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Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

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dc.contributor.authorJoel, S.
dc.contributor.authorEastwick, P. W.
dc.contributor.authorAllison, C. J.
dc.contributor.authorArriaga, X. B.
dc.contributor.authorBaker, Z. G.
dc.contributor.authorBar-Kalifa, E.
dc.contributor.authorBergeron, S.
dc.contributor.authorBirnbaum, G.
dc.contributor.authorBrock, R. L.
dc.contributor.authorBrumbaugh, C. C.
dc.contributor.authorCarmichael, C. L.
dc.contributor.authorChen, S.
dc.contributor.authorClarke, J.
dc.contributor.authorCobb, R. J.
dc.contributor.authorCoolsen, M. K.
dc.contributor.authorDavis, J.
dc.contributor.authorde Jong, D. C.
dc.contributor.authorDebrot, A.
dc.contributor.authorDeHaas, E. C.
dc.contributor.authorDerrick, J. L.
dc.contributor.authorEller, J.
dc.contributor.authorEstrada, M. J.
dc.contributor.authorFaure, R.
dc.contributor.authorFinkel, E. J.
dc.contributor.authorFraley, R. C.
dc.contributor.authorGable, S. L.
dc.contributor.authorGadassi, R.
dc.contributor.authorGirme, Y. U.
dc.contributor.authorGordon, A. M.
dc.contributor.authorGosnell, C. L.
dc.contributor.authorHammond, M. D.
dc.contributor.authorHannon, P. A.
dc.contributor.authorHarasymchuk, C.
dc.contributor.authorHofmann, W.
dc.contributor.authorHorn, A. B.
dc.contributor.authorImpett, E. A.
dc.contributor.authorJamieson, J. P.
dc.contributor.authorKeltner, D.
dc.contributor.authorKim, J. J.
dc.contributor.authorKirchner, J. L.
dc.contributor.authorKluwer, E. S.
dc.contributor.authorKumashiro, M.
dc.contributor.authorLarson, G.
dc.contributor.authorLazarus, G.
dc.contributor.authorLogan, J. M.
dc.contributor.authorLuchies, L. B.
dc.contributor.authorMacDonald, G.
dc.contributor.authorMachia, L. V.
dc.contributor.authorManiaci, M. R.
dc.contributor.authorMaxwell, J. A.
dc.contributor.authorMizrahi, M.
dc.contributor.authorMuise, A.
dc.contributor.authorNiehuis, S.
dc.contributor.authorOgolsky, B. G.
dc.contributor.authorOldham, C. R.
dc.contributor.authorOverall, N. C.
dc.contributor.authorPerrez, M.
dc.contributor.authorPeters, B. J.
dc.contributor.authorPietromonaco, P. R.
dc.contributor.authorPowers, S. I.
dc.contributor.authorProk, T.
dc.contributor.authorPshedetzky-Shochat, R.
dc.contributor.authorRafaeli, E.
dc.contributor.authorRamsdell, E.
dc.contributor.authorReblin, M.
dc.contributor.authorReicherts, M.
dc.contributor.authorReifman, A.
dc.contributor.authorReis, H. T.
dc.contributor.authorRhoades, G. K.
dc.contributor.authorRholes, W. S.
dc.contributor.authorRighetti, F.
dc.contributor.authorRodriguez, L. M.
dc.contributor.authorRogge, R.
dc.contributor.authorRosen, N. O.
dc.contributor.authorSaxbe, D.
dc.contributor.authorSened, H.
dc.contributor.authorSimpson, J. A.
dc.contributor.authorSlotter, E. B.
dc.contributor.authorStanley, S. M.
dc.contributor.authorStocker, S.
dc.contributor.authorSurra, C.
dc.contributor.authorVaughn, A. A.
dc.contributor.authorVicary, A. M.
dc.contributor.authorVisserman, M. L.
dc.contributor.authorWolf, S.
dc.date.accessioned2026-01-19T16:28:06Z
dc.date.available2026-01-19T16:28:06Z
dc.date.issued2020
dc.identifier.citationJoel, S., Eastwick, P. W., Allison, C. J., Arriaga, X. B., Baker, Z. G., Bar-Kalifa, E., Bergeron, S., Birnbaum, G., Brock, R. L., Brumbaugh, C. C., Carmichael, C. L., Chen, S., Clarke, J., Cobb, R. J., Coolsen, M. K., Davis, J., de Jong, D. C., Debrot, A., DeHaas, E. C., Derrick, J. L., Eller, J., Estrada, M. J., Faure, R., Finkel, E. J., Fraley, R. C., Gable, S. L., Gadassi, R., Girme, Y. U., Gordon, A. M., Gosnell, C. L., Hammond, M. D., Hannon, P. A., Harasymchuk, C., Hofmann, W., Horn, A. B., Impett, E. A., Jamieson, J. P., Keltner, D., Kim, J. J., Kirchner, J. L., Kluwer, E. S., Kumashiro, M., Larson, G., Lazarus, G., Logan, J. M., Luchies, L. B., MacDonald, G., Machia, L. V., Maniaci, M. R., Maxwell, J. A., Mizrahi, M., Muise, A., Niehuis, S., Ogolsky, B. G., Oldham, C. R., Overall, N. C., Perrez, M., Peters, B. J., Pietromonaco, P. R., Powers, S. I., Prok, T., Pshedetzky-Shochat, R., Rafaeli, E., Ramsdell, E., Reblin, M., Reicherts, M., Reifman, A., Reis, H. T., Rhoades, G. K., Rholes, W. S., Righetti, F., Rodriguez, L. M., Rogge, R., Rosen, N. O., Saxbe, D., Sened, H., Simpson, J. A., Slotter, E. B., Stanley, S. M., Stocker, S., Surra, C., Vaughn, A. A., Vicary, A. M., Visserman, M. L., & Wolf, S. (2020). Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies. Proceedings of the National Academy of Sciences, 117, 19061-19071. https://doi.org/10.1073/pnas.1917036117
dc.identifier.urihttps://hdl.handle.net/10222/85625
dc.language.isoen
dc.relation.ispartofProceedings of the National Academy of Sciences
dc.titleMachine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
dc.typeArticle

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