The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions

The Graded Response Model (GRM; Samejima, Estimation of ability using a response pattern of graded scores, Psychometric Monograph No.?17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, ??, to underlie the ordinal item scores (Takane & de Leeuw in Psychometrika, 52:393?C408, 1987). Traditionally, a normal distribution is specified for Z implying homoscedastic error variances and a normally distributed ??. In this paper, we present the Heteroscedastic GRM with Skewed Latent Trait, which extends the traditional GRM by incorporation of heteroscedastic error variances and a skew-normal latent trait. An appealing property of the extended GRM is that it includes the traditional GRM as a special case. This enables specific tests on the normality assumption of Z. We show how violations of normality in Z can lead to asymmetrical category response functions. The ability to test this normality assumption is beneficial from both a statistical and substantive perspective. In a simulation study, we show the viability of the model and investigate the specificity of the effects. We apply the model to a dataset on affect and a dataset on alexithymia.     

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.     

Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates

This paper proposes a structural analysis for generalized linear models when some explanatory variables are measured with error and the measurement error variance is a function of the true variables. The focus is on latent variables investigated on the basis of questionnaires and estimated using item response theory models. Latent variable estimates are then treated as observed measures of the true variables. This leads to a two-stage estimation procedure which constitutes an alternative to a joint model for the outcome variable and the responses given to the questionnaire. Simulation studies explore the effect of ignoring the true error structure and the performance of the proposed method. Two illustrative examples concern achievement data of university students. Particular attention is given to the Rasch model.     

Implementing an Intercultural Psychology Undergraduate Unit: Approach,Strategies, and Outcomes

The relative absence of Indigenous and multicultural perspectives in core undergraduate psychology curriculum is said to hamper the preparation of students for engaging with culturally diverse communities. An intercultural unit that includes perspectives on Australia's history of race relations, Indigenous issues, and migration as the basis from which to research and practise intercultural psychology was introduced into the psychology undergraduate syllabus at Victoria University. This article describes the unit framework, structure, and evaluation of the unit. Content analysis of survey data showed that students appeared to develop greater critical awareness of psychological perspectives on cultural and social diversity. Although many students reported positive learning and an increased awareness of cultural matters, there were also student responses that suggested dissatisfaction or disengagement from what was being taught. These responses are consistent with the dynamics of learning about self and other, and of dominance and subjugation. It is argued that promoting an understanding of psychological phenomena as socially, culturally, and historically embedded is important in preparing students to be culturally safe and respectful mental health practitioners.     

Computer Modeling and the Fate of Folk Psychology

Although Paul Churchland and Jerry Fodor both subscribe to the so-called theory-theory – the theory that folk psychology (FP) is an empirical theory of behavior – they disagree strongly about FP's fate. Churchland contends that FP is a fundamentally flawed view analogous to folk biology, and he argues that recent advances in computational neuroscience and connectionist AI point toward development of a scientifically respectable replacement theory that will give rise to a new common-sense psychology. Fodor, however, wagers that FP will be largely preserved and vindicated by scientific investigations of behavior. Recent findings by developmental psychologists, I argue, will push both Churchlandians and Fodorians toward the pessimistic view that FP is a misguided theory that will never be displaced, because it is, so to speak, built into our cognitive systems. I explore the possibility of preserving optimism by rejecting the theory-theory and adopting the simulation theory , a competing view developed by Robert Gordon, Alvin Goldman, and Jane Heal. According to simulationists, common-sense interpretation of behavior is accomplished by means of pretense-like operations that deploy the cognitive system's own reasoning capabilities in a disengaged manner. Since on this view no theory-like set of principles would be needed, the simulation theory seems to enjoy a simplicity advantage over the theory-theory. Steven Stich and Shawn Nichols, however, contend that as the cognitive system would require special mechanisms for disengaged operation, the simplicity question cannot be resolved until suitable computational models are developed. I describe a set of models I have constructed to meet this need, and I discuss the contribution such models can make to determining FP's fate.     

Model Selection with the Linear Mixed Model for Longitudinal Data

Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or hierarchical approach in which the variance-covariance matrix of the random effects is assumed to be positive definite with nonzero values for the variances. When the number of fixed effects and random effects is unknown, the predominant approach to model building is a step-up method in which one starts with a limited model (e.g., few fixed and random intercepts) and then additional fixed effects and random effects are added based on statistical tests. A model building approach that has received less attention in psychology and education is a top-down method. In the top-down method, the initial model has a single random intercept but is loaded with fixed effects (also known as an “overelaborate” model). Based on the overelaborate fixed effects model, the need for additional random effects is determined. There has been little if any examination of the ability of these methods to identify a true population model (i.e., identifying the model that generated the data). The purpose of this article is to examine the performance of the step-up and top-down model building approaches for exploratory longitudinal data analysis. Student achievement data sets from the Chicago longitudinal study serve as the populations in the simulations.     

Using Self Psychology in Psychotherapy,edited by Helene Jackson

No abstract available for this article.     

A Model for Increasing Indigenous Participation in Psychology Degrees

For psychology to truly embrace reconciliation, a greater representation of Indigenous Australian psychology professionals is required. A model for increasing Indigenous participation in psychology degrees is described here. Such an increase has many potential benefits for the discipline and the services that it provides. The model proposed targets the barriers reported by past and potential Indigenous students. Key aspects of this model are as follows: the institutional context that embraces a cultural competence framework, the inclusion of relevant Indigenous curriculum in subjects within the degree, partnership with the local area health service, and involvement of local elders and communities. It also encompasses a system for growing a sustainable mentoring program involving students and staff. Mutually respectful relationships and reciprocal learning are foundational in this approach. The model provides experience of reconciliation in action for all psychology students important in training future professionals. Integral is a process to increase the relevance of the curriculum and pedagogy for serving the needs of Indigenous people, thereby increasing the engagement of Indigenous students.     

Flexible Approaches to Computing Mediated Effects in Generalized Linear Models: Generalized Estimating Equations and Bootstrapping

In behavioral research, interest is often in examining the degree to which the effect of an independent variable X on an outcome Y is mediated by an intermediary or mediator variable M. This article illustrates how generalized estimating equations (GEE) modeling can be used to estimate the indirect or mediated effect, defined as the amount by which the regression coefficient of X on Y changes after adjusting for M. Advantages of this method are: (a) it applies to the class of generalized linear models, including linear, logistic, and Poisson regression as special cases; (b) it allows multiple independent variables and mediators in the same model; and (c) asymptotically valid standard errors and confidence intervals are obtained using standard software. This methodology is compared with the bootstrap, another general methodology that can be applied to the same broad class of models, and is evaluated using simulation in both linear and logistic regression scenarios. The methods are utilized to examine the degree to which the effect of low birthweight status on internalizing symptoms at age 20 is mediated through IQ at age 8.     

Disentangling the Impact of Resistance and Ambivalence on Therapy Outcomes in Cognitive Behavioural Therapy for Generalized Anxiety Disorder

Resistance and ambivalence about change are increasingly recognized as important determinants of treatment outcomes. Moreover, resistance and ambivalence are thought to be theoretically related in that clients who are more ambivalent about change are more likely to demonstrate resistance to the process and tasks of treatment. In the context of cognitive behavioural therapy (CBT) for generalized anxiety disorder, the present study simultaneously examined early resistance and ambivalence using two observer-based coding systems in order to determine their inter-relationship and, importantly, to investigate their relative contributions to outcome. Resistance was also coded during mid-treatment in order to investigate possible mediation pathways. Early ambivalence (clients’ arguments against change or counter-change talk) was found to be no longer related to outcomes when early resistance was taken into account, suggesting that disharmony in the therapeutic relationship is more important to outcomes than ambivalence per se. Moreover, mid-treatment resistance partially mediated the relationship between early resistance and post-treatment worry severity. That is, higher early opposition to therapist direction is related to poorer outcomes, in part because it is associated with greater resistance during the working phase of CBT. The findings underscore the critical need for therapists to be sensitive to identifying resistance early and throughout treatment.     

Discriminant Analysis Using Mixed Continuous,Dichotomous, and Ordered Categorical Variables

This article proposes an intuitive approach for predictive discriminant analysis with mixed continuous, dichotomous, and ordered categorical variables that are defined via an underlying multivariate normal distribution with a threshold specification. The classification rule is based on the comparison of the observed data logarithm probability density functions. To reduce the computational burden, the analysis is conducted in the context of a confirmatory factor analysis model with independent error measurements. Identification of the dichotomous and ordered categorical variables is discussed. Results are obtained by implementations of a Monte Carlo expectation maximization (MCEM)algorithm and a path sampling procedure. Probabilities of misclassification are estimated via the idea of the “jackknife” method. A real example is given to illustrate the proposed method.     

Greater Expectations: Using Hierarchical Linear Modeling to Examine Expectancy for Treatment Outcome as a Predictor of Treatment Response

A client's expectation that therapy will be beneficial has long been considered an important factor contributing to therapeutic outcomes, but recent empirical work examining this hypothesis has primarily yielded null findings. The present study examined the contribution of expectancies for treatment outcome to actual treatment outcome from the start of therapy through 12-month follow-up in a clinical sample of individuals (n = 72) treated for fear of flying with either in vivo exposure or virtual reality exposure therapy. Using a piecewise hierarchical linear model, outcome expectancy predicted treatment gains made during therapy but not during follow-up. Compared to lower levels, higher expectations for treatment outcome yielded stronger rates of symptom reduction from the beginning to the end of treatment on 2 standardized self-report questionnaires on fear of flying. The analytic approach of the current study is one potential reason that findings contrast with prior literature. The advantages of using hierarchical linear modeling to assess interindividual differences in longitudinal data are discussed.     

A Comprehensive Meta-Analysis of Triple P-Positive Parenting Program Using Hierarchical Linear Modeling: Effectiveness and Moderating Variables

A meta-analysis encompassing all studies evaluating the impact of the Triple P-Positive Parenting Program on parent and child outcome measures was conducted in an effort to identify variables that moderate the program's effectiveness. Hierarchical linear models (HLM) with three levels of data were employed to analyze effect sizes. The results (N = 55 studies) indicate that Triple P causes positive changes in parenting skills, child problem behavior and parental well-being in the small to moderate range, varying as a function of the intensity of the intervention. The most salient findings of variables moderating the interventions' impact were larger effects found on parent report as compared to observational measures and more improvement associated with more intensive formats and initially more distressed families. Sample characteristics (e.g., child's age, being a boy) and methodological features (e.g., study quality) exhibited different degrees of predictive power. The analysis clearly identified several strengths of the Triple P system, most importantly its ability to effect meaningful improvement in parents and children. Some limitations pertain to the small evidence-base of certain formats of Triple P and the lack of follow-up data beyond 3 years after the intervention. Many of the present findings may be relevant to other evidence-based parenting programs.     

Predicting Health-related Quality of Life: Testing the Contextual Model Using Structural Equation Modeling

This study aims to investigate the utility of the Contextual Model of Health-Related Quality of Life (HRQOL) to explain the relationship among the domains of HRQOL with a diverse, population-based sample of breast cancer survivors (BCS). We employed a cross-sectional design to investigate HRQOL among 703 multiethnic, population-based BCS. The study methodology was guided by the Contextual Model of HRQOL. Structural Equation Modeling (SEM) was conducted to assess the hypothesized model. SEM identified significant relationships among the bio-psychological domain (general health status, cancer-related factors, and psychological factors), the cultural-socio-ecological domain (health care satisfaction, socio-ecological factor, and socio-economic status), and HRQOL. The best fitting model indicates direct pathways from ‘general health status’, ‘years since diagnosis’, ‘health care satisfaction’ and ‘socio-ecological factor’ to ‘HRQOL’ variables. Additionally, ‘socio-ecological factor’ and ‘socio-economic status’ variables were indirectly associated with HRQOL through ‘general health status’. Findings suggest that the Contextual Model of HRQOL adds valid factors to explain overall HRQOL and increases our understanding of the socio-ecological dimensions predicting HRQOL outcomes. The revelation of inter-relations among the dimensions of HRQOL may inform the translational and clinical utility of the HRQOL construct.

On the Relation Between the Linear Factor Model and the Latent Profile Model

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the unconditional covariances. In particular, a 2-class latent profile model with Gaussian components underestimates the observed covariances but not the variances, when the data are consistent with a unidimensional Gaussian factor model. In explanation of this phenomenon we provide some results relating the unconditional covariances to the goodness of fit of the latent profile model, and to its excess multivariate kurtosis. The analysis also leads to some useful parameter restrictions related to symmetry.