01991cam a22003732u 4500001000800000005001700008007001400025008004100039035002000080037001900100040002700119088001300146091000800159100002200167245012500189260006800314300001000382500010000392500012000492520049200612530007901104533008101183650002801264650002901292650003201321650002401353650002101377650003301398650002301431700003601454710004801490856003701538984004201575522295520181019100950.0he u||024||||080220s1971 xxu ||| b ||| | eng d 9(ericd)ED053166 aED053166bERIC aericdbengcericddMvI aRB-71-16 amfm1 aLord, Frederic M.10aEfficiency of Estimation When There is Only One Common Factorh[microform] /cFrederic M. Lord and Marilyn S. Wingersky. a[Washington, D.C.] :bDistributed by ERIC Clearinghouse,c1971. a17 p. aSponsoring Agency: Office of Naval Research, Washington, DC. Psychological Sciences Div.5ericd aSponsoring Agency: Office of Naval Research, Arlington, VA. Personnel and Training Research Programs Office.5ericd aExplicit formulas are derived for the asymptotic sampling variances and covariances of the maximum likelihood estimators for factor-analysis parameters in the special case where there is just one common factor. The effect of the number of variables on these variances and covariances is indicated. A formula is given showing to what extent the usual covariance between two of a set of variables can be estimated more efficiently when there is known to be just one common factor. (Author) aMay also be available online. Address as at 14/8/18:uhttps://eric.ed.gov/ aMicrofiche.b[Washington D.C.]:cERIC Clearinghouseemicrofiches : positive.17aFactor Analysis.2ericd17aFactor Structure.2ericd17aMathematical Models.2ericd07aMathematics.2ericd07aSampling.2ericd17aStatistical Analysis.2ericd07aStatistics.2ericd1 aWingersky, Marilyn S.,eauthor.2 aEducational Testing Service, Princeton, NJ.41uhttps://eric.ed.gov/?id=ED053166 aANLcmc 2253 ED053166d77000000060091