ABSTRACT:Wright, WC, Fedewa, MV, Aguiar, EJ, Winchester, LJ, Schumacker, RE, and Esco, MR. Accuracy of three field-based devices for measuring counter movement jump performance in elite female volleyball players. J Strength Cond Res 40(1): 112-117, 2026-The purpose of this study was to determine the accuracy of 3 field-based methods for assessing countermovement jump (CMJ) performance in elite female volleyball players, using force plate (FP) data as the criterion standard. Sixteen collegiate female volleyball players (age = 19.4 ± 1.5 years, height = 176.2 ± 10.6 cm, body mass = 71.5 ± 11.1 kg) performed the CMJ while being simultaneously measured by a jump-and-reach device (JR), linear position transducer (LPT), mobile phone application (APP), and a criterion FP system. CMJ height from the FP (36.16 ± 3.88 cm) was significantly lower than the JR (39.53 ± 5.18 cm, p < 0.01) and LPT (48.23 ± 4.44 cm, p < 0.01) but slightly higher than the APP (35.32 ± 3.75 cm, p < 0.01). In addition, the APP displayed the strongest correlation ( r = 0.99, p < 0.01) and smallest standard error of estimate (SEE ±0.27 cm) and 95% limits of agreement (±0.57 cm) compared with the other 2 devices. For CMJ, the mean power from the FP (2,243.00 ± 458.03 W) was significantly different from JR (2,615.69 ± 798.03 W, p = 0.02) and APP (1,354.31 ± 241.98 W, p < 0.01), but not the LPT (2,447.08 ± 559.32 W, p = 0.40). Although the correlations for mean power ( r = 0.61-0.76) between the field devices and criterion were significant ( p < 0.05), the SEE's (range ±310.90 to ±377.09 W) and limits of agreement (range ±655.28 to ±1,064.08 W) were considerably large. Based on the results, the APP seems to provide the most valid CMJ height measures. However, the caution should be used for assessing CMJ mean power with the field tools.

This study examined the psychometric properties of the English version of the 10-item Connor–Davidson Resilience Scale using the Rasch Rating Scale model in a sample of 177 international students and scholars at a U.S. university. The Connor-Davison Resilience Scale was developed to measure individual differences in psychological resilience. Previous studies using item response theory (IRT) approaches to evaluate the scale have not yet considered potential differences in psychometric properties related to participants’ ethnicities and cultures. Thus, this study extended prior investigations by examining possible violations of measurement invariance across participants’ demographic characteristics at the item level using the Rasch rating scale model. Although the CD-RISC-10 demonstrated adequate person separation reliability, visual inspection of individual ratings and the variable map indicates that some participants provided extreme and inconsistent responses. Moreover, the CD-RISC-10 had an apparent ceiling effect, and one item showed differential item functioning across gender groups. Altogether, the results suggest that the English CD-RISC-10 shows adequate psychometric properties within a sample of international participants in the U.S. However, continued research is needed to determine how population differences may affect performance on the instrument and to develop items capable of measuring a broader range of psychological resilience.

An application of Bayesian factor analysis for evaluation of scale reliability is discussed, which is developed within the framework of latent variable modeling. The method permits direct point and interval estimation of the reliability coefficient of multiple-component measuring instruments using Bayesian inference. The approach allows also point and interval estimation of the population discrepancy between the popular coefficient alpha and instrument reliability. The procedure is readily applied in empirical measurement research employing widely available statistical software. The outlined method is illustrated using numerical data.

This paper is concerned with the process of selecting between the increasingly popular bi-factor model and the second-order factor model in measurement research. It is indicated that in certain settings widely used in empirical studies, the second-order model is nested in the bi-factor model and obtained from the latter after imposing appropriate parameter constraints. These restrictions can be directly tested within the framework of the latent variable modeling methodology employing widely circulated software. The outlined model selection procedure provides a readily applied means of choosing between the two models of growing interest to measurement scholars, and is illustrated using numerical data.

Purpose This study extends research on one of the most frequently cited school leadership frameworks by examining the psychometric properties of the instrument designed to assess many of the practices included in that framework. Design/methodology/approach Using data collected from 1,401 teachers the study examined the instrument’s measurement invariance, score reliabilities, as well as construct and predictive validities. Polytomous latent trait models (Many-Facet Rasch model), scale and principal component analysis using second-order Confirmatory Factor Analysis, and Structural Equation Modeling (SEM)-Path modelling were used for these purposes. Findings Findings report levels of score reliability and valid score inferences. Results concerning the predictive validity of the instrument indicate a complex set of relations among the domains of leadership practices measured by the instrument, variables selected as mediators of leaders’ influence, and their direct and indirect effects on student learning. Research limitations/implications This study provides researchers with a reliable and valid instrument for use in their future research. Data for the study were provided by elementary teachers in one US state. The extent to which results of the instrument are valid across different cultural and organizational settings remains to be determined. Practical implications Leadership developers may find the instrument useful for assessing the strengths and weaknesses of those participating in their programs while leaders themselves many find the instrument useful for self-diagnosis. Originality/value This study contributes to the development of school leadership measures by including Rasch modeling among the methods used for examining the instrument’s psychometric properties.

While classroom feedback has been shown to be a key mediating factor in students' learning process and performance, the bulk of current research on feedback in the field of foreign language education has largely focused on how teachers respond to students' linguistic errors. Published research on how students in a foreign language context respond to different kinds of classroom feedback practice has been sparse. Even less frequently reported is how different forms of classroom feedback practice may cater to students' motivation in learning. Taking stock of theoretical perspectives concerning feedback and motivation in both educational psychology and language acquisition, this study intends to fill these gaps by investigating what classroom feedback practices tertiary foreign language students experienced, and how these feedback practices were associated with student foreign language learning motivation. Student self-feedback was found to be the most powerful predictor of their motivation for English learning. The results suggest that there is a need for a qualitative change in feedback practices in university foreign language classrooms in order that feedback processes can be deployed more effectively to benefit students' learning.

This study examined the psychometric properties of the Motivational scale of the Motivated Strategies for Learning Questionnaire in a sample of 656 Chinese secondary students in an English learning context. Exploratory factor analysis and confirmatory factor analysis results suggested that a five-factor motivational structure fit the data better as opposed to the original six-factor motivational model reported by Pintrich and his colleagues. Reliability coefficients of these five motivational subscales (i.e., intrinsic value, extrinsic goal orientation, control of learning beliefs, self-efficacy for learning and performance, and test anxiety) were in the adequate to good range. All motivational subscales except test anxiety were positively correlated with metacognitive regulation and/or students' self-rated English proficiency. The second-order CFA further provided empirical evidence to consider a common and broad motivational factor that can be inferred from the five subscales.

Over the last few decades, researchers have increased interests in synthesizing data using the meta-analysis approach. While this method has been able to provide new insights to the literature with findings drawn from secondary data, scholars in the field of Psychology and Methodology have been proposing the integration of meta-analysis with structural equation modeling approach. In this vein, the method of meta-analytic structural equation modeling (MASEM) with the two-step structural equation modeling (TSSEM) approach have been developed, corresponding with the metaSEM package for the use in R statistic package. Ever since its development in 2015, the metaSEM package as well as the TSSEM approach have still been constantly updated and modified. In order to promote the use, this study aims at providing a software review for the metaSEM package and its codes on the R platform. R codes, figures, as well as initial results interpretations are provided.

BACKGROUND:Parental involvement is one of the most important factors affecting students' academic learning. Different families seem to show similar parental involvement patterns. This study employed a representative sample of 12,575 seventh- and eighth-grade Chinese students' parents to explore the patterns of parental involvement. (2) Methods: Latent class analysis (LCA) was used to identify different parental involvement styles in children's studies at home. Discriminant analysis, MANOVA, post-hoc tests, and effect size were used to verify the LCA results. (3) Results: Four distinctive latent class groups were identified and named: supportive (20%), permissive (54%), restrictive (8%), and neglectful (18%). A discriminant analysis supported the LCA group classification results. The MANOVA results indicated statistically significant differences between the four latent classes using the set of predictor variables. The post-hoc test results and effect sizes showed that the predictor variables had substantial differences among the four latent class groups. Parental education and family income showed statistically significant links to these four parental involvement styles, which, in turn, were linked to students' academic achievement according to the MANOVA, effect sizes, and post-hoc test results. (4) Conclusions: Parental involvement styles in children's learning at home can be identified and categorized into four different latent class styles.

The present study surveyed a sample of school counselors ( N = 220) on American School Counselor Association (ASCA) National Model implementation and role stress related to their job satisfaction. A path analysis was conducted to examine the relationships between the variables. ASCA National Model implementation predicted school counselor job satisfaction with role ambiguity and role incongruity as significant mediating variables. Role conflict was not a significant mediating variable. Implications, limitations, and future research for the school counseling profession are discussed.

While hierarchical linear modeling is often used in social science research, the assumption of normally distributed residuals at the individual and cluster levels can be violated in empirical data. Previous studies have focused on the effects of nonnormality at either lower or higher level(s) separately. However, the violation of the normality assumption simultaneously across all levels could bias parameter estimates in unforeseen ways. This article aims to raise awareness of the drawbacks associated with compounded nonnormality residuals across levels when the number of clusters range from small to large. The effects of the breach of the normality assumption at both individual and cluster levels were explored. A simulation study was conducted to evaluate the relative bias and the root mean square of the model parameter estimates by manipulating the normality of the data. The results indicate that nonnormal residuals have a larger impact on the random effects than fixed effects, especially when the number of clusters and cluster size are small. In addition, for a simple random-effects structure, the use of restricted maximum likelihood estimation is recommended to improve parameter estimates when compounded residuals across levels show moderate nonnormality, with a combination of small number of clusters and a large cluster size.