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Statistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:11.00
Study Course Accepted:06.03.2023 09:03:46
| Study Course Information | |||||||||
| Course Code: | SL_017 | LQF level: | Level 7 | ||||||
| Credit Points: | 4.00 | ECTS: | 6.00 | ||||||
| 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) | 12 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 24 | ||||
| Total Contact Hours | 24 | ||||||||
| Full-Time - Semester No.2 | |||||||||
| Lectures (count) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 12 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 24 | ||||
| Total Contact Hours | 24 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Secondary school knowledge in mathematics and informatics. | ||||||||
| Objective: | Acquire in-depth knowledge and skills in statistical data processing methods (descriptive statistics, inferential statistical methods for estimating differences between various groups and analytical statistics), that are necessary for the processing of research data in the final thesis and in the chosen specialisation. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to statistics, the role of statistics in research process. | Classes | 1.00 | computer room | |||||
| 2 | Data types, measure, data input, data preparation in MS Excel. | Classes | 1.00 | computer room | |||||
| 3 | Introduction to IBM SPSS. Basic operations with data in IBM SPSS. | Classes | 1.00 | computer room | |||||
| 4 | Indicators of descriptive statistics in MS Excel and IBM SPSS. | Classes | 2.00 | computer room | |||||
| 5 | Normal distribution and its descriptive statistics. | Classes | 1.00 | computer room | |||||
| 6 | Tables and diagrams, correct formatting. | Classes | 1.00 | computer room | |||||
| 7 | Types of statistical hypotheses. Hypotheses testing. P value. | Classes | 1.00 | computer room | |||||
| 8 | Parametric statistics for quantitative data. Comparison of independent and dependent samples. | Classes | 2.00 | computer room | |||||
| 9 | Nonparametric statistics for quantitative data. Comparison of independent and dependent samples. | Classes | 2.00 | computer room | |||||
| 10 | Processing of qualitative data. Dependent and independent samples. | Classes | 2.00 | computer room | |||||
| 11 | Reliability Analysis. Estimate of the reliability (Cronbach's alpha). | Classes | 1.00 | computer room | |||||
| 12 | Correlation analysis. Regression analysis (Linear regression). | Classes | 2.00 | computer room | |||||
| 13 | Regression analysis (Binary logistic regression). | Classes | 2.00 | computer room | |||||
| 14 | Analysis of scientific publications. | Classes | 2.00 | computer room | |||||
| 15 | Presentation of independent work. | Classes | 1.00 | computer room | |||||
| 16 | Independent work with data using IBM SPSS. | Classes | 2.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with literature – preparation for each class according to the thematic plan. 2. Independent analysis of a scientific publication. Independent work - each student will be provided with a research data file (or the student can use his/her own research data) with defined research objectives - will have to statistically process the data to achieve the defined objectives using descriptive statistical methods, inferential statistical methods and/or analytical statistical methods and present the results in the last class. | ||||||||
| Assessment Criteria: | In order to successfully master the course material and prepare for the final examination of the study course, the student performs the following activities (compulsory, not graded): 1. Participation in practical classes. A practical assignment for each missed class. 2. Oral presentation of a scientific publication analysis. Presentation of independent work. At the end of 1st semester assessment - practical work with data, which is implemented with participation in all practical classes. Examination at the end of the course - a cumulative mark, where: 50% - test with practical assignments using databases, 50% - examination (multiple-choice test with theoretical and practical questions in statistics). | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | Upon successful completion of the course, students’ knowledge will allow them to: * recognise terminology used in statistics and inferential statistical methods used in different publications; * know the most often used possibilities offered by MS Excel and IBM SPSS in data entry and processing; * know the criteria for using data processing methods; * interpret the most important statistical indicators correctly. | ||||||||
| Skills: | Having completed the course, students will be able to: * input and edit data in computer programs MS Excel and IBM SPSS; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, including statistical hypothesis testing using both basic inferential statistical methods and analytical statistical methods; * process data in IBM SPSS; * create and edit tables and graphs in MS Excel and IBM SPSS programs with the obtained results; * describe the obtained research results precisely. | ||||||||
| Competencies: | Upon successful acquisition of the course, students will be able to critically analyse and evaluate applied statistical methods in scientific publications, independently choose the appropriate inferential and analytical statistical methods in order to achieve the research aim and to apply in practice the learned inferential and analytical statistical methods by using IBM SPSS software. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Statistics for Nursing: A Practical Approach: A Practical Approach by Elizabeth Heavey. Burlington, MA: Jones & Bartlett Learning, 2019. | ||||||||
| 2 | Andy Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
| 3 | Suresh. K. Sharma. Nursing Research and Statistics. Elsevier, 2nd edition, 2014. | ||||||||
