Publications (24)53.93 Total impact
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Article: The Cross-Generational Mother–Daughter–Aunt–Niece Design: Establishing Validity of the MDAN Design with NLSY Fertility Variables
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ABSTRACT: Using National Longitudinal Survey of Youth (NLSY) fertility variables, we introduce and illustrate a new genetically-informative design. First, we develop a kinship linking algorithm, using the NLSY79 and the NLSY-Children data to link mothers to daughters and aunts to nieces. Then we construct mother–daughter correlations to compare to aunt–niece correlations, an MDAN design, within the context of the quantitative genetic model. The results of our empirical illustration, which uses DF Analysis and generalized estimation equations (GEE) to estimate biometrical parameters from NLSY79 sister–sister pairs and their children in the NLSY-Children dataset, provide both face validity and concurrent validity in support of the efficacy of the design. We describe extensions of the MDAN design. Compared to the typical within-generational design used in most behavior genetic research, the cross-generational feature of this design has certain advantages and interesting features. In particular, we note that the equal environment assumption of the traditional biometrical model shifts in the context of a cross-generational design. These shifts raise questions and provide motivation for future research using the MDAN and other cross-generational designs.Behavior Genetics 04/2012; 38(6):567-578. · 2.52 Impact Factor -
Article: Behavior genetic modeling of human fertility: findings from a contemporary danish twin study
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ABSTRACT: Behavior genetic designs and analyses can be used to address issues of central importance to demography. We use this methodology to document genetic influence on human fertility. Our data come from Danish twin pairs born from 1953 to 1959, measured on age at first attempt to get pregnant (FirstTry) and number of children (NumCh). Behavior genetic models were fitted using structural equation modeling and DF analysis. A consistent medium-level additive genetic influence was found for NumCh, equal across genders; a stronger genetic influence was identified for FirstTry, greater for females than for males. A bivariate analysis indicated significant shared genetic variance between NumCh and FirstTry.Demography 04/2012; 38(1):29-42. · 1.93 Impact Factor -
Chapter: Bootstrapping and Monte Carlo methods.
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ABSTRACT: Frequently a researcher is interested in a theoretical distribution or characteristics of that distribution, such as its mean, standard deviation, or 2.5 and 97.5 percentiles. One hundred or even 50 years ago, we were restricted practically by computing limitations to theoretical distributions that are described by an explicit equation, such as the binomial or multivariate normal distribution. Using mathematical models of distributions often requires considerable mathematical ability, and also imposes rather severe and often intractable assumptions on the applied researchers (e.g., normality, independence, variance assumptions, and so on). But computer simulations now provide more flexibility specifying distributions, which in turn provide more flexibility specifying models. One contemporary simulation technique is Markov chain Monte Carlo (MCMC) simulation, which can specify arbitrarily complex and nested multivariate distributions. It can even combine different theoretical families of variates. Another contemporary technique is the bootstrap, which can construct sampling distributions of conventional statistics that are free from most (but not all) assumptions. It can even create sampling distributions for new or exotic test statistics that the researcher created for a specific experiment01/2012: pages 407-425; -
Article: Biodemographic modeling of the links between fertility motivation and fertility outcomes in the NLSY79.
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ABSTRACT: In spite of long-held beliefs that traits related to reproductive success tend to become fixed by evolution with little or no genetic variation, there is now considerable evidence that the natural variation of fertility within populations is genetically influenced and that a portion of that influence is related to the motivational precursors to fertility. We conduct a two-stage analysis to examine these inferences in a time-ordered multivariate context. First, using data from the National Longitudinal Survey of Youth, 1979, and LISREL analysis, we develop a structural equation model in which five hypothesized motivational precursors to fertility, measured in 1979-1982, predict both a child-timing and a child-number outcome, measured in 2002. Second, having chosen two time-ordered sequences of six variables from the SEM to represent our phenotypic models, we use Mx to conduct both univariate and multivariate behavioral genetic analyses with the selected variables. Our results indicate that one or more genes acting within a gene network have additive effects that operate through child-number desires to affect both the timing of the next child born and the final number of children born, that one or more genes acting through a separate network may have additive effects operating through gender role attitudes to produce downstream effects on the two fertility outcomes, and that no genetic variance is associated with either child-timing intentions or educational intentions.Demography 05/2010; 47(2):393-414. · 1.93 Impact Factor -
Article: The epistemology of mathematical and statistical modeling: a quiet methodological revolution.
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ABSTRACT: A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rule-based framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models.American Psychologist 01/2010; 65(1):1-12. · 6.87 Impact Factor -
Article: Fertility motivations of youth predict later fertility outcomes: a prospective analysis of national longitudinal survey of youth data.
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ABSTRACT: We examine how the motivational sequence that leads to childbearing predicts fertility outcomes across reproductive careers. Using a motivational traits-desires-intentions theoretical framework, we test a structural equation model using prospective male and female data from the National Longitudinal Survey of Youth. Specifically, we take motivational data collected during the 1979-1982 period, when the youths were in their teens and early twenties, to predict the timing of the next child born after 1982 and the total number of children born by 2002. Separate models were estimated for males and females but ivith equality constraints imposed unless relaxing these constraints improved the overall model fit. The results indicate substantial explanatory power of fertility motivations for both short-term and long-term fertility outcomes. They also reveal the effects of both gender role attitude and educational intentions on these outcomes. Although some gender differences in model pathways occurred, the primary hypothesized pathways were essentially the same across the genders. Two validity substudies support the soundness of the results. A third sub-study comparing the male and female models across the sample split on the basis of previous childbearing revealed a number of pattern differences within the four gender-by-previous childbearing groups. Several of the more robust of these pattern differences offer interesting insights and support the validity and usefulness of our theoretical framework.Biodemography and Social Biology 01/2010; 56(1):1-23. · 0.52 Impact Factor -
Chapter: Resampling methods
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ABSTRACT: Resampling is a statistical approach that relies on empirical analysis, based on the observed data, instead of asymptotic and parametric theory. The goal of resampling is to make an inferential decision, which is the same goal as that of a parametric statistical test such as the conventional t or ANOVA. The difference is in how the goal is achieved. In this chapter, we will define and describe three resampling procedures: the permutation test, the jackknife and the bootstrap. We place a strong emphasis on the bootstrap because it is the most flexible and most frequently used. We will describe both the concepts and the mechanisms that underlie resampling theory. In the course of this development, we hope that readers new to this area will begin to see ways of incorporating resampling methods into various aspects of their applied research, ways that allow them to address novel questions that traditional parametric approaches cannot easily address. We also hope that practicing methodologists as well will find new applications for resampling methods, and appropriate appreciation for their flexibility and overall value (as well as their limitations).08/2009; , ISBN: 9781412930918 -
Article: Education and cognitive ability as direct, mediating, or spurious influences on female age at first birth: behavior genetic models fit to Danish twin data.
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ABSTRACT: The authors study education and cognitive ability as predictors of female age at first birth (AFB), using monozygotic and dizygotic female twin pairs from the Middle-Aged Danish Twin survey. Using mediated regression, they replicate findings linking education (and not cognitive ability) to AFB. But in a behavior genetic model, both relationships are absorbed within a latent variable measuring the shared family environment. Two interpretations are relevant. First, variance in AFB emerges from differences between families, not differences between sisters within the same family. Second, even in a natural laboratory sensitive to genetic variance in female fertility -- during demographic transition -- the variance in AFB was non-genetic, located instead within the shared environment.American Journal of Sociology 02/2008; 114 Suppl:S202-32. · 3.17 Impact Factor -
Chapter: Behavior Genetics and Adolescent Development: A Review of Recent Literature
01/2008: pages 2 - 23; , ISBN: 9780470756607 -
Article: Bootstrapping to test for nonzero population correlation coefficients using univariate sampling.
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ABSTRACT: This article proposes 2 new approaches to test a nonzero population correlation (rho): the hypothesis-imposed univariate sampling bootstrap (HI) and the observed-imposed univariate sampling bootstrap (OI). The authors simulated correlated populations with various combinations of normal and skewed variates. With alpha set=.05, N> or =10, and rho< or =0.4, empirical Type I error rates of the parametric r and the conventional bivariate sampling bootstrap reached .168 and .081, respectively, whereas the largest error rates of the HI and the OI were .079 and .062. On the basis of these results, the authors suggest that the OI is preferable in alpha control to parametric approaches if the researcher believes the population is nonnormal and wishes to test for nonzero rhos of moderate size.Psychological Methods 12/2007; 12(4):414-33. · 4.45 Impact Factor -
Article: Multivariate Cholesky models of human female fertility patterns in the NLSY.
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ABSTRACT: Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.Behavior Genetics 04/2007; 37(2):345-61. · 2.52 Impact Factor -
Article: Sibling Influence on Smoking Behavior: A Within‐Family Look at Explanations for a Birth‐Order Effect
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ABSTRACT: Using a repeated-measures design, we found a significant birth-order relationship suggesting lower ages of smoking onset in later born siblings of a 1979 National Longitudinal Survey of Youth cohort. Two social learning mechanisms, modeling and opportunity, were explored to help illuminate the causes of trends in the within-family means. When empirical patterns were compared to predictions derived from our specifications of how opportunity and modeling processes should work, the results were unsuccessful in explaining the birth-order effect. As a third explanation of the birth-order effect, telescoping did show a significant influence. The effect size was small, however, and had little effect on the group means assessed. Finally, a pattern did emerge that was consistent with a reformulation of the opportunity process in which sisters play a particularly strong role. We develop future research implications of this pattern and speculate on genetic and social conservatism explanations.Journal of Applied Social Psychology 07/2006; 33(9):1773 - 1795. · 0.63 Impact Factor -
Article: Bio-social determinants of fertility.
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ABSTRACT: This paper reviews several studies that investigated the potential role of genetic factors in determining fertility outcomes. Our review demonstrates convincingly that fertility contains genetic variance; that is, differences between humans in their genetic make-up affects their fertility outcomes and fertility-related behaviours. This finding is robust using both heritabilities and coefficients of genetic variation, and using both direct measures of fertility outcomes and also fertility precursors like fecundity, marriage, fertility expectations and attempts to get pregnant (proception). Moreover, genetic variance can change over short periods of time or across educational levels, specifically for females, and the relevance of genetic variance seems to increase during times of increasing reproductive choice.International Journal of Andrology 03/2006; 29(1):46-53. · 3.59 Impact Factor -
Article: Did fertility go up after the Oklahoma City bombing? An analysis of births in metropolitan counties in Oklahoma, 1990-1999.
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ABSTRACT: Political and sociocultural events (e.g., Brown v. Board of Education in 1954 and the German reunification in 1989) and natural disasters (e.g., Hurricane Hugo in 1989) can affect fertility. In our research, we addressed the question of whether the Oklahoma City bombing in April 1995, a man-made disaster, influenced fertility patterns in Oklahoma. We defined three theoretical orientations--replacement theory, community influence theory, and terror management theory--that motivate a general expectation of birth increases, with different predictions emerging from time and geographic considerations. We used two different empirical methodologies. First, we fitted dummy-variable regression models to monthly birth data from 1990 to 1999 in metropolitan counties. We used birth counts to frame the problem and general fertility rates to address the problem formally. These analyses were organized within two design structures: a control-group interrupted time-series design and a difference-in-differences design. In these analyses, Oklahoma County showed an interpretable, consistent, and significant increase in births. Second, we used graphical smoothing models to display these effects visually. In combination, these methods provide compelling support for a fertility response to the Oklahoma City bombing. Certain parts of each theory helped us organize and understand the pattern of results.Demography 12/2005; 42(4):675-92. · 1.93 Impact Factor -
Article: Reformulating and simplifying the DF analysis model.
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ABSTRACT: DeFries-Fulker (DF) Analysis for unselected populations is reformulated as a no-intercept model with centered variables and only two independent variables. The reformulation serves three purposes. First, the original formulation implicitly estimated two different values for c2 and two values for h2. The new formulation resolves this ambiguity. Second, because the original formulation estimated h2 with the coefficient from a regression-interaction term, whether to center the interaction variables was unclear. The new formulation explicitly resolves this issue. Finally, the new formulation estimates fewer parameters, and therefore improves estimation efficiency and statistical power.Behavior Genetics 04/2005; 35(2):211-7. · 2.52 Impact Factor -
Article: Theory development should begin (but not end) with good empirical fits: a comment on Roberts and Pashler (2000).
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ABSTRACT: S. Roberts and H. Pashler (2000) argued against using goodness of fit as evidence to support theories. The authors agree with their suggestions for how to go beyond good fits but disagree with their starting point. In this comment, the authors argue that good fits are part and parcel of theory development, that they are part and parcel of the processes suggested by S. Roberts and H. Pashler, and that they must be the starting point (though far from the ending point) in theoretical development. The authors discuss historical examples of scientific theory development, recent examples of psychological theory development, and development of a particular theory (social contagion theory; J. L. Rodgers & D. C. Rowe, 1993) that S. Roberts and H. Pashler criticized.Psychological Review 08/2002; 109(3):599-604; discussion 605-7. · 7.76 Impact Factor -
Article: Genetic and Environmental Influences on Delinquency: DF Analysis of NLSY Kinship Data
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ABSTRACT: This paper follows earlier research (Rowe et al., 1992) in evaluating the basis of family influences on adolescent delinquent behavior. Delinquency is measured in a number of different ways to account for important theoretical distinctions that exist in the delinquency literature. We use recently identified kinship structure in a large national data set—the National Longitudinal Survey of Youth—to estimate genetic and shared environmental influences on self-reported delinquency scores. Our analytic model is based on DF analysis, a regression procedure used to estimate parameters reflecting genetic and environmental influence. Results suggest a consistent and moderate genetic basis to sibling similarity in delinquency and little evidence of a shared environmental basis. A large amount of variance is attributable to nonshared influences and/or measurement error. Our findings suggest that the search for environmental influences on adolescent delinquency should focus on those that are not shared by siblings.Journal of Quantitative Criminology 05/2001; 17(2):145-168. · 2.12 Impact Factor -
Article: DF-Analyses of Heritability with Double-Entry Twin Data: Asymptotic Standard Errors and Efficient Estimation
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ABSTRACT: In this paper we establish the asymptotic distribution of DeFries Fulker (1985) regression estimates for heritability and shared environmental influences with double-entry twin data. A simple formula to estimate the covariance matrix of the coefficients in DF-regressions is provided, and applications to simulated data and Danish twin data show that this method can substantially increase the statistical power of the analyses. We also provide an ''efficient DF-analysis'' that yields more precise estimates when additional covariates are included among the explanatory variables.Behavior Genetics 01/2001; 31(2):179-191. · 2.52 Impact Factor -
Article: DF-like Analyses of Binary, Ordered, and Censored Variables Using Probit and Tobit Approaches
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ABSTRACT: Binary and censored variables can lead to erroneous inferences about heritability in family studies if the dichotomous or censored nature of the dependent variable is not properly accounted for. The bivariate probit and tobit models proposed in this paper provide a unified approach to family studies with binary, ordered, and censored variables. Each model in this paper is derived from a similar latent-variable structure which can contain covariates that affect the expected value of the dependent variable, as well as genetic and shared environmental influences that lead to an association among related individuals. We apply the models to the fertility outcome and fertility motivations of Danish twins born 1953–1964 and find relevant genetic influences on the number of children as well as the desired timing of the first child.Behavior Genetics 06/1999; 29(4):221-232. · 2.52 Impact Factor -
Article: Nonlinear dynamic modeling and social contagion: Reply to Stoolmiller (1998).
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ABSTRACT: M. Stoolmiller's (1998) comments about the authors' epidemic models of the onset of social activities (EMOSA models) and about nonlinear modeling in general further stimulate the developmental community to give more attention to this class of methods. The authors review and comment on Stoolmiller's criticisms. Following, they discuss the idea of social contagion as a general theoretical tool. (PsycINFO Database Record (c) 2012 APA, all rights reserved)Developmental Psychology 08/1998; 34(5):1117-1118. · 3.21 Impact Factor
Top co-authors
Top Journals
Institutions
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2012
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University of Southern Denmark
Kolding, South Denmark, Denmark
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1999–2012
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University of Oklahoma
- Department of Psychology
Norman, OK, USA
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2010
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Transnational Family Research Institute
Aptos, CA, USA
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2006
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University of Pennsylvania
- Department of Sociology
Philadelphia, PA, USA
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2001
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The University of Arizona
Tucson, AZ, USA
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