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Odkrivanje zakonitosti in podatkovno rudarjenje v psihologiji : uporaba odločitvenih dreves za napovedovanje dosežka na Lestvici iskanja dražljajev
Kastrin, Andrej (Author)

URLURL - Presentation file, Visit http://www.dlib.si/details/URN:NBN:SI:DOC-SVFZEW8W New window

Abstract
Odkrivanje zakonitosti iz podatkov je interdisciplinarno raziskovalno področje, ki združuje tehnologije in znanja s področij statistike, podatkovnih zbirk, strojnega učenja in umetne inteligentnosti. Najpomembnejši element procesa odkrivanja zakonitosti iz podatkov je podatkovno rudarjenje. Namen prispevka je dvojen. Prvič, strokovno psihološko javnost želimo opozoriti na kvalitativni preskok v znanstvenem raziskovanju, ki se je začel z uveljavitvijo področja odkrivanja zakonitosti iz podatkov, in drugič, na primeru odločitvenih dreves želimo bralcu približati uporabnost metod podatkovnega rudarjenja v psihologiji. Uporabo odločitvenih dreves ilustriramo z gradnjo napovednih modelov dosežka na Zuckermanovi Lestvici iskanja dražljajev (SSS-V) na osnovi medosebnih razlik v bazičnih potezah osebnosti in lastnostih temperamenta. Prediktorske spremenljivke so bile operacionalizirane na osnovi Eysenckovega osebnostnega vprašalnika (EPQ) in slovenske priredbe Strelauovega vprašalnika temperamenta po Pavlovu (SVTP). Ustreznost odločitvenih dreves za napovedovanje dosežka na lestvici SSS-V smo primerjali s klasičnim statističnim modelom multiple linearne regresije. Z vidika napovedne točnosti se je kot najbolj uspešen sicer izkazal multipli regresijski model, kljub temu pa so odločitvena drevesa primerna metoda za začeten pregled podatkov, vizualizacijo in opis podatkovnih zakonitosti z lahko razumljivimi formalizmi.

Language:Slovenian
Keywords:odkrivanje zakonitosti iz podatkov, podatkovno rudarjenje, psihološko ocenjevanje
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FF - Faculty of Arts
Year:2008
Publisher:Društvo psihologov Slovenije
Number of pages:str. 51-72
Numbering:Letn. 17, št. 4
UDC:159.9:311
ISSN on article:1318-1874
COBISS.SI-ID:38864994 Link is opened in a new window
Views:617
Downloads:90
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Record is a part of a journal

Title:Psihološka obzorja
Publisher:Društvo psihologov Slovenije
ISSN:1318-1874
COBISS.SI-ID:3084808 New window

Secondary language

Language:English
Title:Knowledge discovery and data mining in psychology : using decision trees to predict the Sensation Seeking Scale score
Abstract:
Knowledge discovery from data is an interdisciplinary research field combining technology and knowledge from domains of statistics, databases, machine learning and artificial intelligence. Data mining is the most important part of knowledge discovery process. The objective of this paper is twofold. The first objective is to point out the qualitative shift in research methodology due to evolving knowledge discovery technology. The second objective is to introduce the technique of decision trees to psychological domain experts. We illustrate the utility of the decision trees on the prediction model of sensation seeking. Prediction of the Zuckermanćs Sensation Seeking Scale (SSS-V) score was based on the bundle of Eysenckćs personality traits and Pavlovian temperament properties. Predictors were operationalized on the basis of Eysenck Personality Questionnaire (EPQ) and Slovenian adaptation of the Pavlovian Temperament Survey (SVTP). The standard statistical technique of multiple regression was used as a baseline method to evaluate the decision trees methodology. The multiple regression model was the most accurate model in terms of predictive accuracy. However, the decision trees could serve as a powerful general method for initial exploratory data analysis, data visualization and knowledge discovery.

Keywords:knowledge discovery from data, data mining, psychological assessment

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