2013
DOI: 10.1080/10705511.2013.769393
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Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random

Abstract: Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups may be retained even if only one member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to … Show more

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Cited by 15 publications
(11 citation statements)
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“…Given the large percentage of missing data at the level of the care-giving unit, analysis relied on the 223 complete care-giving units. However, when data are not missing completely at random (as is most often the case), ignoring available information by limiting the analysis to complete units results in biased estimates that cannot be generalisable to the entire population from which families were sampled (Acock 2005; Blozis et al 2013; Schafer and Graham 2002). Therefore, an additional sensitivity analysis consisted of the entire sample (818 care-giving units), using multiple imputation to account for missing values (Asparouhov and Muthen 2010).…”
Section: Methodsmentioning
confidence: 99%
“…Given the large percentage of missing data at the level of the care-giving unit, analysis relied on the 223 complete care-giving units. However, when data are not missing completely at random (as is most often the case), ignoring available information by limiting the analysis to complete units results in biased estimates that cannot be generalisable to the entire population from which families were sampled (Acock 2005; Blozis et al 2013; Schafer and Graham 2002). Therefore, an additional sensitivity analysis consisted of the entire sample (818 care-giving units), using multiple imputation to account for missing values (Asparouhov and Muthen 2010).…”
Section: Methodsmentioning
confidence: 99%
“…We also have numerous examples of child effects on a parent characteristic (e.g., Ahmadzadeh et al, 2019), although in the absence of a significant path from a birth parent variable to the child variable, we cannot determine whether these child effects are genetically mediated. We have sought to identify evocative rGE associations in some of our analyses and Adoption process Blozis et al (2013), Ge et al (2008), Martin et al (2011) Genetics and prevention Harold et al (2017), Leve (2017), Leve, Harold et al (2010), Leve et al (2018), , Reiss et al (2009) failed to detect a genetic signal. These null findings could be for several reasons, including: (a) we may not have identified or measured the relevant birth parent variable, including possible incongruence between an adult phenotype and a child phenotype, (b) evocative effects may not appear until later in child development, (c) evocative effects may be masked by interaction effects, as moderated mediation (see Fearon et al, 2015, as an example), (d) we only have birth father data for approximately one-third of our sample and thus are missing a portion of the potential genetic influences, (e) the child variables we have examined may not show intergenerational genetic transmission or (f) the genetic influences involved are nonadditive.…”
Section: Summary Of Resultsmentioning
confidence: 99%
“…Third, the use of retrospective data about events that occurred in the past may have led to recall bias, distorting the results. Fourth, statistical analysis using FIML can involve bias when data are not missing at random, because it is based on the assumption of missing at random or missing completely at random [71]. Comparing values between the FIML method and the list-wise deletion method revealed that model fit indices for Positive reframing indicated not to retain good model fit, and the β coefficient on Venting in Brief COPE scores included zero in 95% CIs.…”
Section: The Main Findings Of the Multiple Mediation Analysismentioning
confidence: 99%