TY - JOUR
T1 - Cross-Sectional Analysis of Longitudinal Mediation Processes
AU - O'Laughlin, Kristine D.
AU - Martin, Monica J.
AU - Ferrer, Emilio
N1 - Publisher Copyright:
© 2018 Taylor & Francis Group, LLC.
PY - 2018/5/4
Y1 - 2018/5/4
N2 - Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.
AB - Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.
KW - cross-sectional mediation
KW - intervening variable
KW - latent difference score models
KW - latent growth models
KW - longitudinal mediation
UR - http://www.scopus.com/inward/record.url?scp=85045068052&partnerID=8YFLogxK
U2 - 10.1080/00273171.2018.1454822
DO - 10.1080/00273171.2018.1454822
M3 - Article
C2 - 29624079
AN - SCOPUS:85045068052
VL - 53
SP - 375
EP - 402
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
SN - 0027-3171
IS - 3
ER -