A note on the derivation of the general latent class model

A general formulation of the latent structure principle is suggested, from which it is possible to derive Lazarsfeld's accounting equations in their most general form. The basic equations of Gibson's latent profile model can thence be derived in a single step.     

Regional Differences in Junior Colleges

This study examines the geographical distribution of various junior college characteristics. Scores for 6 factors or categories of college characteristics, identified in earlier ACT research, were computed for each of 581 accredited junior colleges. When these junior colleges were classified and analyzed by geographical region, significant differences were found among regions on all 6 factors—Cultural Affluence (or Private Control), Technological Specialization, Size, Age (or Conventionalism), Transfer Emphasis, and Business Orientation (or High Cost). The regional differences are discussed and implications are suggested for research and counseling as well as for junior college planning.     

A comparison of four methods of constructing factor scores

Four least-squares methods for constructing factor scores have been described in the literature. The formal properties of these scores are developed, and they are compared in terms of four generally desirable properties of constructed factor scores. In particular, it is shown that two of the methods yield scores that are conditionally unbiassed, and univocal in the sense of Guilford and Michael, though not orthogonal, while one of the other methods yields orthogonal scores.It is shown that constructed factor scores cannot be simultaneously univocal and orthogonal, unless we choose the special basis in factor space given by Canonical Factor Analysis.The general problem of choosing between the methods is discussed, on the basis of the theoretical relations obtained.EDITOR'S NOTE: The reader will quickly discover that this article develops several of the generalizations given in the second part of the preceding article, On Factors and Factor Scores. Independent development of the same generalizations is, of course, not a new phenomenon. Because the Presidential Address automatically is accepted for publication and given space in the December issue, it was decided that the only fair thing to do was to print this article in the same issue.     

Numerical methods for polynomial models in nonlinear factor analysis

The basic concepts of nonlinear factor analysis are introduced and some extensions of the general theory are developed. An elementary account of the class of multiple-factor polynomial models is presented, using more elementary algebraic methods than have been employed in earlier accounts of this theory. Working formulas are developed for the multiple-factor polynomial model without product terms.Some empirical results are presented.This work was carried out while the author was a Visiting Research Fellow at the Educational Testing Service, Princeton, N. J. It made use of computer facilities at Princeton University that are supported in part by National Science Foundation Grant NSF-Gp579.     

Noise methods in pattern perception

This experiment investigated 4 methods of inducing visual noise at 7 levels of noise. Fortysix Ss were presented 4800 sets of 3 pattems, with the task of judging which of 2 noisy patterns was more similar to the prototype pattern. The noisy patterns were generated by adding, moving, deleting or both adding and deleting elements of the prototype. All possible comparisons of each noise method were made at each noise level and the results plotted. The results show that the various methods of introducing noise do not produce identical results, and that the similarity assessment is dependent on noise koel. Explanations are offered to account for the results.