A Review of Developments and Applications in Item Analysis

Moses, Tim

doi:10.1007/978-3-319-58689-2_2

Cited by 29 publications

(30 citation statements)

References 62 publications

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“…Second, the benchmark of the possible underestimation in G was PMC while, perhaps, a coefficient called r-polyreg correlation, an r-polyserial estimated by regression correlation (Livinstone & Dorans, 2004) would be a more proper benchmark. This coefficient, developed to overcome the challenge of the obvious overestimation in biserial and polyserial correlation coefficient, does not exceed 1, nor does it rely on bivariate normality assumptions (see Moses, 2017). More studies may be valuable in this respect.…”

Section: Limitationsmentioning

confidence: 99%

Goodman–Kruskal gamma and Dimension-Corrected Gamma in Educational Measurement Settings

Metsämuuronen¹

2021

INT J EDUC METHODOL

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Although Goodman-Kruskal gamma (G) is used relatively rarely it has promising potential as a coefficient of association in educational settings. Characteristics of G are studied in three sub-studies related to educational measurement settings. G appears to be unexpectedly appealing as an estimator of association between an item and a score because it strictly indicates the probability to get a correct answer in the test item given the score, and it accurately produces perfect latent association irrespective of distributions, degrees of freedom, number of tied pairs and tied values in the variables, or the difficulty levels in the items. However, it underestimates the association in an obvious manner when the number of categories in the item is more than four. Towards this, a dimension-corrected G (G2) is proposed and its characteristics are studied. Both G and G2 appear to be promising alternatives in measurement modelling settings, G with binary items and G2 with binary, polytomous and mixed datasets.

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Section: Limitationsmentioning

confidence: 99%

Goodman–Kruskal gamma and Dimension-Corrected Gamma in Educational Measurement Settings

Metsämuuronen¹

2021

INT J EDUC METHODOL

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“…27% is a common criterion for dividing the ratio of high and low in psychological experiments. This method is named as high-low-27-percent group method [41]. Tables 2 to 4 show the F1-score results of BDI, SAI and TAI class, respectively.…”

Section: Evaluation and Analysis A Experimental Resultsmentioning

confidence: 99%

Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations

et al. 2020

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With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi-sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance. INDEX TERMS Mental health assessment, wearable device, attention-based feature fusion.

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“…Item difficulty indicates how many participants have answered an item above the mean of the scale. For items with multiple response options, item difficulty is calculated as follows:

p = \sum_{i} \frac{italicxi}{k * n}

(Bortz & Döring, 2013; Moses, 2017). Values for each item are summed up across participants ( x i ) and are divided by the number of participants ( n ) multiplied by the item response levels ( k ).…”

Section: Methodsmentioning

confidence: 99%

Developing the Premonitory Urges for Tic Disorders Scale–Revised (PUTS‐R)

Baumung

Müller‐Vahl

Dyke

et al. 2020

Journal of Neuropsychology

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Background. Patients with Gilles de la Tourette syndrome (GTS) or chronic tic disorders frequently experience premonitory urges prior to tics. The 'Premonitory Urges for Tic Disorders Scale' (PUTS) is commonly used in order to assess urge severity in patients with tics. Several studies suggest that the PUTS might measure more than one dimension of urges. These include the quality and severity of premonitory urges. Methods. This study aims to replicate and extend previous findings concerning the psychometric properties of the PUTS and its underlying dimensions in a large sample of 241 patients with GTS including both adults (n = 93; mean age = 34.2 AE 12.84; 73 male) and minors (n = 148; mean age = 11.8 AE 2.86; 123 male), pooled from three different recruitment sites. Results. Data analysis confirmed good reliability across the PUTS items for both minors and adults and acceptable item characteristics for items 2-8. A factor analysis of items 1-8 confirmed the existence of two factors in both age groups. Conclusions. The results suggest that the PUTS might benefit from several further small modifications, such as rephrasing items 1 and 9 to increase convergence with the overall construct of the scale. Finally, we propose a revised version of the PUTS, consisting of two subscales: one for urge severity and another one for urge quality by including several new items. 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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A Review of Developments and Applications in Item Analysis

Cited by 29 publications

References 62 publications

Goodman–Kruskal gamma and Dimension-Corrected Gamma in Educational Measurement Settings

Goodman–Kruskal gamma and Dimension-Corrected Gamma in Educational Measurement Settings

Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations

Developing the Premonitory Urges for Tic Disorders Scale–Revised (PUTS‐R)

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