.
Basic Statistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:1.00
Study Course Accepted:02.05.2023 09:07:55
| Study Course Information | |||||||||
| Course Code: | LF_691 | LQF level: | Level 5 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics | Target Audience: | Medicine | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Dina Barute | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | RSU Liepāja Branch | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | Liepaja, Riņķu iela 24/26, lf rsu[pnkts]lv, +371 63442118, +371 63442119, +371 63484632 | ||||||||
| 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 in mathematics and informatics corresponding to the level of secondary education. | ||||||||
| 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. Data types, scales. | Classes | 1.00 | auditorium | |||||
| 2 | Data preparation for database creation. Introduction to IBM SPSS. Basic operations with data in IBM SPSS. | Classes | 1.00 | auditorium | |||||
| 3 | Indicators of descriptive statistics and ways of obtaining them in the IBM SPSS program. | Classes | 1.00 | auditorium | |||||
| 4 | Normal distribution and its characteristic descriptive statistics indicators. | Classes | 1.00 | auditorium | |||||
| 5 | Creating tables and graphs in IBM SPSS according to data type. | Classes | 1.00 | auditorium | |||||
| 6 | Statistical hypotheses, their types. Hypothesis testing. P value. Confidence intervals. | Classes | 1.00 | auditorium | |||||
| 7 | Parametric data processing methods for quantitative data, for comparing 2 dependent or independent samples. | Classes | 1.00 | auditorium | |||||
| 8 | Non-parametric data processing methods for quantitative or ordinal data, for comparing 2 dependent or independent samples. | Classes | 1.00 | auditorium | |||||
| 9 | Parametric and non-parametric data processing methods, for comparing at least 3 dependent or independent samples. | Classes | 1.00 | auditorium | |||||
| 10 | Qualitative data processing for dependent and independent samples. Odds ratio, relative risk. | Classes | 1.00 | auditorium | |||||
| 11 | Reliability analysis. Coefficient of scale consistency (Cronbach's Alpha). | Classes | 1.00 | auditorium | |||||
| 12 | Practical work with data in IBM SPSS. | Classes | 2.00 | auditorium | |||||
| 13 | Independent work with data in IBM SPSS. | Classes | 2.00 | auditorium | |||||
| 14 | Final thesis presentation. | Classes | 1.00 | auditorium | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with literature - preparation for each lesson, according to the thematic plan. 2. Independent work in pairs – a research data file will be prepared for each pair of students (it is allowed to use their own research data) with defined research tasks. Students will need to statistically process data in order to achieve the defined tasks, using descriptive statistics methods and inferential statistics methods, design the work according to the requirements and present the obtained results in the last lesson. | ||||||||
| Assessment Criteria: | In order to successfully learn the material of the study course and prepare for the final exam of the study course, the student performs the following activities (mandatory, not graded): 1. Participation in practical lessons. For each lesson missed - a practical assignment. 2. Oral presentation of independent work. At the end of the study course, exam - assessment (grade) cumulative: 50% – test with practical tasks using databases, 50% – exam (multiple-answer test with theoretical and practical questions in statistics). | ||||||||
| Final Examination (Full-Time): | Exam | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | After fulfilling the requirements of the study course, students will have acquired knowledge that will allow: * to recognize statistical terminology and basic methods used in various types of publications; * to know the possibilities offered by IBM SPSS in data processing; * know the criteria for using data processing methods; * correctly interpret the most important statistical indicators. | ||||||||
| Skills: | As a result of learning the study course, students will be able to: * enter and edit data in computer programs MS Excel and IBM SPSS; * correctly prepare data for statistical processing; * choose suitable data processing methods, including, be able to perform statistical hypothesis tests; * statistically process research data using IBM SPSS software; * create tables and charts in IBM SPSS program with the obtained results; * correctly describe the obtained research results. | ||||||||
| Competencies: | As a result of learning the study course, students will be able to reasonedly make a decision about the use of statistical data processing methods to achieve the research goal and, using IBM SPSS software, to practically apply the learned basic statistical methods in research data processing. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Andy Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
