Robust Methods for Moderation Analysis with a Two-Level Regression Model

Yang, Miao; Yuan, Ke‐Hai

doi:10.1080/00273171.2016.1235965

Cited by 11 publications

(7 citation statements)

References 45 publications

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“…Fortunately, l * z functions as a standardized residual that we used in this study. Using a standardized residual is similar to the approach used in Yang and Yuan (2016). Next, we needed to choose a weight function.…”

Section: Robust Estimation Of Grm Through Robust Maximum Marginal Likmentioning

confidence: 99%

Robust maximum marginal likelihood (RMML) estimation for item response theory models

Hong

Cheng

2018

Behav Res

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Self-report data are common in psychological and survey research. Unfortunately, many of these samples are plagued with careless responses, due to unmotivated participants. The purpose of this study was to propose and evaluate a robust estimation method to detect careless or unmotivated responders, while leveraging item response theory (IRT) person-fit statistics. First, we outlined a general framework for robust estimation specific for IRT models. Subsequently, we conducted a simulation study covering multiple conditions in order to evaluate the performance of the proposed method. Ultimately, we showed that robust maximum marginal likelihood (RMML) estimation significantly improves detection rates for careless responders and reduces bias in item parameters across conditions. Furthermore, we applied our method to a real data set, to illustrate the utility of the proposed method. Our findings suggest that robust estimation coupled with person-fit statistics offers a powerful procedure to identify careless respondents for further review and to provide more accurate item parameter estimates in the presence of careless responses.

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Section: Robust Estimation Of Grm Through Robust Maximum Marginal Likmentioning

confidence: 99%

Robust maximum marginal likelihood (RMML) estimation for item response theory models

Hong

Cheng

2018

Behav Res

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show abstract

“…* functions as a standardized residual which we use in this study. Using a standardized residual is similar to the approach used in Yang and Yuan (2016). Next, we need to choose a weight function.…”

Section: Robust Estimation Of Grm Through Robust Maximum Marginal Likmentioning

confidence: 99%

Robust Maximum Marginal Likelihood (RMML) Estimation for Item Response Theory Models

Hong¹,

Cheng²

2018

Preprint

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Self-report data are common in psychological and survey research. Unfortunately, many of these samples are plagued with careless responses due to unmotivated participants. The purpose of this study is to propose and evaluate a robust estimation method in order to detect careless, or unmotivated, responders while leveraging Item Response Theory (IRT) person fit statistics. First, we outline a general framework for robust estimation specific for IRT models.Subsequently, we conduct a simulation study covering multiple conditions to evaluate the performance of the proposed method. Ultimately, we show how robust maximum marginal likelihood (RMML) estimation significantly improves detection rates for careless responders and reduce bias in item parameters across conditions. Furthermore, we apply our method to a real dataset to illustrate the utility of the proposed method. Our findings suggest that robust estimation coupled with person fit statistics offers a powerful procedure to identify careless respondents for further review, and to provide more accurate item parameter estimates in presence of careless responses.

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“…Psychotherapy outcome is known to be influenced strongly by extratherapeutic factors (40), which increases the probability of having multivariate outliers. To avoid having outliers influencing slope estimates disproportionally to the bulk of observations we specified a Students' t-distribution with five degrees of freedom to the errors, giving a robust estimation of regression coefficients (37,41).…”

Section: Robust Modelling Of Treatment Moderationmentioning

confidence: 99%

Moderation of treatment effects by parent-adolescent conflict in a randomised controlled trial of Attachment-Based Family Therapy for adolescent depression

Rognli

Waraan

Czajkowski

et al. 2020

Scandinavian Journal of Child and Adolescent Psychiatry and Psychology

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Background: Conflict with parents is frequent in adolescent depression, and has been shown to predict poor treatment outcomes. Attachment Based Family Therapy (ABFT) is a manualised treatment for adolescent depression that may be robust to parent-adolescent conflict. Objective: To evaluate the hypothesis that parent-adolescent conflict moderates the outcome of Attachment-Based Family Therapy compared with treatment as usual. Methods: Data were obtained from a randomised trial comparing 16 weeks of ABFT to treatment as usual, in Norwegian Child and Adolescent Mental Health Services. Sixty adolescents with moderate to severe depression and their parents were recruited. Change in Grid-Hamilton Depression Rating Scale scores from baseline to week 16 was modelled using linear mixed models, and a three-way interaction of time, treatment allocation and a continuous measure of parent-adolescent conflict was fitted to estimate a moderator effect. The moderator model was compared to simpler models using leave-one-out crossvalidation. Results: Better outcomes were predicted for Attachment-Based Family Therapy at high levels of mother-adolescent conflict, and for treatment as usual at low levels of mother-adolescent conflict, giving preliminary support to the moderator hypothesis. Findings for father-adolescent conflict were mixed. Cross-validation did not clearly support the moderator model over a simple effect of time, indicating that the replicability of these findings is uncertain. Conclusion: The results suggest that parent-adolescent conflict should be further studied as a moderator of outcome in Attachment-Based Family Therapy. The trial did not meet its recruitment target and had high attrition, limiting the conclusions that may be drawn.

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Robust Methods for Moderation Analysis with a Two-Level Regression Model

Cited by 11 publications

References 45 publications

Robust maximum marginal likelihood (RMML) estimation for item response theory models

Robust maximum marginal likelihood (RMML) estimation for item response theory models

Robust Maximum Marginal Likelihood (RMML) Estimation for Item Response Theory Models

Moderation of treatment effects by parent-adolescent conflict in a randomised controlled trial of Attachment-Based Family Therapy for adolescent depression

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