Veidlapa Nr. M-3 (8)
Study Course Description
Basic Statistics
Course Code
SL_016
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Nursing Science
LQF
Level 6
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
To get basic knowledge of data processing methods (descriptive statistics, inferential statistics to estimate differences), that can be used in thesis work and in chosen specialty.
Preliminary Knowledge
Knowledge of informatics and mathematics corresponding to the secondary school level.
Learning Outcomes
Knowledge
1.Upon successful completion of the course, students will demonstrate basic knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know IBM SPSS offered data processing tools; * know criteria of data processing methods; * interpret the most important statistical indicators correctly.
Skills
1.After completion of this course, students will demonstrate skills: * to input and edit data in computer programs MS Excel and IBM SPSS; * to prepare data for statistical analysis correctly; * to choose appropriate data processing methods, incl., statistical hypothesis testing; * to analyse research data statistically using IBM SPSS program; * to create tables and graphs in IBM SPSS program with the obtained results; * to describe obtained research results precisely.
Competences
1.After completion of this course, students will be able to argument and make decisions about statistical data processing methods, use them to achieve research aims, using IBM SPSS, use the learned statistical basic methods practically to process research data.
Assessment
Individual work
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Title
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% from total grade
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Grade
|
|---|---|---|
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1.
Individual work |
-
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-
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1. Individual work with the literature – prepare to lectures according to plan.
2. Individual work in pairs – each pair will receive a research data file (it is allowed to use their own research data) with previously defined research tasks. Students will statistically process data to reach defined tasks using descriptive statistics and inferential statistics methods, as well as to present obtained results in the last lecture.
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Examination
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Title
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% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
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|
For successful integration of knowledge and to prepare for the final exam, the student performs the following activities (mandatory, not graded): 1. Participation in practical lectures. A practical assignment for each missed class. 2. Oral presentation of independent work.
After completion of this course – exam. The grade of the course is cumulative, where: 50% – test with practical tasks using datasets, 50% – exam (multiple-choice test with theoretical and practical questions in statistics).
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Study Course Theme Plan
FULL-TIME
Part 1
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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
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Introduction to statistics, the role of statistics in research process. Types of data.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
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Preparation of data for database. Introduction to IBM SPSS. Basic operations with data in IBM SPSS.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics in IBM SPSS.
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-
Class/Seminar
|
Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
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Topics
|
Descriptive statistics of the Normal distribution.
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-
Class/Seminar
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Creation of tables and diagrams in IBM SPSS according to data type.
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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. Confidence intervals.
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
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Topics
|
Parametric statistics for quantitative data for 2 independent or paired samples.
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Non-parametric statistics for quantitative or ordinal data for 2 independent or paired samples.
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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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|---|---|---|
|
On site
|
Computer room
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2
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Topics
|
Parametric and non-parametric data processing methods for 3 or more independent or paired samples.
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-
Class/Seminar
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Modality
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Location
|
Contact hours
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|---|---|---|
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On site
|
Computer room
|
2
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Topics
|
Qualitative data processing for independent and dependent samples. Odds ratio, relative risk.
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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
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Topics
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Reliability analysis. (Cronbach's alpha).
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-
Class/Seminar
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
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2
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Topics
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Practical work with data in IBM SPSS.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practical work with data in IBM SPSS.
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-
Class/Seminar
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
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Analysis of publication.
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-
Class/Seminar
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
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2
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Topics
|
Independent work with data using IBM SPSS.
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-
Class/Seminar
|
Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
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2
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Topics
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Students presentations.
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Total ECTS (Creditpoints):
3.00
Contact hours:
32 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Andy Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.
2.
Statistics for Nursing: A Practical Approach. Elizabeth Heavey. Burlington, MA: Jones & Bartlett Learning, 2019.
3.
Suresh. K. Sharma. Nursing Research and Statistics. Elsevier, 2nd edition, 2014.