An automated test development of parallel tests from a seed test

Binary programming models are presented to generate parallel tests from an itembank. The parallel tests are created to match item for item an existing seed test and match user supplied taxonomic specifications. The taxonomic specifications may be either obtained from the seed test or from some other user requirement. An algorithm is presented along with computational results to indicate the overall efficiency of the process. Empirical findings based on an itembank for the Arithmetic Reasoning section of the Armed Services Vocational Aptitude Battery are given.The Office of Naval Research, Program in Cognitive Science, N00014-87-C-0696 partially supported the work of Douglas H. Jones. The Rutgers Research Resource Committee of the Graduate School of Management partially supported the work of Douglas H. Jones and Ing-Long Wu. A Thomas and Betts research fellowship partially supported the work of Ing-Long Wu. The Human Resources Laboratory, United States Air Force, partially supported the work of Ronald Armstrong. The authors benefited from conversations with Dr. Wayne Shore, Operational Technologies, San Antonio, Texas. The order of authors' names is alphabetical and denotes equal authorship.     

Sentence encoding and implicitly activated memories

Some words have fewer direct associates than others, and, when words varying in set size are studied in a list-learning task, those with smaller sets are more likely to be recalled. This set-size effect is found in cued recall when the words are studied in the absence of related words, but not when studied in the presence of related words. Related words provide context and theoretically inhibit irrelevant associates. The present research determined that set-size effects are found when words are encoded in sentence contexts. In contrast to list-learning experiments, the results of three experiments found such effects even when lexically related words were present in the sentences. Other findings indicated that target-set-size effects were determined by the proximity of related words in the sentence and the nature of the test cue. The results are discussed in relation to a model for explaining set-size effects and to selective findings from the sentence-comprehension literature.     

A social-cognitive,attributional model of personal goals: An empirical evaluation

A model of cognitive and affective antecedents and consequences of personal goals, which integrates constructs from social-cognitive and attribution theories, was examined. Data were gathered before and after course examinations in a large university class. Hierarchical and moderated regression analyses supported the model indicating that (1) relationships between self-efficacy, goals, and performance were positive during repeated trials over 4 1/2 months in a naturally occurring setting; (2) the stability dimension of causal attributions moderated the relationship between goal-performance discrepancies and self-efficacy; and (3) the locus of causality dimension of causal attributions moderated the relationship between goal-performance discrepancies and affective responses.We thank Susan Ashford, Jim Austin, Donald Fedor, Ruth Kanfer, Edwin Locke, Paul Nystrom, Donna Randall, Christina Shalley, Masoud Yasai-Ardekani and two anonymous reviewers for their helpful comments on earlier versions of this article. We also thank Eric Dietenberger for his help with graphics.     

Data generation and analysis using spreadsheets

Modern spreadsheets are powerful and useful tools that can often replace special-purpose programs for generating data and for student analysis of simple statistical problems. The inherent flexibility of spreadsheets makes them especially convenient and extensible. Several templates for generating and analyzing data fort tests and analysis of variance are discussed. Users can specify then, \(\bar X\) , andSD of two or more groups and then execute macros that generate appropriate data. Overwriting the generated data with “real” values turns the spreadsheet into a data analysis program. Spreadsheets hold promise as valuable instructional supplements for simple designs, but they are less suitable for more complex designs, where special-purpose programs may be more appropriate.     

A quasi-nonmetric method for multidimensional scaling VIA an extended euclidean model

An Extended Two-Way Euclidean Multidimensional Scaling (MDS) model which assumes both common and specific dimensions is described and contrasted with the standard (Two-Way) MDS model. In this Extended Two-Way Euclidean model then stimuli (or other objects) are assumed to be characterized by coordinates onR common dimensions. In addition each stimulus is assumed to have a dimension (or dimensions) specific to it alone. The overall distance between objecti and objectj then is defined as the square root of the ordinary squared Euclidean distance plus terms denoting the specificity of each object. The specificity,s _j , can be thought of as the sum of squares of coordinates on those dimensions specific to objecti, all of which have nonzero coordinatesonly for objecti. (In practice, we may think of there being just one such specific dimension for each object, as this situation is mathematically indistinguishable from the case in which there are more than one.)We further assume that _ij =F(d _ij ) +e _ij where _ij is the proximity value (e.g., similarity or dissimilarity) of objectsi andj,d _ij is the extended Euclidean distance defined above, whilee _ij is an error term assumed i.i.d.N(0, ²).F is assumed either a linear function (in the metric case) or a monotone spline of specified form (in the quasi-nonmetric case). A numerical procedure alternating a modified Newton-Raphson algorithm with an algorithm for fitting an optimal monotone spline (or linear function) is used to secure maximum likelihood estimates of the paramstatistics) can be used to test hypotheses about the number of common dimensions, and/or the existence of specific (in addition toR common) dimensions.This approach is illustrated with applications to both artificial data and real data on judged similarity of nations.     

Assessing the relationship of Type A beliefs to cardiovascular disease risk and psychosocial distress

Investigators have begun to take a multimodal approach to the assessment and treatment of psychosocial risk factors for cardiovascular disease (CVD). For instance, cognitive responses have become the focus of contemporary research along with continued examination of overt Type A behaviors. Price (1982) has outlined a set of beliefs purportedly associated with Type A behavior and subsequent CVD risk. The current study examines the validity of this belief set as represented by a newly developed measure, the Type A Cognitive Questionnaire (TACQ). Subjects were 221 employed adults participating in a worksite CVD risk reduction program. They completed the TACQ as part of a pretreatment CVD risk screening protocol. As hypothesized, TACQ scores were significantly associated with Type A behavior, hostility, physiological mediators of CVD, and psychosocial distress. Discussion addresses continued refinement of the Type A belief construct.