Veidlapa Nr. M-3 (8)
Study Course Description
Statistical Methods
Course Code
SL_047
Branch of Science
Other medical sciences
ECTS
3.00
Target Audience
Nursing Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Madara Miķelsone
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
14 Balozu street, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
Acquire in-depth knowledge and skills in statistical data processing methods (descriptive statistics, inferential statistical methods for estimating differences between various groups and analytical statistics), that are necessary for the processing of research data in the final thesis and in the chosen specialisation.
Preliminary Knowledge
Secondary school knowledge in mathematics and informatics.
Learning Outcomes
Knowledge
1.Upon successful completion of the course, students’ knowledge will allow them to: * recognise terminology used in statistics and inferential statistical methods used in different publications; * know the most often used possibilities offered by MS Ex
Skills
1.Having completed the course, students will be able to: * input and edit data in computer programs MS Excel and IBM SPSS; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, including statistical hypothesis testing using both basic inferential statistical methods and analytical statistical methods; * process data in IBM SPSS; * create and edit tables and graphs in MS Excel and IBM SPSS programs with the obtained results; * describe the obtained research results precisely.
Competences
1.Upon successful acquisition of the course, students will be able to critically analyse and evaluate applied statistical methods in scientific publications, independently choose the appropriate inferential and analytical statistical methods in order t
Assessment
Individual work
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Title
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% from total grade
|
Grade
|
|---|---|---|
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1.
Individual work |
-
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-
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1. Individual work with literature – preparation for each class according to the thematic plan.
2. Independent analysis of a scientific publication.
Independent work - each student will be provided with a research data file (or the student can use his/her own research data) with defined research objectives - will have to statistically process the data to achieve the defined objectives using descriptive statistical methods, inferential statistical methods and/or analytical statistical methods and present the results in the last class.
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Examination
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Title
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% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
10 points
|
|
In order to successfully master the course material and prepare for the final examination of the study course, the student performs the following activities (compulsory, not graded): 1. Participation in practical classes. A practical assignment for each missed class. 2. Oral presentation of a scientific publication analysis. Presentation of independent work. At the end of 1st semester assessment - practical work with data, which is implemented with participation in all practical classes. Examination at the end of the course - a cumulative mark, where: 50% - test with practical assignments using databases, 50% - examination (multiple-choice test with theoretical and practical questions in statistics). |
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Study Course Theme Plan
FULL-TIME
Part 4
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Computer room
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2
|
Topics
|
Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Introduction to IBM SPSS. Basic operations with data in IBM SPSS.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Indicators of descriptive statistics in MS Excel and IBM SPSS.
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Types of statistical hypotheses. Hypotheses testing. P value. Normal distribution and its descriptive statistics.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Tables and diagrams, correct formatting.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Parametric statistics for quantitative data.
Comparison of independent and dependent samples.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Nonparametric statistics for quantitative data.
Comparison of independent and dependent samples.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Processing of qualitative data. Dependent and independent samples.
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
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Reliability Analysis. Estimate of the reliability (Cronbach's alpha).
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
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Correlation analysis. Regression analysis (Linear regression).
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
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Regression analysis (Binary logistic regression).
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Class/Seminar
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Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
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2
|
Topics
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Analysis of scientific publications.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
Presentation of independent work.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Independent work with data using IBM SPSS.
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Total ECTS (Creditpoints):
3.00
Contact hours:
28 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Mārtinsone K. un Pipere A. Zinātniskās darbības metodoloģija: starpdisciplāna perspektīva, Rīga, RSU, 2021, 608 lpp.
2.
Andy Field. Discovering Statistics using IBM SPSS Statistics. 2024.Suitable for English stream
3.
Statistics for Nursing: A Practical Approach: A Practical Approach by Elizabeth Heavey. Burlington, MA: Jones & Bartlett Learning, 2022.Suitable for English stream