Data analysis in psychology

Course: Psychology

Structural unit: Faculty of Psychology

Title
Data analysis in psychology
Code
ОНД.13
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
6 Semester
Number of ECTS credits allocated
4
Learning outcomes
LO1. To analyze and interpret mental phenomena, identify psychological problems and suggest ways to resolve them. LO7. To consider and critically evaluate the reliability of the results of the psychological research, make valid conclusions.
Form of study
Full-time form
Prerequisites and co-requisites
1. To know: the structure and general patterns of functioning of the psyche; basic knowledge of mathematical analysis, probability theory and mathematical statistics, basic principles of constructing psychological research, empirical and theoretical methods of psychological research; 2. Be able to: formulate and use to solve practical problems of the general methodological and specific basis of their own research; 3. To posess: computer skills, model and collect the initial data of psychological research.
Course content
The purpose of the discipline is to give students the theoretical knowledge and practical skills necessary to 1) understand professional publications based on empirical data and critically evaluate their quality; 2) independent use of statistical methods of data analysis; 3) preparation of presentations and reports on the results of the analysis. involves the study of: features of measurement in psychology, the distribution of variables and descriptive statistics; logic of hypothesis evaluation and testing; parametric and non-parametric criteria for establishing a statistically significant difference in the performance of two or more independent and dependent groups; ways to relate variables measured in metric and categorical scales; opportunities to check the quality of psychological tools; regression analysis, multidimensional scaling, factor and cluster analysis.
Recommended or required reading and other learning resources/tools
Pohorilska N. I., Khodanovych O.V. Matematychni metody v psykholohichnykh doslidzhenniakh. K.: Kyivskyi universytet, 2015. 127 s. Fagerland M.W. t-tests, non-parametric tests, and large studies—a paradox of statistical practice? BMC Medical Research Methodology. 2012. Vol. 12. Режим доступу: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-12-78?fbclid=IwAR2byTgsQaJCKj1wQju7lCoXtJgKqtqGhWkhBYBAv1585OeDFd97NaPRRg Tomczak M., Tomczak E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. TRENDS IN SPORT SCIENCES. 2014. Vol. 1(21). P. 19-25.
Planned learning activities and teaching methods
Lectures, practical classes, Individual work
Assessment methods and criteria
Semester assessment: Practical work: RT (result of teaching) 1.1; RT 1.2; RT 2.1; RT 2.2; RT 2.3; RT 2.4, RT 3.1, RT 4.1 - 69/38 points Execution of modular control works: RT 1.1; RT 1.2; RT 2.1; RT 2.2; RT 2.3; RT 2.4 - 20/12 The total semester score is multiplied by a factor of 0.67 Final assessment: exam. The minimum threshold level of the examination mark for which the exam is considered passed must be at least 24 out of 40 points. A student is not allowed to take the exam if he scored less than 36 points during the semester.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Nataliya Іванівна Pohorilska
Department of General Psychology
Faculty of Psychology
Roman Yuriiovych Synelnykov
Department of General Psychology
Faculty of Psychology
Serhii Oleksandrovych Shykovets
Department of General Psychology
Faculty of Psychology
Volodymyr Volodymyrovych Abramov
Department of General Psychology
Faculty of Psychology