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Avoiding spurious moderation effects: An information-theoretic approach to moderation analysis

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Avoiding spurious moderation effects: An information-theoretic approach to moderation analysis. / Daryanto, Ahmad.
In: Journal of Business Research, Vol. 103, 22.06.2019, p. 110-118.

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Daryanto A. Avoiding spurious moderation effects: An information-theoretic approach to moderation analysis. Journal of Business Research. 2019 Jun 22;103:110-118. Epub 2019 Jun 22. doi: 10.1016/j.jbusres.2019.06.012

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@article{d32c8b50478644e7b5f9e5c0dfd03e27,
title = "Avoiding spurious moderation effects: An information-theoretic approach to moderation analysis",
abstract = "Researchers typically use moderated regression models to examine the presence of linear moderation effects in their studies. However, researchers rarely conduct a robustness check following a significant moderation effect to investigate whether the moderation effect is spurious. The misleading moderation can occur when a predictor and a moderator variable correlate and the true nature of the relationships between predictors and a dependent variable are nonlinear. In this paper, we propose and illustrate the use of an information theoretic approach in moderation analysis with the aim of avoiding spurious moderation effects. We demonstrate our suggested procedure using Monte Carlo simulations and real data from published studies.",
keywords = "Moderated regression model, Information-theoretic approach, Akaike information criterion, Nonlinearity effects, Robustness check",
author = "Ahmad Daryanto",
year = "2019",
month = jun,
day = "22",
doi = "10.1016/j.jbusres.2019.06.012",
language = "English",
volume = "103",
pages = "110--118",
journal = "Journal of Business Research",
issn = "0148-2963",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Avoiding spurious moderation effects

T2 - An information-theoretic approach to moderation analysis

AU - Daryanto, Ahmad

PY - 2019/6/22

Y1 - 2019/6/22

N2 - Researchers typically use moderated regression models to examine the presence of linear moderation effects in their studies. However, researchers rarely conduct a robustness check following a significant moderation effect to investigate whether the moderation effect is spurious. The misleading moderation can occur when a predictor and a moderator variable correlate and the true nature of the relationships between predictors and a dependent variable are nonlinear. In this paper, we propose and illustrate the use of an information theoretic approach in moderation analysis with the aim of avoiding spurious moderation effects. We demonstrate our suggested procedure using Monte Carlo simulations and real data from published studies.

AB - Researchers typically use moderated regression models to examine the presence of linear moderation effects in their studies. However, researchers rarely conduct a robustness check following a significant moderation effect to investigate whether the moderation effect is spurious. The misleading moderation can occur when a predictor and a moderator variable correlate and the true nature of the relationships between predictors and a dependent variable are nonlinear. In this paper, we propose and illustrate the use of an information theoretic approach in moderation analysis with the aim of avoiding spurious moderation effects. We demonstrate our suggested procedure using Monte Carlo simulations and real data from published studies.

KW - Moderated regression model

KW - Information-theoretic approach

KW - Akaike information criterion

KW - Nonlinearity effects

KW - Robustness check

U2 - 10.1016/j.jbusres.2019.06.012

DO - 10.1016/j.jbusres.2019.06.012

M3 - Journal article

VL - 103

SP - 110

EP - 118

JO - Journal of Business Research

JF - Journal of Business Research

SN - 0148-2963

ER -