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Showing 1 to 12 of 19 entries
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Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update.

Structural equation modeling : a multidisciplinary journal

Lubke GH, Campbell I, McArtor D, Miller P, Luningham J, van den Berg SM.
PMID: 28652682
Struct Equ Modeling. 2017;24(2):230-245. doi: 10.1080/10705511.2016.1252265. Epub 2016 Dec 05.

Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indices such as AIC or BIC, and inference is done based on...

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Lubke GH, Campbell I, McArtor D, et al. Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Struct Equ Modeling. 2016;24(2):230-245doi: 10.1080/10705511.2016.1252265.
Lubke, G. H., Campbell, I., McArtor, D., Miller, P., Luningham, J., & van den Berg, S. M. (2017). Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Structural equation modeling : a multidisciplinary journal, 24(2), 230-245. https://doi.org/10.1080/10705511.2016.1252265
Lubke, Gitta H, et al. "Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update." Structural equation modeling : a multidisciplinary journal vol. 24,2 (2017): 230-245. doi: https://doi.org/10.1080/10705511.2016.1252265
Lubke GH, Campbell I, McArtor D, Miller P, Luningham J, van den Berg SM. Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Struct Equ Modeling. 2017;24(2):230-245. doi: 10.1080/10705511.2016.1252265. Epub 2016 Dec 05. PMID: 28652682; PMCID: PMC5482523.
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Tangle: A metric for quantifying complexity and erratic behavior in short time series.

Psychological methods

Moulder RG, Daniel KE, Teachman BA, Boker SM.
PMID: 33507767
Psychol Methods. 2021 Jan 28; doi: 10.1037/met0000386. Epub 2021 Jan 28.

Temporal complexity refers to qualities of a time series that are emergent, erratic, or not easily described by linear processes. Quantifying temporal complexity within a system is key to understanding the time based dynamics of said system. However, many...

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Moulder RG, Daniel KE, Teachman BA, et al. Tangle: A metric for quantifying complexity and erratic behavior in short time series. Psychol Methods. 2021;doi: 10.1037/met0000386.
Moulder, R. G., Daniel, K. E., Teachman, B. A., & Boker, S. M. (2021). Tangle: A metric for quantifying complexity and erratic behavior in short time series. Psychological methods, . https://doi.org/10.1037/met0000386
Moulder, Robert G, et al. "Tangle: A metric for quantifying complexity and erratic behavior in short time series." Psychological methods vol. (2021). doi: https://doi.org/10.1037/met0000386
Moulder RG, Daniel KE, Teachman BA, Boker SM. Tangle: A metric for quantifying complexity and erratic behavior in short time series. Psychol Methods. 2021 Jan 28; doi: 10.1037/met0000386. Epub 2021 Jan 28. PMID: 33507767; PMCID: PMC8552416.
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Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales.

Structural equation modeling : a multidisciplinary journal

McKee KL, Rappaport LM, Boker SM, Moskowitz DS, Neale MC.
PMID: 30416330
Struct Equ Modeling. 2018;25(6):888-905. doi: 10.1080/10705511.2018.1442224. Epub 2018 Mar 16.

Damped Linear Oscillators estimated by 2nd-order Latent Differential Equation have assumed a constant equilibrium and one oscillatory component. Lower-frequency oscillations may come from seasonal background processes, which non-randomly contribute to deviation from equilibrium at each occasion and confound estimation...

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McKee KL, Rappaport LM, Boker SM, et al. Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales. Struct Equ Modeling. 2018;25(6):888-905doi: 10.1080/10705511.2018.1442224.
McKee, K. L., Rappaport, L. M., Boker, S. M., Moskowitz, D. S., & Neale, M. C. (2018). Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales. Structural equation modeling : a multidisciplinary journal, 25(6), 888-905. https://doi.org/10.1080/10705511.2018.1442224
McKee, Kevin L, et al. "Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales." Structural equation modeling : a multidisciplinary journal vol. 25,6 (2018): 888-905. doi: https://doi.org/10.1080/10705511.2018.1442224
McKee KL, Rappaport LM, Boker SM, Moskowitz DS, Neale MC. Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales. Struct Equ Modeling. 2018;25(6):888-905. doi: 10.1080/10705511.2018.1442224. Epub 2018 Mar 16. PMID: 30416330; PMCID: PMC6223647.
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Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data.

Multivariate behavioral research

Lu ZL, Zhang Z, Lubke G.
PMID: 24790248
Multivariate Behav Res. 2011 Jul 01;46(4):567-597. doi: 10.1080/00273171.2011.589261.

No abstract available.

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Lu ZL, Zhang Z, Lubke G. Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data. Multivariate Behav Res. 2011;46(4):567-597doi: 10.1080/00273171.2011.589261.
Lu, Z. L., Zhang, Z., & Lubke, G. (2011). Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data. Multivariate behavioral research, 46(4), 567-597. https://doi.org/10.1080/00273171.2011.589261
Lu, Zhenqiu Laura, et al. "Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data." Multivariate behavioral research vol. 46,4 (2011): 567-597. doi: https://doi.org/10.1080/00273171.2011.589261
Lu ZL, Zhang Z, Lubke G. Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data. Multivariate Behav Res. 2011 Jul 01;46(4):567-597. doi: 10.1080/00273171.2011.589261. PMID: 24790248; PMCID: PMC4002129.
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Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches.

Structural equation modeling : a multidisciplinary journal

Lubke G, Tueller S.
PMID: 24648712
Struct Equ Modeling. 2010 Oct 01;17(4):605-628. doi: 10.1080/10705511.2010.510050. Epub 2010 Oct 12.

Taxometric procedures such as MAXEIG and factor mixture modeling (FMM) are used in latent class clustering, but they have very different sets of strengths and weaknesses. Taxometric procedures, popular in psychiatric and psychopathology applications, do not rely on distributional...

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Lubke G, Tueller S. Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches. Struct Equ Modeling. 2010;17(4):605-628doi: 10.1080/10705511.2010.510050.
Lubke, G., & Tueller, S. (2010). Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches. Structural equation modeling : a multidisciplinary journal, 17(4), 605-628. https://doi.org/10.1080/10705511.2010.510050
Lubke, Gitta, and Tueller, Stephen. "Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches." Structural equation modeling : a multidisciplinary journal vol. 17,4 (2010): 605-628. doi: https://doi.org/10.1080/10705511.2010.510050
Lubke G, Tueller S. Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches. Struct Equ Modeling. 2010 Oct 01;17(4):605-628. doi: 10.1080/10705511.2010.510050. Epub 2010 Oct 12. PMID: 24648712; PMCID: PMC3955757.
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Blue again: perturbational effects of antidepressants suggest monoaminergic homeostasis in major depression.

Frontiers in psychology

Andrews PW, Kornstein SG, Halberstadt LJ, Gardner CO, Neale MC.
PMID: 21779273
Front Psychol. 2011 Jul 07;2:159. doi: 10.3389/fpsyg.2011.00159. eCollection 2011.

Some evolutionary researchers have argued that current diagnostic criteria for major depressive disorder (MDD) may not accurately distinguish true instances of disorder from a normal, adaptive stress response. According to disorder advocates, neurochemicals like the monoamine neurotransmitters (serotonin, norepinephrine,...

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Andrews PW, Kornstein SG, Halberstadt LJ, et al. Blue again: perturbational effects of antidepressants suggest monoaminergic homeostasis in major depression. Front Psychol. 2011;2:159doi: 10.3389/fpsyg.2011.00159.
Andrews, P. W., Kornstein, S. G., Halberstadt, L. J., Gardner, C. O., & Neale, M. C. (2011). Blue again: perturbational effects of antidepressants suggest monoaminergic homeostasis in major depression. Frontiers in psychology, 2159. https://doi.org/10.3389/fpsyg.2011.00159
Andrews, Paul W, et al. "Blue again: perturbational effects of antidepressants suggest monoaminergic homeostasis in major depression." Frontiers in psychology vol. 2 (2011): 159. doi: https://doi.org/10.3389/fpsyg.2011.00159
Andrews PW, Kornstein SG, Halberstadt LJ, Gardner CO, Neale MC. Blue again: perturbational effects of antidepressants suggest monoaminergic homeostasis in major depression. Front Psychol. 2011 Jul 07;2:159. doi: 10.3389/fpsyg.2011.00159. eCollection 2011. PMID: 21779273; PMCID: PMC3133866.
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Primum non nocere: an evolutionary analysis of whether antidepressants do more harm than good.

Frontiers in psychology

Andrews PW, Thomson JA, Amstadter A, Neale MC.
PMID: 22536191
Front Psychol. 2012 Apr 24;3:117. doi: 10.3389/fpsyg.2012.00117. eCollection 2012.

Antidepressant medications are the first-line treatment for people meeting current diagnostic criteria for major depressive disorder. Most antidepressants are designed to perturb the mechanisms that regulate the neurotransmitter serotonin - an evolutionarily ancient biochemical found in plants, animals, and...

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Andrews PW, Thomson JA, Amstadter A, et al. Primum non nocere: an evolutionary analysis of whether antidepressants do more harm than good. Front Psychol. 2012;3:117doi: 10.3389/fpsyg.2012.00117.
Andrews, P. W., Thomson, J. A., Amstadter, A., & Neale, M. C. (2012). Primum non nocere: an evolutionary analysis of whether antidepressants do more harm than good. Frontiers in psychology, 3117. https://doi.org/10.3389/fpsyg.2012.00117
Andrews, Paul W, et al. "Primum non nocere: an evolutionary analysis of whether antidepressants do more harm than good." Frontiers in psychology vol. 3 (2012): 117. doi: https://doi.org/10.3389/fpsyg.2012.00117
Andrews PW, Thomson JA, Amstadter A, Neale MC. Primum non nocere: an evolutionary analysis of whether antidepressants do more harm than good. Front Psychol. 2012 Apr 24;3:117. doi: 10.3389/fpsyg.2012.00117. eCollection 2012. PMID: 22536191; PMCID: PMC3334530.
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Effect of Body Composition Methodology on Heritability Estimation of Body Fatness.

The open nutrition journal

Elder SJ, Roberts SB, McCrory MA, Das SK, Fuss PJ, Pittas AG, Greenberg AS, Heymsfield SB, Dawson-Hughes B, Bouchard TJ, Saltzman E, Neale MC.
PMID: 25067962
Open Nutr J. 2012 Apr 05;6:48-58. doi: 10.2174/1874288201206010048.

Heritability estimates of human body fatness vary widely and the contribution of body composition methodology to this variability is unknown. The effect of body composition methodology on estimations of genetic and environmental contributions to body fatness variation was examined...

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Elder SJ, Roberts SB, McCrory MA, et al. Effect of Body Composition Methodology on Heritability Estimation of Body Fatness. Open Nutr J. 2012;6:48-58doi: 10.2174/1874288201206010048.
Elder, S. J., Roberts, S. B., McCrory, M. A., Das, S. K., Fuss, P. J., Pittas, A. G., Greenberg, A. S., Heymsfield, S. B., Dawson-Hughes, B., Bouchard, T. J., Saltzman, E., & Neale, M. C. (2012). Effect of Body Composition Methodology on Heritability Estimation of Body Fatness. The open nutrition journal, 648-58. https://doi.org/10.2174/1874288201206010048
Elder, Sonya J, et al. "Effect of Body Composition Methodology on Heritability Estimation of Body Fatness." The open nutrition journal vol. 6 (2012): 48-58. doi: https://doi.org/10.2174/1874288201206010048
Elder SJ, Roberts SB, McCrory MA, Das SK, Fuss PJ, Pittas AG, Greenberg AS, Heymsfield SB, Dawson-Hughes B, Bouchard TJ, Saltzman E, Neale MC. Effect of Body Composition Methodology on Heritability Estimation of Body Fatness. Open Nutr J. 2012 Apr 05;6:48-58. doi: 10.2174/1874288201206010048. PMID: 25067962; PMCID: PMC4110980.
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Genetic and Environmental Influences on Eating Behavior - A Study of Twin Pairs Reared Apart or Reared Together.

The open nutrition journal

Elder SJ, Neale MC, Fuss PJ, Lichtenstein AH, Greenberg AS, McCrory MA, Bouchard TJ, Saltzman E, Roberts SB.
PMID: 25067963
Open Nutr J. 2012 Apr 06;6:59-70. doi: 10.2174/1874288201206010059.

This study examined the relative influence of genetic versus environmental factors on specific aspects of eating behavior. Adult monozygotic twins (22 pairs and 3 singleton reared apart, 38 pairs and 9 singleton reared together, age 18-76 years, BMI 17-43...

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Elder SJ, Neale MC, Fuss PJ, et al. Genetic and Environmental Influences on Eating Behavior - A Study of Twin Pairs Reared Apart or Reared Together. Open Nutr J. 2012;6:59-70doi: 10.2174/1874288201206010059.
Elder, S. J., Neale, M. C., Fuss, P. J., Lichtenstein, A. H., Greenberg, A. S., McCrory, M. A., Bouchard, T. J., Saltzman, E., & Roberts, S. B. (2012). Genetic and Environmental Influences on Eating Behavior - A Study of Twin Pairs Reared Apart or Reared Together. The open nutrition journal, 659-70. https://doi.org/10.2174/1874288201206010059
Elder, Sonya J, et al. "Genetic and Environmental Influences on Eating Behavior - A Study of Twin Pairs Reared Apart or Reared Together." The open nutrition journal vol. 6 (2012): 59-70. doi: https://doi.org/10.2174/1874288201206010059
Elder SJ, Neale MC, Fuss PJ, Lichtenstein AH, Greenberg AS, McCrory MA, Bouchard TJ, Saltzman E, Roberts SB. Genetic and Environmental Influences on Eating Behavior - A Study of Twin Pairs Reared Apart or Reared Together. Open Nutr J. 2012 Apr 06;6:59-70. doi: 10.2174/1874288201206010059. PMID: 25067963; PMCID: PMC4110981.
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A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence.

Multivariate behavioral research

Estabrook R, Neale M.
PMID: 24049215
Multivariate Behav Res. 2013 Jan 01;48(1):1-27. doi: 10.1080/00273171.2012.730072.

Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the existence of missing data have...

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Estabrook R, Neale M. A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence. Multivariate Behav Res. 2013;48(1):1-27doi: 10.1080/00273171.2012.730072.
Estabrook, R., & Neale, M. (2013). A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence. Multivariate behavioral research, 48(1), 1-27. https://doi.org/10.1080/00273171.2012.730072
Estabrook, Ryne, and Neale, Michael. "A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence." Multivariate behavioral research vol. 48,1 (2013): 1-27. doi: https://doi.org/10.1080/00273171.2012.730072
Estabrook R, Neale M. A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence. Multivariate Behav Res. 2013 Jan 01;48(1):1-27. doi: 10.1080/00273171.2012.730072. PMID: 24049215; PMCID: PMC3773873.
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Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots.

Structural equation modeling : a multidisciplinary journal

Neale MC, Clark SL, Dolan CV, Hunter MD.
PMID: 26924921
Struct Equ Modeling. 2016 Mar 01;23(2):221-233. doi: 10.1080/10705511.2014.979932. Epub 2015 Jun 26.

A linear latent growth curve mixture model with regime switching is extended in 2 ways. Previously, the matrix of first-order Markov switching probabilities was specified to be time-invariant, regardless of the pair of occasions being considered. The first extension,...

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Neale MC, Clark SL, Dolan CV, et al. Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots. Struct Equ Modeling. 2015;23(2):221-233doi: 10.1080/10705511.2014.979932.
Neale, M. C., Clark, S. L., Dolan, C. V., & Hunter, M. D. (2016). Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots. Structural equation modeling : a multidisciplinary journal, 23(2), 221-233. https://doi.org/10.1080/10705511.2014.979932
Neale, Michael C, et al. "Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots." Structural equation modeling : a multidisciplinary journal vol. 23,2 (2016): 221-233. doi: https://doi.org/10.1080/10705511.2014.979932
Neale MC, Clark SL, Dolan CV, Hunter MD. Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots. Struct Equ Modeling. 2016 Mar 01;23(2):221-233. doi: 10.1080/10705511.2014.979932. Epub 2015 Jun 26. PMID: 26924921; PMCID: PMC4767507.
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Inference Based on the Best-Fitting Model can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach.

Structural equation modeling : a multidisciplinary journal

Lubke GH, Campbell I.
PMID: 28663687
Struct Equ Modeling. 2016;23(4):479-490. doi: 10.1080/10705511.2016.1141355. Epub 2016 Apr 07.

Inference and conclusions drawn from model fitting analyses are commonly based on a single "best-fitting" model. If model selection and inference are carried out using the same data model selection uncertainty is ignored. We illustrate the Type I error...

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Lubke GH, Campbell I. Inference Based on the Best-Fitting Model can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach. Struct Equ Modeling. 2016;23(4):479-490doi: 10.1080/10705511.2016.1141355.
Lubke, G. H., & Campbell, I. (2016). Inference Based on the Best-Fitting Model can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach. Structural equation modeling : a multidisciplinary journal, 23(4), 479-490. https://doi.org/10.1080/10705511.2016.1141355
Lubke, Gitta H, and Campbell, Ian. "Inference Based on the Best-Fitting Model can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach." Structural equation modeling : a multidisciplinary journal vol. 23,4 (2016): 479-490. doi: https://doi.org/10.1080/10705511.2016.1141355
Lubke GH, Campbell I. Inference Based on the Best-Fitting Model can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach. Struct Equ Modeling. 2016;23(4):479-490. doi: 10.1080/10705511.2016.1141355. Epub 2016 Apr 07. PMID: 28663687; PMCID: PMC5487004.
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Showing 1 to 12 of 19 entries