Veidlapa Nr. M-3 (8)

Study Course Description

Data Analysis in Health Care

Main Study Course Information

Course Code

SL_039

Branch of Science

Economics and Business; Social Economics

ECTS

6.00

Target Audience

Health Management

LQF

Level 7

Study Type And Form

Full-Time

Study Course Implementer

Course Supervisor

Māra Grēve

Structure Unit Manager

Andrejs Ivanovs

Structural Unit

Statistics Unit

Contacts

14a Baložu street, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab

About Study Course

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.

Preliminary Knowledge

Secondary school knowledge in mathematics and informatics.

Learning Outcomes

Knowledge

1.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

1.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.

Competences

1.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.

Assessment

Individual work

Title	% from total grade	Grade
1. Individual 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.

Examination

Title	% from total grade	Grade
1. Examination	-	-
• Activity during interactive lectures – 25%. • Quality and terms of individual and group tasks – 25%. • Accuracy and precision of written exam answers – 50%.

Study Course Theme Plan

FULL-TIME

Part 1

Lecture

Modality	Location	Contact hours
On site	Computer room	2

Topics

Basic algebra and calculus, introduction to linear algebra (work with matrices), graph theory.

Lecture

Modality	Location	Contact hours
On site	Computer room	2

Topics

Introduction to probability theory, Bayes probability.

Lecture

Modality	Location	Contact hours
On site	Computer room	2

Topics

Data scales, data conversion, mathematical transformation with data and data entry.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Data preparation: data entry, data validation, handling of missing data.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Data filtering, transformation, and calculation of new variables, calculation of atypically high or low values.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Data filtering, transformation, and calculation of new variables, calculation of atypically high or low values.

Lecture

Modality	Location	Contact hours
On site	Computer room	2

Topics

Descriptive statistics, theoretical and empirical distributions, confidence intervals.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Running descriptive statistics with Excel and Jamovi, graphical presentation of data, data visualization.

Lecture

Modality	Location	Contact hours
On site	Computer room	2

Topics

Hypotheses testing, one sample tests.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Hypotheses testing, one sample tests.

Lecture

Modality	Location	Contact hours
On site	Computer room	2

Topics

Statistical tests for independent observations. Parametric and non-parametric tests.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Statistical tests for independent observations. Parametric and non-parametric tests.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Statistical tests for independent observations. Parametric and non-parametric tests.

Lecture

Modality	Location	Contact hours
On site	Computer room	2

Topics

Statistical tests for dependent observations. Parametric and non-parametric tests.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Statistical tests for dependent observations. Parametric and non-parametric tests.

Class/Seminar

Modality	Location	Contact hours
On site	Computer room	2

Topics

Statistical tests for dependent observations. Parametric and non-parametric tests.

Lecture

Modality	Location	Contact hours
On site	Computer room	2