Abstract
Dyadic similarity effect hypotheses state that the (dis)similarity between dyad members (e.g. the similarity on a personality dimension) is related to a dyadic outcome variable (e.g. the relationship satisfaction of both partners). Typically, these hypotheses have been investigated by using difference scores or other profile similarity indices as predictors of the outcome variables. These approaches, however, have been vigorously criticized for their conceptual and statistical shortcomings. Here, we introduce a statistical method that is based on polynomial regression and addresses most of these shortcomings: dyadic response surface analysis. This model is tailored for similarity effect hypotheses and fully accounts for the dyadic nature of relationship data. Furthermore, we provide a tutorial with an illustrative example and reproducible R and Mplus scripts that should assist substantive researchers in precisely formulating, testing, and interpreting their dyadic similarity effect hypotheses.
Dokumententyp: | Zeitschriftenartikel |
---|---|
Keywords: | dyadic data; polynomial regression; response surface; analysis similarity |
Fakultät: | Psychologie und Pädagogik > Department Psychologie > Psychologische Methodenlehre und Diagnostik |
Themengebiete: | 100 Philosophie und Psychologie > 150 Psychologie |
URN: | urn:nbn:de:bvb:19-epub-60773-2 |
ISSN: | 1099-0984 |
Sprache: | Englisch |
Dokumenten ID: | 60773 |
Datum der Veröffentlichung auf Open Access LMU: | 28. Feb. 2019, 14:00 |
Letzte Änderungen: | 04. Nov. 2020, 13:39 |