Learning objectives
Learning Objectives
D1 - KNOWLEDGE AND CAPACITY TO UNDERSTAND.
Upon completion of the course, the student should demonstrate knowledge and understanding of:
1. The basic principles and different types of experimental designs
2. The main descriptive statistics and their use, both of continuous variables and frequency measures
3. The results of an analysis of variance or a Chi-Square
4. The relationship between variables
5. The visualization of data
D2 - ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING
Upon completion of the training activity, the student should demonstrate the ability to:
1. Calculate descriptive statistics from collected and/or available data
2. Calculate a sample size
3. Perform mean comparison and analysis of variance
4. Quantify a correlation between variables
5. Study the relationship between a dependent and an independent variable (regression)
6. Perform a chi-square
D3 - AUTONOMY OF JUDGMENT
Upon completion of the training activity, the student will be able to:
1. Interpret a confidence interval of a mean and how to use it to compare the outcome of specific treatments or effects
2. Evaluate an experimental design
3. Perform and interpret the results of an analysis of variance
4. Manage frequency data
5. Be familiar with computer tools to be used to perform data analysis
D4 - COMMUNICATION SKILLS
Upon completion of the training activity, the student should demonstrate the ability to:
1. Express themselves clearly and in appropriate terms while describing a data analysis.
D5 - LEARNING SKILLS
Upon completion of the training activity, the student will be able to:
1. Learn the main concepts related to data analysis
2. Understand how to use basic statistics to answer some research questions.
Prerequisites
none
Course unit content
Why do we Use Statistics? PPDAC Problem-Solving Cycle. Testing a Hypothesis.
Basic Principles of Experimental Designs. Basic Descriptive Statistics (mean, mode, median, standard deviation). Normal and Standardized Distribution (practical applications).
Confidence interval of a Mean and Its Applications: Sample Size Calculation, Paired t-Test, Comparison Between Experimental Groups (practical applications).
One-Factor Analysis of Variance. Completely Randomized Design (practical applications).
Two or More Factors Analysis of Variance. The Special Case of Block Effect. Interaction Effect. Latin Square.
Relationship Between Variables: Correlation and Linear Regression. Logistic Regression.
Frequency Measures and Chi-Square.
Basic Concepts in Data Visualization.
During the course, the R software will also be introduced, a programming language and software environment for statistical computing and graphics.
Full programme
- - -
Bibliography
https://2012books.lardbucket.org/books/beginning-statistics/
Chapter 10. Experimental Design: Statistical Analysis of Data. Descriptive Statistics https://uca.edu/psychology/files/2013/08/Ch10-Experimental-Design_Statistical-Analysis-of-Data.pdf
An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain (https://synapse.koreamed.org/upload/synapsedata/pdfdata/0006jkan/jkan-43-154.pdf)
Herzog, Michael H., Gregory Francis, and Aaron Clarke. Understanding statistics and experimental design: how to not lie with statistics. Springer Nature, 2019. Parts I and II https://library.oapen.org/bitstream/id/553fdb7d-9981-4e99-b0a9-6241622b1ad5/1007132.pdf
Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019. https://clauswilke.com/dataviz/
Wickham, Hadley, and Garrett Grolemund. R for data science. Vol. 2. Sebastopol, CA: O'Reilly, 2017. https://r4ds.hadley.nz/
Teaching methods
Lectures, computer session.
Assessment methods and criteria
Verification of the expected learning outcomes described by Indicator D1 and part of those described by Indicators D2, D3, D4 and D5 will be done by written examination.
The written examination will consist of 30 multiple choice questions and will last a maximum of 40 minutes.
Through the questions regarding the course content, it will be ascertained whether the student has achieved the objective of content knowledge and understanding.
Each question answered correctly will be awarded 1 point, while each question answered incorrectly will be subtracted 0.25 points. Each missing answer will be awarded 0 points.
The grade will be rounded up only in case the score obtained has 75 as a decimal, in all other cases it will be rounded down. For example, 16.75 = 17, while 16.5 = 16.
1 point will be added to the score achieved with the answers to the questions.
Honors will be achieved with a total score of 31.
For students with DSA, appropriate compensatory and dispensatory measures will be put in place
Other information
Lessons frequency is not mandatory, although
strongly encouraged.
2030 agenda goals for sustainable development
Good health and well-being
