Data‐driven approaches to information access

This paper summarizes three lines of research that are motivated by the practical problem of helping users find information from external data sources, most notably computers. The application areas include information retrieval, text categorization, and question answering. A common theme in these applications is that practical information access problems can be solved by analyzing the statistical properties of words in large volumes of real world texts. The same statistical properties constrain human performance, thus we believe that solutions to practical information access problems can shed light on human knowledge representation and reasoning.     

Trait tendencies to forgive,punish, and exonerate oneself: A multi-study investigation

In two studies (N’s = 515 and 359), we examine the utility of the Dual-Process Model of Self-Forgiveness for conceptualizing and measuring trait self-forgiveness with South African and American adults. Exploratory and confirmatory factor analyses of the trait-adapted Dual-Process Self-Forgiveness Scale supported an eight-item, two-factor structure of self-forgivingness consistent with prior theorizing and empirical evidence. The trait-adapted measure predicted incrementally more variance in outcomes beyond an existing measure of trait self-forgiveness. Latent subgroups characterized by tendencies to forgive, punish, and exonerate oneself in response to wrongdoing or failure were identified. Comparisons of mean differences provided preliminary evidence supporting the validity of the subgroups. Implications of the findings for conceptualizing and measuring self-forgiveness are discussed.     

Rare Constructions Are More Often Sentence‐Initial

Main clause phenomena (MCPs) are syntactic constructions that occur predominantly or exclusively in main clauses. I propose a processing explanation for MCPs. Sentence processing is easiest at the beginning of the sentence (requiring less search); this follows naturally from widely held assumptions about sentence processing. Because of this, a wider variety of constructions can be allowed at the beginning of the sentence without overwhelming the sentence‐processing mechanism. Unlike pragmatic and grammatical accounts of MCPs, the processing account predicts avoidance of MCPs in non‐initial main clauses (non‐initial coordinate clauses and premodified clauses). A corpus study supports these predictions, but it is somewhat inconclusive. A further corpus study examines another type of syntactic construction, premodifying adjunct phrases (“openers”); the prediction here is that less common types of opener will be especially avoided in non‐initial contexts. The prediction is confirmed, supporting the processing view of rare constructions.     

My Country,Right or Wrong: Education,Accumulated Democratic Experience,and Political Socialization of Blind Patriotism

Although educational attainment has been identified as a significant factor that reduces uncritical positive attachment to one's country (i.e., blind patriotism), little is known about whether the remedial effect of education on uncritical patriotism is universal across countries with different histories of democratic rule. This article examines the extent to which democracy stock (the accumulated amount of democratic traditions, legacies, and experiences consolidated over time) conditions the relationship between education and blind patriotism. Using international data from 33 countries, the article shows that the remedial effect of education is more pronounced in countries with larger stocks of democracy than in countries with higher levels of democracy (current or recent). The results imply that the political socialization of critical patriotism is a cumulative process in which citizens internalize their potentially democratic competencies (e.g., knowledge, skills, and dispositions) and develop a sense of national attachment based on critical reflection on government policy and practice (i.e., critical loyalty) over an extended period of time.     

Canonical analysis of two convex polyhedral cones and applications

Canonical analysis of two convex polyhedral cones consists in looking for two vectors (one in each cone) whose square cosine is a maximum. This paper presents new results about the properties of the optimal solution to this problem, and also discusses in detail the convergence of an alternating least squares algorithm. The set of scalings of an ordinal variable is a convex polyhedral cone, which thus plays an important role in optimal scaling methods for the analysis of ordinal data. Monotone analysis of variance, and correspondence analysis subject to an ordinal constraint on one of the factors are both canonical analyses of a convex polyhedral cone and a subspace. Optimal multiple regression of a dependent ordinal variable on a set of independent ordinal variables is a canonical analysis of two convex polyhedral cones as long as the signs of the regression coefficients are given. We discuss these three situations and illustrate them by examples.     

A reformulation of the general Euclidean model for the external analysis of preference data

This paper discusses least squares methods for fitting a reformulation of the general Euclidean model for the external analysis of preference data. The reformulated subject weights refer to a common set of reference vectors for all subjects and hence are comparable across subjects. If the rotation of the stimulus space is fixed, the subject weight estimates in the model are uniquely determined. Weight estimates can be guaranteed nonnegative. While the reformulation is a metric model for single stimulus data, the paper briefly discusses extensions to nonmetric, pairwise, and logistic models. The reformulated model is less general than Carroll's earlier formulation.The author is grateful to Christopher J. Nachtsheim for his helpful suggestions.     

A method of optimal scaling for multivariate ordinal data and its extensions

This paper develops a method of optimal scaling for multivariate ordinal data, in the framework of a generalized principal component analysis. This method yields a multidimensional configuration of items, a unidimensional scale of category weights for each item and, optionally, a multidimensional configuration of subjects. The computation is performed by alternately solving an eigenvalue problem and executing a quasi-Newton projection method. The algorithm is extended for analysis of data with mixed measurement levels or for analysis with a combined weighting of items. Numerical examples and simulations are provided. The algorithm is discussed and compared with some related methods.Earlier results of this research appeared in Saito and Otsu (1983). The authors would like to acknowledge the helpful comments and encouragement of the editor.     

Hierarchical classes: Model and data analysis

A discrete, categorical model and a corresponding data-analysis method are presented for two-way two-mode (objects × attributes) data arrays with 0, 1 entries. The model contains the following two basic components: a set-theoretical formulation of the relations among objects and attributes; a Boolean decomposition of the matrix. The set-theoretical formulation defines a subset of the possible decompositions as consistent with it. A general method for graphically representing the set-theoretical decomposition is described. The data-analysis algorithm, dubbed HICLAS, aims at recovering the underlying structure in a data matrix by minimizing the discrepancies between the data and the recovered structure. HICLAS is evaluated with a simulation study and two empirical applications.This research was supported in part by a grant from the Belgian NSF (NFWO) to Paul De Boeck and in part by NSF Grant BNS-83-01027 to Seymour Rosenberg. We thank Iven Van Mechelen for clarifying several aspects of the Boolean algebraic formulation of the model and Phipps Arabie for his comments on an earlier draft.     

Functional measurement approach to self-estimation in multiattribute evaluation

Multiattribute analysis depends on measurement of values and weights. Unless these measures reflect the decision maker's true values and weights, the multiattribute formula may put a less-preferred alternative in first place. To avoid such disordinality requires stringent measurement conditions: First, the values and weights must be on linear (equal interval) or ratio (known zero) scales. Second, these scales must satisfy a condition of common unit across disparate attribute dimensions. Most methods of range adjustment beg both of these measurement questions. Functional measurement theory can solve both problems and so can be useful in multiattribute analysis. Past work has established the operation of a general cognitive algebra as an empirical reality. The averaging model, in particular, makes possible the definition and estimation of weights and values as distinct psychological parameters. It can also solve the problem of common unit. Cognitive algebra thus provides a grounded theoretical foundation on which to develop self-estimation methodology, in which decision makers provide direct estimates of their values and weights. The logic is straightforward. Functional measurement can analyze global judgments to obtain validated psychological scales. These scales may then be used as validational criteria for the self-estimates. Procedures to eliminate biases in the self-estimates can thus be tested and refined in well-learned multiattribute tasks, such as judgments of meals, in which global judgments are trustworthy. Once developed, such self-estimation procedures may be used with some confidence for general multiattribute analysis. A number of studies from 20-odd years of work on the theory of information integration are summarized to show good, although not unmixed promise for self-estimation.     

Factor invariance of the Millon Clinical Multiaxial Inventory

Several factor analyses of the Millon Clinical Multiaxial Inventory (MCMI) have resulted in very similar solutions. Interpretation of this consistency is hampered by the fact that the 20 scales of the inventory share items. Overlapping items cause the scales to be linearly dependent and may create structure in the interscale correlation matrix which is separate from the subject response patterns. A factor analysis was performed on the matrix of item-overlap coefficients which describes the underlying artifactual structure of the instrument. Data from two new subject samples were factor analyzed and compared to previously published studies. Similarity coefficients among factors across studies were calculated.