Кто читает:: Институт образования

Статус:: Курс обязательный

Когда читается:: 1-й курс, 2, 3 модуль

Преподаватели

Грачева Дарья Александровна

Чархаби Мортеза

Юсупова Элен Магомедовна

Full 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

Homework
Writen homework during the course
Group work
Group presentations
Final test
Final test at the end of each module
Exam
Presentation of final project

Interim Assessment

2022/2023 3rd module
0.2 * Final test + 0.4 * Exam + 0.4 * Homework

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

Магистерская программа «Обучение и оценивание как наука»

Statistics I