2013
DOI: 10.1080/10705511.2013.769395
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Analyzing Mixed-Dyadic Data Using Structural Equation Models

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Cited by 92 publications
(87 citation statements)
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“…In conducting a dyadic analysis, it needs to be considered whether distinguishable or indistinguishable dyads are studied. Dyadic distinguishability describes whether the two members of a dyad can be differentiated using one distinctive dichotomous variable, which also needs to be relevant to the research question (Peugh, DiLillo, & Panuzio, 2013). The present study presents a case in which the two dyad members cannot be meaningfully differentiated using a withindyad variable, such as sex (Kenny & Cook, 1999), that is indistinguishable dyads (of same-sex friends).…”
Section: Question 3: How Do Character Strengths Relate To Friendship mentioning
confidence: 99%
“…In conducting a dyadic analysis, it needs to be considered whether distinguishable or indistinguishable dyads are studied. Dyadic distinguishability describes whether the two members of a dyad can be differentiated using one distinctive dichotomous variable, which also needs to be relevant to the research question (Peugh, DiLillo, & Panuzio, 2013). The present study presents a case in which the two dyad members cannot be meaningfully differentiated using a withindyad variable, such as sex (Kenny & Cook, 1999), that is indistinguishable dyads (of same-sex friends).…”
Section: Question 3: How Do Character Strengths Relate To Friendship mentioning
confidence: 99%
“…(2010 Appendix: Data Analytic Strategy Expanded As stated previously, children were paired into dyads based on several important socioeconomic, demographic, and individual achievement characteristics prior to observational testing. For analysis purposes, the dyads were treated as indistinguishable (see Kenny, Kashy, & Cook, 2006), meaning that, after matching children on the aforementioned descriptive characteristics prior to assessment, no other variable relevant to the research question could meaningfully distinguish "child #1" from "child #2" within a given dyad (Peugh, DiLillo, & Panuzio, 2011) for all child participants in the sample. A researcher might reasonably assume under these design conditions that three sources of response variable variation require proper multilevel modeling: (1) variation in repeated response variable scores nested within individuals (Level-1), (2) variation in average response variable scores among individual members nested within dyads (Level-2), and (3) variation in mean response variable scores across all dyads (Level-3).…”
Section: Resultsmentioning
confidence: 99%
“…Mixed-dyadic data (teachers vs. parents evaluations of the same children, as in case of current research) demand additional statistical analyses that appropriately model the variation within dyads and between dyads (Gonzales & Griffin, 2012;Kenny, Kashy, & Cook, 2006;Peugh, DiLillo, & Panuzio, 2013). These dyads data are distinguishable (distinct evaluations of parents and teachers) in contrast to twin studies.…”
Section: Analyzing Mixed-dyadic Data Using Path Analysesmentioning
confidence: 98%
“…However, in our statistical analyses we will apply more advanced path dyadic models (Peugh, DiLillo, & Panuzio, 2013) that are more appropriate to integrate mixed-dyadic data (i.e., teachers' and parents' evaluations of the same children).…”
Section: Introductionmentioning
confidence: 99%