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Health Statistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:8.00
Study Course Accepted:17.06.2022 15:43:39
| Study Course Information | |||||||||
| Course Code: | SL_021 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Communication Science | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Diāna Kalniņa | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | Baložu iela 14, Block A, Riga, statistika rsu[pnkts]lv, +371 67060897 | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 7 | Lecture Length (academic hours) | 2 | Total Contact Hours of Lectures | 14 | ||||
| Classes (count) | 9 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 18 | ||||
| Total Contact Hours | 32 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Basic knowledge of Mathematics. | ||||||||
| Objective: | Promote knowledge acquisition of key issues, statistical indicators and tests used in health statistics. Raise the students’ awareness of the role of statistics in research and interpretation. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to statistics. The role of statistics in the research process. General population and sample. Sample size and structure. Data types. The scales. | Lectures | 2.00 | computer room | |||||
| 2 | Introduction to the statistical data processing program IBM SPSS. Basic operations with data in IBM SPSS. | Classes | 2.00 | computer room | |||||
| 3 | Descriptive statistics for qualitative and quantitative data. Central tendency, distribution and representation indicators. Confidence interval. | Lectures | 1.00 | computer room | |||||
| 4 | Descriptive statistics indicators IBM SPSS. Normal distribution and its characteristic descriptive statistics. | Classes | 1.00 | computer room | |||||
| 5 | Statistical hypotheses. Possible errors in hypothesis test. P-value. | Lectures | 1.00 | computer room | |||||
| 6 | Dependent and independent selections. T-tests. | Lectures | 1.00 | computer room | |||||
| 7 | Parametric data processing methods for quantitative data using IBM SPSS. | Classes | 1.00 | computer room | |||||
| 8 | Publication and presentation of research results. | Lectures | 1.00 | computer room | |||||
| 9 | Correlations analysis. Regression analysis. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 10 | Non-Parametric data processing methods for quantitative data using IBM SPSS. | Classes | 1.00 | computer room | |||||
| 11 | Seminar "Analysis of publications". | Classes | 1.00 | computer room | |||||
| 12 | Final independent work. | Classes | 2.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | Students study literature and e-study materials outside classes and lectures. Analysis of scientific publications to raise awareness of the study course. | ||||||||
| Assessment Criteria: | At the end of the course, students independently do their practical work. The course is successfully completed, if the assessment of the practical work is at least 5 points. | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | 1. Accurate use of basic statistical concepts. 2. Describe measurement data using basic statistical tests and indicators. | ||||||||
| Skills: | 1. Able to enter data in the processing program. 2. Determine the type of data and evaluate their distribution. 3. Able to formulate hypotheses (zero; alternative) and choose the appropriate test. 4. Able to calculate ratios of the regression equation. 5. Able to calculate Pearson and Spearman’s correlation ratios. 6. Able to draw data specific diagrams. | ||||||||
| Competencies: | Students will be able to select appropriate statistical data processing methods, formulate hypotheses, process data and interpret the obtained results. Evaluate the obtained data using statistical and analytical tools. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | K. Mārtinsone, A. Pipere, D. Kamerāde. Pētniecība: Teorija un prakse. Izdevniecība RaKa, 2016 | ||||||||
| 2 | Statistika. /Krastiņš O., Ciemiņa I./ Rīga: LR CSP, 2003. - 267 lpp. | ||||||||
| 3 | OpenIntro Statistics. 3rd ed, 2015, 436 lpp | ||||||||
| Additional Reading | |||||||||
| 1 | Varbūtību teorija un matemātiskā statistika /Vasermanis E., Šķiltere D./ Rīga, 2003. -186 lpp. | ||||||||
| 2 | SPSS statistics for social scientists /Acton, C., et al./ Basingstoke: Palgrave Macmillan, 2009. 363 lpp. | ||||||||
| 3 | Leavy. P. (ed). The Oxford Handbook of Qualitative Research. New York: Oxford University Press, 2014 | ||||||||
| 4 | Бююль А., Цефель П. SPSS: искусство обработки информации. Анализ статистических данных и восстановление скрытых закономерностей: Пер. с нем. СПб.: ООО«DiaSoftЮП», 2005 | ||||||||
| Other Information Sources | |||||||||
| 1 | Choosing the Correct Statistic in SAS, STATA, SPSS and R | ||||||||
| 2 | Latvijas statistikas gadagrāmata, 2017. Rīga: Centrālā statistikas pārvalde, 2018., 550 lpp | ||||||||
| 3 | LR CSP mājas lapa | ||||||||
