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6 /88 M, #$ F #$ $ M$!.%3), e TG Ga< *%) E*@%3 6A / ] *%) E *@%3 6A #GE *%$3 3 F %&] % b$!g D S3 6A :"f M SEM F M3 3 F ] ) g. *N, #$&.%) ) <T] # *%C B, A,.) M, <U,& %&) R& ] 3@ *$ '. - +, = -'; :'; &' > ''- F ] 3 b #$ G$ )) 3@ E/.M, )) E/ L; M3 3@ F3 M3 F3 )) M, )) )) L; M3!3 + G F3 )) L; M3 G $, F3 <T] F 89". 3' 9] ~;.M, F 6A #3 ]#$ 4< M, 8$ *%) =3$ 9;< <T] A <d F #$ #$& M, -! -.! / =3$ 9;< :% 89"., 7% b #$, 3) * %&' 3 6 #$ G$.%) D+3 # *%C B 9;< F MSE) 6A S"+ #3 ] F * %&' =3$ #3 ] ) *%.%3*%) 98 F 6A S"+ #$ $E #$ M, <T] * %&' =3$ 9;< F, l& F ] M, 3 F M3 <d # *%C F $ A T,< R&< %) ) 4 R& #$%&' E A, *%) *% ] ^_ #$ ) ^_ ] 9. B R& %$!3/ b * %&' =3$ 9;< F ^_ *%) *% %&' ] R&.)!3/ %) 3d g. ] #$ / M, $ ] %) * 3!3 ) 8" 6 3 F M3 F M$! < % *@4 #$ SEM F ] M, #$ +3) 6, Lee HW. An Application of Latent Variable Structural Equation Modeling For Experimental Research in Educational Technology. TOJET: The Turkish Online Journal of Educational Technology. ;.. Haase RF, Ellis MV. Multivariate analysis of variance. Journal of Counseling Psychology. 987; 34: Byrne BM. Structural equation modeling with AMOS: Basic 34 concepts, applications, and programming: Routledge; Alwin DF, Hauser RM. The decomposition of effects in path analysis. American sociological review. 975: Yong AG, Pearce S. A beginner s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology. 3; 9:

7 89/ &' / '. - +, )#) &' $% "#! Iranian Journal of Epidemiology Atumn 7; 3 3): Original Article Studying the Performance of Multivariate Analysis of Variance MANOVA) Models and Structural Equation Modeling on Complex Relationships between Variables Abbaszadeh S, Baneshi MR, Zolala F, Jahani Y, Sharifi H 3 - MSc Student in Biostatistics, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran - Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran 3- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Corresponding author: Baneshi MR, rbaneshi@gmail.com Received August 6; Accepted 5 February 7) Background and Objectives: We may sometimes measure the joint effect of correlated independent variables on several dependent variablesthe present study aimed to evaluate the performance of multivariate analysis of variance MANOVA) and structural equation modeling SEM) on complex relationships between variables. Methods: The present study evaluated the knowledge and attitude of 5-8 year-old individuals towards narcotics glass, ecstasy). The effect of independent variables on two latent variables of knowledge and attitudes was studied using SEM and MANOVA modelingthe mean square error of methods were compared. Results: The direction of associations was similar in both methods but their coefficients and p-values were different. only the effect of gender P-value=.7) on knowledge in both methods was significant. Nevertheless, gender P-value <.) and marital status P-value<.) were significantly associated with attitude in both methods. The mean square error of multivariate analysis of variance and structural equation modeling was.98 and. respectively. Conclusion: In the current studythe performance of SEM was better than MANOVA. Therefore, it is suggested that SEM to be used to study the multifactorial relationship between variables. In addition, only gender was effective on knowledge in both methods, while gender and marital status were effective on attitude in both methods. Keywords: Multivariate analysis of variance MANOVA), Structural equation modeling, Information, Attitude, Glass, Ecstasy

Missing Data Techniques

Missing Data Techniques Paul Philippe Pare Department of Sociology, UWO Centre for Population, Aging, and Health, UWO London Criminometrics (www.crimino.biz) 1 Introduction Missing data is a common problem