UPI. University Personality Inventory UPI. Yoshitake UPI UPI

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1 2015 Vol. 31 No Psychological Development and Education DOI /j. cnki. issn * / latent profile analysis LPA % % % 2 Z 2. 6SD % % 8. 36% % ~ % LPA UPI B UPI 0-1 UPI University Personality Inventory UPI 2012 UPI 1966 Yoshitake UPI UPI * 14ZDB JK045. zhangwei@ scnu. edu. cn 350

2 τ 1. 2 Vermunt latent profile model LPM 1968 latent class model LCM Marsh Lüdtke Trautwein & 2008 latent class Morin 2009 analysis LCA I jk k j L I k k C T 1 P I k = T Latent P C = t P I k C = t 1 t = 1 class Latent classes Finite mixture Finite mixtures T P C = t = t = 1 LCA Maarten Naaktgeboren Reitsma Moons & Groot LCA P I k C = t = J P I jk C = t j = P C = t t 2 P I jk = l C = t t 2002 j l LCM L P I jk = l C = t = 1 5 DSM l = 1 Latent Profile Analysis LPA - IV SPD SPD LCA 1 f I k = T P C = t f I k μ t t = 1 t 3 SPD Fossati Krueger τ t Markon Borroni & Maffei 2013 Szatmari 1995 component μ t 10 ICD

3 LCM π i0 i Bertrand Bénichou Grenier & Chastang 2005 π i1 = P X i = 1 Y = 1 5 Antonius Van & Tim π i0 = P X i = 0 Y = UPI UPI Golden Standard LPA person - centered N = ± % UPI UPI 1995 UPI UPI sensitivity 1 UPI specificity π i1 i 1 P X i = 1 Y = 1 Y = 1 1 UPI 20 ~ 25 X i =

4 UPI SCL ~ SCL AIC BIC AIC BIC LMRT 2009 UPI 20 ~ 30 Mplus7. 0 SPSS UPI 55 Kappa UPI LMRT Entropy Entropy % RMSEA = SRMR = CFI = TLI = χ 2 /df = UPI = 64% RMSEA = SRMR = CFI = TLI = χ 2 /df = % 64% Muthén UPI y* y* A B C <

5 D 3 E 3 ~ χ 2 = DF = RMSEA = CFI =. 97 TLI = α = ~ 6 α = UPI 10% UPI T1 n = 6129 T2 n = BIC AIC Entropy LMRT BIC AIC Entropy o LMRT T n = 6129 UPI Z T n = 6752 UPI Z n % n % % ~ 9. 86% UPI UPI T UPI % 354

6 61. 21% % % % 8. 36% % % % SCL90 3 SCL90 T SCL % % % % % % ~ 4 SCL90 0 ~ SCL % % % % % % % % SCL % % % % % % % % 90% 4 61% ~ 84% 10% 4. 1 UPI 355

7 UPI UPI Yoshitake 1995 UPI 2007 UPI 2004 UPI UPI Muthén % ~ % Muthén UPI Parry & McArdle 1991 UPI 2010 SCL LPA 61% ~ 84% 72. 9% 9. 4% % ~ % % SCID-P 8. 36% 10 ICD-10 39% CCMD % 4 13 Z ~ 93. 8% 95. 3% ~ 2. 6SD 97. 0% Tao % ~ % 17. 7% % ~ % LPA 39% 356

8 UPI LPA UPI 52% 64% % % % % % 8. 36% Antonius J. Van R. J. & Tim M. S Online video game addiction identification of addicted adolescent gamers. Addiction Bertrand P. Bénichou J. Grenier P. & Chastang C Hui and Walter s latent-class reference-free approach may be more useful in assessing agreement than diagnostic performance. Journal of % ~ % 5. 2 Clinical Epidemiology Benjamin D. K. DeLong E. & Steinbach 2 Psychopathy in Community-Dwelling Italian Adults Kay N. Li K. Nokkaew N. & Park 3 UPI UPI Education UPI W. J Latent Class Analysis An Illustrative Application for Education in the Assessment of Resident Otoscopic Skills. Ambulatory Pediatrics Fossati A. Krueger R. F Markon K. E. Borroni S. & Maffei C Reliability and Validity of the Personality Inventory for DSM-5 PID-5 Predicting DSM-IV Personality Disorders and Assessment 20 B Hopelessness and suicidal behavior among Chinese Thai and Korean college students and predictive effects of the World Health Organization s WHOQOL-BREF. International Electronic Journal of Health Maarten V. S. M. Naaktgeboren A. Reitsma J. B. Moons K. 357

9 G. M. & Groot J. A. H Latent Class Models in Diagnostic Studies When There is No Reference Standard A Systematic Review. American Journal of Epidemiology Muthén B Contributions to factor analysis of dichotomous variables. Psychometrika Muthén B Dichotomous factor analysis of symptom data. Sociological Methods & Research Muthén B Dichotomous factor analysis of symptom data. Sociological Methods & Research Muthén L. K. Muthén BO Mplus Statistical analysis with latent variables User s guide McDonald R. P Principles and practice in reporting structural equation analysis. Psychological Methods Parry C. D. H. & McArdle J. J An applied comparison of methods for least- squares factor analysis of dichotomous variables Applied Psychological Measurement DOI UPI SCL / Ran T. Xiuqin H. Jinan W. Huimin Z. Ying Z. & Mengchen L Proposed diagnostic Criteria for internet addiction. - Addiction Szatmari P. Volkmar F. & Walter S Evaluation of diagnostic criteria forautism using latent class models. Journal of the American Academyof Child and Adolescent Psychiatry Yoshitake M UPI A study on mental health of freshmen in SCL90 EPQ UPI Toyo Women s College by UPI Yoshitake M UPI A study on validity of UPI UPI SCL SNPs UP

10 Identifying Psychological or Behavioral Problems of College Students Based on Latent Profile Analysis SU Binyuan 1 2 ZHANG Jieting 2 3 YU Chengfu 2 ZHANG Wei 2 1. Psychological Counseling & Research Center South China Normal University Guangzhou School of Psychology /Center for Studies of Psychological Application South China Normal University Guangzhou Shenzhen University Shenzhen Abstract To explore the applicability of latent profile analysis LPA in detecting psychological or behavioral problems a total of college students were tested for psychological health. The psychological status of the 644 students was evaluated by psychologists counselors and class supervisors. Using evaluation results and the 90 Symptom checklist SCL90 positive detection rate as the golden standard for diagnostic accuracy sensitivity and specificity were compared between LPA and the traditional demarcation method. The results showed that 1 Student s psychological and behavioral problems can be divided into three sub-groups high risk group 9. 86% mental confusion group % and healthy group %. 2 High risk groups were characterized by prominent mental symptoms Z 2. 6SD. The positive symptom of mental health risk in high risk group is % which is far above that of mental confusion group % and mental health group 8. 36%. 3 LPA improved sensitivity by 8. 93% % and showed better diagnostic accuracy comparing with the traditional demarcation method. Key words latent profile analysis LPA university personality inventory UPI psychological or behavioral problems college students 359