Model Selection in Continuous Test Norming With GAMLSS

Voncken, Lieke; Albers, Casper J.; Timmerman, Marieke E.

doi:10.1177/1073191117715113

Cited by 32 publications

(45 citation statements)

References 27 publications

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“…The GAMLSS method is an extension of the Lambda-Median-Sigma method, which was introduced by Rigby and Stasinopoulos to address some of the limitations associated with generalized linear models and generalized additive models. [ 10 , 11 ] To more clearly represent the dynamic changes during the first 2 years of life, separate charts were constructed to depict the CBC results. Percent of stacked area charts were used to show the proportions of differential WBCs.…”

Section: Methodsmentioning

confidence: 99%

Age-dependent changes of total and differential white blood cell counts in children

Peng

Yan

et al. 2020

Chinese Medical Journal

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Background Total and differential white blood cell counts are important for the diagnostic evaluation of suspected diseases. To facilitate the interpretation of total and differential white blood cell counts in pediatric patients, the present study investigated age-dependent changes in total and differential white blood cell counts in healthy reference children. Methods Data were obtained from the Pediatric Reference Intervals in China study (PRINCE), which aims to establish and verify pediatric reference intervals for Chinese children based on a nationwide multicenter cross-sectional study from January 2017 to December 2018. Quantile curves were calculated using the generalized additive models for location, shape, and scale method. The 2.5th, 50th, and 97.5th quantile curves were calculated for both total and differential white blood counts. Percents of stacked area charts were used to demonstrate the proportions of differential white blood cells. All statistical analyses were performed using R software. Results Both 50th and 97.5th quantiles of total white blood cell count and monocyte count were highest at birth, then rapidly decreased in the first 6 months of life; relatively slow reduction continued until 2 years of age. The lymphocyte count was low during infancy and increased to its highest level at 6 months of age; it then exhibited moderate and continuous reduction until approximately 9 years of age. The pattern of neutrophil count changed with age in a manner opposite to that of lymphocyte count. Besides, there were two inter-sections of lymphocyte count and neutrophil count during infancy and at approximately 5 years of age, based on locally weighted regression (LOESS) analysis. There were no apparent age-related changes in eosinophil or basophil counts. Conclusion These data regarding age-related changes in total and differential white blood cell counts can be used to assess the health of pediatric patients and guide clinical decisions.

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Section: Methodsmentioning

confidence: 99%

Age-dependent changes of total and differential white blood cell counts in children

Peng

Yan

et al. 2020

Chinese Medical Journal

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“…In the case of intelligence assessment or developmental tests, the raw scores change rather quickly with age. If, in such cases, the age brackets are chosen too large, significant jumps between the norm tables of the subsamples will occur (Lenhard et al, 2016; Bracken, 1988; Voncken et al, 2019a; Zachary & Gorsuch, 1985). These jumps lead to errors because the age of a test person might deviate considerably from the average age of the respective age bracket.…”

Section: The Significance Of Norm Scores In Applied Psychometricsmentioning

confidence: 99%

“…The assumption of an underlying continuum has, however, been proven to be generally valid for modeling purposes (cf. Hansen, 2004) and applies to all forms of continuous norming (e.g., Oosterhuis et al, 2016; Voncken et al, 2019a).…”

Section: The Significance Of Norm Scores In Applied Psychometricsmentioning

confidence: 99%

Improvement of Norm Score Quality via Regression-Based Continuous Norming

Lenhard

Lenhard²

2020

Educational and Psychological Measurement

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The interpretation of psychometric test results is usually based on norm scores. We compared semiparametric continuous norming (SPCN) with conventional norming methods by simulating results for test scales with different item numbers and difficulties via an item response theory approach. Subsequently, we modeled the norm scores based on random samples with varying sizes either with a conventional ranking procedure or SPCN. The norms were then cross-validated by using an entirely representative sample of N = 840,000 for which different measures of norming error were computed. This process was repeated 90,000 times. Both approaches benefitted from an increase in sample size, with SPCN reaching optimal results with much smaller samples. Conventional norming performed worse on data fit, age-related errors, and number of missings in the norm tables. The data fit in conventional norming of fixed subsample sizes varied with the granularity of the age brackets, calling into question general recommendations for sample sizes in test norming. We recommend that test norms should be based on statistical models of the raw score distributions instead of simply compiling norm tables via conventional ranking procedures.

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“…For example, the mean, standard deviation and skewness of the test score can vary conditional on age. A frequentist distributional regression framework (i.e., generalized additive models for location, scale and shape (GAMLSS); Rigby & Stasinopoulos, 2005) has successfully been applied to estimate normed scores for different types of psychological tests (e.g., developmental tests, intelligence tests and neuropsychological tests; Bayley, 2006;Rommelse et al, 2018;Voncken, Albers, & Timmerman, 2019;Voncken et al, 2018). The normed scores of these tests are estimated conditional on age, and sometimes (i.e., in neuropsychological tests) also conditional on the additional predictors sex and/or education level.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Gaussian distributional regression models for more efficient norm estimation

Voncken

Kneib

Albers

et al. 2020

Brit J Math & Statis

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A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost-efficient high-quality norms. The method is illustrated using empirical normative data from the IDS-2 intelligence test. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Model Selection in Continuous Test Norming With GAMLSS

Cited by 32 publications

References 27 publications

Age-dependent changes of total and differential white blood cell counts in children

Age-dependent changes of total and differential white blood cell counts in children

Improvement of Norm Score Quality via Regression-Based Continuous Norming

Bayesian Gaussian distributional regression models for more efficient norm estimation

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