FSS:PSYb1170 Statistics - Course Information
PSYb1170 Statistical Analysis in PsychologyFaculty of Social Studies
- Extent and Intensity
- 1/1/0. 6 credit(s). Type of Completion: zk (examination).
- doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Mgr. Karel Rečka (seminar tutor)
Bc. et Bc. Andrea Stašková (seminar tutor)
Mgr. Jan Širůček, Ph.D. (seminar tutor)
- Guaranteed by
- doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology - Faculty of Social Studies
Contact Person: doc. Mgr. Stanislav Ježek, Ph.D.
Supplier department: Department of Psychology - Faculty of Social Studies
- Wed 8:00–9:40 P31 Posluchárna A. I. Bláhy
- Timetable of Seminar Groups:
PSYb1170/02: Wed 11:00–11:50 PC25, K. Rečka
PSYb1170/03: Wed 12:00–12:50 PC25, A. Stašková
PSYb1170/04: Wed 13:00–13:50 PC25, S. Ježek
- ! PSY117 Statistics
The course assumes students have basic knowledge of the prinicples and procedures of research in psychology. It also assumes knowledge and proficiency in the basics of high-school algebra.
- Course Enrolment Limitations
- The course is only offered to the students of the study fields the course is directly associated with.
- fields of study / plans the course is directly associated with
- Psychology (programme FSS, B-HE)
- Psychology (programme FSS, B-HS)
- Psychology (programme FSS, B-KS)
- Psychology (programme FSS, B-MS)
- Psychology (programme FSS, B-PL)
- Psychology (programme FSS, B-PS) (2)
- Psychology (programme FSS, B-PSY) (9)
- Psychology (programme FSS, B-SO)
- Psychology (programme FSS, B-SP)
- Course objectives
- The main objective of the course is to introduce statistics as a part of psychology and the elementary statistical concepts used in psychological research. Students will learn to passively and actively use these concepts - statistica literacy. They learn to prepare data for analysis, compute elementary statistics, test elementary hypotheses. They also learn to read and communicate statistical findings both in Czech and English.
- Learning outcomes
- Student who successfully passes the course will be able: - to arrange data in conventional data matrix; to compute basic descriptive statistics describing the distribution of individual variables and relationships among them; to draw graphs describing the distribution of individual variables and relationships among them; to compute confidence intervals for basic descriptive statistics; to test elementary hypotheses; to use linear regression with one predictor; to use conditional probabilities to compute the indices of diagnostic utility of tests.
- 1. Data matrix, types of variables, coding, measurement levels, data checking.
- 2. Graphical representation of data. Cumulative, absolute and relative frequencies and distribution. Tables, minimum, maximum, outliers. Normal distribution and areas under the curve. Bar chart, histogram.
- 3. Measures of central tendency and variability, percentiles, standard scores. Boxplot.
- 4. Measures of association (Pearson, Spearmann, Kendall) and graphical representation of relationship. Scatterplot. Linear, positive, negative association. Partial and part correlation.
- 5. Linear regression. Statistical prediction, linear vs. non-linear regression. Estimate, model, residual. Least squares method. Regression and residual variance, susms of squares. Coefficient of determination. Homoscedascity.
- 6. Probability, conditional probability, probability distributions. Bayes' theorem, indices of diagnostic utility.
- 7. Statistical inference, point vs. interval estimates. Statistics vs parameters. Sampling distribution, standard error. Central limit theorem.
- 8. Statistical hypothesis testing, Bayesian, Fisherian and Neymann-Pearson approach. Level of significance, Type I and II error, power, effect size, t-tests, tests for Pearson correlations.
- 9. Basic tests for nominal and ordinal variables.
- 10. One-way ANOVA.
- required literature
- HOWELL, David C. Statistical methods for psychology. 8th ed. Belmont, CA: Wadsworth Cengage Learning, 2013. xix, 770. ISBN 9781111840853. info
- not specified
- CUMMING, Geoff and Robert CALILN-JAGEMAN. Introduction to the new statistics : estimation, open science, and beyond. First published. New York: Routledge, Taylor & Francis Group, 2017. xxviii, 56. ISBN 9781138825512. info
- HENDL, Jan. Přehled statistických metod : analýza a metaanalýza dat. Páté, rozšířené vydán. Praha: Portál, 2015. 734 stran. ISBN 9788026209812. info
- Teaching methods
- lecture, problem solution demonstration, group discussion, online discussion forum, critical reading, homework
- Assessment methods
- Midterm exams
There are three midterm exams. Each is worth 20 points. Their dates are listed in the interactive template. They will take place at the end of tle lecture time slot. Those who miss the midterm exams can retake any of them during the last week of the semester.
There is a team-work assignment worth 10 points.
The final exam is is worth 30 points. To pass the exam the student must earn at least 15 points. The test covers all materials listed in this syllabus and in the imteractive template. A student is allowed to taje the final exam if she has earned a minimum of 15 point from the midterms.
Midterm, project, and final exams add up to a maximum of 100 points. To pass the course at least 50% is needed. The grading scale is following:
A: 100 - 89p B: 88 – 78p C: 77 – 67p D: 66 – 56p E: 55 – 50p F: 49 and less points.
- Language of instruction
- Follow-Up Courses
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
General note: Cvičení je děleno na tři skupiny.
- Listed among pre-requisites of other courses
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fss/spring2021/PSYb1170