A general model for the analysis of multilevel data

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of the model include repeated measures designs, multiple matrix samples, multilevel latent variable models, multiple time series, and variance and covariance component models.We would like to acknowledge the helpful comments of Ruth Silver. We also wish to thank the referees for helping to clarify the paper. This work was partly carried out with research funds provided by the Economic and Social Research Council (U.K.).     

An investigation of the ability to process inferences in language following right hemisphere brain damage

Twenty-two right hemisphere brain-damaged and 22 non-brain-damaged patients were given a multiple-choice recognition task which contained true statements, statements which were inferentially true but not actually heard before, and false statements. It was hypothesized that if right hemisphere brain damage disturbs the ability to comprehend inferences, these subjects, unlike their normal counterparts, would not falsely recognize true inferences as heard before. This hypothesis was not confirmed. However, the right hemisphere group was poorer than controls at rejecting false statements. This behavior was speculated to be a retrieval difficulty, which was exacerbated if the information contained spatial or semantically similar material.     

Diverse middle school students’ future career autobiographies and socioeconomic status factors: A content analysis

The future career autobiography (FCA) is a narrative, discovery-based career intervention designed to assess individuals’ career hopes and dreams. In total, 134 low-income, ethnically and racially diverse middle school students participated in an FCA intervention through a yearlong grant-funded career development initiative. Using content analysis, we analyzed participants’ FCAs for socioeconomic status factors and lifestyle indicators. We found that participants articulated occupational and lifestyle factors more than education and income, and that there were differences based on biological sex, race, and ethnicity. We present pertinent results and discuss implications for counseling practice and future research.     

Three common factor models for groups of variables

For the case of two or more groups of variables (batteries), generalizations on three common factor models, the Equal Residual Variances model, the Image model of Jöreskog, and the Canonical Factor Analysis model, are described and illustrated with empirical examples.The author is indebted to Dr. E. J. Burr for his valuable criticisms.     

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.     

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.