Veidlapa Nr. M-3 (8)
Study Course Description
Data Analysis in Health Care
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.Knowledge of the principles of quantitative research and the application of statistical methods in health research.
Individual work and tests
Self-test tests • Create a table • Descriptive statistics • Comparing groups • Correlation and regression
2.Knowledge of data types, measurement scales, and data sources used in health care and public health research.
Individual work and tests
Comparing groups • Descriptive statistics • Data types and measurement scales • Create a table • Self-test tests
3.Ability to recognise statistical terminology and commonly used statistical methods applied in scientific publications in the health field.
Individual work and tests
Self-test tests • Individual work with literature • Create a table • Data types and measurement scales • Comparing groups • Correlation and regression • Descriptive statistics
4.Knowledge of data preparation and data processing principles for statistical analysis, including data quality requirements and methodological assumptions.
Individual work and tests
Create a table
5.Understanding of key statistical indicators (e.g., confidence intervals, p‑values, effect size measures) and principles of their interpretation.
Individual work and tests
Create a table • Correlation and regression • Comparing groups • Self-test tests
Skills
1.Prepare and structure data for statistical analysis using MS Excel for data organisation, coding, and cleaning.
Individual work and tests
Create a table
2.Identify data types and measurement scales and assess data suitability for selected statistical methods.
Individual work and tests
Create a table • Self-test tests • Correlation and regression • Descriptive statistics • Comparing groups
3.Select and apply appropriate statistical data processing methods, including hypothesis testing.
Individual work and tests
Create a table
4.Perform statistical data analysis using Jamovi statistical software.
Individual work and tests
Create a table
5.Present analysis results in tables and diagrams and correctly interpret statistical results.
Individual work and tests
Create a table
Competences
1.Able to independently justify decisions regarding data types, data sources, and statistical analysis methods in health research.
Individual work and tests
Description of statistical data analysis methods
2.Able to select and purposefully apply appropriate statistical methods to achieve research objectives using Jamovi.
Individual work and tests
Create a table
3.Able to critically evaluate statistical analysis results in relation to the research question and study design.
Individual work and tests
Description of statistical data analysis methods
4.Able to integrate statistical data analysis into the methodology of a master’s thesis and accurately describe the applied methods in academic work.
Individual work and tests
Description of statistical data analysis methods
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work with literature |
-
|
-
|
|
Preparation for each lesson according to the thematic plan using class presentations and required reading. |
||
|
2.
Self-test tests |
5.00% from total grade
|
10 points
|
|
Self-assessment tests based on theoretical topics enable self-assessment and strengthening of one's own understanding and preparedness, without time limitations or restrictions on the number of completions. |
||
|
3.
Create a table |
-
|
Test
|
|
Creating a table with a comparison of two independent groups by designing them according to the requirements of publications - the student will be given data (variables of different types) to be analysed as previously acquired topics, and the results will be summarised in the form of a table, an example of which will be given. |
||
|
4.
Course evaluation questionnaire |
-
|
-
|
|
A kind request to complete the study course assessment questionnaire on the student Portal to evaluate the quality of the study course as a whole. |
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Description of statistical data analysis methods |
25.00% from total grade
|
10 points
|
|
Development of a description of statistical data analysis methods according to the chosen topic, aim of research, tasks, data types to be analysed and the selected statistical methods of the Master’s thesis. |
||
|
2.
Data types and measurement scales |
10.00% from total grade
|
10 points
|
|
3.
Descriptive statistics |
20.00% from total grade
|
10 points
|
|
4.
Comparing groups |
20.00% from total grade
|
10 points
|
|
5.
Correlation and regression |
20.00% from total grade
|
10 points
|
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Master’s thesis methodology. Research in the field of health.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Qualitative research methods.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Quantitative research methods. Data types and measurement scales.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Hypothesis testing.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Preparation of data for analysis. Useful MS Excel functions.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Introduction to the data analysis program Jamovi.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics, confidence intervals.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics and confidence intervals with the data analysis program Jamovi.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
One-sample statistical tests for categorical variables.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
One-sample statistical tests for quantitative variables and variables on an ordinal scale.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Comparing independent groups for categorical variables.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Comparing independent groups for quantitative variables and variables on an ordinal scale.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Comparing dependent groups for categorcal variables.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Comparing dependent groups for quantitative variables and variables on an ordinal scale.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practice with data, group comparison.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practice with data, group comparison.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Correlation coefficients.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Linear regression.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Binomial logistic regression.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Multinomial and ordinal logistic regression.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Measuring instruments and scales in questionnaires.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Cronbach alpha factor. Exploratory and confirmatory factoring analysis.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Calculation of the number of participants required. Creating a description of data analysis methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practical work: Creating a description of data analysis methods.
|
Total ECTS (Creditpoints):
6.00
Contact hours:
48 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Lang S. A First Course in Calculus, 5th edition, Springer-Verlag New York, 1986. (klasisks teorijas avots)Suitable for English stream
2.
Ross S. A First Course in Probability, 8th edition, Pearson Education, 2020.Suitable for English stream
3.
Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal, 2nd edition, John Wiley & Sons, 2014.Suitable for English stream
4.
Petrie A. & Sabin C. Medical Statistics at a Glance, 4th edition, Wiley-Blackwell, 2020.Suitable for English stream
5.
Field A. Discovering Statistics using IBM SPSS Statistics, 5th edition, Sage Publications, 2018.Suitable for English stream