Veidlapa Nr. M-3 (8)
Study Course Description
Basic Statistics
Course Code
LF_691
Branch of Science
Mathematics
ECTS
3.00
Target Audience
Medicine
LQF
Level 5
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Dina Barute
Structure Unit Manager
Dita Role
Structural Unit
RSU Liepāja Branch
Contacts
Liepaja, Riņķu iela 24/26, lf@rsu.lv, +371 63442118, +371 63442119, +371 63484632
About Study Course
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.
Preliminary Knowledge
Knowledge in mathematics and informatics corresponding to the level of secondary education.
Learning Outcomes
Knowledge
1.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
1.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.
Competences
1.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.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual 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.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
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).
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Introduction to Statistics. Data types, scales.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data preparation for database creation. Introduction to IBM SPSS. Basic operations with data in IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Indicators of descriptive statistics and ways of obtaining them in the IBM SPSS program.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Normal distribution and its characteristic descriptive statistics indicators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Creating tables and graphs in IBM SPSS according to data type.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Statistical hypotheses, their types. Hypothesis testing. P value. Confidence intervals.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Parametric data processing methods for quantitative data, for comparing 2 dependent or independent samples.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Non-parametric data processing methods for quantitative or ordinal data, for comparing 2 dependent or independent samples.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Parametric and non-parametric data processing methods, for comparing at least 3 dependent or independent samples.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Qualitative data processing for dependent and independent samples. Odds ratio, relative risk.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Reliability analysis. Coefficient of scale consistency (Cronbach's Alpha).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Practical work with data in IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Practical work with data in IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Independent work with data in IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Independent work with data in IBM SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Final thesis presentation.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
32 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Andy Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.