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Madara Miķelsone

Study Course Description

Course Description Statuss:Approved
Course Description Version:4.00
Study Course Accepted:26.09.2019
Study Course Information
Course Code:SL_016LQF level:Level 6
Credit Points:2.00ECTS:3.00
Branch of Science:Mathematics; Theory of Probability and Mathematical StatisticsTarget Audience:Nursing Science
Study Course Supervisor
Course Supervisor:Madara Miķelsone
Study Course Implementer
Structural Unit:Statistical Unit
The Head of Structural Unit:Andrejs Ivanovs
Contacts:Kapseļu iela 23, 2.stāvs, Rīga, +371 67060897, statistikaatrsu[pnkts]lv,
Study Course Planning
Full-Time - 1. Semester No.
Lectures (number)5Lecture Length (academic hours)2Total Contact Hours of Lectures10
Classes (number)11Class Length (academic hours)2Total Contact Hours of Classes22
Total Contact Hours32
Study course description
Preliminary Knowledge:
Secondary school knowledge in mathematics and informatics.
To get basic knowledge in data processing methods (descriptive statistics, inferential statistics to estimate differences), that can be used in thesis work and in chosen specialty.
Topic Layout (Full-Time)
No.TopicType of ImplementationNumberVenue
1Introduction to statistics, the role of statistics in research process. Types of data.Lectures1.00computer room
2Preparation of data for database. Introduction to IBM SPSS. Basic operations with data in IBM SPSS.Lectures1.00computer room
3Descriptive statistics in IBM SPSS.Classes1.00computer room
4Descriptive statistics of the Normal distribution.Lectures1.00computer room
5Creation of tables and diagrams in IBM SPSS according to data type.Classes1.00computer room
6Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Confidence intervals.Lectures1.00computer room
7Parametric statistics for quantitative data for 2 independent or paired samples.Classes1.00computer room
8Nonparametric statistics for quantitative or ordinal data for 2 independent or paired samples.Classes1.00computer room
9Parametric and nonparametric data processing methods for 3 or more independent or paired samples.Classes1.00computer room
10Qualitative data processing for independent and dependent samples. Odds ratio, relative risk.Classes1.00computer room
11Reliability analysis. (Cronbach's alpha).Lectures1.00computer room
12Practical work with data in IBM SPSS.Classes2.00computer room
13Analysis of publication.Classes1.00computer room
14Independent work with data using IBM SPSS.Classes1.00computer room
15Students presentations.Classes1.00computer room
Unaided Work:
1. Individual work with the literature – prepare to lectures according to plan. 2. Individual analysis of scientific publication, that has to be presented in the last lecture. 3. Individual work – every student will receive a research data file (or student can use their own) with previously defined research tasks. Student will statistically process data to reach defined tasks using descriptive statistics, inferential statistics and/or analytical statistics methods. As well as to present obtained results in the last lecture, using defined formatting style.
Assessment Criteria:
Participation in practical lectures. For every missed lecture – practical tasks and control questions about missed topic. After completion of this course: 1. Oral presentation of publication analysis and independent work. 2. Multiple choice test with theoretical questions in statistics.
Final Examination (Full-Time):Exam
Final Examination (Part-Time):
Learning Outcomes
Knowledge:After completion of this 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 data processing method criterias; * correctly interpret the most important statistical indicators.
Skills: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., will be able to do statistical hypothesis testing; * statistically analyse research data using IBM SPSS program; * create tables and graphs in IBM SPSS program with obtained results; * precisely describe obtained research results.
Competencies: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, practically use learned statistical basic methods to process research data.
1Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp.
2Petrie A. & Sabin C. Medical Statistics at a Glance, 3rd edition, 2009. ISBN: 978-1-405-18051-1
3Field A. Discovering Statistics using IBM SPSS Statistics, 4th edition, ISBN-13: 978-1446249185, 2013.