Canonical Analysis and Factor Comparison

SAGE Publications, Inc, 1977 soft cover in near fine condition. 1st Edition. Soft cover. Near Fine.

Ninth Printing There is a number "6" on the front cover. The copyright page has two suggested citations that would list the issue number as "07-006". The book is unread. The spine is not creased, the binding is tight and the pages are clean. There is no shelf wear. The back cover has a gentle crease near the bottom left. Please see… Read more about this item

SAGE Publications, Inc, 1977 soft cover in near fine condition. Soft cover. Near Fine.

Sage Publications, Inc, 1977. Paperback. Like New/No dust jacket. 8.4 x 4.5 x 0.2 inches. Free 1st Class upgrade! Practically Brand New! The covers look great. The binding is tight. The title page is stamped. The interior pages are clean and unmarked. Electronic delivery tracking will be issued free of charge. Quantitative Applications in the Social… Read more about this item

SAGE Publications, Inc, April 1977. Trade Paperback. Used - Very Good.

NY: Sage Publications, 1977. 11th printing. Paperback. Used: Very Good. Softcover, 62p, VG. Series: Quantitative Applications in the Social Sciences.

Century, 1984. Paperback. Used: Acceptable. Paperback - 1984 - good condition -

SAGE Publications, Inc, 1977-04-01. Paperback. Used:Good.

SAGE Publications, Incorporated. Used - Good. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

SAGE Publications, Incorporated. Used - Good. Used book that is in clean, average condition without any missing pages.

Sage. Good. 1977. Soft Cover. (O115) .

Sage Publications , pp. 64 . Papeback. New.

New. New Book; Fast Shipping from UK; Not signed; Not First Edition; The Canonical Analysis and Factor Comparison.

SAGE Publications, Inc, 1977-04-01. Paperback. New. New. In shrink wrap. Looks like an interesting title!

Paperback / softback. New. An advanced study which presumes a knowledge of multiple regression and factor analysis techniques, this paper considers two techniques for comparing entire sets of data, and develops the canonical correlation model as an extension of regression analysis in which there are several dependent variables.