ESTIMATION FOR THE MULTIPLE FACTOR MODEL WHEN DATA ARE MISSING

被引:112
作者
FINKBEINER, C
机构
[1] Invorydale Technical Center, 3W76, The Procter and Gamble Co., Cincinnati, 45217, Ohio
关键词
factor analysis; missing data;
D O I
10.1007/BF02296204
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with 5 heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation over the heuristic methods but is considerably more costly computationally. © 1979 The Psychometric Society.
引用
收藏
页码:409 / 420
页数:12
相关论文
共 23 条
[1]   MISSING OBSERVATIONS IN MULTIVARIATE STATISTICS .1. IEW OF LITERATURE [J].
AFIFI, AA ;
ELASHOFF, RM .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1966, 61 (315) :595-&
[2]  
ANDERSON TW, 1956, 3RD P BERK S MATH ST, V5, P111
[3]   A NOTE ON THE GENERATION OF RANDOM NORMAL DEVIATES [J].
BOX, GEP ;
MULLER, ME .
ANNALS OF MATHEMATICAL STATISTICS, 1958, 29 (02) :610-611
[4]   A COMPARISON OF FACTOR ANALYTIC TECHNIQUES [J].
BROWNE, MW .
PSYCHOMETRIKA, 1968, 33 (03) :267-&
[5]   FACTOR ANALYSIS - AN INTRODUCTION TO ESSENTIALS .2. ROLE OF FACTOR ANALYSIS IN RESEARCH [J].
CATTELL, RB .
BIOMETRICS, 1965, 21 (02) :405-&
[6]   FACTOR ANALYSIS - AN INTRODUCTION TO ESSENTIALS .I. PURPOSE AND UNDERLYING MODELS [J].
CATTELL, RB .
BIOMETRICS, 1965, 21 (01) :190-&
[7]   MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM [J].
DEMPSTER, AP ;
LAIRD, NM ;
RUBIN, DB .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1977, 39 (01) :1-38
[8]   ALTERNATIVE WEIGHTING SCHEMES FOR LINEAR PREDICTION [J].
DORANS, N ;
DRASGOW, F .
ORGANIZATIONAL BEHAVIOR AND HUMAN PERFORMANCE, 1978, 21 (03) :316-345
[9]  
FINKBEINER CT, 1976, THESIS U ILLINOIS
[10]   PROPOSAL FOR HANDLING MISSING DATA [J].
GLEASON, TC ;
STAELIN, R .
PSYCHOMETRIKA, 1975, 40 (02) :229-252