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--- - - Journal of Educational Measurement, Volume 55, Issue 3, Page 355-356, Fall 2018.

--- - |2 Abstract This research derived information functions and proposed new scalar information indices to examine the quality of multidimensional forced choice (MFC) items based on the RANK model. We also explored how GGUM‐RANK information, latent trait recovery, and reliability varied across three MFC formats: pairs (two response alternatives), triplets (three alternatives), and tetrads (four alternatives). As expected, tetrad and triplet measures provided substantially more information than pairs, and MFC items composed of statements with high discrimination parameters were most informative. The methods and findings of this study will help practitioners to construct better MFC items, make informed projections about reliability with different MFC formats, and facilitate the development of MFC triplet‐ and tetrad‐based computerized adaptive tests. - Journal of Educational Measurement, Volume 55, Issue 3, Page 357-372, Fall 2018.

--- - |2 Abstract Person reliability parameters (PRPs) model temporary changes in individuals’ attribute level perceptions when responding to self‐report items (higher levels of PRPs represent less fluctuation). PRPs could be useful in measuring careless responding and traitedness. However, it is unclear how well current procedures for estimating PRPs can recover parameter estimates. This study assesses these procedures in terms of mean error (ME), average absolute difference (AAD), and reliability using simulated data with known values. Several prior distributions for PRPs were compared across a number of conditions. Overall, our results revealed little differences between using the χ or lognormal distributions as priors for estimated PRPs. Both distributions produced estimates with reasonable levels of ME; however, the AAD of the estimates was high. AAD did improve slightly as the number of items increased, suggesting that increasing the number of items would ameliorate this problem. Similarly, a larger number of items were necessary to produce reasonable levels of reliability. Based on our results, several conclusions are drawn and implications for future research are discussed. - Journal of Educational Measurement, Volume 55, Issue 3, Page 421-432, Fall 2018.

--- - |2 Abstract This study investigates the effect of multidimensionality on extraction of latent classes in mixture Rasch models. In this study, two‐dimensional data were generated under varying conditions. The two‐dimensional data sets were analyzed with one‐ to five‐class mixture Rasch models. Results of the simulation study indicate the mixture Rasch model tended to extract more latent classes than the number of dimensions simulated, particularly when the multidimensional structure of the data was more complex. In addition, the number of extracted latent classes decreased as the dimensions were more highly correlated regardless of multidimensional structure. An analysis of the empirical multidimensional data also shows that the number of latent classes extracted by the mixture Rasch model is larger than the number of dimensions measured by the test. - Journal of Educational Measurement, Volume 55, Issue 3, Page 403-420, Fall 2018.

--- - |2 Abstract Bayesian methods incorporate model parameter information prior to data collection. Eliciting information from content experts is an option, but has seen little implementation in Bayesian item response theory (IRT) modeling. This study aims to use ethical reasoning content experts to elicit prior information and incorporate this information into Markov Chain Monte Carlo (MCMC) estimation. A six‐step elicitation approach is followed, with relevant details at each stage for two IRT items parameters: difficulty and guessing. Results indicate that using content experts is the preferred approach, rather than noninformative priors, for both parameter types. The use of a noninformative prior for small samples provided dramatically different results when compared to results from content expert–elicited priors. The WAMBS (When to worry and how to Avoid the Misuse of Bayesian Statistics) checklist is used to aid in comparisons. - Journal of Educational Measurement, Volume 55, Issue 3, Page 373-402, Fall 2018.

Cross‐level invariance in a multilevel item response model can be investigated by testing whether the within‐level item discriminations are equal to the between‐level item discriminations. Testing the cross‐level invariance assumption is important to understand constructs in multilevel data. However, in most multilevel item response model applications, the cross‐level invariance is assumed without testing of the cross‐level invariance assumption. In this study, the detection methods of differential item discrimination (DID) over levels and the consequences of ignoring DID are illustrated and discussed with the use of multilevel item response models. Simulation results showed that the likelihood ratio test (LRT) performed well in detecting global DID at the test level when some portion of the items exhibited DID. At the item level, the Akaike information criterion (AIC), the sample‐size adjusted Bayesian information criterion (saBIC), LRT, and Wald test showed a satisfactory rejection rate (>.8) when some portion of the items exhibited DID and the items had lower intraclass correlations (or higher DID magnitudes). When DID was ignored, the accuracy of the item discrimination estimates and standard errors was mainly problematic. Implications of the findings and limitations are discussed.

When dealing with missing responses, two types of omissions can be discerned: items can be skipped or not reached by the test taker. When the occurrence of these omissions is related to the proficiency process the missingness is nonignorable. The purpose of this article is to present a tree‐based IRT framework for modeling responses and omissions jointly, taking into account that test takers as well as items can contribute to the two types of omissions. The proposed framework covers several existing models for missing responses, and many IRTree models can be estimated using standard statistical software. Further, simulated data is used to show that ignoring missing responses is less robust than often considered. Finally, as an illustration of its applicability, the IRTree approach is applied to data from the 2009 PISA reading assessment.

This study proposes a structured constructs model (SCM) to examine measurement in the context of a multidimensional learning progression (LP). The LP is assumed to have features that go beyond a typical multidimentional IRT model, in that there are hypothesized to be certain cross‐dimensional linkages that correspond to requirements between the levels of the different dimensions. The new model builds on multidimensional item response theory models and change‐point analysis to add cut‐score and discontinuity parameters that embody these substantive requirements. This modeling strategy allows us to place the examinees in the appropriate LP level and simultaneously to model the hypothesized requirement relations. Results from a simulation study indicate that the proposed change‐point SCM recovers the generating parameters well. When the hypothesized requirement relations are ignored, the model fit tends to become worse, and the model parameters appear to be more biased. Moreover, the proposed model can be used to find validity evidence to support or disprove initial theoretical hypothesized links in the LP through empirical data. We illustrate the technique with data from an assessment system designed to measure student progress in a middle‐school statistics and modeling curriculum.

Linking functions adjust for differences between identifiability restrictions used in different instances of the estimation of item response model parameters. These adjustments are necessary when results from those instances are to be compared. As linking functions are derived from estimated item response model parameters, parameter estimation error automatically propagates into linking error. This article explores an optimal linking design approach in which mixed‐integer programming is used to select linking items to minimize linking error. Results indicate that the method holds promise for selection of linking items.

The early detection of item drift is an important issue for frequently administered testing programs because items are reused over time. Unfortunately, operational data tend to be very sparse and do not lend themselves to frequent monitoring analyses, particularly for on‐demand testing. Building on existing residual analyses, the authors propose an item index that requires only moderate‐to‐small sample sizes to form data for time‐series analysis. Asymptotic results are presented to facilitate statistical significance tests. The authors show that the proposed index combined with time‐series techniques may be useful in detecting and predicting item drift. Most important, this index is related to a well‐known differential item functioning analysis so that a meaningful effect size can be proposed for item drift detection.

Response accuracy and response time data can be analyzed with a joint model to measure ability and speed of working, while accounting for relationships between item and person characteristics. In this study, person‐fit statistics are proposed for joint models to detect aberrant response accuracy and/or response time patterns. The person‐fit tests take the correlation between ability and speed into account, as well as the correlation between item characteristics. They are posited as Bayesian significance tests, which have the advantage that the extremeness of a test statistic value is quantified by a posterior probability. The person‐fit tests can be computed as by‐products of a Markov chain Monte Carlo algorithm. Simulation studies were conducted in order to evaluate their performance. For all person‐fit tests, the simulation studies showed good detection rates in identifying aberrant patterns. A real data example is given to illustrate the person‐fit statistics for the evaluation of the joint model.

Test assembly is the process of selecting items from an item pool to form one or more new test forms. Often new test forms are constructed to be parallel with an existing (or an ideal) test. Within the context of item response theory, the test information function (TIF) or the test characteristic curve (TCC) are commonly used as statistical targets to obtain this parallelism. In a recent study, Ali and van Rijn proposed combining the TIF and TCC as statistical targets, rather than using only a single statistical target. In this article, we propose two new methods using this combined approach, and compare these methods with single statistical targets for the assembly of mixed‐format tests. In addition, we introduce new criteria to evaluate the parallelism of multiple forms. The results show that single statistical targets can be problematic, while the combined targets perform better, especially in situations with increasing numbers of polytomous items. Implications of using the combined target are discussed.

As noted by Fremer and Olson, analysis of answer changes is often used to investigate testing irregularities because the analysis is readily performed and has proven its value in practice. Researchers such as Belov, Sinharay and Johnson, van der Linden and Jeon, van der Linden and Lewis, and Wollack, Cohen, and Eckerly have suggested several statistics for detection of aberrant answer changes. This article suggests a new statistic that is based on the likelihood ratio test. An advantage of the new statistic is that it follows the standard normal distribution under the null hypothesis of no aberrant answer changes. It is demonstrated in a detailed simulation study that the Type I error rate of the new statistic is very close to the nominal level and the power of the new statistic is satisfactory in comparison to those of several existing statistics for detecting aberrant answer changes. The new statistic and several existing statistics were shown to provide useful information for a real data set. Given the increasing interest in analysis of answer changes, the new statistic promises to be useful to measurement practitioners.

The focus of this article is on scale score transformations that can be used to stabilize conditional standard errors of measurement (CSEMs). Three transformations for stabilizing the estimated CSEMs are reviewed, including the traditional arcsine transformation, a recently developed general variance stabilization transformation, and a new method proposed in this article involving cubic transformations. Two examples are provided and the three scale score transformations are compared in terms of how well they stabilize CSEMs estimated from compound binomial and item response theory (IRT) models. Advantages of the cubic transformation are demonstrated with respect to CSEM stabilization and other scaling criteria (e.g., scale score distributions that are more symmetric).

The development of cognitive diagnostic‐computerized adaptive testing (CD‐CAT) has provided a new perspective for gaining information about examinees' mastery on a set of cognitive attributes. This study proposes a new item selection method within the framework of dual‐objective CD‐CAT that simultaneously addresses examinees' attribute mastery status and overall test performance. The new procedure is based on the Jensen‐Shannon (JS) divergence, a symmetrized version of the Kullback‐Leibler divergence. We show that the JS divergence resolves the noncomparability problem of the dual information index and has close relationships with Shannon entropy, mutual information, and Fisher information. The performance of the JS divergence is evaluated in simulation studies in comparison with the methods available in the literature. Results suggest that the JS divergence achieves parallel or more precise recovery of latent trait variables compared to the existing methods and maintains practical advantages in computation and item pool usage.

In equating, when common items are internal and scoring is conducted in terms of the number of correct items, some pairs of total scores (X) and common‐item scores (V) can never be observed in a bivariate distribution of X and V; these pairs are called structural zeros. This simulation study examines how equating results compare for different approaches to handling structural zeros. The study considers four approaches: the no‐smoothing, unique‐common, total‐common, and adjusted total‐common approaches. This study led to four main findings: (1) the total‐common approach generally had the worst results; (2) for relatively small effect sizes, the unique‐common approach generally had the smallest overall error; (3) for relatively large effect sizes, the adjusted total‐common approach generally had the smallest overall error; and, (4) if sole interest focuses on reducing bias only, the adjusted total‐common approach was generally preferable. These results suggest that, when common items are internal and log‐linear bivariate presmoothing is performed, structural zeros should be maintained, even if there is some loss in the moment preservation property.

Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors model) to account for the effect of collaboration on individual learning, including having the help of a partner and getting to observe/help a partner. We find evidence that models that include these collaborative features have a better fit than the original models for performance data and that learning rates estimated using the extended models provide insights into how collaboration benefits individual students’ learning outcomes.

Collaborative problem solving (CPS) assessment is a new academic research field with a number of educational implications. In 2015, the Programme for International Student Assessment (PISA) assessed CPS with a computer‐simulated human‐agent (H‐A) approach that claimed to measure 12 individual CPS skills for the first time. After reviewing the approach, we conceptually embedded a computer‐based collaborative behavior assessment (COLBAS) into the overarching PISA 2015 CPS approach. COLBAS is an H‐A CPS assessment instrument that can be used to measure certain aspects of CPS. In addition, we applied a model‐based cluster analysis to the embedded COLBAS aspects. The analysis revealed three types of student collaborator profiles that differed in cognitive performance and motivation: (a) passive low‐performing (non‐)collaborators, (b) active high‐performing collaborators, and (c) compensating collaborators.

This article summarizes assessment of cognitive skills through collaborative tasks, using field test results from the Assessment and Teaching of 21st Century Skills (ATC21S) project. This project, sponsored by Cisco, Intel, and Microsoft, aims to help educators around the world enable students with the skills to succeed in future career and college goals. In this article, ATC21S collaborative assessments focus on the project's “ICT Literacy—Learning in digital networks” learning progression. The article includes a description of the development of the learning progression, as well as examples and the logic behind the instrument construction. Assessments took place in random pairs of students in a demonstration digital environment. Modeling of results employed unidimensional and multidimensional item response models, with and without random effects for groups. The results indicated that, based on this data set, the models that take group into consideration in both the unidimensional and the multidimensional analyses fit better. However, the group‐level variances were substantially higher than the individual‐level variances. This indicates that a total individual estimate of group plus individual is likely a more informative estimate than individual alone but also that the performances of the pairs dominated the performances of the individuals. Implications are discussed in the results and conclusions.

This article addresses performance assessments that involve collaboration among students. We apply the Hawkes process to infer whether the actions of one student are associated with increased probability of further actions by his/her partner(s) in the near future. This leads to an intuitive notion of engagement among collaborators, and we consider a model‐based index that can be used to quantify this notion. The approach is illustrated using a simulation‐based task designed for science education, in which pairs of collaborators interact using online chat. We also consider the empirical relationship between chat engagement and task performance, finding that less engaged collaborators were less likely to revise their responses after being given an opportunity to share their work with their partner.

Simulations and games offer interactive tasks that can elicit rich data, providing evidence of complex skills that are difficult to measure with more conventional items and tests. However, one notable challenge in using such technologies is making sense of the data generated in order to make claims about individuals or groups. This article presents a novel methodological approach that uses the process data and performance outcomes from a simulation‐based collaborative science assessment to explore the propensities of dyads to interact in accordance with certain interaction patterns. Further exploratory analyses examine how the approach can be used to answer important questions in collaboration research regarding gender and cultural differences in collaborative behavior and how interaction patterns relate to performance outcomes.

In order to understand potential applications of collaborative problem‐solving (CPS) assessment tasks, it is necessary to examine empirically the multifaceted student performance that may be distributed across collaboration methods and purposes of the assessment. Ideally, each student should be matched with various types of group members and must apply the skills in varied contexts and tasks. One solution to these assessment demands is to use computer‐based (virtual) agents to serve as the collaborators in the interactions with students. This article proposes a human‐to‐agent (H‐A) approach for formative CPS assessment and describes an international pilot study aimed to provide preliminary empirical findings on the use of H‐A CPS assessment to inform collaborative learning. Overall, the findings showed promise in terms of using a H‐A CPS assessment task as a formative tool for structuring effective groups in the context of CPS online learning.

Collaborative problem solving is a complex skill set that draws on social and cognitive factors. The construct remains in its infancy due to lack of empirical evidence that can be drawn upon for validation. The differences and similarities between two large‐scale initiatives that reflect this state of the art, in terms of underlying assumptions about the construct and approach to task development, are outlined. The goal is to clarify how definitions of the nature of the construct impact the approach to design of assessment tasks. Illustrations of two different approaches to the development of a task designed to elicit behaviors that manifest the construct are presented. The method highlights the degree to which these approaches might constrain a comprehensive assessment of the construct.

Background:

Evolving technology and scientific advancement have increased the chances of survival of the extremely premature baby; however, such survival can be associated with some severe long-term morbidities.

Research question:

The research investigates the caregiving and ethical dilemmas faced by neonatal nurses when caring for extremely premature babies (defined as ≤24 weeks’ gestation). This article explores the issues arising for neonatal nurses when they considered the philosophical question of ‘what if it was me and my baby’, or what they believed they would do in the hypothetical situation of going into premature labour and delivering an extremely premature baby.

Participants:

Data were collected via a questionnaire to Australian neonatal nurses and semi-structured interviews with 24 neonatal nurses in New South Wales, Australia.

Ethical considerations:

Relevant ethical approvals have been obtained by the researchers.

Findings:

A qualitative approach was used to analyse the data. The theme ‘imagined futures’ was generated which comprised three sub-themes: ‘choice is important’, ‘not subjecting their own baby to treatment’ and ‘nurses and outcome predictions’. The results offer an important and unique understanding into the perceptions of nursing staff who care for extremely premature babies and their family, see them go home and witness their evolving outcomes. The results show that previous clinical and personal experiences led the nurses in the study to choose to have the belief that if in a similar situation, they would choose not to have their own baby resuscitated and subjected to the very treatment that they provide to other babies.

Conclusion:

The theme ‘imagined futures’ offers an overall understanding of how neonatal nurses imagine what the life of the extremely premature baby and his or her family will be like after discharge from neonatal intensive care. The nurses’ past experience has led them to believe that they would not want this life for themselves and their baby, if they were to deliver at 24 weeks’ gestation or less.

The purpose of this scoping review was to survey the most recent (5 years) acute care, community health, and mental health nursing workload literature to understand themes and research avenues that may be applicable to school nursing workload research. The search for empirical and nonempirical literature was conducted using search engines such as Google Scholar, PubMed, CINAHL, and Medline. Twenty-nine empirical studies and nine nonempirical articles were selected for inclusion. Themes that emerged consistent with school nurse practice include patient classification systems, environmental factors, assistive personnel, missed nursing care, and nurse satisfaction. School nursing is a public health discipline and population studies are an inherent research priority but may overlook workload variables at the clinical level. School nurses need a consistent method of population assessment, as well as evaluation of appropriate use of assistive personnel and school environment factors. Assessment of tasks not directly related to student care and professional development must also be considered in total workload.

Background:

The research question for this study was as follows: Is the Code of Ethics for Nurses in Italy (Code) a valid or useful decision-making instrument for nurses faced with ethical problems in their daily clinical practice?

Method:

Focus groups were conducted to analyze specific ethical problems through 11 case studies. The analysis was conducted using sections of the Code as well as other relevant documents. Each focus group had a specific theme and nurses participated freely in the discussions according to their respective clinical competencies.

Ethical considerations:

The executive administrative committee of the local nursing licensing council provided approval for conducting this project. Measures were taken to protect the confidentiality of consenting participants.

Findings:

The answer to the research question posed for this investigation was predominantly positive. Many sections of the Code were useful for discussion and identifying possible solutions for the ethical problems presented in the 11 cases.

Conclusion:

We concluded that the Code of Ethics for Nurses in Italy can be a valuable aid in daily practice in most clinical situations that can give rise to ethical problems.

Introduction: African American (AA) high school-age girls are more likely to have had sex before age 13 years and have higher rates of all sexually transmitted infections. Cognition and religion/spirituality are associated with adolescent sexuality, therefore, the purpose of this study was to identify cognitive and religious substrates of AA girls’ risky sexual behaviors. Method: A descriptive study was conducted with 65 AA girls aged 15 to 20 years using computerized questionnaires and cognitive function tasks. Results: Average age was 17.8 ± 1.9 years and average sexual initiation age was 15.5 ± 2.6 years. Overall, 57.6% reported a history of vaginal sex. Girls who reported low/moderate religious importance were significantly younger at vaginal sex initiation than girls for whom religion was very/extremely important. Girls who attended church infrequently reported significantly more sexual partners. Implications: Health care providers can use these findings to deliver culturally congruent health care by assessing and addressing these psychosocial factors in this population.

The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

A lack of knowledge and skills in pediatric palliative care may create hesitation in caring for children with serious life-threatening conditions and their families. Our research examined the effectiveness of pediatric palliative care training for pediatric clinicians. A pretest–posttest study provided educational training in pediatric palliative care to pediatric clinicians and used a pretest and a posttest to assess outcomes. Fifty pediatric clinicians attended this research with 83.3% response rate. After training, participants reported significantly increased confidence in a variety of areas, including providing emotional support to clinicians, personal knowledge, skills, and communication; ethical and legal concerns; and providing emotional support to dying children and their families. Results showed a significant main effect of training on confidence levels (p < .000). This suggests that education can effectively boost pediatric clinicians’ confidence regarding providing pediatric palliative care and therefore should regularly be provided to clinicians.

The purpose of this systemic literature review is to unveil a greater understanding of Physical Activity in Latinas. We used PubMed and PsycInfo databases to search for articles published between 1991 and August 2016, examining physical activity and its correlates exclusively in adult Latinas. Only 21 primary studies met the inclusion criteria. Although physical activity derives from body movements in various domains, the leisure domain led the focus of research interest. The use of self-reports, cross-sectional design, and the ecological framework predominated. Only one study included examining the occupational domain, albeit as disaggregate in studies with a general physical activity approach. Occupational physical activity showed negative associations with education and acculturation. Positive associations were found between leisure-time physical activity, education, acculturation, and perceived health status. Despite the limited evidence to reach overall conclusions, this review uncovered methodological challenges and opportunities to advance knowledge about physical activity in Latinas.

Workplace bullying has been experienced by 27% to 80% of nurses who have participated in studies. Bullying behaviors negatively impact the health of nurses. This study examined whether nurses’ resilience had an impact on the effects of bullying on the nurse’s health. This cross-sectional descriptive study surveyed licensed registered nurses in one state. The sample (N = 345) was predominately female (89%) and Caucasian (84%), with an average age of 46.6 years. In this sample, 40% of nurses were bullied. Higher incidence of bullying was associated with lower physical health scores (p = .002) and lower mental health scores (p = .036). Nurses who are bullied at work experience lower physical and mental health, which can decrease the nurses’ quality of life and impede their ability to deliver safe, effective patient care.

Provision of anticipatory guidance to parents during well-child visits is a defining component of pediatric primary care. Using nationally representative data from the 2000 U.S. National Survey of Early Childhood Health, this study addressed the questions of whether, how, and what mothers of children below age 3 with different ethnic/racial background could benefit from anticipatory guidance. Structural equation modeling results showed that across ethnic/racial groups, guidance was directly associated with decreased maternal stress and directly and indirectly associated with effective parenting practices in child development, discipline, and safety. Moreover, guidance was consistently associated with decreased stress (effect size = .159/.123/.252) and increased effective parenting practices in child development (total effect = .090/.179/.296), discipline (total effect = .079/.138/.111), and safety (total effect = .186/.204/.229) in White/Black/Latino groups, respectively. Finally, whereas guidance was directly associated with child safety in all groups, it was indirectly contributed to child development and discipline via maternal stress in the Latino group only.

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce entire reliability estimates with smaller sample requirements. The free CBID software can be accessed through a website and used by clinical investigators in new instrument development. Demonstrations are presented of the three approaches using the CBID software: (a) traditional confirmatory factor analysis (CFA), (b) Bayesian CFA using flat uninformative prior, and (c) Bayesian CFA using content expert data (informative prior). Outcomes of usability testing demonstrate the need to make the user-friendly, free CBID software available to interdisciplinary researchers. CBID has the potential to be a new and expeditious method for instrument development, adding to our current measurement toolbox. This allows for the development of new instruments for measuring determinants of health in smaller diverse populations or populations of rare diseases.

The current study examines whether person–job fit moderates the relationship between transformational leadership and work engagement. Data were collected using cross-sectional design from 224 (15 male and 209 female) hospital nurses. Participants completed measures of transformational leadership, person–job fit, and work engagement. Moderated multiple regression results showed that transformational leadership had a significant positive predictive relationship with work engagement, and person–job fit had a significant positive predictive relationship with work engagement. Simple slope analysis showed that person–job fit moderated the relationship between transformational leadership and work engagement such that transformational leadership was more positively related to work engagement for nurses with high person–job fit compared with those with low person–job fit. Thus, all the hypotheses were confirmed. The findings were discussed, and suggestions for future research were offered.

Many individuals, including dementia caregivers, use blogs to share their experiences. These blogs contain rich narratives representing an untapped resource for understanding the psychosocial impact of caring for a person with dementia at the family level. The present study used blogs written by caregivers of persons with dementia to explore how these individuals leveraged this medium as part of the caregiving experience. Blogs written by self-identified informal caregivers of persons with dementia were identified using a systematic search method, and data were analyzed using a qualitative thematic analysis. Four themes emerged from the narratives: social support through communication and engagement, information gathering and seeking, reminiscing and legacy building, and altruism. By understanding the ways in which individuals providing care for persons with dementia use social media as part of the caregiving experience, family nurses can develop interventions and services aimed at improving caregiver burden and quality of life.

Providing timely palliative and end-of-life care (PC/EOL) information to parents of children with a serious illness is a national health care priority. The goals of this study were to determine feasibility, acceptability, and parent responses related to a PC/EOL communication intervention, titled "Communication Plan: Early through End of Life (COMPLETE)" to parents of children with a brain tumor. The study was a 2-site prospective, single-group pilot study targeting parents’ stress and coping outcomes. The sample included 13 parents of 11 children (ie, 11 families). During the first 6 months postdiagnosis, we evaluated parent outcomes at 4 time points (baseline and 3 post-sessions). Our findings included significant decline in decision regret (P = .0089); strong, significantly increased hope (P ≤ .0001); and significantly decreased uncertainty (P = .04). Over time, more than half of the parents (61.5%) preferred to receive information about their child’s current condition and PC/EOL options. Our findings provide evidence to suggest that the COMPLETE intervention is feasible and acceptable and produces promising effects on 3 parent outcomes (ie, decision regret, hope, and uncertainty) in parents of children with a brain tumor. Further research is indicated to evaluate COMPLETE with a larger sample of parents of children with cancer and with a control group.

In this study, we describe what factors influence the observed score correlation between an (external) anchor test and a total test. We show that the anchor to full‐test observed score correlation is based on two components: the true score correlation between the anchor and total test, and the reliability of the anchor test. Findings using an analytical approach suggest that making an anchor test a miditest does not generally maximize the anchor to total test correlation. Results are discussed in the context of what conditions maximize the correlations between the anchor and total test.

This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale‐free networks (e.g., a word adjacency model) from which typical network features, such as the in‐/out‐degrees, clustering coefficient (CC), and dynamic networks, were obtained. The CDES integrates the classical concepts of network feature representation and essay score series variation. Several experiments indicated that the network measures different essay qualities and can be clearly demonstrated to develop complex networks for autoscoring tasks. The average agreement of the CDES and human rater scores was 86.5%, and the average Pearson correlation was .77. The results indicate that the CDES produced functional complex systems and autoscored Chinese essays in a method consistent with human raters. Our research suggests potential applications in other areas of educational assessment.

In observed‐score equipercentile equating, the goal is to make scores on two scales or tests measuring the same construct comparable by matching the percentiles of the respective score distributions. If the tests consist of different items with multiple categories for each item, a suitable model for the responses is a polytomous item response theory (IRT) model. The parameters from such a model can be utilized to derive the score probabilities for the tests and these score probabilities may then be used in observed‐score equating. In this study, the asymptotic standard errors of observed‐score equating using score probability vectors from polytomous IRT models are derived using the delta method. The results are applied to the equivalent groups design and the nonequivalent groups design with either chain equating or poststratification equating within the framework of kernel equating. The derivations are presented in a general form and specific formulas for the graded response model and the generalized partial credit model are provided. The asymptotic standard errors are accurate under several simulation conditions relating to sample size, distributional misspecification and, for the nonequivalent groups design, anchor test length.

This article introduces the Diagnostic Profiles (DP) standard setting method for setting a performance standard on a test developed from a cognitive diagnostic model (CDM), the outcome of which is a profile of mastered and not‐mastered skills or attributes rather than a single test score. In the DP method, the key judgment task for panelists is a decision on whether or not individual cognitive skill profiles meet the performance standard. A randomized experiment was carried out in which secondary mathematics teachers were randomly assigned to either the DP method or the modified Angoff method. The standard setting methods were applied to a test of student readiness to enter high school algebra (Algebra I). While the DP profile judgments were perceived to be more difficult than the Angoff item judgments, there was a high degree of agreement among the panelists for most of the profiles. In order to compare the methods, cut scores were generated from the DP method. The results of the DP group were comparable to the Angoff group, with less cut score variability in the DP group. The DP method shows promise for testing situations in which diagnostic information is needed about examinees and where that information needs to be linked to a performance standard.

For a certification, licensure, or placement exam, allowing examinees to take multiple attempts at the test could effectively change the pass rate. Change in the pass rate can occur without any change in the underlying latent trait, and can be an artifact of multiple attempts and imperfect reliability of the test. By deriving formulae to compute the pass rate under two definitions, this article provides tools for testing practitioners to compute and evaluate the change in the expected pass rate when a certain (maximum) number of attempts are allowed without any change in the latent trait. This article also includes a simulation study that considers change in ability and differential motivation of examinees to retake the test. Results indicate that the general trend shown by the analytical results is maintained—that is, the marginal expected pass rate increases with more attempts when the testing volume is defined as the total number of test takers, and decreases with more attempts when the testing volume is defined as the total number of test attempts.

This study adapted an effect size measure used for studying differential item functioning (DIF) in unidimensional tests and extended the measure to multidimensional tests. Two effect size measures were considered in a multidimensional item response theory model: signed weighted P‐difference and unsigned weighted P‐difference. The performance of the effect size measures was investigated under various simulation conditions including different sample sizes and DIF magnitudes. As another way of studying DIF, the χ2 difference test was included to compare the result of statistical significance (statistical tests) with that of practical significance (effect size measures). The adequacy of existing effect size criteria used in unidimensional tests was also evaluated. Both effect size measures worked well in estimating true effect sizes, identifying DIF types, and classifying effect size categories. Finally, a real data analysis was conducted to support the simulation results.

The purpose of this descriptive comparative study is to compare the levels of decisional involvement of staff nurses between one Midwestern health care system in the United States with a nongovernmental University hospital in Turkey. The Decisional Involvement Scale was used for data collection. U.S. (n = 163) and Turkey (n = 50) staff nurses were included in the study. Both samples preferred more decisional involvement than they currently experienced. However, Turkish nurses experienced and preferred lower levels of decisional involvement than the U.S. sample. Shared governance structures may be a strategy used to enhance staff nurse decisional involvement.

The purpose was to examine the effects of auricular acupressure to relieve constipation in patients with breast cancer who were undergoing chemotherapy. Participants were 52 patients with breast cancer receiving chemotherapy at E University Hospital, Seoul, Korea, randomized into two groups of equal size. For the experimental group, auricular acupressure was applied to seven auricular acupoints for 6 weeks using vaccaria seeds, whereas the control group received the usual care. Constipation-assessment scores of the experimental group were significantly lower compared with the control group (p < .001). Stool-form scores of the experimental group were significantly higher compared with the control group (p = .003). Patient Assessment of Constipation–Quality of Life scores of the experimental group were significantly lower compared with the control group (p < .001). Auricular acupressure was effective at relieving constipation in patients with breast cancer receiving chemotherapy. Auricular acupressure was also a safe and acceptable nursing intervention.

Although following a low-sodium diet (LSD) for heart failure (HF) has been recommended for decades, little is known about factors related to long-term patient adherence. The purposes of this study were to (a) compare sodium intake and factors affecting adherence in a long-term adherent group and in a non-adherent group and (b) examine predictors of membership in the long-term adherent group. Patients with HF (N = 74) collected 24-hr urine samples and completed the Dietary Sodium Restriction Questionnaire and the Patient Health Questionnaire-9. Long-term adherence was determined using the Stage of Dietary Behavior Change Scale. The long-term adherent group had lower sodium intake (3,086 mg vs. 4,135 mg, p = .01) and perceived more benefits from LSD than the non-adherent group. Only positive attitudes toward LSD predicted membership in the long-term adherence group (odds ratio [OR] = 1.18, p = .005). Interventions focused on enhancing positive perceptions of the benefits of an LSD may improve long-term dietary adherence in patients with HF.

This article describes the analysis of regression-discontinuity designs (RDDs) using the R packages rdd, rdrobust, and rddtools. We discuss similarities and differences between these packages and provide directions on how to use them effectively. We use real data from the Carolina Abecedarian Project to show how an analysis of an RDD can be performed from start to finish.

Path models with observed composites based on multiple items (e.g., mean or sum score of the items) are commonly used to test interaction effects. Under this practice, researchers generally assume that the observed composites are measured without errors. In this study, we reviewed and evaluated two alternative methods within the structural equation modeling (SEM) framework, namely, the reliability-adjusted product indicator (RAPI) method and the latent moderated structural equations (LMS) method, which can both flexibly take into account measurement errors. Results showed that both these methods generally produced unbiased estimates of the interaction effects. On the other hand, the path model—without considering measurement errors—led to substantial bias and a low confidence interval coverage rate of nonzero interaction effects. Other findings and implications for future studies are discussed.

Aim:

The aim of the study was to assess the effectiveness of exercise training on depression, anxiety, physical capacity and sympatho-vagal balance in patients after myocardial infarction and compare differences between men and women.

Methods:

Thirty-two men aged 56.3±7.6 years and 30 women aged 59.2±8.1 years following myocardial infarction underwent an 8-week training programme consisting of 24 interval trainings on cycloergometer, three times a week. Before and after completing the training programme, patients underwent: depression intensity assessment with the Beck depression inventory; anxiety assessment with the state–trait anxiety inventory; a symptom-limited exercise test during which were analysed: maximal workload, duration, double product.

Results:

In women the initial depression intensity was higher than in men, and decreased significantly after the training programme (14.8±8.7 vs. 10.5±8.8; P<0.01). The anxiety manifestation for state anxiety in women was higher than in men and decreased significantly after the training programme (45.7±9.7 vs. 40.8±0.3; P<0.01). Of note, no depression and anxiety manifestation was found in men. Physical capacity improved significantly after the training programme in all groups, and separately in men and in women. Moreover, an 8-week training programme favourably modified the parasympathetic tone.

Conclusions:

Participating in the exercise training programme contributed beneficially to a decrease in depression and anxiety manifestations in women post-myocardial infarction. Neither depression nor anxiety changed significantly in men. The impact of exercise training on physical capacity and autonomic balance was beneficial and comparable between men and women.

In psychometric practice, the parameter estimates of a standard item-response theory (IRT) model can become biased when item-response data, of persons’ individual responses to test items, contain outliers relative to the model. Also, the manual removal of outliers can be a time-consuming and difficult task. Besides, removing outliers leads to data information loss in parameter estimation. To address these concerns, a Bayesian IRT model that includes person and latent item-response outlier parameters, in addition to person ability and item parameters, is proposed and illustrated, and is defined by item characteristic curves (ICCs) that are each specified by a robust, Student’s t-distribution function. The outlier parameters and the robust ICCs enable the model to automatically identify item-response outliers, and to make estimates of the person ability and item parameters more robust to outliers. Hence, under this IRT model, it is unnecessary to remove outliers from the data analysis. Our IRT model is illustrated through the analysis of two data sets, involving dichotomous- and polytomous-response items, respectively.

Purpose: To improve health and reduce health disparities, research partnerships with American Indian and Alaska Native communities should build on existing traditions and strengths. The overall goal of this pilot project was to clarify the needs of tribal community members and determine strengths and resources available to the community. Design: Community-based participatory research was the approach used to obtain community input. Data collection methods included, Photovoice (n = 16), digital storytelling (n = 4), and community capacity surveys (n = 128). Results: Quantitative and qualitative findings supported the need to (1) address youth suicide and substance abuse on the reservation and (2) use preexisting resources available in the community. The results led to the development of a strengths-based intervention incorporating the Gathering of Native American’s curriculum. Conclusion: Integral to the development, implementation, and sustainability of the intervention was the truly reciprocal relationship developed between community and university partners.