Literature DB >> 32351423

Using Multilevel Mediation Model to Measure the Contribution of Beliefs to Judgments of Learning.

Xiao Hu1, Jun Zheng1, Tian Fan1, Ningxin Su1, Chunliang Yang2, Liang Luo1,2.   

Abstract

Recent studies on judgments of learning (JOLs) suggest that beliefs play an important role in the formation of JOLs. The current article introduces a multilevel mediation model to quantify the contribution of beliefs to JOL formation when both JOLs and global belief-based predictions are measured from the same group of participants. Our examples of fitting the multilevel mediation model to hypothetical and published datasets revealed that it is feasible to use the multilevel mediation model to examine the mediation effect of beliefs on the relationship between a cue and JOLs, and quantitatively compare the effects of beliefs and processing fluency on JOLs in one model. Then we compared the current multilevel mediation model and the multilevel moderation model implemented in previous studies, and discussed their similarities and differences. Finally, a data simulation was performed to explain the inflation of Type I error for the multilevel mediation model when we regress global belief-based predictions on the cue, and suggestions about appropriate steps for conducting multilevel mediation analysis are proposed.
Copyright © 2020 Hu, Zheng, Fan, Su, Yang and Luo.

Entities:  

Keywords:  beliefs; judgments of learning; metamemory; multilevel linear model; multilevel mediation model

Year:  2020        PMID: 32351423      PMCID: PMC7174663          DOI: 10.3389/fpsyg.2020.00637

Source DB:  PubMed          Journal:  Front Psychol        ISSN: 1664-1078


  30 in total

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4.  Evidence that judgments of learning are causally related to study choice.

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Review 6.  Self-regulated learning: beliefs, techniques, and illusions.

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7.  Test Framing Generates a Stability Bias for Predictions of Learning by Causing People to Discount their Learning Beliefs.

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8.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

Authors:  R M Baron; D A Kenny
Journal:  J Pers Soc Psychol       Date:  1986-12

9.  Metacognitive unawareness of the errorful generation benefit and its effects on self-regulated learning.

Authors:  Chunliang Yang; Rosalind Potts; David R Shanks
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2017-01-23       Impact factor: 3.051

10.  Why is knowledge updating after task experience incomplete? Contributions of encoding experience, scaling artifact, and inferential deficit.

Authors:  Michael L Mueller; John Dunlosky; Sarah K Tauber
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  1 in total

1.  A Bayesian inference model for metamemory.

Authors:  Xiao Hu; Jun Zheng; Ningxin Su; Tian Fan; Chunliang Yang; Yue Yin; Stephen M Fleming; Liang Luo
Journal:  Psychol Rev       Date:  2021-05-27       Impact factor: 8.247

  1 in total

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