000			04513nla2a2200505 4500
001			660233
005			20231030041658.0
035			_a(RuTPU)RU\TPU\network\29304
035			_aRU\TPU\network\29294
090			_a660233
100			_a20190515a2018 k y0engy50 ba
101	0		_aeng
102			_aCH
105			_ay z 100zy
135			_adrcn ---uucaa
181		0	_ai
182		0	_ab
200	1		_aSten Score Method And Cluster Analysis: Identifying Respondents Vulnerable To Drug Abuse _fN. A. Lukianova, Yu. B. Burkatovskaya, E. V. Fell
203			_aText _celectronic
300			_aTitle screen
320			_a[References: p. 788-789 (17 tit.)]
330			_aIn this article, the authors assess the methodological reliability of big data processing in sociological research. The authors compare sten score method and cluster analysis as methods of processing the results of socio-psychological tests aimed at identifying groups of young people potentially vulnerable to drug addiction. The survey was conducted in eight universities in a city in Siberia with a large student population where 22884 students aged from 18 to 25 were questioned. First, the obtained results were processed by using the sten score method. Then, cluster analysis was conducted to define a high-risk group of students having a propensity for drug consumption. Advantages and disadvantages of the two methods for processing a large sample of data are compared. The results of this comparison demonstrate that the cluster analysis method is the most appropriate method for this type of research as it produces statistically correct data. The use of cluster analysis makes it possible to work with any type of information, both qualitative and qualitative data. On the other hand, the sten scores method can only be applied in certain conditions, i.e. where the original distribution resembles a normal distribution; where some theoretical basis to expect normal distribution exists, and where there is certainty that the normalization group is sufficiently large and representative to be a true reflection of the population.
461		0	_0(RuTPU)RU\TPU\network\11959 _tThe European Proceedings of Social & Behavioural Sciences (EpSBS)
463		0	_0(RuTPU)RU\TPU\network\29259 _tVol. 35 : Research Paradigms Transformation in Social Sciences (RPTSS 2017) _oInternational Conference, 18-21 May 2017, Tomsk, Russia _o[proceedings] _fNational Research Tomsk Polytechnic University (TPU) ; eds. I. B. Ardashkin [et al.] _v[P. 779-789] _d2018
610	1		_aэлектронный ресурс
610	1		_aтруды учёных ТПУ
610	1		_asten score method
610	1		_acluster analysis
610	1		_adrug use
610	1		_adrug addiction
610	1		_asociological research
610	1		_astatistical information
610	1		_aкластерный анализ
610	1		_aнаркомания
610	1		_aсоциологические исследования
610	1		_aстатистическая информация
700		1	_aLukianova _bN. A. _cspecialist in the field of psychology and law _cProfessor of Tomsk Polytechnic University, Doctor of philosophy sciences _f1971- _gNatalia Aleksandrovna _2stltpush _3(RuTPU)RU\TPU\pers\33131
701		1	_aBurkatovskaya _bYu. B. _cmathematician _cassociate Professor of Tomsk Polytechnic University, candidate of physico-mathematical Sciences _f1973- _gYuliya Borisovna _2stltpush _3(RuTPU)RU\TPU\pers\36259
701		1	_aFell _bE. V. _cspecialist in the field of law _cAssociate Professor of Tomsk Polytechnic University _f1975- _gElena Vladimirovna _2stltpush _3(RuTPU)RU\TPU\pers\33614
712	0	2	_aНациональный исследовательский Томский политехнический университет _bШкола базовой инженерной подготовки _bОтделение социально-гуманитарных наук _h8033 _2stltpush _3(RuTPU)RU\TPU\col\23512
712	0	2	_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515
801		2	_aRU _b63413507 _c20190517 _gRCR
856	4		_uhttp://dx.doi.org/10.15405/epsbs.2018.02.92
856	4		_uhttp://earchive.tpu.ru/handle/11683/53272
942			_cCF