Skip to main content
SpringerLink
Log in

Advancing the Psychometric Study of Human Life History Indicators

K Does Not Measure Life History Speed, but Theory and Evidence Suggest It Deserves Further Attention

  • Published:
Human Nature Aims and scope Submit manuscript

Abstract

In this article we attend to recent critiques of psychometric applications of life history (LH) theory to variance among humans and develop theory to advance the study of latent LH constructs. We then reanalyze data (n = 4,244) previously examined by Richardson et al. (Evolutionary Psychology, 15(1), 2017, https://doi.org/10.1177/1474704916666840 to determine whether (a) previously reported evidence of multidimensionality is robust to the modeling approach employed and (b) the structure of LH indicators is invariant by sex. Findings provide further evidence that a single LH dimension is implausible and that researchers should cease interpreting K-factor scores as empirical proxies for LH speed. In contrast to the original study, we detected a small inverse correlation between mating competition and Super-K that is consistent with a trade-off. Tests of measurement invariance across the sexes revealed evidence of metric invariance (i.e., equivalence of factor loadings), consistent with the theory that K is a proximate cause of its indicators; however, evidence of partial scalar invariance suggests use of scores likely introduces bias when the sexes are compared. We discuss limitations and identify approaches that researchers may use to further evaluate the validity of the K-factor and other applications of LH to human variation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. There are 21 scores total because two indicators selected in the original study are two-dimensional.

  2. Measurement invariance testing via multiple groups structural equation modeling (SEM) is closely related to MIMIC modeling. If strict scalar invariance by sex holds for the K-factor in the former approach (i.e., all loadings and intercepts invariant), then there will be no direct effects of sex on K-factor indicators in the latter approach, as well as no moderation of K-factor loadings by sex. In a MIMIC model containing the K-factor, direct effects on reflective indicators of K would represent evidence of scalar noninvariance, or that intercepts vary by sex. By entering a K × sex interaction term into the MIMIC model, researchers can also test whether sex appears to moderate effects of K on its indicators. Moderation by sex, in this case, is evidence of metric noninvariance or that loadings vary between the sexes. Multiple groups SEM offers several advantages beyond MIMIC models, including the possibility of testing for differences in variances between groups and more straightforward testing for differences in loadings and covariances.

References

  • Adams, H. A., Luevano, V. X., & Jonason, P. K. (2014). Risky business: Willingness to be caught in an extra-pair relationship, relationship experience and the dark triad. Personality and Individual Differences, 66, 204–207. https://doi.org/10.1016/j.paid.2014.01.008

    Article  Google Scholar 

  • Andrews-Hanna, J. R. (2012). The brain’s default network and its adaptive role in internal mentation. The Neuroscientist, 18, 251–270. https://doi.org/10.1177/1073858411403316

    Article  Google Scholar 

  • Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397–438. https://doi.org/10.1080/10705510903008204

    Article  Google Scholar 

  • Beall, A. T., & Schaller, M. (2019). Evolution, motivation, and the mating/parenting trade-off. Self and Identity, 18(1), 39–59. https://doi.org/10.1080/15298868.2017.1356366

    Article  Google Scholar 

  • Belsky, J., Steinberg, L., & Draper, P. (1991). Childhood experience, interpersonal development, and reproductive strategy: An evolutionary theory of socialization. Child Development, 62(4), 647–670. https://doi.org/10.1111/j.1467-8624.1991.tb01558.x

    Article  Google Scholar 

  • Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E. J., Berk, R., & Cesarini, D. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6–25. https://doi.org/10.1038/s41562-017-0189-z

    Article  Google Scholar 

  • Black, C. J., Figueredo, A. J., & Jacobs, W. J. (2017). Substance, history, and politics: An examination of the conceptual underpinnings of alternative approaches to the life history narrative. Evolutionary Psychology, 15(1). https://doi.org/10.1177/1474704916670402

  • Bollen, K. (1989). Structural equations with latent variables. Wiley Series in Probability and Mathematical Statistics. Wiley.

  • Bonifay, W., Lane, S. P., & Reise, S. P. (2017). Three concerns with applying a bifactor model as a structure of psychopathology. Clinical Psychological Science, 5(1), 184–186.

    Article  Google Scholar 

  • Bricker, J. B., Stallings, M. C., Corley, R. P., Wadsworth, S. J., Bryan, A., Timberlake, D. S., & DeFries, J. C. (2006). Genetic and environmental influences on age at sexual initiation in the Colorado Adoption Project. Behavior Genetics, 36(6), 820–832.

    Article  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage.

    Google Scholar 

  • Byrne, B. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming (1st ed.). Lawrence Erlbaum Associates.

    Google Scholar 

  • Campbell, A. (2009). What kind of selection? Behavioral and Brain Sciences, 32, 272–273.

    Article  Google Scholar 

  • Carranza, J. (2009). Defining sexual selection as sex-dependent selection. Animal Behaviour, 77, 749–751.

    Article  Google Scholar 

  • Carter, G. L., Campbell, A. C., & Muncer, S. (2014). The dark triad: Beyond a ‘male’ mating strategy. Personality and Individual Differences, 56, 159–164.

    Article  Google Scholar 

  • Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41(2), 189–225.

    Article  Google Scholar 

  • Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255.

    Article  Google Scholar 

  • Copping, L. T., & Richardson, G. B. (2020). Studying sex differences in psychosocial life history indicators. Evolutionary Psychological Science, 6(1), 47–59. https://doi.org/10.1007/s40806-019-00211-2

    Article  Google Scholar 

  • Copping, L. T., Campbell, A., & Muncer, S. (2014). Psychometrics and life history strategy: The structure and validity of the high K strategy scale. Evolutionary Psychology, 12(1), 200–222.

    Article  Google Scholar 

  • Copping, L. T., Campbell, A., Muncer, S., & Richardson, G. B. (2017). The psychometric evaluation of human life histories: A reply to Figueredo, Cabeza de Baca, Black, Garcia, Fernandes, Wolf, and Woodley (2015). Evolutionary Psychology, 15(1). https://doi.org/10.1177/1474704916663727

  • Darwin, C. (1871). The descent of man and selection in relation to sex. Penguin Classics (2004 edition).

  • Del Giudice, M. (2009). Sex, attachment and the development of reproductive strategies. Behavioral and Brain Sciences, 32, 1–67. https://doi.org/10.1017/S0140525X09000016

    Article  Google Scholar 

  • Del Giudice, M. (2020). Rethinking the fast-slow continuum of individual differences. Evolution & Human Behavior, 41, 536–549. https://doi.org/10.1016/j.evolhumbehav.2020.05.004

    Article  Google Scholar 

  • Draper, P., & Harpending, H. (1982). Father absence and reproductive strategy: An evolutionary perspective. Journal of Anthropological Research, 38(3), 255–273.

    Article  Google Scholar 

  • Dunne, M. P., Martin, N. G., Statham, D. J., Slutske, W. S., Dinwiddie, S. H., Bucholz, K. K., & Heath, A. C. (1997). Genetic and environmental contributions to variance in age at first sexual intercourse. Psychological Science, 8(3), 211–216.

    Article  Google Scholar 

  • Ellis, B. J., Figueredo, A. J., Brumbach, B. H., & Schlomer, G. L. (2009). Fundamental dimensions of environmental risk. The impact of harsh versus unpredictable environments on the evolution and development of life history strategies. Human Nature, 20, 204–268.

    Article  Google Scholar 

  • Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017). Generalized network psychometrics: Combining network and latent variable models. Psychometrika, 82(4), 904–927.

    Article  Google Scholar 

  • Figueredo, A. J., de Baca, T. C., Black, C. J., García, R. A., Fernandes, H. B. F., Wolf, P. S. A., & Anthony, M. (2015). Methodologically sound: Evaluating the psychometric approach to the assessment of human life history [reply to]. Evolutionary Psychology, 13(2), 147470491501300200.

    Article  Google Scholar 

  • Figueredo, A. J., Vasquez, G., Brumbach, B. H., & Schneider, S. M. (2004). The heritability of life history strategy: The K-factor, covitality, and personality. Social Biology, 51(3–4), 121–143.

    Google Scholar 

  • Figueredo, A. J., Vásquez, G., Brumbach, B. H., & Schneider, S. M. (2007). The K-factor, covitality, and personality. Human Nature, 18(1), 47–73.

    Article  Google Scholar 

  • Figueredo, A. J., Vásquez, G., Brumbach, B. H., Schneider, S. M., Sefcek, J. A., Tal, I. R., & Jacobs, W. J. (2006). Consilience and life history theory: From genes to brain to reproductive strategy. Developmental Review, 26(2), 243–275.

    Article  Google Scholar 

  • Figueredo, A. J., Vásquez, G., Brumbach, B. H., Sefcek, J. A., Kirsner, B. R., & Jacobs, W. J. (2005). The K-factor: Individual differences in life history strategy. Personality and Individual Differences, 39(8), 1349–1360.

    Article  Google Scholar 

  • Figueredo, A. J., Wolf, P. S. A., Olderbak, S. G., Gladden, P. R., Fernandes, H. B. F., Wenner, C., & Jacobs, W. J. (2014). The psychometric assessment of human life history strategy: A meta-analytic construct validation. Evolutionary Behavioral Sciences, 8(3), 148–185.

    Article  Google Scholar 

  • Franić, S., Dolan, C. V., Borsboom, D., Hudziak, J. J., van Beijsterveldt, C. E., & Boomsma, D. I. (2013). Can genetics help psychometrics? Improving dimensionality assessment through genetic factor modeling. Psychological Methods, 18(3), 406–433.

    Article  Google Scholar 

  • Garcia, R. A., de Baca, T. C., Black, C. J., Sotomayor-Peterson, M., Smith-Castro, V., & Figueredo, A. J. (2016). Measures of domain-specific resource allocations in life history strategy: Indicators of a latent common factor or ordered developmental sequence? Journal of Methods and Measurement in the Social Sciences, 7(1), 23–51.

    Article  Google Scholar 

  • Gignac, G. E., & Kretzschmar, A. (2017). Evaluating dimensional distinctness with correlated-factor models: Limitations and suggestions. Intelligence, 62, 138–147.

    Article  Google Scholar 

  • Gruijters, S. L., & Fleuren, B. P. (2018). Measuring the unmeasurable. Human Nature, 29, 33–44.

    Article  Google Scholar 

  • Harden, K. P., Mendle, J., Hill, J. E., Turkheimer, E., & Emery, R. E. (2008). Rethinking timing of first sex and delinquency. Journal of Youth and Adolescence, 37(4), 373–385. https://doi.org/10.1007/s10964-007-9228-9

    Article  Google Scholar 

  • Hershberger, S. L. (2005). Factor scores. In B. S. Everitt (Ed.), Encyclopedia of statistics in behavioral science (pp. 636–644). Wiley.

    Google Scholar 

  • Hill, T. P. (2017). An elementary theory for the variability hypothesis. New York Journal of Mathematics, 23, 1641–1655.

    Google Scholar 

  • Hsiao, Y. Y., & Lai, M. H. C. (2018). The impact of partial measurement invariance on testing moderation for single and multi-level data. Frontiers in Psychology, 9, 740. https://doi.org/10.3389/fpsyg.2018.00740

    Article  Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

    Article  Google Scholar 

  • James, J., Ellis, B. J., Schlomer, G. L., & Garber, J. (2012). Sex specific pathways to early puberty, sexual debut and sexual risk taking: Test of an integrated evolutionary-developmental model. Developmental Psychology, 48, 687–702. https://doi.org/10.1037/a0026427

    Article  Google Scholar 

  • Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70(351a), 631–639.

    Article  Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford.

    Google Scholar 

  • Kogan, S. M., Cho, J., Simons, L. G., Allen, K. A., Beach, S. R. H., Simons, R. L., & Gibbons, F. X. (2014). Pubertal timing and sexual risk behaviors among rural African American male youth: Testing a model based on life history theory. Archives of Sexual Behavior, 44(3), 609–618. https://doi.org/10.1007/s10508-014-0410-3

    Article  Google Scholar 

  • Kohler, H. P., Rodgers, J. L., & Christensen, K. (1999). Is fertility behavior in our genes? Findings from a Danish twin study. Population and Development Review, 25(2), 253–288.

    Article  Google Scholar 

  • Knafo, A., & Plomin, R. (2006). Prosocial behavior from early to middle childhood: Genetic and environmental influences on stability and change. Developmental Psychology, 42(5), 771–786. https://doi.org/10.1037/0012-1649.42.5.771

    Article  Google Scholar 

  • Kruger, D. J. (2017). Brief self-report scales assessing life history dimensions of mating and parenting effort. Evolutionary Psychology, 15(1). https://doi.org/10.1177/1474704916673840

  • Lai, M. H., Richardson, G. B., & Mak, H. W. (2019). Quantifying the impact of partial measurement invariance in diagnostic research: An application to addiction research. Addictive behaviors, 94, 50–56.

    Article  Google Scholar 

  • Li, W., Mai, X., & Liu, C. (2014). The default mode network and social understanding of others: what do brain connectivity studies tell us. Frontiers in Human Neuroscience, 8(74). https://doi.org/10.3389/fnhum.2014.00074

  • Lowe, J. R., Edmundson, M., & Widiger, T. A. (2009). Assessment of dependency, agreeableness, and their relationship. Psychological Assessment, 21(4), 543–553.

    Article  Google Scholar 

  • Mansolf, M., & Reise, S. P. (2017). When and why the second-order and bifactor models are distinguishable. Intelligence, 61, 120–129.

    Article  Google Scholar 

  • Maes, H. H., Silberg, J. L., Neale, M. C., & Eaves, L. J. (2007). Genetic and cultural transmission of antisocial behavior: an extended twin parent model. Twin Research and Human Genetics, 10(1), 136–150. https://doi.org/10.1375/twin.10.1.136

    Article  Google Scholar 

  • Međedović, J. (2018). Exploring the links between psychopathy and life history in a sample of college females: A behavioral ecological approach. Evolutionary Psychological Science, 4(4), 466–473. https://doi.org/10.1007/s40806-018-0157-5

    Article  Google Scholar 

  • Međedović, J. (2019). Harsh environment facilitates psychopathy’s involvement in mating-parenting trade-off. Personality and Individual Differences, 139, 235–240. https://doi.org/10.1016/j.paid.2018.11.034

    Article  Google Scholar 

  • Muncer, S. (2011). The general factor of personality: Evaluating the evidence from meta-analysis, confirmatory factor analysis and evolutionary theory. Personality and Individual Differences, 51, 775–778. https://doi.org/10.1016/j.paid.2011.06.029

    Article  Google Scholar 

  • Musek, J. (2007). A general factor of personality: Evidence for the Big One in the five-factor model. Journal of Research in Personality, 61, 622–631.

    Google Scholar 

  • Mustanski, B., Viken, R. J., Kaprio, J., Winter, T., & Rose, R. J. (2007). Sexual behavior in young adulthood: A population-based twin study. Health Psychology, 26(5), 610–617.

    Article  Google Scholar 

  • Muthén, B., du Toit, S., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Accessible at https://www.statmodel.com/download/Article_075.pdf

  • Neale, M., & Cardon, L. (2013). Methodology for genetic studies of twins and families (Nato Science Series D, 67). Dordrecht: Springer Science & Business Media. (originally published in 1992).

  • Nettle, D. (2006). The evolution of personality variation in humans and other animals. American Psychologist, 61(6), 622–631.

    Article  Google Scholar 

  • Olderbak, S., Gladden, P., Wolf, P. S. A., & Figueredo, A. J. (2014). Comparison of life history strategy measures. Personality and Individual Differences, 58, 82–88.

    Article  Google Scholar 

  • Penke, L., Denissen, J. A., & Miller, G. F. (2007). The evolutionary genetics of personality. European Journal of Personality, 21, 549–587. https://doi.org/10.1002/per.629

    Article  Google Scholar 

  • Polderman, T. J., Benyamin, B., De Leeuw, C. A., Sullivan, P. F., Van Bochoven, A., Visscher, P. M., & Posthuma, D. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics, 47(7), 702–709.

    Article  Google Scholar 

  • Richardson, G. B., Chen, C. C., Dai, C. L., Brubaker, M. D., & Nedelec, J. L. (2017c). The psychometrics of the Mini-K: Evidence from two college samples. Evolutionary Psychology, 15(1). https://doi.org/10.1177/1474704916682034

  • Richardson, G. B., Chen, C.-C., Dai, C.-L., Hardesty, P. H., & Swoboda, C. M. (2014). Life history strategy and young adult substance use. Evolutionary Psychology, 12(5), 932–957.

    Article  Google Scholar 

  • Richardson, G. B., Dai, C.-L., Chen, C.-C., Nedelec, J. L., Swoboda, C. M., & Chen, W.-W. (2016). Adolescent life history strategy in the intergenerational transmission and developmental stability of substance use. Journal of Drug Issues, 46(2), 102–121

    Article  Google Scholar 

  • Richardson, G. B., Dariotis, J. K., & Lai, M. H. (2017a). From environment to mating competition and Super-K in a predominantly urban sample of young adults. Evolutionary Psychology, 15(1). https://doi.org/10.1177/1474704916670165

  • Richardson, G. B., Sanning, B. K., Lai, M. H., Copping, L. T., Hardesty, P. H., & Kruger, D. J. (2017b). On the psychometric study of human life history strategies: State of the science and evidence of two independent dimensions. Evolutionary Psychology, 15(1). https://doi.org/10.1177/1474704916666840

  • Rodgers, J. L., Kohler, H. P., Kyvik, K. O., & Christensen, K. (2001). Behavior genetic modeling of human fertility: Findings from a contemporary Danish twin study. Demography, 38(1), 29–42.

    Article  Google Scholar 

  • Sear, R., & Mace, R. (2008). Who keeps children alive? A review of the effects of kin on child survival. Evolution and Human Behavior, 29, 1–18.

    Article  Google Scholar 

  • Snyder, H. R., Young, J. F., & Hankin, B. L. (2017). Strong homotypic continuity in common psychopathology-, internalizing-, and externalizing-specific factors over time in adolescents. Clinical Psychological Science, 5(1), 98–110.

    Article  Google Scholar 

  • Stearns, S. C. (1992). The evolution of life histories. Oxford University Press.

    Google Scholar 

  • Steinmetz, H. (2013). Analyzing observed composite differences across groups: Is partial measurement invariance good enough? Methodology, 9, 1–12.

    Article  Google Scholar 

  • Stiver, K. A., & Alonzo, S. H. (2009). Parental and mating effort: Is there necessarily a trade-off? (Invited Review). Ethology, 115(12), 1101–1126. https://doi.org/10.1111/j.1439-0310.2009.01707.x.

  • Trivers, R. L. (1972). Parental investment and sexual selection. In B. Campbell (Ed.), Sexual selection and the descent of man 1871–1971 (pp. 136–179). Aldine.

  • van der Linden, D., Schermer, J. A., de Zeeuw, E., Dunkel, C. S., Pekaar, K. A., Bakker, A. B., & Petrides, K. V. (2018). Overlap between the general factor of personality and trait emotional intelligence: a genetic correlation study. Behavior Genetics, 48(2), 147–154. https://doi.org/10.1007/s10519-017-9885-8

    Article  Google Scholar 

  • van der Linden, D., Pekaar, K. A., Bakker, A. B., Schermer, J. A., Vernon, P. A., Dunkel, C. S., & Petrides, K. V. (2017). Overlap between the general factor of personality and emotional intelligence: A meta-analysis. Psychological Bulletin, 143(1), 1–65.

    Google Scholar 

  • van der Linden, D., van Klaveren, D., & Dunkel, C. S. (2015). Emotional intelligence (EI) is an indicator of a slow life history strategy: A test of ability and trait EI. Personality and Individual Differences, 73, 84–87.

    Article  Google Scholar 

  • Verweij, K. J. H., Zietsch, B. P., Bailey, J. M., & Martin, N. G. (2009). Shared aetiology of risky sexual behaviour and adolescent misconduct: Genetic and environmental influences. Genes, Brain and Behavior, 8(1), 107–113.

    Article  Google Scholar 

  • Waldron, M., Heath, A. C., Turkheimer, E., Emery, R., Bucholz, K. K., Madden, P. A., & Martin, N. G. (2007). Age at first sexual intercourse and teenage pregnancy in Australian female twins.  Twin Research and Human Genetics, 10(3), 440–449.

  • Wang, S., Chen, C. C., Dai, C. L., & Richardson, G. B. (2018). A call for, and beginner’s guide to, measurement invariance testing in evolutionary psychology. Evolutionary Psychological Science, 4, 166–178

    Article  Google Scholar 

  • Wei, Y., De Lange, S. C., Scholtens, L. H., Watanabe, K., Ardesch, D. J., Jansen, P. R., ... & Posthuma, D. (2019). Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nature Communications, 10 (4839). https://doi.org/10.1038/s41467-019-12764-8

  • West-Eberhard, M. J. (1979). Sexual selection, social competition, and evolution. Proceedings of the American Philosophical Society, 123, 222–234

    Google Scholar 

  • Xu, T., Nenning, K. H., Schwartz, E., Hong, S. J., Vogelstein, J. T., Fair, D. A., ... & Langs, G. (2019). Cross-species functional alignment reveals evolutionary hierarchy within the connectome. bioRxiv, https://doi.org/10.1101/692616

  • Zietsch, B. P., & Sidari, M. J. (2020). A critique of life history approaches to human trait covariation. Evolution and Human Behavior, 41, 527–535. https://doi.org/10.1016/j.evolhumbehav.2019.05.007

    Article  Google Scholar 

  • Zietsch, B. P., Verweij, K. J. H., Bailey, J. M., Wright, M. J., & Martin, N. G. (2010). Genetic and environmental influences on risky sexual behaviour and its relationship with personality. Behavior Genetics, 40(1), 12–21.

    Article  Google Scholar 

Download references

Acknowledgments

Publically available data from the MIDUS study was used for this research. Since 1995, the MIDUS study has been funded by the following: John D. and Catherine T. MacArthur Foundation Research Network, National Institute on Aging (P01-AG020166), and National Institute on Aging (U19-AG051426).

Author information

Authors and Affiliations

  1. University of Cincinnati, Cincinnati, OH, USA

    George B. Richardson & Nathan McGee

  2. Teesside University, Middlesbrough, UK

    Lee T. Copping

Authors
  1. George B. Richardson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nathan McGee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lee T. Copping
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George B. Richardson.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 149 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Richardson, G.B., McGee, N. & Copping, L.T. Advancing the Psychometric Study of Human Life History Indicators . Hum Nat 32, 363–386 (2021). https://doi.org/10.1007/s12110-021-09398-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12110-021-09398-5

Keywords

Search

Navigation