The Development of the Santa Clara Brief Compassion Scale: An Abbreviation of Sprecher and Fehr’s Compassionate Love Scale

This purpose of this study is to develop a brief version of Sprecher and Fehr’s Compassionate Love Scale (2005). This was accomplished by administering the 21-item scale to college student participants and subsequently selecting five items for a brief version. The five items were selected based on the evaluation of high correlation coefficients between individual item responses and the overall total 21 questions from the original scale, the results of factor analysis, and items that had moderate means and high standard deviations. The correlation between the original and brief version is 0.96, while the internal reliability of the brief version, using Cronbach’s alpha, is 0.90.     

Regularized Multiple-Set Canonical Correlation Analysis

Multiple-set canonical correlation analysis (Generalized CANO or GCANO for short) is an important technique because it subsumes a number of interesting multivariate data analysis techniques as special cases. More recently, it has also been recognized as an important technique for integrating information from multiple sources. In this paper, we present a simple regularization technique for GCANO and demonstrate its usefulness. Regularization is deemed important as a way of supplementing insufficient data by prior knowledge, and/or of incorporating certain desirable properties in the estimates of parameters in the model. Implications of regularized GCANO for multiple correspondence analysis are also discussed. Examples are given to illustrate the use of the proposed technique. The work reported in this paper is supported by Grants 10630 and 290439 from the Natural Sciences and Engineering Research Council of Canada to the first and the second authors, respectively. The authors would like to thank the two editors (old and new), the associate editor, and four anonymous reviewers for their insightful comments on earlier versions of this paper. Matlab programs that carried out the computations reported in the paper are available upon request.     

The impact of perceived racial discrimination on the mental health of Asian American and Latino college students

The authors examined the impact of perceived racial discrimination on various mental health outcomes for Asian American and Latino college students within an emic and etic framework. Results indicate that Asian American and Latino college students experienced similar exposure and reactions to various kinds of discrimination. However, Latino students were more likely than Asian American students to have been accused of doing something wrong, such as cheating and breaking the law, and more likely to appraise these experiences as stressful. Asian Americans evidenced higher risk for trait anxiety. Regardless of ethnicity, perceived racial discrimination was associated with several negative mental health outcomes, including higher psychological distress, suicidal ideation, state anxiety, trait anxiety, and depression. Findings highlight the need to address discrimination across multiple social and professional settings and to understand the broad array of mental health outcomes.     

The effect of a collision warning system on the driving performance of young drivers at intersections

This study investigated the manner in which the driving performance of young people was affected by a collision warning system when they encountered a driver running a red light at an intersection. Furthermore, the causal relationship among driving performance, traffic factors and intersection accidents was examined using Path Analysis. Participants drove a driving simulator with an intersection collision warning system (ICWS) in a simulated urban area. The driving performance measures recorded were reaction time, speed, lateral position deviation and crash events. Experimental results indicated that drivers who drove a vehicle with an ICWS audio signal at an intersection had a shorter reaction time, a lower speed and a reduced accident rate than those observed while driving a vehicle without ICWS audio signal. Furthermore, Path Analysis showed that the ICWS had an indirect effect on accident rate reduction through improved driving performance. The location of intersection accident had both direct and indirect effects on the accident rate. The number of driving days per week had a direct effect on accident rate reduction.     

Coexistence of Unconditionality and Conditionality of the Davidic Covenant in Chronicles

The Davidic covenant is the basis of the hope for a restoration of the Davidic kingdom in Chronicles. The Chronicler's retention of both the unconditionality and conditionality of the Davidic covenant does not mean that he was inconsistent; in my view, he views the two as complementary. The royal promise is conditional in the sense that the Davidic kings are disciplined and punished, and the kingdom ceases to exist when it does not meet the conditions set down by YHWH; it is unconditional in that YHWH's will never depart from the kingdom, as seen in 1 Chron. 17:13 where YHWH distinguishes his promise to David from his promise to Saul. Following this line of thought, the Chronicler yearns for further change in the postexilic temple‐centered society through a restoration of the lost kingdom by appealing to the Davidic covenant. The latter subtends the preservation of the Davidic kingdom in the preexilic period and a possible restoration in the postexilic period.     

Psycho-educational group treatment for the severely and persistently mentally ill: how much leader training is necessary?

Psycho-educational groups (PEGs) have been shown to be a particularly effective form of treatment for patients classified under the rubric of severely and persistently mentally ill (SPMI). However, recent surveys suggest that certain professionals, such as nurses and clinical psychologists, called on to conduct these groups may have limited to no training. This study tested three methods (self-instructional, workshop, and workshop plus clinical supervision) designed to provide on-the-job training to practicing clinicians. Specifically, training was based on two manuals; one that operationalized the basic knowledge and skills needed to run an effective PEG and the second being a commonly used PEG program for symptom management targeted at SPMI patients. Eight nurses from four adult units at a state psychiatric hospital initially received self-instructional or workshop training and then conducted a 12-session symptom management PEG composed of four to five SPMI patients (e.g., schizophrenia, schizoaffective or major depression disorders). In a second wave of additional training, nurses received workshop training or clinical supervision and then conducted a second PEG. Differences between training methods were assessed by nurse- and patient-completed measures that tapped the knowledge and skills emphasized in the symptom management and PEG (nurses only) manuals. Results indicated limited support for the superiority of the workshop method on the nurse measures alone. Implications for training working professionals in group treatments and assessing outcomes with chronic SPMI patients are discussed.     

Bayesian generalized structured component analysis

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well as to accommodate a greater variety of complex analyses such as latent moderation analysis, the capturing of cluster-level heterogeneity, and regularized analysis. To date, however, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this paper, a novel extension of GSCA, called BGSCA, is proposed that estimates parameters within the Bayesian framework. BGSCA can be more attractive than the original GSCA for various reasons. For example, it can infer the probability distributions of random parameters, account for error variances in the measurement model, provide additional fit measures for model assessment and comparison from the Bayesian perspectives, and incorporate external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters. We utilize a Markov chain Monte Carlo method, the Gibbs sampler, to update the posterior distributions for the parameters of BGSCA. We conduct a simulation study to evaluate the performance of BGSCA. We also apply BGSCA to real data to demonstrate its empirical usefulness.     

Cesarean childbirth in sweden: Effects on the mother and father-infant relationship

The effects of Cesarean delivery on father-infant and mother-infant interaction were studied in a sample of 30 Swedish, two-parent families with a full-term healthy first-born infant. Fifteen infants were delivered vaginally, and 15 infants were delivered under full anesthesia by Cesarean section. The infants and parents were observed at home when the infants were 3 and 8 months old. The findings indicate that mothers of infants delivered by Cesarean section reacted less positively when they saw their infants for the first time and encountered more difficulties breastfeeding than did mothers of vaginally delivered infants. At 3 months post-partum, fathers of infants who were Cesarean-delivered participated in a greater amount of caregiving activities. There were no differences between Cesarean and vaginally delivered mothers at 3 months after delivery. At 8 months, there were no differences between the two groups (Cesarean and vaginal) on either the interview questions or on-any of the behavioral measures. Results from this study do not support reports from the USA of long-lasting effects of Cesarean delivery on parent-infant interaction. The results suggest the need to study cultural variations that surround Cesarean section delivery so as to understand in what way the subsequent parent-infant relationship is influenced by this event.     

Functional Parallel Factor Analysis for Functions of One- and Two-dimensional Arguments

Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses. The existing functional PARAFAC methods handle functions of a one-dimensional argument (e.g., time) only. In this paper, we propose a new extension of functional PARAFAC for handling three-way data whose responses are sequenced along both a two-dimensional domain (e.g., a plane with x- and y-axis coordinates) and a one-dimensional argument. Technically, the proposed method combines PARAFAC with basis function expansion approximations, using a set of piecewise quadratic finite element basis functions for estimating two-dimensional smooth functions and a set of one-dimensional basis functions for estimating one-dimensional smooth functions. In a simulation study, the proposed method appeared to outperform the conventional PARAFAC. We apply the method to EEG data to demonstrate its empirical usefulness.