Parenting can be very challenging, especially when raising a disabled child. Children with disabilities require more supports and are more likely to be abused. The parent-child relationship is an important factor in ensuring child welfare. Little research has focused on identifying the impact of parenting characteristics on raising a child with a disability. The purpose of this study was to examine whether parenting style, parenting competence, and parenting stress were predictors of parent-child relationship quality in parents of children with disabilities ages 3 to 12 years. This study was quantitative and used multiple linear regression to identify predictor variables of the quality of the parent-child relationship. A convenience sample of 244 parents identified through a Qualtrics participant pool completed online surveys. Minuchin's structural family theory was used to guide this research and identify how challenges, such as raising a child with a disability, can cause distress when families are unable to adapt and parents are unable to maintain authority. Parenting factors were assessed using the Parenting Stress Index-4 Competence subscale, the Parenting Stress Index-4 SF, and the Parenting Styles and Dimensions Questionnaire. The quality of the parent-child relationship was assessed using the Parent-Child Relationship Inventory. The results of this study indicated that all parenting factors examined were significant predictors of the parent-child relationship quality. Age of the child was not a predictor. These findings have positive social change implications and can be used to increase practitioner knowledge of the impact of these parenting characteristics on parent-child relationship quality. Modification of treatment models could improve parenting behaviors, reduce parental stress and incidents of child abuse, and assess for the most conducive parenting styles for raising a disabled child.

The purpose of the study is to fill the gap in the literature regarding sibling bereavement. This study explored how sibling bereavement from unnatural causes of death affects surviving siblings. Bereavement affects millions of Americans every year. Most grieve naturally, but some experience complicated grief or depression. Many studies address parental and spousal bereavement, but few focus on sibling bereavement. This study fills that gap in the literature so that mental health care professionals and the general public understand what bereaved siblings experience after the death of a sibling. The study was a phenomenological study using social constructivism as a theoretical lens to explore how sibling bereavement affects surviving siblings. Open-ended interviews were collected from 10 bereaved siblings. Those interviews were then transcribed and categorized using a 7 step process to review and organize all relevant statements. Results of this study shows that sibling bereavement can be a life changing event for surviving siblings affecting all aspects of life and leaving unanswered questions and feelings of guilt. Participants also state they felt overlooked after the death leading to delayed grief. Participants concluded that sibling grief is subjective, so any treatment plan should be catered to the individual based on their relationship to the deceased sibling and the role the sibling played. This study can bring about positive social change by helping mental health care workers understand sibling bereavement better so that they may help those suffering from complicated grief following the loss of a sibling.

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In recent years much attention has been given to the application of computer technology to psychometric methods, but researchers have concentrated on adapting traditional methods of psychological testing to the new technology instead of utilizing it to develop innovative methods of assessment. The purpose of this study was to determine whether a computerized assessment battery designed to evaluate cognitive functioning and attention could demonstrate reliability and validity. The Computerized Cognitive Assessment Battery (CCAB) was developed according to the PASS Model of Cognitive Functioning and administered via a Macintosh computer and test results included response style variables (mouse movement and response time). Children having attention problems (N = 25) in grades three through five were compared to a random group of children (N = 29). On the newly developed CCAB, the majority of the sectional variables displayed significant intercorrelations (p {dollar}<{dollar}.01) indicating internal consistency of this measure. The reliability of the Sequential component of the CCPT was found to be.90 for Scale 1 and.83 for Scale 2. No relationship was found between the covert measures on the CCAB and Conners' parent and teacher rating scales. Evidence indicated that the covert measures are related to the Planning Factor. The attention measure of the CCPT was able to discriminate between the two groups as well as the Gordon Diagnostic System. The total CCAB was able to predict group membership with one hundred percent accuracy using the classification results of the discriminant function analysis. Consistent with the prediction of the PASS model, the Attention component (CCPT) was the only area in which the scores of the two groups differed. The present study demonstrated the feasibility and practicality of a fully-computerized cognitive assessment battery to aid in the assessment process. The results of this research indicate that the potential exists to evaluate cognitive functioning by a computer-based assessment system. Not only could such a test provide an index of intellectual ability based on a well researched and extensively used IQ test (Raven), it could also yield a great deal of information related to meta-cognitive skills, self-regulatory behavior, processing styles and compensatory mechanisms.

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No description available.

The Federal Aviation Administration (FAA) has experienced decreased return on investment caused by hiring too many air traffic controller specialists (ATCSs) who performed poorly in field training, thus failing to become certified professional controllers (CPCs). Based on Schmidt and Hunter's theory of job performance and biodata theory, this quantitative, archival study examined whether factors of cognitive ability and biodata could predict job performance status of 2 generations of ATCSs, poststrike (PS) and next generation (NG) controllers. For each generation of controllers, binary logistic regression analysis was conducted to determine if any of the independent variables---transmuted composite (TMC) score for PS controllers, Air Traffic and Selection and Training (AT-SAT) test score for NG controllers, average of high school arithmetic/math letter grade, overall high school average letter grade, self-estimation of time to become fully effective in the ATCS role, self-estimation of percentile ranking in the FAA program relative to the class, size of neighborhood raised, or socioeconomic status---are significant predictors of job performance status for controllers as measured by whether they pass the field OJT (i.e., certified or still in training, or failed certification or left training). The regression results for the PS and NG controllers were found to be statistically significant (chi2 (23) = 68.377, p < .001) and (chi 2 (17) = 99.496, p < .001), respectively. Findings that overall high school grade point average and socioeconomic status significantly predicted ATCS job performance for both PS and NG controllers could influence the FAA's use of revised biodata to better predict ATCS job performance. Further research should include studies of socioeconomic status, gender, and race to address new evidence that the AT-SAT has adverse impact.

No description available.

Background: In health status assessment, patient-reported outcome (PRO) measures are tools used to elicit important and measurable information from patients to better understand the impact of health conditions on their lives. Such impacts are considered latent constructs, or variables that cannot be observed or measured directly. Instruments intended to assess latent constructs must satisfy certain development, psychometric, and scaling standards through the generation of both qualitative and quantitative evidence to demonstrate the adequacy of its measurement properties. Health-related quality of life (HRQOL), or the subjective perception of health, is a core concept within the field of PROs. The Short Form 36 (SF-36) is one of the most commonly used PROs used to assess health-related quality of life (HRQOL).Objectives: To provide a better understanding of the performance and dimensionality of the SF-36 version 2 in a cross-sectional sample of patients with multiple sclerosis (MS) on an item, subscale, and higher-order factor structure level using different measurement methods grounded in classical test theory (CTT), factor analysis, and item response theory (IRT).Methods: This was a post hoc analysis of a cross-sectional dataset. Patients with MS were recruited to participate in an online survey asking a variety of questions related to their health and treatment seeking behaviors. The SF-36 was one of the questionnaires included in the survey. Items and individual subscales were evaluated using a multi-trait/multi-item correlation matrix to assess item-to-subscale relationships, including item discriminant validity with other subscales. Unidimensionality for select SF-36 subscales was assessed through confirmatory factor analysis (CFA). Internal consistency reliability (Cronbach's alpha) was evaluated for each subscale. Patient-reported disability, depression, and current symptom exacerbation status were evaluated relative to SF-36 subscale scores to assess convergent validity, discriminant validity, and known-groups validity. Higher-order factor models of the SF-36 were tested to evaluate dimensionality of the instrument, including a two-factor second-order factor model, a bifactor model, and a statistical comparison between the bifactor model and its corresponding nested model. Unidimensionality was further evaluated through the use of graded response IRT models. The relative fit of traditional versus discrimination-constrained models was tested using a -2 loglikelihood ratio test, followed by an evaluation of item-level properties for fit (S-X² statistics), local dependence, and further assessment of model parameters (discrimination parameters, location parameters, option response functions, and test information curves). Person location parameters were also estimated to compare scale information to the location of patients along the latent construct. Results: A total of 1,052 respondents completed the survey. Unidimensionality of individual subscales evaluated via CFA all had confirmatory fit indices (CFI)>0.90, butroot mean square error of approximation [RMSEA] values all exceeded 0.08. All IRT graded response models showed a statistically significant improvement in model fit when item discrimination was freely estimated. Each subscale from the IRT models had at least one mis-fitting item across all unidimensional scales tested (S-X² p-value>0.05), and nearly all subscales tested showed item pairs with signs of local dependence. Cronbach's alpha was>0.80 for all subscales except for General Health [GH] (alpha = 0.78). SF-36 subscales most closely related to physical aspects of health status had the strongest relationship to disability status (physical functioning [PF], r = -0.82, and role physical [RP], r = -0.57). Subscales more closely related to mental health had the largest effect sizes between patients with versus without depression (0.88 for mental health [MH] subscale) and the smallest effect sizes between patients reporting currently experiencing versus not experiencing an exacerbation of their symptoms (0.48 for role emotional [RE]subscale). Both CFA and IRT analyses showed lack of compelling evidence supporting unidimensionality upon combining items from the PF, RP, bodily pain [BP], and GH subscales to form the Physical-21, and upon combining items from the VT, role emotional (RE), social functioning (SF), and MH subscales to form the Mental-14. Higher-order factor models showed good model fit, with CFI>0.90 in all cases and lower RMSEA values than seen for the individual subscales (0.077 to 0.107). The bifactor model fit significantly better than its nested second-order version, however, the best-fitting (i.e., highest CFI and lowest RMSEA) higher-order factor model was the preliminary first-order model with eight first-order factors consistent with the eight subscales of the SF-36 (CFI=0.996, RMSEA=0.077, X² = 3872.14, p<0.001). Conclusions: The SF-36 version 2 performed well when evaluated within the CTT framework, but both CFA and IRT methods revealed several limitations at the item and factor level across all subscales, due to item wording (i.e., positive versus negative), items not being sufficiently related to its latent construct, and local dependence of items within and across subscales. The appropriateness of equal weighting of responses to produce a single summary score for each subscale, as well as their further aggregation into the Physical Component Summary and Mental Component Summary scores should be reevaluated.

<p> The role of superior human capital in creating and maintaining competitive advantage in the global marketplace is an increasing priority. The problem for business leaders is that despite the purported benefits of superior human capital, contemporary human capital assessments prove incapable of reliably identifying the best employees to hire, promote, or retain. Research into the use of mental models as a means to differentiate expert and novice performers suggested that a simple and reliable means of eliciting and comparing mental models might address this gap. Prior studies following the Structural Dimensional Analysis of Mental Representations (SDA-M) methodology produced reliable and repeatable differentiation of expert and novice performers in professional sports, but had not been applied to the knowledge-work driving today&rsquo;s businesses. The purpose of this quantitative, quasi-experimental study was to evaluate the use of SDA-M in the identification and classification of expert knowledge workers. Specifically, this research sought to answer two specific research questions concerning the use of SDA-M: what is the similarity of expert knowledge worker mental models produced via SDA-M analysis; and, what is the similarity of novice knowledge worker mental models when compared to those of experts? Following the SDA-M methodology, customer support personnel purposefully selected to represent expert (<i>n</i> = 6) and novice (<i>n</i> = 6) performers completed a sorting task designed to elicit and record individual mental models of a critical job task. Experts were selected from senior-level positions with a minimum of five years organizational tenure; novices were selected from entry-level positions with no more than one year of organizational tenure. The individual mental models where then compared using Qsplit to determine the degree of similarity between them. SDA-M analysis proved ineffective at differentiating expert and novice performers on the study stimulus task. Although SDA-M analysis differentiated subjects into two groups, these groups did not align with the ex ante definitions of expert and novice performers. Additional analysis suggested an incompatibility between the job task selected for eliciting mental models and the criteria used in sampling; industry tenure proved a better indicator of expert performance of the study task than organizational tenure. Although the results of this study can only be considered inconclusive, SDA-M analysis demonstrated the ability to clearly differentiate participants based on the structure and consistency of their mental model constructions. Continued research into the use of SDA-M as a means to identify superior human capital is warranted.</p>