Thematic seminar: Uses of data mining in psychological research

General data

Course ID:	WF-R-PS-STWDM
Erasmus code / ISCED:	14.4 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (unknown)
Course title:	Thematic seminar: Uses of data mining in psychological research
Name in Polish:	Seminarium tematyczne: Wykorzystanie data mining w badaniach psychologicznych
Organizational unit:	Institute of Psychology
Course groups:
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Subject level:	intermediate
Learning outcome code/codes:	SD_ PS _W01 SD_ PS _W03 SD_ PS _U02 SD_ PS _U03 SD_ PS _K02
Short description:	During the course, students acquire basic knowledge about the use of data mining methods in psychological research. They do exercises using two methods Classification and Regression Tree (C&RT) and clustering analysis conducted by by data mining algorithms in STATISTICA's Data Miner package
Full description:	1. Introduction to modeling using data mining algorithms - basic information 2. Overview of Statistica's Data Miner package 3. Assumptions of modeling with the usage of Classification and Regression Tree (C&RT) algorithm 4. Building trees with usage of Classification and Regression Tree (C & RT) - Exercise 1 5. Building trees with usage of Classification and Regression Tree (C & RT) - Exercise 2 6. Building trees with usage of Classification and Regression Tree (C & RT) - Exercise 3 7. Building trees with usage of Classification and Regression Tree (C & RT) - Exercise 4 8. Building trees with usage of Classification and Regression Tree (C & RT) - Exercise 4 9. Assumptions of clustering analysis conducted by by data mining algorithms 10. Building the profiles using cluster analysis - exercises 1 11. Building the profiles using cluster analysis - exercises 2 12. Building the profiles using cluster analysis - exercises 3 13. Building the profiles using cluster analysis - exercises 4 14. Building the profiles using cluster analysis - exercises 5 15. Submission of final reports
Bibliography:	Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Burlington, MA: Academic Press (Elsevier). Elder, J., Hill, T., Miner, G., Nisbet, B., Delen, D., & Fast, A. (2012). Practical Text Mining and Statistical Analysis for Nono-structured Text Data Application. Oxford: Elsevier. Szymańska, A. (2017). Wykorzystanie analizy skupień metodą data mining do wykreślania profili osób badanych w badaniach psychologicznych [Using cluster analysis in the data mining method to draw profiles of participants surveyed in psychological research]. Studia Psychologiczne. Szymańska, A. (2017). Wykorzystanie algorytmów Text Mining do analizy danych tekstowych w psychologii [Usage of text mining algorithms to analyze textual data in psychology]. Socjolingwistyka.
Efekty kształcenia i opis ECTS:	Knowledge - the student correctly describes the operation of algorithms used to build decision trees. Skills - the student selects the appropriate algorithms for data analysis; correctly interprets the results; knows how to search and select sources that will be used to enrich his knowledge and skills. Competences - strives for reliable and compliant methodology for collecting empirical data and analyzing them using algorithms learned in class. credits: participation in classes: 20 preparation for the test: 10 Total hours: 30 NUMBER OF ECTS: 4

This course is not currently offered.

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