Basic Statistics

Study Course Description

Course Description Statuss:Approved

Course Description Version:7.00

Study Course Accepted:09.08.2023 11:30:11

Study Course Information
Course Code:		SL_019		LQF level:		Level 6
Credit Points:		2.00		ECTS:		3.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Social Welfare and Social Work
Study Course Supervisor
Course Supervisor:		Ināra Kantāne
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		Baložu iela 14, Block A Riga, +371 67060897, statistikarsu[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
Part-Time - Semester No.1
Lectures (count)		0	Lecture Length (academic hours)		0	Total Contact Hours of Lectures		0
Classes (count)		10	Class Length (academic hours)		2	Total Contact Hours of Classes		20
Total Contact Hours								20
Study course description
Preliminary Knowledge:
Secondary level knowledge in mathematics and informatics.
Objective:
To provide students with the opportunity to acquire basic knowledge and skills in statistical methods of data processing (descriptive statistics, inferential statistical methods in difference estimation) which are necessary for the final work development and statistical indicators for use in specific field.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction in statistics and the role of statistics in research.			Classes		1.00	computer room
2	Types of data, scales, data input, data preparation in MS Excel.			Classes		1.00	computer room
3	Introduction with IBM SPSS. Basic work with data in IBM SPSS.			Classes		1.00	computer room
4	Descriptive statistics indicators in MS Excel and IMB SPSS.			Classes		1.00	computer room
5	Normal distribution and its descriptive statistics indicators.			Classes		1.00	computer room
6	Statistical hypothesis and its types. Hypothesis testing. P value.			Classes		1.00	computer room
7	Parametric data processing methods for quantitative data. One sample t-test. Compare independent sample. Student`s t-test. Analysis of variance (ANOVA).			Classes		1.00	computer room
8	Comparison of dependent samples. Paired sample t-test. Analysis of variance for repeated samples.			Classes		1.00	computer room
9	Non-parametric data processing methods for quantitative data. Compare independent sample. Manna Whitneya test. Kruskola Valisa test.			Classes		1.00	computer room
10	Comparison of dependent samples. Wilcoxon test. McNemar`s test.			Classes		1.00	computer room
11	Qualitative data processing. Dependent and independent samples. Pearson`s Chi-Squared test and Fisher`s Exact test.			Classes		1.00	computer room
12	Reliability analysis. Scale correlation coefficient (Cronbach`s Alpha).			Classes		1.00	computer room
13	Scientific publication analysis.			Classes		1.00	computer room
14	Individual work with data in IBM SPSS.			Classes		1.00	computer room
15	Individual work with data in IBM SPSS.			Classes		1.00	computer room
16	Individual work presentation.			Classes		1.00	computer room
Topic Layout (Part-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction in statistics and the role of statistics in research.			Classes		0.50	computer room
2	Types of data, scales, data input, data preparation in MS Excel.			Classes		0.50	computer room
3	Introduction with IBM SPSS. Basic work with data in IBM SPSS.			Classes		1.00	computer room
4	Descriptive statistics indicators in MS Excel and IMB SPSS.			Classes		1.00	computer room
5	Normal distribution and its descriptive statistics indicators.			Classes		0.50	computer room
6	Statistical hypothesis and its types. Hypothesis testing. P value.			Classes		0.50	computer room
7	Parametric data processing methods for quantitative data. One sample t-test. Compare independent sample. Student`s t-test. Analysis of variance (ANOVA).			Classes		0.50	computer room
8	Comparison of dependent samples. Paired sample t-test. Analysis of variance for repeated samples.			Classes		0.50	computer room
9	Non-parametric data processing methods for quantitative data. Compare independent sample. Manna Whitneya test. Kruskola Valisa test.			Classes		0.50	computer room
10	Comparison of dependent samples. Wilcoxon test. McNemar`s test.			Classes		0.50	computer room
11	Qualitative data processing. Dependent and independent samples. Pearson`s Chi-Squared test and Fisher`s Exact test.			Classes		1.00	computer room
12	Reliability analysis. Scale correlation coefficient (Cronbach`s Alpha).			Classes		0.50	computer room
13	Scientific publication analysis.			Classes		0.50	computer room
14	Individual work with data in IBM SPSS.			Classes		0.50	computer room
15	Individual work with data in IBM SPSS.			Classes		0.50	computer room
16	Individual work presentation.			Classes		1.00	computer room
Assessment
Unaided Work:
1. Individual work with the literature – prepare to lectures accordingly to plan; 2. Individual analysis of scientific publication. 3. Individual work – every student will receive a research data file (or student can use their own) with previously defined research tasks. Student will statistically process data to reach defined tasks using descriptive statistic, inferential statistic and/or analytical statistics methods. As well as to report obtained results in final paper, using defined formatting style and to present obtained results in the last lecture. In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal.
Assessment Criteria:
Participation in practical lectures. The practical application of the obtained statistical terms and methods. On completion of this course: 1. Oral presentation of independent work – 50%. 2. Exam – multiple choice test with theoretical questions in statistics – 50%. For every missed lecture – summary has to be written using given literature (min. 1 A4 page).
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):		Exam (Written)
Learning Outcomes
Knowledge:		After completion of this course, students will demonstrate basic knowledge that allow: • to recognise terminology and used methods in different kind of publications; • to know MS Excel and IBM SPSS offered data processing tools; • to know data processing method criteria; • correctly interpret the most important statistical indicators.
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., are able to do statistical hypothesis testing; • statistically analyse research data using computer programs MS Excel and IBM SPSS; • create tables and graphs in MS Excel and IBM SPSS programmes with obtained results; • precisely describe obtained research results.
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 computer programs MS Excel and IBM SPSS, practically use learned statistical basic methods to process research data.
Bibliography
No.	Reference
Required Reading
1	Petrie A. & Sabin C. Medical Statistics at a Glance. 3rd edition, 2009.
2	Field A. Discovering Statistics using IBM SPSS Statistics. 4th edition, 2013.
3	Teibe U. Bioloģiskā statistika. Rīga: LU 2007, – 156 lpp.
Other Information Sources
1	How to choose a statistical test.