appreciate economic, social, and ecological connections that link the world’s nations and people (Civic and Global Learning);

9. integrate theory and practice (Applied and Collaborative Learning);

10. demonstrate and master the ability to elicit other views, mediate

disagreements, and help reach conclusions in group settings (Applied and Collaborative Learning);

11. engage with the arts and draw meaning and value from artistic expression (Aesthetic Engagement); and

12. access, manage, integrate, evaluate, create, and communicate information purposefully, knowledgeably, technically, and ethically (Information and Communication Technology Literacy).

VI. COURSE DESCRIPTION

This is an introductory course on the principles of biostatistics and applications of statistical techniques in data collection, summarization and analysis of health-related data. The course includes fundamental concepts related to sampling, probability distributions and statistical inference. It also provides an overview of the common statistical techniques applied to health data covering simple estimation and hypothesis of population parameters, analysis of variance, correlation analysis and regression analysis.

The course emphasizes more on the applications of these techniques rather than their theoretical foundations and computational requirements.

Elementary statistical analysis will be generated using available statistical software.

VII. COURSE LEARNING OUTCOMES

At the end of the course, the students should be able to:

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4 1. describe the roles biostatistics serves in nursing and public health

research;

2. assess data sources and data quality for the purpose of selecting appropriate data for specific research questions;

3. translate research objectives into clear, testable statistical hypotheses;

4. describe basic principles and the practical importance of key concepts from probability and inference, inductive versus deductive reasoning, including random variation, systematic error, sampling error, measurement error, hypothesis testing, type I and type II errors, and confidence bounds;

5. apply numerical, tabular, and graphical descriptive techniques commonly used to characterize and summarize health data;

6. identify appropriate statistical methods to be applied in a given research setting, apply these methods, and acknowledge the limitations of those methods;

7. evaluate computer output containing statistical procedures and graphics and interpret it in a public health context;

8. differentiate between quantitative problems that can be addressed with standard, commonly used statistical methods and those requiring input from a professional biostatistician; and,

9. generate statistical analysis using an available statistical software.

VIII. COURSE CONTENT

Time Allotment and Expected Outcomes

Course Content Teaching- Learning Activities

Evaluation

Week 1

Describe the basic concepts in

biostatistics

Distinguish among the

Introduction to Biostatistics

 Nature of Biostatistics

 Applications of Biostatistics

 Lecture- Discussion

 Small group discussion

 Paper and pencil test

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5 different measurement

scales and the implications for selection of statistical methods to be used based on these distinctions.

Determine an

appropriate sampling plan for an analysis Apply descriptive and inferential

methodologies according to the type of study design for answering a particular research question.

 Variables and Measurement

 Descriptive and Inferential Statistics Sampling

 Advantages and Disadvantages

 Criteria for Sampling Designs

 Non-Probability Sampling Designs

 Probability ampling Designs

 Sample Size Data Collection

 Primary and Secondary Data

 Methods of Data Collection

 Tools Used for Data Collection

Weeks 2

Apply descriptive techniques commonly used to summarize data.

Describe basic concepts of

probability, random variation and commonly used statistical probability distributions.

Data Summarization and Presentation

 Frequency Tabulation

 Graphical Presentation

 Measures of Central Tendency

 Measures of Dispersion

 Measures of Location Demography Rates and Ratios Probability

Distributions

 Discrete Distributions

 Continuous

 Assigned reading

 Reporting

 Student feedback

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6 Distributions

Statistical Tables Week 3

Define and distinguish between population parameters and sample statistics.

Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.

Apply common

statistical methods for inference.

Introduction to Statistical Inference

 Parameters and Statistics

 Sampling Distributions

 Estimation

 Hypothesis Testing

 Parametric and Non- parametric Tests Inference for Single Populations

 Estimation of Single Mean

 Estimation of Single Proportion

 Test of Hypothesis for Single Mean

 Test of Hypothesis for Single Proportion

 Discussion

Week 4

Apply common

Inference for Two Populations

 Estimation of Difference of Two Means

 Estimation of Difference of Two Proportions

 Test of Hypothesis for Difference of Two Means

 Test of Hypothesis for Difference of Two Proportions

Chi-Square Tests

 Test of Difference of More than Two Proportions

 Discussion

Week 5 Analysis of Variance

 Test for Differences  Assigned reading

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7 Apply common

of More than Two Means

 Testing for

Significant Sources of Variation

Correlation Analysis

 Measures of Correlation for Quantitative Variables

 Measures of Association for Qualitative Variables

 Test of Hypothesis for Correlation

 Test of Hypothesis for Association Regression Analysis

 Simple Linear Regression

 Multiple Linear Regression

 Logistic Regression

 Discussion

IX. SUMMATIVE EVALUATION GUIDE

1. Satisfactory attendance and participation in class: 30%

2. Two Exams - Midterm (25%), Final (25%)

3. Paper detailing statistical analysis of data from a health research: 20%

X. REFERENCES

Burt, G. B. (2015). Basic biostatistics: Statistics for public health. Burlington, MA:

Jones & Bartlett Learning.

Katz, D. L., Elmore, J. G., Wild, D. M. G., & Lucan, S. C. (2014). Jekel’s

epidemiology, biostatistics, preventive medicine, and public health (4thed.).

Philadelphia, PA: Saunders.

Kros, J. F., & Rosenthal, D. A. (2015).Statistics for health care management and administration: Working with Excel (3rd ed.). San Francisco, CA: Jossey-Bass A Wiley Brand

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8 Lesaffre, E., & Lawson, A. B. (2012).Bayesian biostatistics. San Francisco, CA: John

Wiley & Sons.

Merrill, R. M. (2013). Fundamentals of epidemiology and biostatistics: Combining the basics. Burlington, MA: Jones & Bartlett Learning.

Merrill, R. M. (2016). Statistical methods in epidemiologic research. Burlington, MA:

Jones & Bartlett Learning.

Rosner, B. (2016). Fundamentals of biostatistics (8thed.). Boston, MA: Cengage Learning, Australia

Sullivan, L. M. (2012). Essentials of biostatistics in public health (2nded.). Burlington, MA: Jones & Bartlett Learning

Suchmacher, M. (2012).Practical biostatistics: A user-friendly approach for evidence-based medicine. Amsterdam: Elsevier.

http://www.gpower.hhu.de/en.html http://libguides.lib.msu.edu/biostatistics http://onlinestatbook.com/2/

Prepared by Marilyn E. Crisostomo,MSPH Biostat., MPH Guest Lecturer

Checked by Al D. Biag, RN, EdD

Program Coordinator Date