Journal of applied measurement最新文献

Rasch's logistic model for growth is explained by reviewing his analysis of piglet growth. An early formulation was given in India when Rasch visited with Rao to describe the statistic metameter as the distinguishing characteristic for determining the rate of growth. Next, several examples are given demonstrating growth with plots of growth over time using characteristic time with truncated data. The results of these growth plots and analyses are summarized given their implications and restraints for using this approach in determining rate of growth.

Aim: To explore the psychometric properties of the general movements optimality score (GMOS) by examining its dimensionality, rating scale functioning, and item hierarchies using Rasch measurement.

Methods: Secondary data analysis of the GMOS data for video-recording of 383 infants with uni-, multidimensional, and mixed Rasch partial credit models. Videos were scored based on the global General Movement Assessment categories, and on the amplitude, speed, spatial range, proximal and distal rotations, onset and offset, tremulous and cramped components of the upper and lower extremities (21 items), resulting in the GMOS.

Results: The GMOS data fits best to a unidimensional mixed Rasch model with three different classes of infants, with all but two items contributing to the infants' separation. Rating scales functioned well for 19 items. Item difficulty hierarchies varied depending on infants' class. No floor effect and no substantive gaps between item difficulty estimates were found.

Conclusion: The GMOS has strong psychometric properties to distinguish infants with different functional motor performance and provides a quantitative measure of quality of movement.

Interpretation: The GMOS can be confidently used to assist with early diagnosis, grade motor performance, and provide a solid base to study individual general movement developmental trajectories.

Residual-based fit statistics are among the most common indicators of fit to the Rasch model. There is considerable discussion in the literature of the efficacy of item fit statistics in detecting measurement disturbances. However, to date there has been no investigation of whether these fit statistics are robust to interactions between item discrimination and item difficulty. This study uses simulations to investigate whether interaction effects occur for fit statistics commonly used with the Rasch model. It is found that when the parameters are estimated with the Rasch model, the values of certain item fit statistics vary depending on the interaction between location and discrimination. Specifically, in the study, OUTFIT MNSQ and INFIT MNSQ provide a relatively consistent index of item discrimination across a range of item difficulties, whereas the t-statistic and the log transformed fit residual vary in a systematic fashion that depends on item location.

Researchers and practitioners have used the Modern Language Aptitude Test (MLAT) to assess language aptitude and identify possible language learning deficiencies in examinees since the 1950s. However, researchers have not assessed its psychometric properties using modern measurement theory methods. We use the dichotomous Rasch model to explore the psychometric properties of the MLAT, including data-model fit indices, item difficulty and student ability calibrations, reliability of separation, and differences in achievement across gender subgroups based on a sample of undergraduate and graduate university students (N=204). Our findings suggest that the MLAT has acceptable psychometric properties such that it can be meaningfully interpreted as a measure of language proficiency. Our findings confirm previous research that language performance across gender groups significantly differs. We found no significant interactions between gender subgroups and the difficulty of the five domains of the assessment. We discuss these results in terms of their implications for research and practice.

Because modern, simultaneously estimated longitudinal Rasch models are unable to handle many timepoints, new methods of producing person and item estimates and evaluating test function are necessary. Longitudinal anchoring, in which a common scale of item parameters is used to estimate trait levels over multiple occasions, is a potential solution. With proper anchoring procedures, person and item estimates can be obtained without limiting the number of timepoints that can be analyzed. A simulation study examining the performance of six longitudinal anchoring methods (Floated, Racked, Time One, Mean, Random, and Stacked) was conducted. The Mean and the Stacked anchoring methods best recovered the population change over time, person and item estimates, and model fit. The Racked method could not produce reliable change estimates and should be avoided. Longitudinal anchoring is an easily implemented solution when analyzing large longitudinal datasets and shows promise as a low-computation method of producing latent trait estimates.

Rasch/Guttman Scenario (RGS) scales produce scores that map onto interpretable descriptions of individuals at different levels of hierarchically progressive constructs. The unique scenario item format provides actionable and rich content-relevant feedback about (a) respondent status, (b) intervention design, and (c) longitudinal change on a construct. This article presents a seven-step methodological framework for the development of RGS scales. We also reflect on plausible challenges that may arise in the applications of RGS scale development and propose future research directions for the methodology.

The Burn-Specific Pain Anxiety Scale (BSPAS) estimates pain-related anxiety and determines the effect of treatment in patients with burns, especially regarding wound care. This study aimed to analyze the 9-item and the abbreviated 5-item BSPAS by the Rasch model. This prospective study included 161 patients admitted to Dutch burn centres. The BSPAS was administered during hospital stay resulting in 314 self-reports and was analysed using the Rasch unidimensional measurement model 2030 (RUMM 2030). Unidimensionality of the 9-item and 5-item BSPAS was confirmed. Initially, both versions did not fit the model due to response dependency. After creating subtests, fit to the model improved. After deleting 'feeling insecure about my healing' and creating two subtests with three items, fit of the 9-item BSPAS was obtained, while the 5-item BSPAS fitted after creating a subtest with two items. The Rasch model demonstrated that both versions were unidimensional and were able to fit the model after adjusting for response dependency. Moreover, the 5-item BSPAS could be further improved by deleting 'worrying about the possible pain.' A 4-item abbreviated BSPAS (BSPAS-4I) captures pain-related anxiety and is proposed to be used in future studies and daily practice.

While several proofs exist that the number keyed (or number correct) score is a sufficient statistic to estimate person measure (or ability, beta) in the dichotomous Rasch model, there are few proofs about the direct mathematical link from beta to the number correct score. This manuscript proves that the estimation link going from score to beta is the test response function, which is the sum of the probabilities correct for all items given the difficulty (delta) values and beta.

Mental toughness (MT) predicts outcomes across several high-stress contexts such as athletics, the military, and the workplace. Despite this, researchers have struggled to reach consensus regarding how best to conceptualize and measure MT. MT assessments have focused on measuring general MT rather than domain-specific MT. The current study proposed a measurement model of MT grounded in social-cognitive theory and introduced an assessment of MT within a situational judgment context relevant to the workplace. Participants completed the new MT measure as well as assessments to establish construct validity. Both exploratory and confirmatory factor analyses suggested a three-factor solution fit the data best, consisting of task persistence, emotional control, and utilization of feedback. Cross-structure analyses indicated that the new assessment avoided common-method bias in responding, evidenced by weak correlations with measures of other constructs. The results provided initial evidence to continue research on using a situational judgment test to measure MT.

The present study developed and validated a short form of the Cross-Cultural (Chinese) Personality Assessment Inventory for adolescents (CPAI-A; Form B) focusing on the personality scales by unidimensional and multidimensional Rasch models. Multiple evidence from unidimensional Rasch models (item fit, DIF statistics, dimensionality, reliability indices, construct coverage) were evaluated in order to create a short scale with optimal psychometric properties. Further, multidimensional Rasch model, canonical analysis, and predictive validity were performed and evaluated to validate the CPAI-A-SF further. As a result, 65 of 277 items were selected in the short measure with a four-dimensional structure. The infit and outfit mean-squares (MNSQ) of the personality scale items ranged between .81 and 1.25. Good construct coverage was displayed on the item-person map, and all four dimensions demonstrate reasonable EAP/PV reliability ranging from .81 to .87. The personality scores of CPAI-A-SF predicted life satisfaction as well as the scores from the original inventory.