The Consequences of Selective Participation on Behavioral-Genetic Findings: Evidence from Simulated and Real Data

Taylor, Alan

doi:10.1375/1369052042335241

Cited by 17 publications

(14 citation statements)

References 55 publications

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“…The resulting restriction of environmental range may result in the understatement of shared environmental influences (Stoolmiller, 1999;Taylor, 2004). Such understatement only takes place when the variable of interest is associated with the variable on which environmental range is restricted (Taylor, 2004), but there is evidence for such an association between SES and academic achievement (White, 1982). We cannot use SIBS to measure these associations, of course, because the restriction of range in SES will also act to attenuate the correlations.…”

Section: Limitations Of This Studymentioning

confidence: 99%

“…This reflects economic advantage, as well as the educational advantage of the mothers that we noted in our description of the sample in the Methods section of the paper. The resulting restriction of environmental range may result in the understatement of shared environmental influences (Stoolmiller, 1999;Taylor, 2004). Such understatement only takes place when the variable of interest is associated with the variable on which environmental range is restricted (Taylor, 2004), but there is evidence for such an association between SES and academic achievement (White, 1982).…”

Section: Limitations Of This Studymentioning

confidence: 99%

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How parents influence school grades: Hints from a sample of adoptive and biological families

Johnson

Iacono

2007

Learning and Individual Differences

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Using the biological and adoptive families in the Minnesota-based Sibling Interaction and Behavior Study, we investigated the associations among genetic and environmental influences on IQ, parenting, parental expectations for offspring educational attainment, engagement in school, and school grades. All variables showed substantial genetic influence, and very modest shared environmental influence. No gender differences were evident. There were significant genetic influences common to IQ and parental expectations of educational attainment, parenting and engagement in school, school grades and engagement in school, parental expectations for offspring educational attainment and school grades, and IQ and school grades. A possible interpretation of the common genetic influences involving parenting is that parents use their own experience with school in shaping the ways in which they parent their offspring.Among developmental and clinical psychologists, parenting is considered to be an important influence on children's academic outcomes (e.g., Gadeyne, Ghesquiere, & Onghena, 2004;Steinberg, Elmen, & Mounts, 1989). This is supported by a large volume of evidence from longitudinal (e.g., Steinberg, Lamborn, Darling, Mounts, & Dornbusch, 1994) and experimental intervention studies (e.g, Forgatch & DeGarmo, 1999), as well as by studies of concurrent associations. Investigations of the mechanisms by which parents exert their effects on academic outcomes tend to follow one of two general traditions (Kellaghan, Sloane, Alvarez, & Bloom, 1993): examination of the effects on children of parental actions, or "what parents do", and exploration of the effects on children of "who parents are". The classic parenting research focusing on socialization activities by parents in the form of emotional tone, disciplinary practices, responsiveness, and expectations (e.g., Baumrind, 1991;Maccoby & Martin, 1983) comes from the "what parents do" tradition, as does research on the quality of the home learning environment provided to children (e.g., Bradley, 1994;de Jong & Leseman, 2001). The "who parents are" tradition is represented by studies positing that socioeconomic and cultural factors carry with them trait-like parental socialization practices with a variety of contextual influences that affect development (e.g., Gallimore & Goldenberg, 2001;Gutman & McLoyd, 2000). Of course, the two research traditions do not necessarily reflect distinctCorresponding author: Wendy Johnson, c/o Department of Psyhology, University of Minnesota -Twin Cities, 75 East River Road, Minneapolis, MN 55455, 952-473-1673, wendy.Johnson@ed.ac.uk. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could...

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Section: Limitations Of This Studymentioning

confidence: 99%

Section: Limitations Of This Studymentioning

confidence: 99%

How parents influence school grades: Hints from a sample of adoptive and biological families

Johnson

Iacono

2007

Learning and Individual Differences

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“…Considering the effect of missingness as a result of attrition, it should be kept in mind that we restricted ourselves to the discussion of response models under the assumption that the data are MAR or MCAR. When the data are actually missing not at random (MNAR), maximum likelihood estimation cannot correct for the missingness, and genetic and environmental influences may either be overestimated or underestimated, depending on the nonresponse model Taylor, 2004).…”

Section: Table 3amentioning

confidence: 99%

Statistical Power to Detect Genetic and Environmental Influences in the Presence of Data Missing at Random

Derks¹,

Dolan

Boomsma

2007

Twin Res Hum Genet

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We study the situation in which a cheap measure (X) is observed in a large, representative twin sample, and a more expensive measure (Y) is observed in a selected subsample. The aim of this study is to investigate the optimal selection design in terms of the statistical power to detect genetic and environmental influences on the variance of Y and on the covariance of X and Y. Data were simulated for 4000 dizygotic and 2000 monozygotic twins. Missingness (87% vs. 97%) was then introduced in accordance with 7 selection designs: (i) concordant low + individual high design; (ii) extreme concordant design; (iii) extreme concordant and discordant design (EDAC); (iv) extreme discordant design; (v) individual score selection design; (vi) selection of an optimal number of MZ and DZ twins; and (vii) missing completely at random. The statistical power to detect the influence of additive and dominant genetic and shared environmental effects on the variance of Y and on the covariance between X and Y was investigated. The best selection design is the individual score selection design. The power to detect additive genetic effects is high irrespective of the percentage of missingness or selection design. The power to detect shared environmental effects is acceptable when the percentage of missingness is 87%, but is low when the percentage of missingness is 97%, except for the individual score selection design, in which the power remains acceptable. The power to detect D is low, irrespective of selection design or percentage of missingness. The individual score selection design is therefore the best design for detecting genetic and environmental influences on the variance of Y and on the covariance of X and Y. However, the EDAC design may be preferred when an additional purpose of a study is to detect quantitative trait loci effects.

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“…The longitudinal studies of aging in biodemography needed for causal inference are challenged by drop-outs due to death and "usual non-response" that is often associated with severe disease. Currently, the techniques used are (multiple) imputations, inverse probability weighting, GEE (Generalized Estimating Equations), EM Algorithms (Expectation-Maximization Algorithms), and multilevel models assuming that the missing values are "missing at random" (conditional on the last measurement)which in most cases is an assumption that is likely to be violated (Raudenbush and Chan 1993, Dufouil et al 2004, Taylor 2004). Furthermore, bio-demographic aging research meets special challenges because the number of dropouts due to death considerably exceeds what is usually seen in other studies.…”

Section: Individual Versus Population Differencesmentioning

confidence: 99%

Human Biodemography: Some challenges and possibilities

Christensen¹

2008

DemRes

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This opinion report -in a series on the future of biodemography -focuses on promising areas that I think will be valuable to develop in the future in order to get a better understanding of the determinants of the health and well-being of elderly people. I discuss two major themes: i) the benefits of strengthening the ties between biodemography and medical-clinical disciplines to better understand the link between functioning/diseases/vulnerability and mortality, ii) the male-female health-survival paradox (i.e., males report better health than females, but encounter higher mortality at all ages), and how this paradox may shed light on fundamental aging processes.

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The Consequences of Selective Participation on Behavioral-Genetic Findings: Evidence from Simulated and Real Data

Cited by 17 publications

References 55 publications

How parents influence school grades: Hints from a sample of adoptive and biological families

How parents influence school grades: Hints from a sample of adoptive and biological families

Statistical Power to Detect Genetic and Environmental Influences in the Presence of Data Missing at Random

Human Biodemography: Some challenges and possibilities

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