PSYd0028 Data Analysis – Quantitative Research

Faculty of Social Studies
Spring 2021
Extent and Intensity
0/0/0. 15 credit(s). Type of Completion: z (credit).
Teacher(s)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology - Faculty of Social Studies
Supplier department: Department of Psychology - Faculty of Social Studies
Prerequisites
! PSY028 Data Analysis – Quantitative Research
The course requires basic undergraduate knowledge of statistics used in psychology or social sciences. This includes descriptive statistics, the description of statistical relationships, foundations of statistical inference and user knowledge of analysis of variance and linear regression.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 10 student(s).
Current registration and enrolment status: enrolled: 7/10, only registered: 0/10
fields of study / plans the course is directly associated with
there are 20 fields of study the course is directly associated with, display
Course objectives
The course aims to refresh students knowledge from previous courses and add new knowledge required for autonomous research work, critical reading of published literature, and further learning of statistical models and analyses. Besides the understanding of key concepts and ideas of descriptive and inferential statistics, the course also deals with practical analytical issues. At its core the course introduces students to a wide range of linear models with manifest and latent variables used across psychology.
Learning outcomes
The outcome is a passive knowledge of a range of statistical models, their use, interpretation, strengths and weaknesses.
Syllabus
  • 1. Theory a. Probability distributions b. Statistical inference i. confidence intervals ii. significance tests iii. alternatives to NHST c. Effect size, power analysis, replicability… 2. Practical issues a. Data management and "messy" data b. Missing data 3. Univariate analyses – models predicting one variables a. Model b. Linear regression model c. Analysis of variance as a special parameterization of linear regression model d. Interaction and contrasts e. Generalised linear model – discrete and non-normal outcomes f. Multilevel linear model 4. Analyses with latent variables a. Confirmatory factor analysis b. Exploratory factor analysis c. SEM model
Literature
  • KLINE, Rex B. Principles and practice of structural equation modeling. Fourth edition. London: The Guilford Press, 2016. xvii, 534. ISBN 9781462523344. info
  • BAGULEY, Thomas. Serious stats : a guide to advanced statistics for the behavioral sciences. New York: Palgrave Macmillan, 2012. xxiii, 830. ISBN 9780230577183. info
  • HAIR, Joseph F. Multivariate data analysis : a global perspective. 7th ed. Upper Saddle River, NJ: Pearson, 2010. xxviii, 80. ISBN 9780135153093. info
Teaching methods
Consultations
Assessment methods
Oral exam
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: in blocks.
The course is also listed under the following terms Autumn 2019, Spring 2020, Autumn 2020.
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