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Items: 17

1.

What Technology Can and Cannot Do to Support Assessment of Non-cognitive Skills.

Simmering VR, Ou L, Bolsinova M.

Front Psychol. 2019 Sep 25;10:2168. doi: 10.3389/fpsyg.2019.02168. eCollection 2019.

2.

Modeling Differences Between Response Times of Correct and Incorrect Responses.

Bolsinova M, Tijmstra J.

Psychometrika. 2019 Dec;84(4):1018-1046. doi: 10.1007/s11336-019-09682-5. Epub 2019 Aug 28.

PMID:
31463656
3.

A heteroscedastic hidden Markov mixture model for responses and categorized response times.

Molenaar D, Rózsa S, Bolsinova M.

Behav Res Methods. 2019 Apr;51(2):676-696. doi: 10.3758/s13428-019-01229-x.

4.

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

Tijmstra J, Bolsinova M.

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

5.

Nonlinear Indicator-Level Moderation in Latent Variable Models.

Bolsinova M, Molenaar D.

Multivariate Behav Res. 2019 Jan-Feb;54(1):62-84. doi: 10.1080/00273171.2018.1486174. Epub 2018 Dec 4.

PMID:
30513219
6.

Modeling Nonlinear Conditional Dependence Between Response Time and Accuracy.

Bolsinova M, Molenaar D.

Front Psychol. 2018 Sep 7;9:1525. doi: 10.3389/fpsyg.2018.01525. eCollection 2018.

7.

On the Importance of the Speed-Ability Trade-Off When Dealing With Not Reached Items.

Tijmstra J, Bolsinova M.

Front Psychol. 2018 Jun 13;9:964. doi: 10.3389/fpsyg.2018.00964. eCollection 2018.

8.

General mixture item response models with different item response structures: Exposition with an application to Likert scales.

Tijmstra J, Bolsinova M, Jeon M.

Behav Res Methods. 2018 Dec;50(6):2325-2344. doi: 10.3758/s13428-017-0997-0.

9.

A semi-parametric within-subject mixture approach to the analyses of responses and response times.

Molenaar D, Bolsinova M, Vermunt JK.

Br J Math Stat Psychol. 2018 May;71(2):205-228. doi: 10.1111/bmsp.12117. Epub 2017 Oct 17.

PMID:
29044460
10.

Improving precision of ability estimation: Getting more from response times.

Bolsinova M, Tijmstra J.

Br J Math Stat Psychol. 2018 Feb;71(1):13-38. doi: 10.1111/bmsp.12104. Epub 2017 Jun 21.

PMID:
28635139
11.

A heteroscedastic generalized linear model with a non-normal speed factor for responses and response times.

Molenaar D, Bolsinova M.

Br J Math Stat Psychol. 2017 May;70(2):297-316. doi: 10.1111/bmsp.12087. Epub 2017 Feb 3.

12.

Using expert knowledge for test linking.

Bolsinova M, Hoijtink H, Vermeulen JA, Béguin A.

Psychol Methods. 2017 Dec;22(4):705-724. doi: 10.1037/met0000124. Epub 2017 Apr 3.

PMID:
28368175
13.

Conditional Dependence between Response Time and Accuracy: An Overview of its Possible Sources and Directions for Distinguishing between Them.

Bolsinova M, Tijmstra J, Molenaar D, De Boeck P.

Front Psychol. 2017 Feb 16;8:202. doi: 10.3389/fpsyg.2017.00202. eCollection 2017.

14.

Modelling Conditional Dependence Between Response Time and Accuracy.

Bolsinova M, de Boeck P, Tijmstra J.

Psychometrika. 2017 Dec;82(4):1126-1148. doi: 10.1007/s11336-016-9537-6. Epub 2016 Oct 13.

PMID:
27738955
15.

Response moderation models for conditional dependence between response time and response accuracy.

Bolsinova M, Tijmstra J, Molenaar D.

Br J Math Stat Psychol. 2017 May;70(2):257-279. doi: 10.1111/bmsp.12076. Epub 2016 Sep 12.

PMID:
27618470
16.

Can IRT Solve the Missing Data Problem in Test Equating?

Bolsinova M, Maris G.

Front Psychol. 2016 Jan 5;6:1956. doi: 10.3389/fpsyg.2015.01956. eCollection 2015.

17.

A test for conditional independence between response time and accuracy.

Bolsinova M, Maris G.

Br J Math Stat Psychol. 2016 Feb;69(1):62-79. doi: 10.1111/bmsp.12059. Epub 2015 Jun 8.

PMID:
26059168

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