No abstract

Two studies were conducted to demonstrate that maladaptive aspects of high and low Openness to Experience were related to characterological impairment and that this aspect of personality may define a new domain of personality dysfunction. The 55-item Experiential Permeability Inventory (EPI; containing 4 scales) was developed and demonstrated to have acceptable psychometric properties. Evidence of convergent, discriminant, and incremental validity was provided. These studies provide a methodological framework for identifying and developing aspects of personality dysfunction that can expand the comprehensiveness of the current set of Axis II disorders. Theoretical implications of the EPI are discussed.

Despite their wide usage, the constructs of spirituality and religiosity have no universally accepted definitions, and very little research has examined how these numinous constructs relate both to one another and to established personality dimensions. Two studies are presented that examined the factor structure of a motivationally based measure of spirituality, the Spiritual Transcendence Scale (STS) and a behaviorally based measure of religiosity, the Religious Involvement Scale (RIS). Three causal models examining their relationships to one another and to psychological measures of growth and maturity, as well as their incremental validity in predicting a wide array of psychosocial outcomes over the influence of the Five-Factor Model domains were examined. Employing self and observer ratings and American and Filipino samples, the results demonstrated that these robust, cross-culturally generalizable scales provided insights into people not contained by traditional personality variables. The conceptual implications of these results were discussed.

We ground Cultural-Historical Activity Theory (CHAT) in studies of workplace practices from a mathematical point of view. We draw on multiple case study visits by college students and teacher-researchers to workplaces. By asking questions that 'open boxes', we 'outsiders and boundary-crossers' sought to expose contradictions between College and work, induce breakdowns and identify salient mathematics. Typically, we find that mathematical processes have been historically crystallised in 'black boxes' shaped by workplace cultures: its instruments, rules and divisions of labour tending to disguise or hide mathematics. These black boxes are of two kinds, signalling two key processes by which mathematics is put to work. The first involves automation, when the work of mathematics is crystallised in instruments, tools and routines: this process tends to distribute and hide mathematical work, but also evolves a distinct workplace 'genre' of mathematical practice. The second process involves sub-units of the community being protected from mathematics by a division of labour supported by communal rules, norms and expectations. These are often regulated by boundary objects that are the object of activity on one side of the boundary but serve as instruments of activity on the other side. We explain contradictions between workplace and College practices in analyses of the contrasting functions of the activity systems that structure them and that consequently provide for different genres and distributions of mathematics, and finally draw inferences for better alignment of College programmes with the needs of students.

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their implications for educational research. We illustrate the issues with an educational, longitudinal survey in which missing data was significant, but for which we were able to collect much of these missing data through subsequent data collection. We thus compare methods, that is, step-wise regression (basically ignoring the missing data) and MI models, with the model from the actual enhanced sample. The value of MI is discussed and the risks involved in ignoring missing data are considered. Implications for research practice are discussed.