Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support

Recovering a Basic Space From a Set of Issue Scales

Keith T. Poole
American Journal of Political Science
Vol. 42, No. 3 (Jul., 1998), pp. 954-993
DOI: 10.2307/2991737
Stable URL: http://www.jstor.org/stable/2991737
Page Count: 40
  • Read Online (Free)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
Recovering a Basic Space From a Set of Issue Scales
Preview not available

Abstract

This paper develops a scaling procedure for estimating the latent/unobservable dimensions underlying a set of manifest/observable variables. The scaling procedure performs, in effect, a singular value decomposition of a rectangular matrix of real elements with missing entries. In contrast to existing techniques such as factor analysis which work with a correlation or covariance matrix computed from the data matrix, the scaling procedure shown here analyzes the data matrix directly. The scaling procedure is a general-purpose tool that can be used not only to estimate latent/unobservable dimensions but also to estimate an Eckart-Young lower-rank approximation matrix of a matrix with missing entries. Monte Carlo tests show that the procedure reliably estimates the latent dimensions and reproduces the missing elements of a matrix even at high levels of error and missing data. A number of applications to political data are shown and discussed.

Page Thumbnails

  • Thumbnail: Page 
[954]
    [954]
  • Thumbnail: Page 
955
    955
  • Thumbnail: Page 
956
    956
  • Thumbnail: Page 
957
    957
  • Thumbnail: Page 
958
    958
  • Thumbnail: Page 
959
    959
  • Thumbnail: Page 
960
    960
  • Thumbnail: Page 
961
    961
  • Thumbnail: Page 
962
    962
  • Thumbnail: Page 
963
    963
  • Thumbnail: Page 
964
    964
  • Thumbnail: Page 
965
    965
  • Thumbnail: Page 
966
    966
  • Thumbnail: Page 
967
    967
  • Thumbnail: Page 
968
    968
  • Thumbnail: Page 
[969]
    [969]
  • Thumbnail: Page 
[970]
    [970]
  • Thumbnail: Page 
[971]
    [971]
  • Thumbnail: Page 
972
    972
  • Thumbnail: Page 
973
    973
  • Thumbnail: Page 
974
    974
  • Thumbnail: Page 
975
    975
  • Thumbnail: Page 
976
    976
  • Thumbnail: Page 
977
    977
  • Thumbnail: Page 
978
    978
  • Thumbnail: Page 
979
    979
  • Thumbnail: Page 
[980]
    [980]
  • Thumbnail: Page 
[981]
    [981]
  • Thumbnail: Page 
982
    982
  • Thumbnail: Page 
983
    983
  • Thumbnail: Page 
[984]
    [984]
  • Thumbnail: Page 
[985]
    [985]
  • Thumbnail: Page 
[986]
    [986]
  • Thumbnail: Page 
987
    987
  • Thumbnail: Page 
988
    988
  • Thumbnail: Page 
989
    989
  • Thumbnail: Page 
990
    990
  • Thumbnail: Page 
991
    991
  • Thumbnail: Page 
992
    992
  • Thumbnail: Page 
993
    993