Data Analysis in Health Care

Study Course Description

Course Description Statuss:Approved

Course Description Version:3.00

Study Course Accepted:07.08.2023 09:49:47

Study Course Information
Course Code:		SL_039		LQF level:		Level 7
Credit Points:		4.00		ECTS:		6.00
Branch of Science:		Economics; Social Economics		Target Audience:		Health Management
Study Course Supervisor
Course Supervisor:		Māra Grēve
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		14a Baložu street, Riga, +371 67060897, statistikarsu[pnkts]lv, www.rsu.lv/statlab
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		10	Lecture Length (academic hours)		2	Total Contact Hours of Lectures		20
Classes (count)		14	Class Length (academic hours)		2	Total Contact Hours of Classes		28
Total Contact Hours								48
Study course description
Preliminary Knowledge:
Secondary school knowledge in mathematics and informatics.
Objective:
This module “Data analysis in health care” is subdivided into three sub-modules 1. Mathematics applied in health management 2. Types and processing of data in health care 3. Statistics and statistical tools applied in health management Sub-Module: “Mathematics applied to health management” This module aims to ensure students’ understanding of basic theoretical foundations of statistical data analysis and advantages and limitations of quantitative methods. Sub-Module: “Types and processing of data in health care” This module aims to familiarize students with the classification of data used in health care, available data sources and pre-processing of the data for quantitative analysis. Sub-Module: “Statistics and statistical tools applied to health management” This module aims to provide knowledge and skills in the most widely used descriptive and inferential statistics, regression and correlation analysis. The teaching and learning activities for all 3 Sub-Modules will include presentations, lectures, case-studies, discussions and practical work.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
2	Basic algebra and calculus, introduction to linear algebra (work with matrices), graph theory.			Lectures		1.00	computer room
3	Introduction to probability theory, Bayes probability.			Lectures		1.00	computer room
5	Data scales, data conversion, mathematical transformation with data and data entry.			Lectures		1.00	computer room
6	Data preparation: data entry, data validation, handling of missing data.			Classes		1.00	computer room
7	Data filtering, transformation, and calculation of new variables, calculation of atypically high or low values.			Classes		2.00	computer room
9	Descriptive statistics, theoretical and empirical distributions, confidence intervals.			Lectures		1.00	computer room
10	Running descriptive statistics with Excel and Jamovi, graphical presentation of data, data visualization.			Classes		1.00	computer room
11	Hypotheses testing, one sample tests.			Lectures		1.00	computer room
				Classes		1.00	computer room
12	Statistical tests for independent observations. Parametric and non-parametric tests.			Lectures		1.00	computer room
				Classes		2.00	computer room
13	Statistical tests for dependent observations. Parametric and non-parametric tests.			Lectures		1.00	computer room
				Classes		2.00	computer room
14	Correlation analysis. Regression analysis – linear and logistic (binomial, multinomial and ordinal) regression.			Lectures		2.00	computer room
				Classes		4.00	computer room
15	Faktoranalize – izpētošā un apstiprinošā			Lectures		1.00	computer room
				Classes		1.00	computer room
Assessment
Unaided Work:
• Published research study literature on Data Analysis and Statistical Methods. • Development and presentation of the individual and group class work. In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal.
Assessment Criteria:
• Activity during interactive lectures – 25%. • Quality and terms of individual and group tasks – 25%. • Accuracy and precision of written exam answers – 50%.
Final Examination (Full-Time):		Exam
Final Examination (Part-Time):
Learning Outcomes
Knowledge:		Upon successful completion of the module´s course the students will: • demonstrate knowledge with the basic ideas of linear algebra including concepts of linear systems, independence, theory of matrices, linear transformations; • know data types and data sources in health care; • recognize terminology used in statistics and basic methods used in research publications; • know commonly used data processing tools in MS Excel and IBM SPSS; • know data processing criteria of various statistical methods; • interpret correctly the most important statistical indicators.
Skills:		The students will be able to: • apply solution methods of linear system for various problems; • input and edit data in computer programs MS Excel and IBM SPSS, identify data types and validate the data; • prepare data for statistical analysis correctly; • choose appropriate data processing methods, incl., will be able to do statistical hypothesis testing; • statistically analyse research data using computer programs MS Excel and IBM SPSS; • create tables and graphs in MS Excel and IBM SPSS programs for obtained results; • describe obtained research results correctly.
Competencies:		Students will be able to: • argue and make decisions about statistical data types, sources and processing methods; • recognize the appropriate tools of calculus to solve applied problems; • use appropriate statistical methods to achieve research aims, using computer programs MS Excel and IBM SPSS; • practically use learned statistical methods to process research data.
Bibliography
No.	Reference
Required Reading
1	Lang S. A First Course in Calculus, 5th edition, Springer-Verlag New York, 1986. (klasisks teorijas avots)
2	Ross S. A First Course in Probability, 8th edition, Pearson Education, 2020.
3	Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal, 2nd edition, John Wiley & Sons, 2014.
4	Petrie A. & Sabin C. Medical Statistics at a Glance, 4th edition, Wiley-Blackwell, 2020.
5	Field A. Discovering Statistics using IBM SPSS Statistics, 5th edition, Sage Publications, 2018.