Resilience and well-being among persons with spinal cord injury/disorders.

PURPOSE: We examined positive behavioral resources and characteristics that might distinguish resilient personality prototypes among persons with chronic spinal cord injury/disorder (SCID). Positive psychology variables with clear linkages to existing psychological interventions were examined as potential mediators of the resilience-well-being relationship. Research Method and Design: A cross-sectional, self-report study was conducted. Two hundred and ninety-eight consenting members of the Paralyzed Veterans of America (268 male; 236 self-identified as white) provided useable survey data for analysis (including 161 veterans with tetraplegia, 107 with paraplegia, 30 with cauda equina). Cluster analysis of Big Five personality traits identified resilient and nonresilient personality profiles. Tests of mean differences between resilient and nonresilient participants on behavioral resources and characteristics were performed. Path models predicting well-being and health-related quality of life (HRQL) were conducted. RESULTS: One hundred and sixty-three respondents had resilient personality profiles and 135 had nonresilient profiles. Resilient individuals reported significantly more optimal scores on every positive psychology variable, and greater well-being and HRQL than nonresilient respondents. Path models found the relationship of resilience to well-being was explained through its beneficial associations with psychological flexibility, use of personal strengths, meaning in life (MIL), and gratitude. Psychological flexibility also mediated the resilience-HRQL relationship. Cauda equina was significantly associated with higher pain interference and lower HRQL. CONCLUSIONS: Higher gratitude, MIL, use of personal strengths, and psychological flexibility appear to characterize resilience and well-being among persons with chronic SCID. Further studies are needed to understand the impact of pain interference on HRQL among individuals with cauda equina. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An important component to investigating STEM persistence: the development and validation of the science identity (SciID) scale.

Background: Science, technology, engineering, and mathematics (STEM) influence almost every aspect of our daily lives. However, despite the high demand for STEM occupational talent, the STEM pipeline continues leaking, with less than one-sixth of high school students pursuing STEM majors and only 50% of entering STEM college majors matriculating into STEM fields. Science identity has been identified as the most powerful predictor of high school students pursuing an undergraduate STEM major as reported by Chang (Machine learning approach to predicting STEM college major choice, American Educational Research Association (AERA), San Francisco, 2020). Though the construct is gaining lots of attention, it remains largely ill-defined, not operationalized at the high school level, and not based upon traditional identity theory. The purpose of this study was to develop a valid and reliable instrument that measures high school students' science identity, the Science Identity (SciID) Scale. Results: Subject experts and a small group of high school students provided content validation for the proposed scale. Exploratory factor analysis revealed an optimal two-factor solution, reflecting the traditional two-dimensions of identity theory: Exploration and Commitment. Cronbach's alpha revealed good internal consistency for both factors. Finally, structural equation modeling confirmed the convergent validity of the instrument with the external variables of science achievement and science career interest. Furthermore, the divergent validity between science identity and science self-concept was also confirmed. Conclusions: Initial results indicate that the SciID Scale is a valid and reliable instrument that accurately measures a high school student's standing on this construct. The soundness of this instrument will enable policy makers and practitioners to design more effective intervention programs aimed at cultivating high school students' science identity.

Parental Child-feeding in the Context of Child Temperament and Appetitive Traits: Evidence for a Biopsychosocial Process Model of Appetite Self-Regulation and Weight Status.

Pediatric obesity is a serious public health challenge and there is a need for research that synthesizes the various linkages among the child and parental factors that contribute to pediatric overweight and obesity. The main objective of this study was to examine potential mechanisms and pathways that might explain how child temperament is indirectly related to child body composition through appetitive traits and parental child-feeding practices. Participants consisted of 221 children between 4-6 years of age (51% males, mean age = 4.80 years, standard deviation = 0.85) and their parents (90.5% biological mothers, (Mage) = 32.02 years, (SDage) = 6.43) with 71% of the parents being married. Study variables included child temperament (negative affectivity and effortful control), child appetitive traits (food avoidance and food approach), controlling parental child-feeding practices (restrictive feeding and pressure to eat), and child body composition. Body composition were indexed by parent perceptions, body mass index (BMI), and percent body fat. Results showed that children with low levels of effortful control are more prone to exhibit food avoidance, which in turn is likely to elicit parental pressure to eat that in turn is linked to high child weight status. In addition, children with high levels of negative affectivity are prone to exhibit a food approach, which in turn is likely to elicit restrictive feeding from parents that in turn is linked to high child objective weight status. Findings situate controlling parental child-feeding practices in the context of child temperament and appetitive traits using a biopsychosocial framework of appetite self-regulation and weight. Results highlight that child appetite self-regulation processes and parental child-feeding practices could be essential components to target in childhood obesity preventive interventions.

Analyzing Complex Longitudinal Data in Educational Research: A Demonstration With Project English Language and Literacy Acquisition (ELLA) Data Using xxM.

When analyzing complex longitudinal data, especially data from different educational settings, researchers generally focus only on the mean part (i.e., the regression coefficients), ignoring the equally important random part (i.e., the random effect variances) of the model. By using Project English Language and Literacy Acquisition (ELLA) data, we demonstrated the importance of taking the complex data structure into account by carefully specifying the random part of the model, showing that not only can it affect the variance estimates, the standard errors, and the tests of significance of the regression coefficients, it also can offer different perspectives of the data, such as information related to the developmental process. We used xxM (Mehta, 2013), which can flexibly estimate different grade-level variances separately and the potential carryover effect from each grade factor to the later time measures. Implications of the findings and limitations of the study are discussed.

Advancing Stage 2 Research on Measures for Monitoring Kindergarten Reading Progress.

Although several measures exist for frequently monitoring early reading progress, little research has specifically investigated their technical properties when administered on a frequent basis with kindergarten students. In this study, kindergarten students ( N = 137) of whom the majority was receiving supplemental intervention for reading skills were monitored using Letter Sound Fluency, Phoneme Segmentation Fluency, Word Reading Fluency, Nonsense Word Fluency, Highly Decodable Passages, and Spelling on a biweekly basis between February and May. Acceptable reliability was observed for all measures. Analyses of slope validity using latent growth models, latent change score models, and slope differences according to level of year-end achievement indicated that the relation of slope to overall reading skills varied across the measures. A suggested approach to kindergarten students' reading progress is offered that includes Letter Sound Fluency and a measure of word-reading skills to provide a comprehensive picture of student growth toward important year-end reading outcomes.

Impact of Not Addressing Partially Cross-Classified Multilevel Structure in Testing Measurement Invariance: A Monte Carlo Study.

In educational settings, researchers are likely to encounter multilevel data with cross-classified structure. However, due to the lack of familiarity and limitations of statistical software for cross-classified modeling, most researchers adopt less optimal approaches to analyze cross-classified multilevel data in testing measurement invariance. We conducted two Monte Carlo studies to investigate the performances of testing measurement invariance with cross-classified multilevel data when the noninvarinace is at the between-level: (a) the impact of ignoring crossed factor using conventional multilevel confirmatory factor analysis (MCFA) which assumes hierarchical multilevel data in testing measurement invariance and (b) the adequacy of the cross-classified multiple indicators multiple causes (MIMIC) models with cross-classified data. We considered two design factors, intraclass correlation (ICC) and magnitude of non-invariance. Generally, MCFA demonstrated very low statistical power to detect non-invariance. The low power was plausibly related to the underestimated factor loading differences and the underestimated ICC due to the redistribution of the variance component from the ignored crossed factor. The results demonstrated possible incorrect statistical inferences with conventional MCFA analyses that assume multilevel data as hierarchical structure for testing measurement invariance with cross-classified data (non-hierarchical structure). On the contrary, the cross-classified MIMIC model demonstrated acceptable performance with cross-classified data.

Factor structure and factorial invariance of the Multidimensional Acculturative Stress Inventory.

Using a national data set, this study examined the factor structure and factorial invariance of the Multidimensional Acculturative Stress Inventory (MASI) across Latino and Asian Americans, gender, and nativity (U.S.- vs. foreign-born). Results showed that a 4-factor model of acculturative stress provided good fit to the data. Tests of factorial invariance provided evidence of measurement equivalence across all of the groupings tested. These findings suggest that the MASI operationalizes acculturative stress in an equivalent manner across Latino and Asian American students, gender, and nativity.

Exploring the various interpretations of "test bias".

Test bias is a hotly debated topic in society, especially as it relates to diverse groups of examinees who often score low on standardized tests. However, the phrase "test bias" has a multitude of interpretations that many people are not aware of. In this article, we explain five different meanings of "test bias" and summarize the empirical and theoretical evidence related to each interpretation. The five meanings are as follows: (a) mean group differences, (b) differential predictive validity, (c) differential item functioning, (d) differing factor structures of tests, and (e) unequal consequences of test use for various groups. We explain in this article why meanings (a) and (e) are not actual forms of test bias and that there are serious concerns about (b). In our conclusion, we discuss the benefits of standardized testing for diverse examinees and urge readers to be careful and precise in their use of the phrase "test bias."

A comparison of sequential and nonsequential specification searches in testing factorial invariance.

The purpose of this study was to examine the performance of modification indices (MIs) in finding correct partial invariance models when testing factorial invariance across groups of interest. In the present study, we examined the two commonly used approaches in this area-namely, nonsequential search and sequential search. Using the nonsequential search procedure, partial invariance models can be found by relaxing at once all the MIs larger than a certain cutoff value (usually, 3.84) at the first place, whereas in the sequential search method, the models are modified by relaxing one constrained parameter (usually the parameter with the largest MI) at a time and reanalyzing the models after each parameter has been relaxed. Our simulation results showed that the nonsequential search can lead to extremely high false positive rates and that it is highly likely that some invariant items will be incorrectly identified as noninvariant. In consequence, the nonsequential search method will likely lead to inadequately modifying scales. However, the sequential search method using MIs performed well and produced good true positive and false positive rates across all simulation conditions. Recommendations based on the findings are provided, and limitations of the study are discussed.

Confirmation of a two-factor model for interpretation of the comprehensive trail making test with children.

Exploratory factor analyses (EFAs) of the Comprehensive Trail Making Test suggested a possible two-factor solution that might better reflect the differences in Trails 1-3 and Trails 4 and 5 as opposed to a single Composite Index for the total standardization sample. The purpose of this study was to conduct a confirmatory factor analysis (CFA) of the two-factor structure with a subset of the standardization sample ages 18 or younger that had completed all five-trail tasks. The sample included 251 boys and 306 girls, ages 8-18, with a mean age of 12.76 (SD = 3.07). Data were collected across 16 states with representation from all regions of the USA. Standardized scores on each of the trails (1-5) tasks were considered. The results of CFA using M-plus indicated a good fit for the two-factor model, χ(2)(4) = 18.686, p = .0009, root mean-square error of approximation = 0.081, comparative fit index = 0.986 and standardized root-mean-squared residual = 0.021. A one-factor model was not supported. As suggested by the EFA in the manual, Trails 1-3 and Trails 4 and 5, while related, appear to be different in subtle ways that may be most meaningful in conjunction with evaluation of children with neurodevelopmental differences. Implications and possible explanations for this difference are discussed.

Covariances between regression coefficient estimates in a single mediator model.

This study presents formulae for the covariances between parameter estimates in a single mediator model. These covariances are necessary to build confidence intervals (CI) for effect size measures in mediation studies. We first analytically derived the covariances between the parameter estimates in a single mediator model. Using the derived covariances, we computed the multivariate-delta standard errors, and built the 95% CIs for the effect size measures. A simulation study evaluated the accuracy of the standard errors as well as the Type I error, power, and coverage of the CIs using various parameter values and sample sizes. Finally, we presented a numerical example and a SAS MACRO that calculates the CIs for the effect size measures.

Analyzing Longitudinal Data with Multilevel Models: An Example with Individuals Living with Lower Extremity Intra-articular Fractures.

The use and quality of longitudinal research designs has increased over the past two decades, and new approaches for analyzing longitudinal data, including multi-level modeling (MLM) and latent growth modeling (LGM), have been developed. The purpose of this paper is to demonstrate the use of MLM and its advantages in analyzing longitudinal data. Data from a sample of individuals with intra-articular fractures of the lower extremity from the University of Alabama at Birmingham's Injury Control Research Center is analyzed using both SAS PROC MIXED and SPSS MIXED. We start our presentation with a discussion of data preparation for MLM analyses. We then provide example analyses of different growth models, including a simple linear growth model and a model with a time-invariant covariate, with interpretation for all the parameters in the models. More complicated growth models with different between- and within-individual covariance structures and nonlinear models are discussed. Finally, information related to MLM analysis such as online resources is provided at the end of the paper.

Outcomes of a prospective trial of student-athlete drug testing: the Student Athlete Testing Using Random Notification (SATURN) study.

PURPOSE: To assess the effects of random drug and alcohol testing (DAT) among high school athletes. METHODS: This was a 2-year prospective randomized controlled study of a single cohort among five intervention high schools with a DAT policy and six schools with a deferred policy, serially assessed by voluntary, confidential questionnaires. DAT school athletes were at risk for random testing during the full academic year. Positive test results were reported to parents or guardians, with mandatory counseling. Indices of illicit drug use, with and without alcohol use, were assessed at the beginning and end of each school year for the past month and prior year. Potential mediating variables were evaluated. RESULTS: Student-athletes from intervention and control schools did not differ in past 1-month use of illicit drug or a combination of drug and alcohol use at any of the four follow-up periods. At the end of the initial school year and after 2 full school years, student-athletes at DAT schools reported less drug use during the past year (p < .01) compared to athletes at the deferred policy schools. Combining past year drug and alcohol use together, student-athletes at DAT schools reported less use at the second and third follow-up assessments (p < .05). Paradoxically, DAT athletes across all assessments reported less athletic competence (p < .001), less belief authorities were opposed to drug use (p < .01), and indicated greater risk-taking (p < .05). At the final assessment, DAT athletes believed less in testing benefits (p < .05) and less that testing was a reason not to use drugs (p < .01). CONCLUSIONS: No DAT deterrent effects were evident for past month use during any of four follow-up periods. Prior-year drug use was reduced in two of four follow-up self-reports, and a combination of drug and alcohol use was reduced at two assessments as well. Overall, drug testing was accompanied by an increase in some risk factors for future substance use. More research is needed before DAT is considered an effective deterrent for school-based athletes.