Archive for the Psychology of Religion, Ahead of Print.
This article investigates whether there are differences in religious and spiritual (R/S) beliefs, attitudes, practices, training, and self-assessed competence between counselors and psychologists in Canada. Researchers surveyed 307 mental health professionals in Canada with two standardized measures (the Assessment of Spirituality and Religious Sentiments Scale and the Duke University Religion Index) and various other questions corresponding to variables investigated or alluded to in past research. We hypothesized that, compared with psychologists, counselors would (a) have stronger personal R/S beliefs, (b) demonstrate more positive attitudes about the appropriateness of using R/S techniques with clients, (c) utilize R/S techniques more in sessions, (d) possess more positive attitudes toward training in this area, and (e) have higher self-assessed competence for working with R/S clients. These hypotheses were generally supported except for the last one: there were no significant differences found between counselors and psychologists in self-assessed competence in working with R/S clients. We compare our findings to those of extant research, particularly the study by Plumb who examined counselors in Canada. On the basis of our findings, we advocate for more systematic cultivation of R/S competence in programs for both counselors and psychologists in Canada.
Patient Perspectives of a University-Based Intensive Comprehensive Aphasia Program for Stroke Survivors with Aphasia
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Aggressive behavior: examining the psychological and demographic factors among university students in Nigeria
Cognitive processing differences between stereotype activation and semantic activation
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Education bias in typical brief cognitive tests used for the detection of dementia in elderly population with low educational level: a critical review
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An Explanatory Multidimensional Random Item Effects Rating Scale Model
Educational and Psychological Measurement, Ahead of Print.
Random item effects item response theory (IRT) models, which treat both person and item effects as random, have received much attention for more than a decade. The random item effects approach has several advantages in many practical settings. The present study introduced an explanatory multidimensional random item effects rating scale model. The proposed model was formulated under a novel parameterization of the nominal response model (NRM), and allows for flexible inclusion of person-related and item-related covariates (e.g., person characteristics and item features) to study their impacts on the person and item latent variables. A new variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm designed for latent variable models with crossed random effects was applied to obtain parameter estimates for the proposed model. A preliminary simulation study was conducted to evaluate the performance of the MH-RM algorithm for estimating the proposed model. Results indicated that the model parameters were well recovered. An empirical data set was analyzed to further illustrate the usage of the proposed model.
Random item effects item response theory (IRT) models, which treat both person and item effects as random, have received much attention for more than a decade. The random item effects approach has several advantages in many practical settings. The present study introduced an explanatory multidimensional random item effects rating scale model. The proposed model was formulated under a novel parameterization of the nominal response model (NRM), and allows for flexible inclusion of person-related and item-related covariates (e.g., person characteristics and item features) to study their impacts on the person and item latent variables. A new variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm designed for latent variable models with crossed random effects was applied to obtain parameter estimates for the proposed model. A preliminary simulation study was conducted to evaluate the performance of the MH-RM algorithm for estimating the proposed model. Results indicated that the model parameters were well recovered. An empirical data set was analyzed to further illustrate the usage of the proposed model.
Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation
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The Impact of Maternal Depression on Internet-Parent–Child Interaction Therapy for Child Attention-Deficit/Hyperactivity Disorder: A Case Study
Clinical Case Studies, Volume 22, Issue 4, Page 363-382, August 2023.
Conduct disorders and attention-deficit/hyperactivity disorder (ADHD) are highly comorbid, with an estimated prevalence rate of 51.5% for children between 2–17 years of age (Centers for Disease Control and Prevention, 2020). Parent–Child Interaction Therapy (PCIT) is an empirically supported behavioral parent training program for children with disruptive behavior. PCIT research consistently demonstrates decreases in disruptive behaviors and increases in positive parenting strategies among families of young children with ADHD; however, PCIT has yet to become widely recognized as a treatment for ADHD. This case study presents the treatment of a 6-year-old boy with ADHD and severe behavior problems. The case was further impacted by the single mother’s depressive symptoms and internet delivery of PCIT during the COVID-19 pandemic. Findings from this case report documented an improvement in disruptive child behaviors and emotion regulation and increased positivity during parent–child interactions, despite worsening maternal depressive symptoms. This case study highlights the utility of PCIT to improve child disruptive behaviors and ADHD symptoms in the midst of several complicating factors.
Conduct disorders and attention-deficit/hyperactivity disorder (ADHD) are highly comorbid, with an estimated prevalence rate of 51.5% for children between 2–17 years of age (Centers for Disease Control and Prevention, 2020). Parent–Child Interaction Therapy (PCIT) is an empirically supported behavioral parent training program for children with disruptive behavior. PCIT research consistently demonstrates decreases in disruptive behaviors and increases in positive parenting strategies among families of young children with ADHD; however, PCIT has yet to become widely recognized as a treatment for ADHD. This case study presents the treatment of a 6-year-old boy with ADHD and severe behavior problems. The case was further impacted by the single mother’s depressive symptoms and internet delivery of PCIT during the COVID-19 pandemic. Findings from this case report documented an improvement in disruptive child behaviors and emotion regulation and increased positivity during parent–child interactions, despite worsening maternal depressive symptoms. This case study highlights the utility of PCIT to improve child disruptive behaviors and ADHD symptoms in the midst of several complicating factors.
Practical parsimony and complexity in conceptualising habit
Evaluating the Effects of Missing Data Handling Methods on Scale Linking Accuracy
Educational and Psychological Measurement, Ahead of Print.
For large-scale assessments, data are often collected with missing responses. Despite the wide use of item response theory (IRT) in many testing programs, however, the existing literature offers little insight into the effectiveness of various approaches to handling missing responses in the context of scale linking. Scale linking is commonly used in large-scale assessments to maintain scale comparability over multiple forms of a test. Under a common-item nonequivalent group design (CINEG), missing data that occur to common items potentially influence the linking coefficients and, consequently, may affect scale comparability, test validity, and reliability. The objective of this study was to evaluate the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on IRT scale linking accuracy when missing data occur to common items. Under a set of simulation conditions, the relative performance of the six missing data treatment methods under two missing mechanisms was explored. Results showed that RF, MI, and FIML produced less errors for conducting scale linking whereas LWD was associated with the most errors regardless of various testing conditions.
For large-scale assessments, data are often collected with missing responses. Despite the wide use of item response theory (IRT) in many testing programs, however, the existing literature offers little insight into the effectiveness of various approaches to handling missing responses in the context of scale linking. Scale linking is commonly used in large-scale assessments to maintain scale comparability over multiple forms of a test. Under a common-item nonequivalent group design (CINEG), missing data that occur to common items potentially influence the linking coefficients and, consequently, may affect scale comparability, test validity, and reliability. The objective of this study was to evaluate the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on IRT scale linking accuracy when missing data occur to common items. Under a set of simulation conditions, the relative performance of the six missing data treatment methods under two missing mechanisms was explored. Results showed that RF, MI, and FIML produced less errors for conducting scale linking whereas LWD was associated with the most errors regardless of various testing conditions.