Conference Object

Impact of Time Intervals on Lagged Moderated Regression Effects

Author(s) / Creator(s)

Dormann, Christian
Cortina, Jose

Abstract / Description

(a) Background: Moderated regression analysis is the most frequently applied statistical method to analyze interaction/moderation effects in the applied psychology literature, and one of the most common statistical techniques of any kind (e.g., Aguinis, & Stone-Romero, 1997; Cohen, Cohen, West, & Aiken, 2003). Further, appreciation of the need for longitudinal studies has led to an increase in the number of studies that used lagged moderated regression analysis or related methods such as multi-sample structural equation models. It is, however, not well-known that results of lagged moderation analysis could be misleading if time intervals are not appropriately modelled. This was shown, e.g., in a recent meta-analysis of longitudinal studies (Guthier, Dormann, & Voelkle, 2020), but it also applies to primary studies. (b) Objectives/Research question(s): The objective is to identify conditions that lead to misleading results (e.g., wrong signs) from lagged moderation analysis and provide a solution. (d) Method/Approach: Monte Carlo Simulation of longitudinal data and analysis of generated data with different multiple regression models and moderated ctsem (e) Results/Findings: If more than a single effect in a causal system is moderated, length of interval is particularly consequential. Truly positive moderating effects can manifest as negative moderating effects and vice versa (sign-flipping) if moderated regression models are used. In particular, this happens if more than the focal lagged effect is moderated (e.g., a lagged effect in the 'reversed' causal direction) but with a different sign. Contrary, moderated ctsem yields less biased and in some cases unbiased estimates. (f) Conclusions and implications: Interpretations of previously published longitudinal moderation analyses should be treated with caution, and moderated ctsem instead of moderated regression analysis should be to analyze moderation with longitudinal data.

Keyword(s)

continuous time structural equation modeling moderation

Persistent Identifier

Date of first publication

2021-05-19

Is part of

Research Synthesis & Big Data, 2021, online

Publisher

ZPID (Leibniz Institute for Psychology)

Citation

Dormann, C., & Cortina, J. (2021). Impact of Time Intervals on Lagged Moderated Regression Effects. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.4836
  • Author(s) / Creator(s)
    Dormann, Christian
  • Author(s) / Creator(s)
    Cortina, Jose
  • PsychArchives acquisition timestamp
    2021-05-14T16:13:20Z
  • Made available on
    2021-05-14T16:13:20Z
  • Date of first publication
    2021-05-19
  • Abstract / Description
    (a) Background: Moderated regression analysis is the most frequently applied statistical method to analyze interaction/moderation effects in the applied psychology literature, and one of the most common statistical techniques of any kind (e.g., Aguinis, & Stone-Romero, 1997; Cohen, Cohen, West, & Aiken, 2003). Further, appreciation of the need for longitudinal studies has led to an increase in the number of studies that used lagged moderated regression analysis or related methods such as multi-sample structural equation models. It is, however, not well-known that results of lagged moderation analysis could be misleading if time intervals are not appropriately modelled. This was shown, e.g., in a recent meta-analysis of longitudinal studies (Guthier, Dormann, & Voelkle, 2020), but it also applies to primary studies. (b) Objectives/Research question(s): The objective is to identify conditions that lead to misleading results (e.g., wrong signs) from lagged moderation analysis and provide a solution. (d) Method/Approach: Monte Carlo Simulation of longitudinal data and analysis of generated data with different multiple regression models and moderated ctsem (e) Results/Findings: If more than a single effect in a causal system is moderated, length of interval is particularly consequential. Truly positive moderating effects can manifest as negative moderating effects and vice versa (sign-flipping) if moderated regression models are used. In particular, this happens if more than the focal lagged effect is moderated (e.g., a lagged effect in the 'reversed' causal direction) but with a different sign. Contrary, moderated ctsem yields less biased and in some cases unbiased estimates. (f) Conclusions and implications: Interpretations of previously published longitudinal moderation analyses should be treated with caution, and moderated ctsem instead of moderated regression analysis should be to analyze moderation with longitudinal data.
  • Publication status
    unknown
  • Review status
    unknown
  • Citation
    Dormann, C., & Cortina, J. (2021). Impact of Time Intervals on Lagged Moderated Regression Effects. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.4836
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/4273
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.4836
  • Language of content
    eng
  • Publisher
    ZPID (Leibniz Institute for Psychology)
  • Is part of
    Research Synthesis & Big Data, 2021, online
  • Keyword(s)
    continuous time structural equation modeling
  • Keyword(s)
    moderation
  • Dewey Decimal Classification number(s)
    150
  • Title
    Impact of Time Intervals on Lagged Moderated Regression Effects
  • DRO type
    conferenceObject
  • Visible tag(s)
    ZPID Conferences and Workshops