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Basic Statistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:10.00
Study Course Accepted:12.08.2022 09:14:13
| Study Course Information | |||||||||
| Course Code: | SL_016 | LQF level: | Level 6 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Nursing Science | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Madara Miķelsone | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | 14 Balozu street, 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) | 16 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 32 | ||||
| Total Contact Hours | 32 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Knowledge of informatics and mathematics corresponding to the secondary school level. | ||||||||
| 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. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to statistics, the role of statistics in research process. Types of data. | Classes | 1.00 | computer room | |||||
| 2 | Preparation of data for database. Introduction to IBM SPSS. Basic operations with data in IBM SPSS. | Classes | 1.00 | computer room | |||||
| 3 | Descriptive statistics in IBM SPSS. | Classes | 1.00 | computer room | |||||
| 4 | Descriptive statistics of the Normal distribution. | Classes | 1.00 | computer room | |||||
| 5 | Creation of tables and diagrams in IBM SPSS according to data type. | Classes | 1.00 | computer room | |||||
| 6 | Types of statistical hypotheses. Hypotheses testing. P value. Confidence intervals. | Classes | 1.00 | computer room | |||||
| 7 | Parametric statistics for quantitative data for 2 independent or paired samples. | Classes | 1.00 | computer room | |||||
| 8 | Non-parametric statistics for quantitative or ordinal data for 2 independent or paired samples. | Classes | 1.00 | computer room | |||||
| 9 | Parametric and non-parametric data processing methods for 3 or more independent or paired samples. | Classes | 1.00 | computer room | |||||
| 10 | Qualitative data processing for independent and dependent samples. Odds ratio, relative risk. | Classes | 1.00 | computer room | |||||
| 11 | Reliability analysis. (Cronbach's alpha). | Classes | 1.00 | computer room | |||||
| 12 | Practical work with data in IBM SPSS. | Classes | 2.00 | computer room | |||||
| 13 | Analysis of publication. | Classes | 1.00 | computer room | |||||
| 14 | Independent work with data using IBM SPSS. | Classes | 1.00 | computer room | |||||
| 15 | Students presentations. | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 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. | ||||||||
| Assessment Criteria: | 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). | ||||||||
| Final Examination (Full-Time): | Exam | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | 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: | 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. | ||||||||
| 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, use the learned statistical basic methods practically to process research data. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| 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. | ||||||||
