# MATH09005 2019 Statistics and Data Analysis

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**Description**

This module will equip the student with advanced statistical techniques for analysing data in the Public Health and Health Promotion fields. It provides the students with the ability to apply appropriate statistical techniques to data sets gathered during project work and also to comment on and rate techniques used in existing studies.

### Learning Outcomes

*On completion of this module the learner will/should be able to;*

**1.**

Formulate conclusions on the health of a population by applying a range of advanced statistical techniques in the analysis of population health data.

**2.**

Model the relationships between variables using regression analysis

**3.**

Explain the concept of survival analysis and apply the most commonly used mortality/survival functions

**4.**

Justify the selection and application of appropriate quantitative analysis in global health literature and reports.

**5.**

Use a statistical software package to carry out analysis on health related datasets.

### Teaching and Learning Strategies

The teaching methods used will be a combination of online lectures, self study, on line tutorials, problem solving exercises and computer based learning

### Module Assessment Strategies

The student will be assessed by means of both summative and formative assessment. The summative assessment will consist of two practical based projects where the student will be examined on both their theoretical knowledge of data analysis and statistics and their use of statistical analysis software to apply this knowledge with the emphasis on the practical application of statistics. The student will also have to complete an online end of module exam which will be in the form of an open book quiz.

The student will also have access to online self-assessment quizzes as part of the formative assessment. These quizzes will allow the student to monitor their own progress on the module as well as identify any knowledge gaps they may have.

### Repeat Assessments

The student will be given a opportunity to do a repeat practical project covering the learning outcomes assessed in the projects detailed in the assessment strategy if they do not meet the requirements to pass the module and their project work was of a sub standard level.

### Indicative Syllabus

**Formulate conclusions on the health of a population by applying a range of advanced statistical techniques in the analysis of population health data.**

- Introduction to Biostatistics
- Birth Rates/Death Rates
- Standardisation of Death/Birth Rates (both Direct and Indirect)
- Incidence Rates/Prevalence
- Odds Ratio/Relative Risk/2 x 2 Tables and Chi-Square test
- Sample Size Calculations/Effect Size/Power
- One Sample, Independent Samples and Paired Samples t-tests
- One-Way ANOVA and related Post Hoc Tests
- Repeated Measures ANOVA and related Post Hoc Tests
- Two Way ANOVA and related Post Hoc Tests
- z-tests for proportion size
- Non Parametric Tests
- Mann-Whitney test
- Kruskal Wallis test
- Wilcoxon signed-rank test

- Reliability Analysis
- Factor Analysis

**Model the relationships between variables using regression analysis**

- Pearson's Correlation/Spearman's Correlation
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Stepwise Regression

**Explain the concept of survival analysis and apply the most commonly used mortality/survival functions**

- Hazard Functions
- Survival Functions
- Censoring
- Kaplan-Meier
- Hazard Ratio
- Log Rank Test
- Cox Proportional Hazards Model

**Justify the selection and application of appropriate quantitative analysis in global health literature and reports.**

- Identification of statistical methods used in Research
- Critical Appraisal of Statistical Methods in research

**Use a statistical software package to carry out analysis on health related datasets**

- The Latest version of IBM SPSS Statistics will be used to perform a number of the statistical procedures detailed earlier in the Syllabus.

### Coursework & Assessment Breakdown

**Coursework & Continuous Assessment**

### Coursework Assessment

Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
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1 | End of Module Online Test | Continuous Assessment | Open Book Exam | 30 % | Week 13 | 1,2,3,4 |

2 | Application of Statistical Methods Project | Continuous Assessment | Assignment | 35 % | Week 6 | 1,2,5 |

3 | Application of Statistical Methods Project 2 | Continuous Assessment | Assignment | 35 % | End of Term | 3,4,5 |

### Online Learning Mode Workload

Type | Location | Description | Hours | Frequency | Avg Workload |
---|---|---|---|---|---|

Lecture | Distance Learning Suite | Online Lecture | 1 | Weekly | 1.00 |

Independent Learning | Not Specified | Weekly Practical Activities | 2 | Weekly | 2.00 |

Independent Learning | Not Specified | Self Study | 2 | Weekly | 2.00 |

### Required & Recommended Book List

**Recommended Reading**

2011-08-17

*Biostatistics: An Applied Introduction for the Public Health Practitioner*Cengage Learning

ISBN 9781133708957 ISBN-13 1133708951

BIOSTATISTICS: AN APPLIED INTRODUCTION FOR THE PUBLIC HEALTH PRACTITIONER is designed to help public health researchers, practitioners, and students understand and apply essential biostatistics concepts. This innovative new text emphasizes real-world public health problems and the research questions they inspire. This text provides a unique introduction to statistical concepts and methods used by working professionals during investigations. Unlike other texts that assume a strong knowledge of mathematics or rely heavily on formulas, BIOSTATISTICS consistently emphasizes the public health context, making even complex material both accessible and relevant. The first chapter introduces common statistical terminology by explaining them in clear language, while subsequent chapters explore the most useful and versatile statistical methods for a variety of public health research questions. For each type of question, the author presents a range of applicable methods, from descriptions of data to simple statistical tests, generalized linear models, and multiple variable regression. The text's step-by-step coverage of fundamental concepts is perfect for students new to the field, but its depth and detail also make it ideal for two-course series in M.P.H. or M.H.A. programs, or for working professionals. Readers at all stages of their professional lives can draw on this invaluable resource to help them interpret and conduct statistical studies and support effective evidence-based practice. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

**Recommended Reading**

2017-02-17

*Essentials of Biostatistics in Public Health*Jones & Bartlett Learning

ISBN 9781284108194 ISBN-13 1284108198

Study designs -- Quantifying the extent of disease -- Summarizing data collected in the sample -- The role of probability -- Confidence interval estimates -- Hypothesis testing procedures -- Power and sample size determination -- Multivariable methods -- Nonparametric tests -- Survival analysis -- Data visualization

**Recommended Reading**

2018-11-13

*Biostatistics*Wiley

ISBN 9781119282372 ISBN-13 1119282373

The ability to analyze and interpret enormous amounts of data has become a prerequisite for success in allied healthcare and the health sciences. Now in its 11th edition, Biostatistics: A Foundation for Analysis in the Health Sciences continues to offer in-depth guidance toward biostatistical concepts, techniques, and practical applications in the modern healthcare setting. Comprehensive in scope yet detailed in coverage, this text helps students understandand appropriately useprobability distributions, sampling distributions, estimation, hypothesis testing, variance analysis, regression, correlation analysis, and other statistical tools fundamental to the science and practice of medicine. Clearly-defined pedagogical tools help students stay up-to-date on new material, and an emphasis on statistical software allows faster, more accurate calculation while putting the focus on the underlying concepts rather than the math. Students develop highly relevant skills in inferential and differential statistical techniques, equipping them with the ability to organize, summarize, and interpret large bodies of data. Suitable for both graduate and advanced undergraduate coursework, this text retains the rigor required for use as a professional reference.

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