Master
2022/2023
Statistics I
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Compulsory course (Science of Learning and Assessment)
Area of studies:
Psychology
Delivered by:
Institute of Education
Where:
Institute of Education
When:
1 year, 2, 3 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Science of Learning and Assessment
Language:
English
ECTS credits:
6
Contact hours:
72
Course Syllabus
Abstract
Within the course Statistics I, types and properties of distributions of variables are discussed as well as general rules for testing statistical hypotheses, methods of descriptive statistics, correlation coefficients, linear and logistic regression analysis, factorial and cluster analysis and latent classes are studied. The work takes place in the R program. In this course students will learn how to set research goals and choose appropriate statistical methods for the analysis, implement and interpret quantitative data analysis results, use statistical packages and work with open datasets. Besides, student will become familiar with current research studies in education and its methodology
Learning Objectives
- Be able to understand fundamental concepts and important terminology in statistics
- Be able to choose an appropriate statistical method to answer research questions
- Be able to apply basic statistical methods using R software
- Be able to critically analyse and interprete results of statistical analysis
Expected Learning Outcomes
- Able to learn the concept of normal distribution
- Able to calculate Z-scores
- Able to calculate point estimates and interpret confidence intervals
- Able to differentiate between types of measurement scales
- Able to differentiate between the measures of central tendency and variation for different scales
- Able to learn the concept of contingency table
- Able to state the relevant null and alternative hypothesis
- Able to learn the concept of p-value and level of significance
- Able to calculate correlation coefficients
- Able to differentiate between the types of correlations
- Able to conduct ANOVA
- Able to run the regression model
- Able to interpret the linear regression’s coefficients
- Able to compute the coefficient of determination
- Able to test regression model assumptions
- Able to run the regression model with different types of variables
- Able to interpret the logistic regression’s coefficients
- Able to differentiate between PCA and FA
- Able to conduct PCA and FA
- Able to interpret the result of factor analysis
- Able to differentiate between Hierarchical clustering and k-means
- Able to conduct cluster analysis
- Able to interpret the result of cluster analysis
- Able to conduct latent class analysis
- Able to interpret the result of latent class analysis
Course Contents
- Introduction to statistics
- Normal distribution
- Introduction to hypothesis testing
- Correlation analysis
- Hypothesis testing for means & Analysis of variance
- Introduction to Linear Regression
- Linear & Logistic regression
- Factor Analysis
- Cluster Analysis
- Latent Class Analysis
Assessment Elements
- HomeworkWriten homework during the course
- Group workGroup presentations
- Final testFinal test at the end of each module
- ExamPresentation of final project
Bibliography
Recommended Core Bibliography
- Agresti, A., & Finlay, B. (2014). Statistical Methods for the Social Sciences: Pearson New International Edition (Vol. Pearson new international ed., 4. ed). Harlow England: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418314
- Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064
Recommended Additional Bibliography
- Математические методы психологического исследования : анализ и интерпретация данных: учеб. пособие, Наследов, А. Д., 2006