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Author: Miller, Warren B.
Resulting in 6 citations.
1. Miller, Warren B.
Bard, David E.
Pasta, David J.
Rodgers, Joseph Lee
Biodemographic Modeling of the Links Between Fertility Motivation and Fertility Outcomes in the NLSY79
Demography 47,2 (May 2010): 393-414.
Also: http://muse.jhu.edu/login?uri=/journals/demography/v047/47.2.miller.html
Cohort(s): NLSY79
Publisher: Population Association of America
Keyword(s): Childbearing; Fertility; Gender Attitudes/Roles; Genetics; LISREL; Modeling; Modeling, Multilevel

Permission to reprint the abstract has not been received from the publisher.

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 childnumber 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. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Miller, Warren B., David E. Bard, David J. Pasta and Joseph Lee Rodgers. "Biodemographic Modeling of the Links Between Fertility Motivation and Fertility Outcomes in the NLSY79." Demography 47,2 (May 2010): 393-414.
2. Miller, Warren B.
Rodgers, Joseph Lee
Pasta, David J.
The Fertility Motivations of Youth Predict Later Fertility Outcomes: A Prospective Analysis of National Longitudinal Survey of Youth Data
Biodemography and Social Biology 56,1 (January 2010): 1-23.
Also: http://www.tandfonline.com/doi/abs/10.1080/19485561003709131
Cohort(s): NLSY79
Publisher: Taylor & Francis
Keyword(s): Childbearing; Fertility; Gender Attitudes/Roles; Gender Differences; Modeling

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 with 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 sub-studies 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. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Miller, Warren B., Joseph Lee Rodgers and David J. Pasta. "The Fertility Motivations of Youth Predict Later Fertility Outcomes: A Prospective Analysis of National Longitudinal Survey of Youth Data." Biodemography and Social Biology 56,1 (January 2010): 1-23.
3. Rodgers, Joseph Lee
Bard, David E.
Johnson, Amber
D'Onofrio, Brian M.
Miller, Warren B.
The Cross-Generational Mother–Daughter–Aunt–Niece Design: Establishing Validity of the MDAN Design with NLSY Fertility Variables
Behavior Genetics 38,6 (November 2008): 567-578.
Also: http://www.springerlink.com/content/x75521h0l957w296/
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Behavior Genetics Association
Keyword(s): Behavior; Fertility; Genetics; Inheritance; Kinship; Mothers and Daughters; Siblings

Permission to reprint the abstract has not been received from the publisher.

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. [ABSTRACT FROM AUTHOR]

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Bibliography Citation
Rodgers, Joseph Lee, David E. Bard, Amber Johnson, Brian M. D'Onofrio and Warren B. Miller. "The Cross-Generational Mother–Daughter–Aunt–Niece Design: Establishing Validity of the MDAN Design with NLSY Fertility Variables." Behavior Genetics 38,6 (November 2008): 567-578.
4. Rodgers, Joseph Lee
Bard, David E.
Miller, Warren B.
Mother-Daughter-Aunt-Niece (MDAN) Design, Applied to Cross-Generational NLSY
Presented: Storrs, CT, Behavior Genetics Association, 36th Annual Annual Conference, June 2006
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Behavior Genetics Association
Keyword(s): Age at First Intercourse; Age at Menarche/First Menstruation; Genetics; Intergenerational Patterns/Transmission; Mothers and Daughters; Self-Reporting

Permission to reprint the abstract has not been received from the publisher.

A new biometrical design – called the MDAN design – emerges from the complex longitudinal survey design of the National Longitudinal Survey of Youth (NLSY) data. Using the crossgenerational structure available in the NLSY, we link mothers to daughters and aunts to nieces, creating an MDAN (mother-daughter-aunt-niece) design. The cross-generational data include NLSY-females who are only mothers, those who are only aunts, and those who are both mothers and aunts. Further, there is within-generational biometrical information linking NLSY-Youth females to one another as cousins, half-siblings, full-siblings, and twins; and linking NLSYChildren females to one another as cousins, half siblings, full siblings, and twins. We create linking files identifying the various within- and between-generational links, and fit preliminary biometrical models using those links. Phenotypes are fertility variables, typically measured across the two generations at approximately the same age and using identical measurement instruments. Specific measures on which we focus include self-reported age at menarche and self-reported age at first intercourse. Previous research using biometrical models have studied these phenotypes within each generation; the current research substantially extends both the empirical results and the methodological innovation by taking advantage of the ability to fit three different types of genetically- and environmentally-informed structure simultaneously.
Bibliography Citation
Rodgers, Joseph Lee, David E. Bard and Warren B. Miller. "Mother-Daughter-Aunt-Niece (MDAN) Design, Applied to Cross-Generational NLSY." Presented: Storrs, CT, Behavior Genetics Association, 36th Annual Annual Conference, June 2006.
5. Rodgers, Joseph Lee
Bard, David E.
Miller, Warren B.
Multivariate Cholesky Models of Human Female Fertility Patterns in the NLSY
Behavior Genetics 37,2 (March 2007): 345-361.
Also: http://www.springerlink.com/content/mt8j270588g24168/
Cohort(s): Children of the NLSY79
Publisher: Behavior Genetics Association
Keyword(s): Fertility; Genetics; Life Course; Modeling, Multilevel; Siblings

Permission to reprint the abstract has not been received from the publisher.

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.
Bibliography Citation
Rodgers, Joseph Lee, David E. Bard and Warren B. Miller. "Multivariate Cholesky Models of Human Female Fertility Patterns in the NLSY." Behavior Genetics 37,2 (March 2007): 345-361.
6. Rodgers, Joseph Lee
Hughes, Kimberly
Kohler, Hans-Peter
Christensen, Kaare
Doughty, Debby
Rowe, David C.
Miller, Warren B.
Genetic Influence Helps Explain Variation in Human Fertility: Evidence from Recent Behavioral and Molecular Genetic Studies
Current Directions in Psychological Science 10, 5 (October 2001): 184-188
Cohort(s): NLSY79
Publisher: Blackwell Publishing, Inc. => Wiley Online
Keyword(s): Fertility; Genetics

To search for genetic influence on human fertility differentials appears inconsistent with past empirical research and prior interpretations of Fisher's fundamental theorem of natural selection. We discuss Fisher's theorem and give reasons why genetic influences may indeed account for individual differences in human fertility. We review recent empirical studies showing genetic influence on variance in fertility outcomes and precursors to fertility. Further, some of the genetic variance underlying fertility outcomes overlaps with that underlying fertility precursors. Findings from different cultures, different times, different levels of data, and both behavioral and molecular genetic designs lead to the same conclusion: Fertility differentials are genetically influenced, and at least part of the influence derives from behavioral precursors that are under volitional control, which are themselves genetically mediated.
Bibliography Citation
Rodgers, Joseph Lee, Kimberly Hughes, Hans-Peter Kohler, Kaare Christensen, Debby Doughty, David C. Rowe and Warren B. Miller. "Genetic Influence Helps Explain Variation in Human Fertility: Evidence from Recent Behavioral and Molecular Genetic Studies." Current Directions in Psychological Science 10, 5 (October 2001): 184-188.