MATH07026 2019 HEALTH STATISTICS AND DATA ANALYSIS

General Details

Full Title
HEALTH STATISTICS AND DATA ANALYSIS
Transcript Title
HEALTH STATISTICS AND DATA ANA
Code
MATH07026
Attendance
N/A %
Subject Area
MATH - Mathematics
Department
HEAL - Health & Nutritional Sciences
Level
07 - NFQ Level 7
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
Author(s)
Padraig McGourty
Programme Membership
SG_SHEAL_B07 201900 Bachelor of Science in Health Science and Physiology SG_SHUMA_H08 201900 Bachelor of Science (Honours) in Human Nutrition SG_SHEAL_H08 201900 Bachelor of Science (Honours) in Health Science and Physical Activity SG_SHUMA_B07 201900 Bachelor of Science in Human Nutrition
Description

This module is designed for health science and nutrition students to provide them with, the knowledge of how to summarise and analyse data, to provide an insight into the concepts of probability and probability distributions, understanding of statistical methods for testing hypotheses, and to provide skills in using statistical software to analyse datasets.

 

Learning Outcomes

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

1.

Describe the rationale for and methods of selecting appropriate samples for a study

2.

Graphically display and numerically summarise data using appropriate descriptive statistics

3.

Apply probability and probability distributions to data analysis

4.

Choose and apply appropriate tests of hypotheses based on a research problem and the characteristics of a dataset

5.

Model the relationships between variables using linear regression analysis

6.

Use an appropriate statistical software package to perform statistical analysis of data

Teaching and Learning Strategies

The teaching methods used will be a combination of lectures, self-study, labs, tutorials, and any combination of discussion, case study, 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 continuous assessments where the student will be examined on both their theoretical knowledge of statistics and their use of statistical analysis software to apply this knowledge with the emphasis on the practical application of statistics.  They will also take an end of module exam which will concentrate on their theoretical knowledge.

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

Where the student fails to achieve the pass mark in the module, they may be asked to repeat the final exam or complete a practical laboratory assessment or a combination of both.

The repeat practical laboratory assessment will be held in the first week of September prior to the autumn exam boards taking place.

Indicative Syllabus

1. Describe the rationale for and methods of selecting appropriate samples for a study

  • Sample selection methods based on study design
  • Sample Size
  • Sampling Issues

2. Graphically display and numerically summarise data using appropriate descriptive statistics

  • Graphical Representation of data including frequency tables and charts
  • Measures of Central Tendency, Position and Dispersion.
  • Confidence Intervals for Means and Proportion size

3. Apply probability and probability distributions to data analysis

  • Probability Experiments
  • Probability Trees
  • Classical Probability
  • Experimental Probability
  • Addition and Multiplication Rules of Probability
  • Counting Rules
  • Discrete Probability Distributions
    • Binomial Distribution
    • Poisson Distribution
  • The Normal Distribution
    • Applications of the standard Normal Distribution
    • Assessing Normality
    • The Central Limit Theorem

4. Choose and apply appropriate tests of hypotheses based on a research problem and the characteristics of a dataset

  • Introduction to Hypothesis Testing
  • Writing hypotheses for statistical tests
  • 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
  • z-tests for proportion size
  • Non Parametric Tests
    • Introduction to Non-Parametric hypothesis tests
    • Chi-Square test for association and Independence
    • Mann-Whitney test
    • Kruskal Wallis test
    • Wilcoxon signed-rank test

5. Model the relationships between variables using linear regression analysis

  • Relationship Modelling
  • Pearson's Correlation Co-efficient
  • Significance of the correlation co-efficient
  • Spearman's Rho

6. Use an appropriate statistical software package to perform statistical analysis of data

  • Introduction to SPSS
  • Using SPSS to carry out the various statistical tests detailed in the syllabus above.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
50 %
End of Semester / Year Formal Exam
50 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Multiple Choice Self Assessment Quizzes Formative UNKNOWN - % OnGoing 1,2,3,4,5
2 Descriptive Statistics - Practical Assessment Continuous Assessment Practical Evaluation 25 % Week 7 2,6
3 Inferential Statistics - Practical Assessment Continuous Assessment Practical Evaluation 25 % Week 13 4,5,6

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam Theory based final Exam Final Exam UNKNOWN 50 % End of Term 1,2,3,4,5
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Laboratory Practical Computer Laboratory Statistics Practical 2 Weekly 2.00
Lecture Lecture Theatre Theory Lecture 2 Weekly 2.00
Independent Learning UNKNOWN Student Self Study 3 Weekly 3.00
Total Full Time Average Weekly Learner Contact Time 4.00 Hours

Required & Recommended Book List

Recommended Reading
2012-04-04 Statistics for the Health Sciences SAGE Publications
ISBN 9781849203364 ISBN-13 1849203369
Recommended Reading
2016-04-01 SPSS Survival Manual
ISBN 033526154X ISBN-13 9780335261543

The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software. In her bestselling guide, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting data output and an example of how to present the results in a report. For both beginners and experienced users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is an essential text. Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing. This sixth edition is fully revised and updated to accommodate changes to IBM SPSS procedures, screens and output. It covers new SPSS tools for generating graphs and non-parametric statistics, importing data, and calculating dates. 5 star Amazon review: "This is the book I wish I had whilst studying SPSS and experimental design on my MSc in social research methods. It is the clearest guide to SPSS that I have come across and it is very practical and easy to use. It has allowed me to revise statistical methods in a matter of days and I have gained a better understanding of these techniques than I had through using other much lengthier texts."

Recommended Reading
2017-08-22 SPSS Demystified Routledge
ISBN 9781351976350 ISBN-13 1351976354

Without question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of students anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its third edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS. Key features of the text: Step-by-step instruction and screenshots Designed to be hands-on with the user performing the analyses alongside on their computer as they read through each chapter Call-out boxes provided, highlighting important information as appropriate SPSS output explained, with written results provided using the popular, widely recognized APA format End-of-chapter exercises included, allowing for additional practice ? Features and updates to this edition include: material updated to IBM SPSS 24 (available Fall 2016), including screenshots and data sets/end-of-chapter exercises.

Module Resources

Non ISBN Literary Resources

N/A

Journal Resources

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Other Resources

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Additional Information

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