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Biostatistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:8.00
Study Course Accepted:09.08.2023 11:28:32
| Study Course Information | |||||||||
| Course Code: | SL_015 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Rehabilitation | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Ināra Kantāne | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | Baložu iela 14, Block A, Riga, +371 67060897, statistika rsu[pnkts]lv, www.rsu.lv/statlab | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 8 | Class Length (academic hours) | 3 | Total Contact Hours of Classes | 24 | ||||
| Total Contact Hours | 24 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Secondary school level knowledge in Mathematics and Informatics. | ||||||||
| Objective: | To get in-depth knowledge in data processing methods (descriptive statistics, inferential statistics to estimate characteristics of the entire population), that can be used in thesis work and in their speciality. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Statistics role in the research process. Data types, measures and data preparation in computer programs Excel and IBM SPSS Statistics. Main activities with data in IBM SPSS Statistics. | Classes | 0.50 | computer room | |||||
| 2 | Descriptive statistics in Excel and IBM SPSS Statistics. Usage of descriptive statistics measures. | Classes | 0.50 | computer room | |||||
| 3 | Descriptive statistics of the Normal distribution. | Classes | 0.50 | computer room | |||||
| 4 | Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. | Classes | 0.50 | computer room | |||||
| 5 | Parametric statistics for quantitative data. Comparison of independent and dependent samples. | Classes | 1.00 | computer room | |||||
| 6 | Nonparametric statistics for quantitative data. Comparison of independent and dependent samples. | Classes | 1.00 | computer room | |||||
| 7 | Qualitative data processing. Independent and dependent samples. | Classes | 1.00 | computer room | |||||
| 8 | Correlation analysis. Regression analysis (Linear regression). | Classes | 1.00 | computer room | |||||
| 9 | Regression analysis (Binary logistic regression). | Classes | 0.50 | computer room | |||||
| 10 | Analysis of scientific publications. | Classes | 0.50 | computer room | |||||
| 11 | Independent work with data using IBM SPSS Statstics. | Classes | 0.50 | computer room | |||||
| 12 | Student presentations. | Classes | 0.50 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with the literature – prepare for lectures accordingly to the plan. 2. Individual analysis of a scientific publication. 3. Individual work – every student will receive a research data file (or the student can use their own) with previously defined research tasks. Student will process data to reach defined tasks using descriptive statistics, inferential statistics and/or analytical statistics methods. To report obtained results in final paper, using defined formatting style and to present obtained results in the last lecture. In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal. | ||||||||
| Assessment Criteria: | Participation in practical lectures. For every missed lecture – a summary has to be written using given literature (min. 1 A4 page). To complete this study course: 1. Oral presentation of scientific publication: 20% of the final grade. 2. Oral presentation of independent work: 30% of the final grade. 3. At the end of the study course, examination: test with theoretical and practical questions (30 questions): 50% of the final grade. | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | On completion of the study course, students will demonstrate knowledge that allows to: * recognise terminology used in statistics and methods used in different publications; * know Excel and IBM SPSS Statistics offered data processing tools; * know data processing method criteria; * correctly interpret obtained research results. | ||||||||
| Skills: | On completion of this course, students will demonstrate skills to: * prepare data for statistical analysis correctly; * choose appropriate statistic data processing methods; * statistically analyse research data using computer programs Microsoft Excel and IBM SPSS Statistics; * create tables and graphs in Excel and IBM SPSS Statistics programs with obtained results; * precisely describe the obtained research results. | ||||||||
| Competencies: | On 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 Excel and IBM SPSS Statistics, practically use acquired statistical methods to process research data. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Field A. Discovering Statistics using IBM SPSS Statistics. 4th edition, 2013. | ||||||||
| 2 | Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition, Wiley-Blackwell, 2019. | ||||||||
| Additional Reading | |||||||||
| 1 | Teibe U. Bioloģiskā statistika. Rīga: LU Akadēmiskais apgāds, 2007, p 155. | ||||||||
