Exploring the effects of congruence and Holland’s personality codes on job satisfaction: An application of hierarchical linear modeling techniques

This study applied hierarchical linear modeling to investigate the effect of congruence on intrinsic and extrinsic aspects of job satisfaction. Particular focus was given to differences in job satisfaction by gender and by Holland’s first-letter codes. The study sample included nationally represented 1462 female and 1280 male college graduates who held full-time jobs in 2000. Congruence was measured by using actual occupational and academic major codes. After controlling for the differences across three-letter Holland personality codes, results indicate that job satisfaction was largely a function of individual attributes rather than disparities in personality types. Holland’s congruence was most effective in explaining intrinsic dimensions of job satisfaction for females, and less effective for extrinsic job satisfaction for both genders. Social females and males were less satisfied with extrinsic satisfaction, while Conventional and Social females were more satisfied with intrinsic features in their vocational environments.     

When should clinicians switch treatments? An application of signal detection theory to two treatments for women with alcohol use disorders

Statistical application of signal detection theory has been used to study the clinical utility of early treatment response in a range of treatments and psychiatric disorders. The current study sought to examine the predictive value of weekly within-treatment drinking using receiver operator curves (ROCs) and zero-inflated Poisson (ZIP) regression in 102 women with alcohol use disorders (AUDs) randomized to either alcohol behavioral individual treatment (ABIT; n = 52) or alcohol behavioral couples treatment (ABCT; n = 50). ROC analyses indicated that failure to achieve or sustain abstinence by the end-of-treatment and one-year follow-up was predicted with reasonable accuracy by week 4 percent days abstinent (PDA) in ABIT. ZIP models yielded similar results with evidence for within-treatment PDA with week 6 PDA predicting both the abstinence as well as percent days drinking at the end-of-treatment and one-year follow-up. Within-treatment PDA was a significantly better predictor of outcomes for ABIT than ABCT, despite a better overall treatment response for ABCT. Implications for stepped care models of alcohol treatment are discussed and recommendations for future research made. When Should Clinicians Switch Treatments: An Application of Signal Detection Theory to Two Treatments for Women with Alcohol Use Disorders.     

全息项目因素分析在心理学研究中的应用

文章首先指出了经典线性因素分析的种种局限;然后介绍了一种基于“被试—项目矩阵”而非仅仅协方差矩阵或相关矩阵的全息项目因素分析,该方法能克服经典线性因素分析的种种局限,在心理学研究中尤其是人格研究中有着其他因素分析方法不可替代的作用。     

Poisson Multilevel Models with Small Samples

Recent methodological studies have investigated the properties of multilevel models with small samples. Previous work has primarily focused on continuous outcomes and little attention has been paid to count outcomes. The estimation of count outcome models can be difficult because the likelihood has no closed-form solution, meaning that approximation methods are required. Although adaptive Gaussian quadrature (AGQ) is generally seen as the gold standard, its comparative performance has been investigated with larger samples. AGQ approximates the full likelihood, a function that is known to produce biased estimates with small samples with continuous outcomes. Conversely, penalized quasi-likelihood (PQL) is considered to be a less desirable approximation; however, it can approximate the restricted likelihood function, a function that is known to perform well with smaller samples with continuous outcomes. The goal of this paper is to compare the small sample bias of full likelihood methods to the linearization bias of PQL with restricted likelihood. Simulation results indicate that the linearization bias of PQL is preferable to the finite sample bias of AGQ with smaller samples.     

Examining career-related mentoring and managerial performance across cultures: A multilevel analysis

The benefits of the mentoring relationship for protégés have been a primary focus in the mentoring literature. Researchers have recently begun to examine how mentoring can benefit the mentor. The purpose of the present study is to examine whether direct report-ratings of a manager’s career-related mentoring behaviors are related to boss-ratings of that manager’s performance. In addition, this study assesses whether the cultural background of the manager moderates the career-related mentoring-performance relationship via multilevel methodology. Results reveal that managers who are rated by their direct reports as engaging in career-related mentoring behaviors are perceived as better performers by their bosses. Moreover, the GLOBE societal culture dimension of Performance Orientation was a significant cross-level moderator of the career-related mentoring-performance relationship. Implications for the practice of mentoring in cross-cultural contexts across multiple disciplines are discussed.     

The generalized Logit-Linear Item Response Model for Binary-Designed Items

This paper introduces the generalized logit-linear item response model (GLLIRM), which represents the item-solving process as a series of dichotomous operations or steps. The GLLIRM assumes that the probability function of the item response is a logistic function of a linear composite of basic parameters which describe the operations, and the coefficients depend on three design matrices X, Y and Z. The GLLIRM provides a tool for testing hypotheses on the item-solving process and generalizes existing models. An empirical application is included, in which the model is applied to evaluate sources of difficulty and pairwise item interactions in a logical analysis test. This research was supported by the Comunidad de Madrid grant CCG06-UAM/ESP-0043.     

Regularized Partial and/or Constrained Redundancy Analysis

Methods of incorporating a ridge type of regularization into partial redundancy analysis (PRA), constrained redundancy analysis (CRA), and partial and constrained redundancy analysis (PCRA) were discussed. The usefulness of ridge estimation in reducing mean square error (MSE) has been recognized in multiple regression analysis for some time, especially when predictor variables are nearly collinear, and the ordinary least squares estimator is poorly determined. The ridge estimation method was extended to PRA, CRA, and PCRA, where the reduced rank ridge estimates of regression coefficients were obtained by minimizing the ridge least squares criterion. It was shown that in all cases they could be obtained in closed form for a fixed value of ridge parameter. An optimal value of the ridge parameter is found by G-fold cross validation. Illustrative examples were given to demonstrate the usefulness of the method in practical data analysis situations. We thank Jim Ramsay for his insightful comments on an earlier draft of this paper. The work reported in this paper is supported by Grants 10630 from the Natural Sciences and Engineering Research Council of Canada to the first author.     

Light Affine Set Theory: A Naive Set Theory of Polynomial Time

In [7], a naive set theory is introduced based on a polynomial time logical system, Light Linear Logic (LLL). Although it is reasonably claimed that the set theory inherits the intrinsically polytime character from the underlying logic LLL, the discussion there is largely informal, and a formal justification of the claim is not provided sufficiently. Moreover, the syntax is quite complicated in that it is based on a non-traditional hybrid sequent calculus which is required for formulating LLL.In this paper, we consider a naive set theory based on Intuitionistic Light Affine Logic (ILAL), a simplification of LLL introduced by [1], and call it Light Affine Set Theory (LAST). The simplicity of LAST allows us to rigorously verify its polytime character. In particular, we prove that a function over {0, 1}* is computable in polynomial time if and only if it is provably total in LAST.     

Varieties of Linear Calculi

A uniform calculus for linear logic is presented. The calculus has the form of a natural deduction system in sequent calculus style with general introduction and elimination rules. General elimination rules are motivated through an inversion principle, the dual form of which gives the general introduction rules. By restricting all the rules to their single-succedent versions, a uniform calculus for intuitionistic linear logic is obtained. The calculus encompasses both natural deduction and sequent calculus that are obtained as special instances from the uniform calculus. Other instances give all the invertibilities and partial invertibilities for the sequent calculus rules of linear logic. The calculus is normalizing and satisfies the subformula property for normal derivations.     

Equivalent linear logistic test models

This paper is about the Linear Logistic Test Model (LLTM). We demonstrate that there are infinitely many equivalent ways to specify a model. An implication is that there may well be many ways to change the specification of a given LLTM and achieve the same improvement in model fit. To illustrate this phenomenon, we analyze a real data set using a Lagrange multiplier test for the specification of the model. This Lagrange multiplier test is similar to the modification index used in structural equation modeling.