Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 16 entries
Sorted by: Best Match Show Resources per page
Reanalysis of the German PISA Data: A Comparison of Different Approaches for Trend Estimation With a Particular Emphasis on Mode Effects.

Frontiers in psychology

Robitzsch A, Lüdtke O, Goldhammer F, Kroehne U, Köller O.
PMID: 32528352
Front Psychol. 2020 May 26;11:884. doi: 10.3389/fpsyg.2020.00884. eCollection 2020.

International large-scale assessments, such as the Program for International Student Assessment (PISA), are conducted to provide information on the effectiveness of education systems. In PISA, the target population of 15-year-old students is assessed every 3 years. Trends show whether...

Cite
Robitzsch A, Lüdtke O, Goldhammer F, et al. Reanalysis of the German PISA Data: A Comparison of Different Approaches for Trend Estimation With a Particular Emphasis on Mode Effects. Front Psychol. 2020;11:884doi: 10.3389/fpsyg.2020.00884.
Robitzsch, A., Lüdtke, O., Goldhammer, F., Kroehne, U., & Köller, O. (2020). Reanalysis of the German PISA Data: A Comparison of Different Approaches for Trend Estimation With a Particular Emphasis on Mode Effects. Frontiers in psychology, 11884. https://doi.org/10.3389/fpsyg.2020.00884
Robitzsch, Alexander, et al. "Reanalysis of the German PISA Data: A Comparison of Different Approaches for Trend Estimation With a Particular Emphasis on Mode Effects." Frontiers in psychology vol. 11 (2020): 884. doi: https://doi.org/10.3389/fpsyg.2020.00884
Robitzsch A, Lüdtke O, Goldhammer F, Kroehne U, Köller O. Reanalysis of the German PISA Data: A Comparison of Different Approaches for Trend Estimation With a Particular Emphasis on Mode Effects. Front Psychol. 2020 May 26;11:884. doi: 10.3389/fpsyg.2020.00884. eCollection 2020. PMID: 32528352; PMCID: PMC7264417.
Copy Download .nbib Format: NLM
The Onset of Rapid-Guessing Behavior Over the Course of Testing Time: A Matter of Motivation and Cognitive Resources.

Frontiers in psychology

Lindner MA, Lüdtke O, Nagy G.
PMID: 31396120
Front Psychol. 2019 Jul 23;10:1533. doi: 10.3389/fpsyg.2019.01533. eCollection 2019.

Digital tests make it possible to identify student effort by means of response times, specifically, unrealistically fast responses that are defined as rapid-guessing behavior (RGB). In this study, we used latent class and growth curve models to examine (1)...

Cite
Lindner MA, Lüdtke O, Nagy G. The Onset of Rapid-Guessing Behavior Over the Course of Testing Time: A Matter of Motivation and Cognitive Resources. Front Psychol. 2019;10:1533doi: 10.3389/fpsyg.2019.01533.
Lindner, M. A., Lüdtke, O., & Nagy, G. (2019). The Onset of Rapid-Guessing Behavior Over the Course of Testing Time: A Matter of Motivation and Cognitive Resources. Frontiers in psychology, 101533. https://doi.org/10.3389/fpsyg.2019.01533
Lindner, Marlit Annalena, et al. "The Onset of Rapid-Guessing Behavior Over the Course of Testing Time: A Matter of Motivation and Cognitive Resources." Frontiers in psychology vol. 10 (2019): 1533. doi: https://doi.org/10.3389/fpsyg.2019.01533
Lindner MA, Lüdtke O, Nagy G. The Onset of Rapid-Guessing Behavior Over the Course of Testing Time: A Matter of Motivation and Cognitive Resources. Front Psychol. 2019 Jul 23;10:1533. doi: 10.3389/fpsyg.2019.01533. eCollection 2019. PMID: 31396120; PMCID: PMC6664071.
Copy Download .nbib Format: NLM
Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach.

Behavior research methods

Grund S, Lüdtke O, Robitzsch A.
PMID: 34027594
Behav Res Methods. 2021 Dec;53(6):2631-2649. doi: 10.3758/s13428-020-01530-0. Epub 2021 May 23.

Multilevel models often include nonlinear effects, such as random slopes or interaction effects. The estimation of these models can be difficult when the underlying variables contain missing data. Although several methods for handling missing data such as multiple imputation...

Cite
Grund S, Lüdtke O, Robitzsch A. Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach. Behav Res Methods. 2021;53(6):2631-2649doi: 10.3758/s13428-020-01530-0.
Grund, S., Lüdtke, O., & Robitzsch, A. (2021). Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach. Behavior research methods, 53(6), 2631-2649. https://doi.org/10.3758/s13428-020-01530-0
Grund, Simon, et al. "Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach." Behavior research methods vol. 53,6 (2021): 2631-2649. doi: https://doi.org/10.3758/s13428-020-01530-0
Grund S, Lüdtke O, Robitzsch A. Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach. Behav Res Methods. 2021 Dec;53(6):2631-2649. doi: 10.3758/s13428-020-01530-0. Epub 2021 May 23. PMID: 34027594; PMCID: PMC8613130.
Copy Download .nbib Format: NLM
Alleviating estimation problems in small sample structural equation modeling-A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches.

Psychological methods

Ulitzsch E, Lüdtke O, Robitzsch A.
PMID: 34928675
Psychol Methods. 2021 Dec 20; doi: 10.1037/met0000435. Epub 2021 Dec 20.

Small sample structural equation modeling (SEM) may exhibit serious estimation problems, such as failure to converge, inadmissible solutions, and unstable parameter estimates. A vast literature has compared the performance of different solutions for small sample SEM in contrast to...

Cite
Ulitzsch E, Lüdtke O, Robitzsch A. Alleviating estimation problems in small sample structural equation modeling-A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches. Psychol Methods. 2021;doi: 10.1037/met0000435.
Ulitzsch, E., Lüdtke, O., & Robitzsch, A. (2021). Alleviating estimation problems in small sample structural equation modeling-A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches. Psychological methods, . https://doi.org/10.1037/met0000435
Ulitzsch, Esther, et al. "Alleviating estimation problems in small sample structural equation modeling-A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches." Psychological methods vol. (2021). doi: https://doi.org/10.1037/met0000435
Ulitzsch E, Lüdtke O, Robitzsch A. Alleviating estimation problems in small sample structural equation modeling-A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches. Psychol Methods. 2021 Dec 20; doi: 10.1037/met0000435. Epub 2021 Dec 20. PMID: 34928675.
Copy Download .nbib Format: NLM
The Dimensionality of Reading Self-Concept: Examining Its Stability Using Local Structural Equation Models.

Assessment

Basarkod G, Marsh HW, Sahdra BK, Parker PD, Guo J, Dicke T, Lüdtke O.
PMID: 35037486
Assessment. 2022 Jan 15;10731911211069675. doi: 10.1177/10731911211069675. Epub 2022 Jan 15.

For results from large-scale surveys to inform policy and practice appropriately, all participants must interpret and respond to items similarly. While organizers of surveys assessing student outcomes often ensure this for achievement measures, doing so for psychological questionnaires is...

Cite
Basarkod G, Marsh HW, Sahdra BK, et al. The Dimensionality of Reading Self-Concept: Examining Its Stability Using Local Structural Equation Models. Assessment. 2022;10731911211069675doi: 10.1177/10731911211069675.
Basarkod, G., Marsh, H. W., Sahdra, B. K., Parker, P. D., Guo, J., Dicke, T., & Lüdtke, O. (2022). The Dimensionality of Reading Self-Concept: Examining Its Stability Using Local Structural Equation Models. Assessment, 10731911211069675. https://doi.org/10.1177/10731911211069675
Basarkod, Geetanjali, et al. "The Dimensionality of Reading Self-Concept: Examining Its Stability Using Local Structural Equation Models." Assessment vol. (2022): 10731911211069675. doi: https://doi.org/10.1177/10731911211069675
Basarkod G, Marsh HW, Sahdra BK, Parker PD, Guo J, Dicke T, Lüdtke O. The Dimensionality of Reading Self-Concept: Examining Its Stability Using Local Structural Equation Models. Assessment. 2022 Jan 15;10731911211069675. doi: 10.1177/10731911211069675. Epub 2022 Jan 15. PMID: 35037486.
Copy Download .nbib Format: NLM
Erratum to: Maximum Likelihood Estimation of a Social Relations Structural Equation Model.

Psychometrika

Nestler S, Lüdtke O, Robitzsch A.
PMID: 34331189
Psychometrika. 2021 Sep;86(3):842. doi: 10.1007/s11336-021-09793-y.

No abstract available.

Cite
Nestler S, Lüdtke O, Robitzsch A. Erratum to: Maximum Likelihood Estimation of a Social Relations Structural Equation Model. Psychometrika. 2021;86(3):842doi: 10.1007/s11336-021-09793-y.
Nestler, S., Lüdtke, O., & Robitzsch, A. (2021). Erratum to: Maximum Likelihood Estimation of a Social Relations Structural Equation Model. Psychometrika, 86(3), 842. https://doi.org/10.1007/s11336-021-09793-y
Nestler, Steffen, et al. "Erratum to: Maximum Likelihood Estimation of a Social Relations Structural Equation Model." Psychometrika vol. 86,3 (2021): 842. doi: https://doi.org/10.1007/s11336-021-09793-y
Nestler S, Lüdtke O, Robitzsch A. Erratum to: Maximum Likelihood Estimation of a Social Relations Structural Equation Model. Psychometrika. 2021 Sep;86(3):842. doi: 10.1007/s11336-021-09793-y. PMID: 34331189; PMCID: PMC8502134.
Copy Download .nbib Format: NLM
The Stability of Extreme Response Style and Acquiescence Over 8 Years.

Assessment

Wetzel E, Lüdtke O, Zettler I, Böhnke JR.
PMID: 25986062
Assessment. 2016 Jun;23(3):279-91. doi: 10.1177/1073191115583714. Epub 2015 May 18.

This study investigated the stability of extreme response style (ERS) and acquiescence response style (ARS) over a period of 8 years. ERS and ARS were measured with item sets drawn randomly from a large pool of items used in...

Cite
Wetzel E, Lüdtke O, Zettler I, et al. The Stability of Extreme Response Style and Acquiescence Over 8 Years. Assessment. 2015;23(3):279-91doi: 10.1177/1073191115583714.
Wetzel, E., Lüdtke, O., Zettler, I., & Böhnke, J. R. (2016). The Stability of Extreme Response Style and Acquiescence Over 8 Years. Assessment, 23(3), 279-91. https://doi.org/10.1177/1073191115583714
Wetzel, Eunike, et al. "The Stability of Extreme Response Style and Acquiescence Over 8 Years." Assessment vol. 23,3 (2016): 279-91. doi: https://doi.org/10.1177/1073191115583714
Wetzel E, Lüdtke O, Zettler I, Böhnke JR. The Stability of Extreme Response Style and Acquiescence Over 8 Years. Assessment. 2016 Jun;23(3):279-91. doi: 10.1177/1073191115583714. Epub 2015 May 18. PMID: 25986062.
Copy Download .nbib Format: NLM
Synergistic Effects of Expectancy and Value on Homework Engagement: The Case for a Within-Person Perspective.

Multivariate behavioral research

Nagengast B, Trautwein U, Kelava A, Lüdtke O.
PMID: 26741849
Multivariate Behav Res. 2013 May;48(3):428-60. doi: 10.1080/00273171.2013.775060.

Historically, expectancy-value models of motivation assumed a synergistic relation between expectancy and value: motivation is high only when both expectancy and value are high. Motivational processes were studied from a within-person perspective, with expectancies and values being assessed or...

Cite
Nagengast B, Trautwein U, Kelava A, et al. Synergistic Effects of Expectancy and Value on Homework Engagement: The Case for a Within-Person Perspective. Multivariate Behav Res. 2013;48(3):428-60doi: 10.1080/00273171.2013.775060.
Nagengast, B., Trautwein, U., Kelava, A., & Lüdtke, O. (2013). Synergistic Effects of Expectancy and Value on Homework Engagement: The Case for a Within-Person Perspective. Multivariate behavioral research, 48(3), 428-60. https://doi.org/10.1080/00273171.2013.775060
Nagengast, Benjamin, et al. "Synergistic Effects of Expectancy and Value on Homework Engagement: The Case for a Within-Person Perspective." Multivariate behavioral research vol. 48,3 (2013): 428-60. doi: https://doi.org/10.1080/00273171.2013.775060
Nagengast B, Trautwein U, Kelava A, Lüdtke O. Synergistic Effects of Expectancy and Value on Homework Engagement: The Case for a Within-Person Perspective. Multivariate Behav Res. 2013 May;48(3):428-60. doi: 10.1080/00273171.2013.775060. PMID: 26741849.
Copy Download .nbib Format: NLM
The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test.

Journal of Intelligence

Ziegler M, Schroeter TA, Lüdtke O, Roemer L.
PMID: 31162462
J Intell. 2018 Aug 01;6(3). doi: 10.3390/jintelligence6030035.

The Openness-Fluid-Crystallized-Intelligence (OFCI) model posits long-term relations between Openness and cognitive abilities and has been successfully tested with longitudinal data. However, research on the developmental interplay between cognitive abilities and personality exists only sparsely. The current paper focuses on...

Cite
Ziegler M, Schroeter TA, Lüdtke O, et al. The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test. J Intell. 2018;6(3)doi: 10.3390/jintelligence6030035.
Ziegler, M., Schroeter, T. A., Lüdtke, O., & Roemer, L. (2018). The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test. Journal of Intelligence, 6(3), . https://doi.org/10.3390/jintelligence6030035
Ziegler, Matthias, et al. "The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test." Journal of Intelligence vol. 6,3 (2018). doi: https://doi.org/10.3390/jintelligence6030035
Ziegler M, Schroeter TA, Lüdtke O, Roemer L. The Enriching Interplay between Openness and Interest: A Theoretical Elaboration of the OFCI Model and a First Empirical Test. J Intell. 2018 Aug 01;6(3). doi: 10.3390/jintelligence6030035. PMID: 31162462; PMCID: PMC6480751.
Copy Download .nbib Format: NLM
Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error.

Multivariate behavioral research

Marsh HW, Lüdtke O, Robitzsch A, Trautwein U, Asparouhov T, Muthén B, Nagengast B.
PMID: 26801796
Multivariate Behav Res. 2009 Nov 30;44(6):764-802. doi: 10.1080/00273170903333665.

This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the...

Cite
Marsh HW, Lüdtke O, Robitzsch A, et al. Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error. Multivariate Behav Res. 2009;44(6):764-802doi: 10.1080/00273170903333665.
Marsh, H. W., Lüdtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B., & Nagengast, B. (2009). Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error. Multivariate behavioral research, 44(6), 764-802. https://doi.org/10.1080/00273170903333665
Marsh, Herbert W, et al. "Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error." Multivariate behavioral research vol. 44,6 (2009): 764-802. doi: https://doi.org/10.1080/00273170903333665
Marsh HW, Lüdtke O, Robitzsch A, Trautwein U, Asparouhov T, Muthén B, Nagengast B. Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error. Multivariate Behav Res. 2009 Nov 30;44(6):764-802. doi: 10.1080/00273170903333665. PMID: 26801796.
Copy Download .nbib Format: NLM
Causal Inference with Multilevel Data: A Comparison of Different Propensity Score Weighting Approaches.

Multivariate behavioral research

Fuentes A, Lüdtke O, Robitzsch A.
PMID: 34128730
Multivariate Behav Res. 2021 Jun 15;1-24. doi: 10.1080/00273171.2021.1925521. Epub 2021 Jun 15.

Propensity score methods are a widely recommended approach to adjust for confounding and to recover treatment effects with non-experimental, single-level data. This article reviews propensity score weighting estimators for multilevel data in which individuals (level 1) are nested in...

Cite
Fuentes A, Lüdtke O, Robitzsch A. Causal Inference with Multilevel Data: A Comparison of Different Propensity Score Weighting Approaches. Multivariate Behav Res. 2021;1-24doi: 10.1080/00273171.2021.1925521.
Fuentes, A., Lüdtke, O., & Robitzsch, A. (2021). Causal Inference with Multilevel Data: A Comparison of Different Propensity Score Weighting Approaches. Multivariate behavioral research, 1-24. https://doi.org/10.1080/00273171.2021.1925521
Fuentes, Alvaro, et al. "Causal Inference with Multilevel Data: A Comparison of Different Propensity Score Weighting Approaches." Multivariate behavioral research vol. (2021): 1-24. doi: https://doi.org/10.1080/00273171.2021.1925521
Fuentes A, Lüdtke O, Robitzsch A. Causal Inference with Multilevel Data: A Comparison of Different Propensity Score Weighting Approaches. Multivariate Behav Res. 2021 Jun 15;1-24. doi: 10.1080/00273171.2021.1925521. Epub 2021 Jun 15. PMID: 34128730.
Copy Download .nbib Format: NLM
A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models With Small Sample Sizes.

Frontiers in psychology

Lüdtke O, Ulitzsch E, Robitzsch A.
PMID: 33995176
Front Psychol. 2021 Apr 29;12:615162. doi: 10.3389/fpsyg.2021.615162. eCollection 2021.

With small to modest sample sizes and complex models, maximum likelihood (ML) estimation of confirmatory factor analysis (CFA) models can show serious estimation problems such as non-convergence or parameter estimates outside the admissible parameter space. In this article, we...

Cite
Lüdtke O, Ulitzsch E, Robitzsch A. A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models With Small Sample Sizes. Front Psychol. 2021;12:615162doi: 10.3389/fpsyg.2021.615162.
Lüdtke, O., Ulitzsch, E., & Robitzsch, A. (2021). A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models With Small Sample Sizes. Frontiers in psychology, 12615162. https://doi.org/10.3389/fpsyg.2021.615162
Lüdtke, Oliver, et al. "A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models With Small Sample Sizes." Frontiers in psychology vol. 12 (2021): 615162. doi: https://doi.org/10.3389/fpsyg.2021.615162
Lüdtke O, Ulitzsch E, Robitzsch A. A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models With Small Sample Sizes. Front Psychol. 2021 Apr 29;12:615162. doi: 10.3389/fpsyg.2021.615162. eCollection 2021. PMID: 33995176; PMCID: PMC8118082.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 16 entries