Skip to main content
Supervisor
Andrejs Ivanovs

Study Course Description

Course Description Statuss:Approved
Course Description Version:4.00
Study Course Accepted:14.03.2019
Study Course Information
Course Code:SL_013LQF level:Level 6
Credit Points:2.00ECTS:3.00
Branch of Science:Mathematics; Theory of Probability and Mathematical StatisticsTarget Audience:Rehabilitation
Study Course Supervisor
Course Supervisor:Andrejs Ivanovs
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, www.rsu.lv/statlab
Study Course Planning
Full-Time - 1. Semester No.
Lectures (number)Lecture Length (academic hours)Total Contact Hours of Lectures0
Classes (number)11Class Length (academic hours)3Total Contact Hours of Classes33
Total Contact Hours33
Part-Time - 1. Semester No.
Lectures (number)Lecture Length (academic hours)Total Contact Hours of Lectures
Classes (number)8Class Length (academic hours)3Total Contact Hours of Classes24
Total Contact Hours24
Study course description
Preliminary Knowledge:
Secondary school knowledge in mathematics and informatics.
Objective:
To get basic knowledge in data processing methods (descriptive statistic, inferential statistic to estimate differences), that can be used in thesis work and in their speciality.
Topic Layout (Full-Time)
No.TopicType of ImplementationNumberVenue
2Introduction to IBM SPSS. Basic actions with data in the IBM SPSS program.Classes1.00computer room
3Descriptive statistics in MS Excel and IBM SPSS.Classes1.00computer room
4Descriptive statistics of the Normal distribution. Creation of tables and diagrams, correct design.Classes1.00computer room
6Parametric statistics for quantitative data. Comparison of independent samples (Students t test, Analysis of Variance (ANOVA)). Comparison of dependent samples (Paired Student t test, Analysis of Variance (MANOVA)).Classes1.00computer room
7Nonparametric statistics for quantitative data. Comparison of independent samples (Mann–Whitney U test, Kruskal-Wallis Test). Comparison of dependent samples (Wilcoxon test, McNemar's test).Classes1.00computer room
8Qualitative data processing. Independent and dependent samples. Pearson chi square tests and Fisher's exact test.Classes1.00computer room
9Reliability analysis. Internal consistency measure (Cronbach's alpha).Classes1.00computer room
10Analysis of scientific publications.Classes1.00computer room
11Independent work with data using IBM SPSS.Classes1.00computer room
12Students' presentations.Classes1.00computer room
Topic Layout (Part-Time)
No.TopicType of ImplementationNumberVenue
4Descriptive statistics of the Normal distribution. Creation of tables and diagrams, correct design.Classes1.00computer room
6Parametric statistics for quantitative data. Comparison of independent samples (Students t test, Analysis of Variance (ANOVA)). Comparison of dependent samples (Paired Student t test, Analysis of Variance (MANOVA)).Classes1.00computer room
7Nonparametric statistics for quantitative data. Comparison of independent samples (Mann–Whitney U test, Kruskal-Wallis Test). Comparison of dependent samples (Wilcoxon test, McNemar's test).Classes1.00computer room
8Qualitative data processing. Independent and dependent samples. Pearson chi square tests and Fisher's exact test.Classes1.00computer room
Assessment
Unaided Work:
1. Individual work with the literature – prepare to lectures accordingly to a plan. 2. Individual analysis of a scientific publication. 3. Individual work - each student will receive a research data file (or students can use their own) with previously defined research tasks. Student will statistically process data to reach defined tasks using descriptive statistic, inferential statistic and/ or analytical statistics methods. As well as to report obtained results in final paper, using defined formatting style and to present obtained results in the last lecture.
Assessment Criteria:
Participation in practical lectures. Test on the use of statistical methods and terminology in statistics. After completion of this course: 1. Multiple choice test with theoretical questions in statistics. 2. Oral presentation of independent work. For every missed lecture - summary has to be written using given literature (min. one A4 page).
Final Examination (Full-Time):Test
Final Examination (Part-Time):Test
Learning Outcomes
Knowledge:Upon 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 MS Excel and IBM SPSS offered data processing tools; * know data processing method criteria; * correctly interpret the most important statistical indicators.
Skills:Upon completion of this course, students will demonstrate skills to: * Input and edit data in computer programs MS Excel and IBM SPSS; * Prepare data for statistical analysis correctly; * Choose appropriate data processing methods, incl., ability 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 programme with obtained results; * Correctly describe obtained research results.
Competencies:Upon 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 computer programs MS Excel and IBM SPSS, practically use learned statistical basic methods to process research data.
Bibliography
No.Reference
1Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp.
2Field A. Discovering Statistics using IBM SPSS Statistics, 4th edition, ISBN-13: 978-1446249185, 2013.
3Petrie A. & Sabin Caroline. Medical Statistics at a Glance, 3rd edition, Willey Blackwell, 2009.