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Select item 90186531.

Memory and Proactive Interference for spatially distributed items

Ansgar D. Endress

Mem Cognit. 2022; 50(4): 782–816. Published online 2022 Feb 4. doi: 10.3758/s13421-021-01239-1

PMCID:: PMC9018653

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Does strict invariance matter? Valid group mean comparisons with ordered-categorical items

Winnie Wing-Yee Tse, Mark H. C. Lai, Yichi Zhang

Behav Res Methods. 2024; 56(4): 3117–3139. Published online 2023 Nov 29. doi: 10.3758/s13428-023-02247-6

PMCID:: PMC11133089

Abstract Article PDF–1.7M

Select item 94832203.

Diagnostic Classification Models for a Mixture of Ordered and Non-ordered Response Options in Rating Scales

Ren Liu, Haiyan Liu, Dexin Shi, Zhehan Jiang

Appl Psychol Meas. 2022 Oct; 46(7): 622–639. Published online 2022 Jun 24. doi: 10.1177/01466216221108132

PMCID:: PMC9483220

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Item Response Thresholds Models: A General Class of Models for Varying Types of Items

Gerhard Tutz

Psychometrika. 2022; 87(4): 1238–1269. Published online 2022 Apr 27. doi: 10.1007/s11336-022-09865-7

PMCID:: PMC9636304

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Differential item functioning analyses of the Patient Reported Outcomes Measurement Information System (PROMIS®) measures: Methods, challenges, advances, and future directions

Jeanne A. Teresi, Chun Wang, Marjorie Kleinman, Richard N. Jones, David J. Weiss

Psychometrika. Author manuscript; available in PMC 2022 Mar 2.

Published in final edited form as: Psychometrika. 2021 Sep; 86(3): 674–711. Published online 2021 Jul 12. doi: 10.1007/s11336-021-09775-0

PMCID:: PMC8889890

Abstract Article PDF–561K

Select item 74885776.

Structural validity of the Boston Carpal Tunnel Questionnaire and its short version, the 6-Item CTS symptoms scale: a Rasch analysis one year after surgery

Juhani Multanen, Jari Ylinen, Teemu Karjalainen, Joona Ikonen, Arja Häkkinen, Jussi P. Repo

BMC Musculoskelet Disord. 2020; 21: 609. Published online 2020 Sep 12. doi: 10.1186/s12891-020-03626-2

PMCID:: PMC7488577

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Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

Alexander Robitzsch

J Intell. 2020 Sep; 8(3): 30. Published online 2020 Aug 14. doi: 10.3390/jintelligence8030030

PMCID:: PMC7555561

Abstract Article PDF–489K

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Item analysis of the Eating Assessment Tool (EAT-10) by the Rasch model: a secondary analysis of cross-sectional survey data obtained among community-dwelling elders

Tina Hansen, Annette Kjaersgaard

Health Qual Life Outcomes. 2020; 18: 139. Published online 2020 May 13. doi: 10.1186/s12955-020-01384-2

PMCID:: PMC7222581

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Linking Self-Perceived Cognitive Functioning Questionnaires Using Item Response Theory: The Subjective Cognitive Decline Initiative

Laura A. Rabin, Sietske A.M. Sikkes, Douglas Tommet, Richard N. Jones, Paul K. Crane, Milushka M. Elbulok-Charcape, Mark A. Dubbelman, Rebecca Koscik, Rebecca E. Amariglio, Rachel F. Buckley, Mercè Boada, Gaël Chételat, Bruno Dubois, Kathryn A. Ellis, Katherine A. Gifford, Angela L. Jefferson, Frank Jessen, Sterling Johnson, Mindy J. Katz, Richard B. Lipton, Tobias Luck, Eleni Margioti, Paul Maruff, Jose Luis Molinuevo, Audrey Perrotin, Ronald C. Petersen, Lorena Rami, Barry Reisberg, Dorene M. Rentz, Steffi G. Riedel-Heller, Shannon L. Risacher, Octavio Rodriguez-Gomez, Perminder S. Sachdev, Andrew J. Saykin, Nikolaos Scarmeas, Colette Smart, Beth E. Snitz, Reisa A. Sperling, Vanessa Taler, Wiesje M. van der Flier, Argonde C. van Harten, Michael Wagner, Steffen Wolfsgruber, Alzheimer’s Disease Neuroimaging Initiative, the Canadian Longitudinal Study on Aging, & the Health and Aging Brain Study: Health Disparities (HABS-HD) Study Team, on behalf of the ISTAART Subjective Cognitive Decline Professional Interest Area

Neuropsychology. Author manuscript; available in PMC 2024 May 1.

Published in final edited form as: Neuropsychology. 2023 May; 37(4): 463–499. doi: 10.1037/neu0000888

PMCID:: PMC10564559

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Differential Item Functioning of the Mini-BESTest Balance Measure: A Rasch Analysis Study

Antonio Caronni, Michela Picardi, Stefano Scarano, Peppino Tropea, Giulia Gilardone, Nadia Bolognini, Valentina Redaelli, Giuseppe Pintavalle, Evdoxia Aristidou, Paola Antoniotti, Massimo Corbo

Int J Environ Res Public Health. 2023 Mar; 20(6): 5166. Published online 2023 Mar 15. doi: 10.3390/ijerph20065166

PMCID:: PMC10049698

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Select item 1097109211.

Explanatory Cognitive Diagnosis Models Incorporating Item Features

Manqian Liao, Hong Jiao, Qiwei He

J Intell. 2024 Mar; 12(3): 32. Published online 2024 Mar 11. doi: 10.3390/jintelligence12030032

PMCID:: PMC10971092

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Psychometric performance of the PROMIS® depression item bank: a comparison of the 28- and 51-item versions using Rasch measurement theory

Sophie Cleanthous, Skye Pamela Barbic, Sarah Smith, Antoine Regnault

J Patient Rep Outcomes. 2019 Dec; 3: 47. Published online 2019 Jul 30. doi: 10.1186/s41687-019-0131-4

PMCID:: PMC6663962

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Select item 957909613.

Scale length does matter: Recommendations for measurement invariance testing with categorical factor analysis and item response theory approaches

E. Damiano D’Urso, Kim De Roover, Jeroen K. Vermunt, Jesper Tijmstra

Behav Res Methods. 2022; 54(5): 2114–2145. Published online 2021 Dec 15. doi: 10.3758/s13428-021-01690-7

PMCID:: PMC9579096

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Select item 933402314.

Psychometric evaluation of the Positive Mental Health (PMH) scale using item response theory

Lusine Vaganian, Maren Boecker, Sonja Bussmann, Michael Kusch, Hildegard Labouvie, Jürgen Margraf, Alexander L. Gerlach, Jan C. Cwik

BMC Psychiatry. 2022; 22: 512. Published online 2022 Jul 28. doi: 10.1186/s12888-022-04162-0

PMCID:: PMC9334023

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Select item 1002910815.

Attention Allocation Between Item and Order Information in Short-Term Memory

Dominic Guitard, Nelson Cowan

Q J Exp Psychol (Hove) Author manuscript; available in PMC 2023 Jun 1.

Published in final edited form as: Q J Exp Psychol (Hove). 2023 Jun; 76(6): 1391–1409. Published online 2022 Aug 24. doi: 10.1177/17470218221118451

PMCID:: PMC10029108

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Select item 1100844216.

Refinement and Validation of the Empowerment Audiology Questionnaire: Rasch Analysis and Traditional Psychometric Evaluation

Rebecca J. Bennett, Josefina Larsson, Sarah Gotowiec, Melanie Ferguson

Ear Hear. 2024 May-Jun; 45(3): 583–599. Published online 2023 Dec 12. doi: 10.1097/AUD.0000000000001449

PMCID:: PMC11008442

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Select item 943573417.

Tradeoffs between Item and Order Information in Short-Term Memory

Dominic Guitard, Jean Saint-Aubin, Nelson Cowan

J Mem Lang. Author manuscript; available in PMC 2022 Sep 1.

Published in final edited form as: J Mem Lang. 2022 Feb; 122: 104300. Published online 2021 Nov 12. doi: 10.1016/j.jml.2021.104300

PMCID:: PMC9435734

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Polytomous Item Explanatory Item Response Theory Models

Jinho Kim, Mark Wilson

Educ Psychol Meas. 2020 Aug; 80(4): 726–755. Published online 2019 Dec 13. doi: 10.1177/0013164419892667

PMCID:: PMC7307487

Abstract Article PDF–608K

Select item 732822619.

Item-Focused Trees for the Detection of Differential Item Functioning in Partial Credit Models

Stella Bollmann, Moritz Berger, Gerhard Tutz

Educ Psychol Meas. 2018 Oct; 78(5): 781–804. Published online 2017 Sep 25. doi: 10.1177/0013164417722179

PMCID:: PMC7328226

Abstract Article PDF–341K

Select item 682044920.

Bayes Factors for Evaluating Latent Monotonicity in Polytomous Item Response Theory Models

Jesper Tijmstra, Maria Bolsinova

Psychometrika. 2019; 84(3): 846–869. Published online 2019 Feb 21. doi: 10.1007/s11336-019-09661-w

PMCID:: PMC6820449

Abstract Article PDF–400K

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PMC Images search for ordered set of item【数】

Fig 1Illustration of the Genetic Algorithm.

In the first iteration, the Genetic Algorithm randomly selects four item sets, which are highlighted in color, from an initial pool of 10 item sets. Each two of these sets are used to produce a new item set based on two principles: 1) subsetting and recombination (= crossover) and 2) random changes to the item set (= mutation). The newly assembled short versions are evaluated and ordered according to an optimization function (= fitness evaluation). The ordering influences the selection probability of an item to be assembled in a short version in the next iteration.

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm

PLoS One. 2016;11(11):e0167110.
Figure 1.

Schematic illustrating the prediction task. Electronic health record data from 57 624 patients are processed into a 35M by 20K feature matrix. Using this feature matrix, 2 different response vectors are created for order set usage and individual clinical items 24 hours after every instance an item is ordered. We then train and evaluate neural networks to predict order set usage and individual clinical items.

ClinicNet: machine learning for personalized clinical order set recommendations

JAMIA Open. 2020 Jul;3(2):216-224.
Figure 2

Plot showing mapping of person and item measures in logits for the set of questions about perceived helpfulness of the bioptic telescope. Participants (color-coded by group) are on the left of the vertical line and items (visual driving tasks) are on the right. Participants are ordered from those who perceived the bioptic to be very helpful at the bottom of the scale to those who perceived it to be of little help at the top of the scale. Items are ordered from the tasks for which the bioptic was most helpful at the bottom to the least helpful at the top. The telescope was most helpful for reading street names and traffic signs. The distributions of person abilities and item difficulties are well matched (overlapping).

Bioptic Telescope Use and Driving Patterns of Drivers with Age-Related Macular Degeneration

Transl Vis Sci Technol. 2016 Sep;5(5):5.
Fig 1Schematic illustration of edge inference by PPIDM in a tripartite graph setting .

Z is here PPI, a set of PPIs, X and Y are D_L and D_R, two sets of Pfam domains. Each item in PPI is an ordered pair of proteins ppi_i = (L_i, R_i) with Id(L_i) ≤ Id(R_i). Domains in D_L and D_R are connected to their common neighbor item ppi_i in PPI through L_i and R_i respectively. The (d1, d2) edge comes from the Gold-Standard dataset of DDIs. With PPIDM, new edges are inferred between domains of D_L and domains of D_R if their adjacency vectors in PPI are similar. Here, the (d3, d2) edge is inferred because d3 and d2 are found in ppi1 and ppi2, and (d3, d4) is inferred because d3 and d4 are found in ppi2 and ppi3. However, the score of (d3, d2) will be lower than the score of (d1, d2) because d3 has one neighbor that does not contain d2 (namely ppi3).

PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

PLoS Comput Biol. 2021 Aug;17(8):e1008844.