The integration of science and practice: Unique perspectives from counseling psychology students

Counseling psychology training programs predominately subscribe to the scientist–practitioner training model, which emphasizes the mutual integration of science and practice. There has been extensive debate surrounding the applicability of the scientist–practitioner training model to the field of counseling psychology, and existing commentary from both trainers and trainees has documented the potential challenges to adequately integrating science and practice. In the current article, three counseling psychology doctoral students outline their experiences in a program that adheres to the scientist–practitioner training model. In particular, they describe their involvement in both clinical and scholarly related activities, their experiences with the integration of science and practice, and how the scientist–practitioner model has influenced their overall professional development. Specific examples of how the scientist–practitioner model can shape the perspectives and career goals of psychologists-in-training are reviewed.     

Cycle commuting intention: A model based on theory of planned behaviour and social identity

Although cycling as a mode of transport can provide various important benefits to cities and their transport systems, it accounts for only a small proportion of commuter trips in southern Europe. The aim of this study was to develop a new model based on Ajzen’s (1991) theory of planned behaviour (TPB), but including social identity as an additional predictor variable to improve the explanatory capability of the TPB. We conducted a telephone survey of a representative sample of 595 non-cycle commuters in the Spanish city of Vitoria-Gasteiz, which has a moderate proportion of bicycle users (6.9%). Confirmatory factor analysis to test the model showed satisfactory overall measurement fit, and all sub-scales had high reliability and validity coefficients. The findings demonstrated the value of incorporating social identity into the TPB to capture motivational factors relevant to cycle commuting. The relationships between the factors in the model indicated that there is a strong link between identifying as ‘a cyclist’ and perceived self-efficacy with respect to cycling. Furthermore, the results suggest that a more specific measure of perceived self-efficacy, targeting concrete behaviours could be used to inform development of initiatives to promote urban cycling. Our data also revealed that, excluding control variables such as journey time, economic cost and distance, the psychosocial variables included in the model predicted 32% of the variance in car users’ intention to start commuting by bicycle.     

Extending the 3 × 2 achievement goal model to the sport domain: The 3 × 2 Achievement Goal Questionnaire for Sport

In the present research, we sought to extend the 3 × 2 achievement goal model recently proffered in the school domain to the sport domain. We did so by conducting two studies focused on the development and initial validation of the 3 × 2 Achievement Goal Questionnaire for Sport (3 × 2 AGQ-S). Study 1 (n = 679), devised items for the questionnaire and demonstrated that data from the questionnaire nicely fit the proposed 3 × 2 model, showed a better fit to the 3 × 2 model than to alternative models, and indicated that each goal variable had good internal consistency. Study 2 again documented the strong psychometric properties of the measure, and additionally linked the goal variables to other constructs central to the achievement goal literature. The establishment of this measure allows extensive study of the 3 × 2 achievement goal model in the sport domain, and promises to yield deeper insights into the nature of achievement motivation in such contexts.     

Polytomous multilevel testlet models for testlet‐based assessments with complex sampling designs

A pplications of standard item response theory models assume local independence of items and persons. This paper presents polytomous multilevel testlet models for dual dependence due to item and person clustering in testlet‐based assessments with clustered samples. Simulation and survey data were analysed with a multilevel partial credit testlet model. This model was compared with three alternative models – a testlet partial credit model (PCM), multilevel PCM, and PCM – in terms of model parameter estimation. The results indicated that the deviance information criterion was the fit index that always correctly identified the true multilevel testlet model based on the quantified evidence in model selection, while the Akaike and Bayesian information criteria could not identify the true model. In general, the estimation model and the magnitude of item and person clustering impacted the estimation accuracy of ability parameters, while only the estimation model and the magnitude of item clustering affected the item parameter estimation accuracy. Furthermore, ignoring item clustering effects produced higher total errors in item parameter estimates but did not have much impact on the accuracy of ability parameter estimates, while ignoring person clustering effects yielded higher total errors in ability parameter estimates but did not have much effect on the accuracy of item parameter estimates. When both clustering effects were ignored in the PCM, item and ability parameter estimation accuracy was reduced.     

A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.     

The hoarding dimension of OCD: psychological comorbidity and the five-factor personality model

Although hoarding has been associated with several psychological disorders, it is most frequently linked to obsessive-compulsive disorder (OCD). The present study assessed hoarding obsessions and compulsions in 204 individuals with OCD, and evaluated how hoarding was related to obsessive-compulsive symptom severity, psychological comorbidity, and personality as measured by the five-factor model. Results indicated that hoarding in OCD is a dimensional variable that is positively associated with dysphoria, total number of lifetime Axis I disorders, and lifetime histories of bipolar I, PTSD, and body dysmorphic disorder. Hoarding was negatively correlated with the NEO-Personality Inventory-Revised (NEO-PI-R) factor of Conscientiousness and positively associated with the NEO-PI-R factor of Neuroticism. When all personality and psychopathology variables were entered into a regression equation, dysphoria, bipolar II disorder, Conscientiousness, age, and Extraversion emerged as significant predictors of hoarding severity. Recommendations are made for clinicians and for future research.     

Modeling response signal and response time data

The diffusion model (Ratcliff, 1978) and the leaky competing accumulator model (LCA, Usher & McClelland, 2001) were tested against two-choice data collected from the same subjects with the standard response time procedure and the response signal procedure. In the response signal procedure, a stimulus is presented and then, at one of a number of experimenter-determined times, a signal to respond is presented. The models were fit to the data from the two procedures simultaneously under the assumption that responses in the response signal procedure were based on a mixture of decision processes that had already terminated at response boundaries before the signal and decision processes that had not yet terminated. In the latter case, decisions were based on partial information in one variant of each model or on guessing in a second variant. Both variants of the diffusion model fit the data well and both fit better than either variant of the LCA model, although the differences in numerical goodness-of-fit measures were not large enough to allow decisive selection between the models.     

Decision making and confidence given uncertain advice

We study human decision making in a simple forced-choice task that manipulates the frequency and accuracy of available information. Empirically, we find that people make decisions consistent with the advice provided, but that their subjective confidence in their decisions shows 2 interesting properties. First, people's confidence does not depend solely on the accuracy of the advice. Rather, confidence seems to be influenced by both the frequency and accuracy of the advice. Second, people are less confident in their guessed decisions when they have to make relatively more of them. Theoretically, we develop and evaluate a type of sequential sampling process model—known as a self-regulating accumulator—that accounts for both decision making and confidence. The model captures the regularities in people's behavior with interpretable parameter values, and we show its ability to fit the data is not due to excessive model complexity. Using the model, we draw conclusions about some properties of human reasoning under uncertainty.