Skip to main content
SpringerLink
Log in

On the Incongruence between Psychometric and Psychosocial-Biodemographic Measures of Life History

  • Published:
Human Nature Aims and scope Submit manuscript

Abstract

In evolutionary psychology, it is customary to measure life-history via psychometric inventories such as the Arizona Life History Battery (ALHB). The validity of this approach has been questioned: it is argued that these measures are not congruent with biological life history events, such as the number of children, age at first birth, or pubertal timing. However, empirical data to test this critique are lacking. We therefore administered the ALHB to a convenience sample of young adults in Serbia (N = 447). We also collected information on psychosocial-biodemographic life history parameters closely related to biological life history traits: pubertal timing, onset of sexual behavior, short- and long-term mating, number of children, timing of reproduction, parenthood values, and environmental harshness. We found that correlations between these two sets of measures were rare, unsystematic, and mostly low in magnitude. Stable patterns of relations emerged only between the indicators of environmental conditions from both sets of measures. Furthermore, some ALHB indicators were found to be positively related with early fertility, which is incongruent with the conceptual foundation of ALHB. Finally, network analysis and factor analysis within each set of measures revealed different structures and that the hypothesis of unidimensionality, on which the ALHB was founded, cannot be applied to psychosocial-biodemographic life history indicators. Our results support the critique of ALHB as a set of measures lacking validity to capture biodemographic life-history parameters. ALHB measures are indeed relevant for understanding life-history variation, but they cannot be used as a substitute for specific life history characteristics. Our findings are a warning to researchers to use direct measures of biological events in order to measure life-history dynamics.

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. Other potentially important results are shown in the Electronic Supplementary Material: (1) additional descriptive information on psychosocial-biodemographic life history indicators; (2) correlations (both Pearson’s and Spearman’s coefficients) between and within two sets of life history indicators obtained on raw measures (before normalization); (3) regression function with reproductive success as a criterion measure and all life history indicators as predictor variables; (4) differences between individuals with and without children on all other examined variables; (5) loadings of the variables on the canonical factors in Canonical Correlation Analysis; (6) more detailed information regarding the centrality indices of the nodes in network model; and (7) networks models with all life history indicators estimated for males and females separately.

  2. Perhaps we can be even more precise here. Reproductive success and survival/longevity are considered the two most crucial fitness components. However, with the substantial improvement in medical care and standard of life in general in many contemporary human populations, the selection on survival/longevity is quite weak. Since early-age mortality is low, most individuals live to their reproductive period and beyond. Consequently, this leaves only reproductive output as a major aspect of fitness—the one through which selection operates. Of course, the role of survival/longevity is contingent on environmental conditions—in harsh and hostile environments, the selection on survival/longevity is stronger. However, for most contemporary human populations, reproductive fitness is probably the main driver of biological evolution.

References

  • Belsky, J. (2012). The development of human reproductive strategies: Progress and prospects. Current Directions in Psychological Science, 21, 310–316.

    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

  • Blom, G. (1958). Statistical estimates and transformed beta-variables. New York: Wiley.

    Google Scholar 

  • Blume, M. (2009). The reproductive benefits of religious affiliation. In E. Voland & W. Schiefenhovel (Eds.), The biological evolution of religious mind and behavior (pp. 117–126). New York: Springer.

    Google Scholar 

  • Chisholm, J. S., Quinlivan, J. A., Petersen, R. W., & Coall, D. A. (2005). Early stress predicts age at menarche and first birth, adult attachment, and expected lifespan. Human Nature, 16(3), 233–265.

    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, 147470491401200115.

    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, 1474704916663727.

  • Dalege, J., Borsboom, D., van Harreveld, F., & van der Maas, H. L. (2017). Network analysis on attitudes: A brief tutorial. Social Psychological and Personality Science, 8, 528–537.

    Google Scholar 

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

  • Dunkel, C. S., Mathes, E. W., Kesselring, S. N., Decker, M. L., & Kelts, D. J. (2015). Parenting influence on the development of life history strategy. Evolution and Human Behavior, 36, 374–378.

    Google Scholar 

  • Ellis, B. J., Figueredo, A. J., Brumbach, B. H., & Schlomer, G. L. (2009). Fundamental dimensions of environmental risk. Human Nature, 20, 204–268.

    Google Scholar 

  • Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195–212.

    Google Scholar 

  • Fieder, M., & Huber, S. (2016). The association between religious homogamy and reproduction. Proceedings of the Royal Society of London B: Biological Sciences, 283: 20160294.

  • Figueredo, A. J. (2007). The Arizona Life History Battery [Electronic version]. Retrieved from http://www.u.arizona.edu/~ajf/alhb.html.

  • Figueredo, A. J., Cabeza de Baca, T., Black, C. J., Garcia, R. A., Fernandes, H. B. F., & Wolf, P. S. A., and Woodley of Menie, M. A. (2015). Methodologically sound: Evaluating the psychometric approach to the assessment of human life history [Reply to Copping, Campbell, and Muncer, 2014]. Evolutionary Psychology, 13, 299–338.

  • Figueredo, A. J., Garcia, R. A., Menke, J. M., Jacobs, W. J., Gladden, P. R., Bianchi, J., … and Jiang, Y. (2017). The K-SF-42: A new short form of the Arizona Life History Battery. Evolutionary Psychology, 15, 1474704916676276.

    Google Scholar 

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

    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, 1349–1360.

    Google Scholar 

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

    Google Scholar 

  • Gagnon, A., Smith, K. R., Tremblay, M., Vézina, H., Paré, P. P., & Desjardins, B. (2009). Is there a trade-off between fertility and longevity? A comparative study of women from three large historical databases accounting for mortality selection. American Journal of Human Biology, 21, 533–540.

    Google Scholar 

  • Gillespie, D. O., Russell, A. F., & Lummaa, V. (2008). When fecundity does not equal fitness: evidence of an offspring quantity versus quality trade-off in pre-industrial humans. Proceedings of the Royal Society B: Biological Sciences, 275, 713–722.

    Google Scholar 

  • Giosan, C. (2006). High-K strategy scale: A measure of the high-K independent criterion of fitness. Evolutionary Psychology, 4, 394–405.

    Google Scholar 

  • Griskevicius, V., Delton, A. W., Robertson, T. E., & Tybur, J. M. (2011). Environmental contingency in life history strategies: the influence of mortality and socioeconomic status on reproductive timing. Journal of Personality and Social Psychology, 100(2), 241–254.

    Google Scholar 

  • Gruijters, S. L., & Fleuren, B. P. (2018). Measuring the unmeasurable: The psychometrics of life history strategy. Human Nature, 29, 33–44.

    Google Scholar 

  • Jasienska, G., Bribiescas, R. G., Furberg, A. S., Helle, S., & Núñez-de la Mora, A. (2017). Human reproduction and health: an evolutionary perspective. The Lancet, 390, 510–520.

    Google Scholar 

  • Kogan, S. M., Cho, J., Simons, L. G., Allen, K. A., Beach, S. R., Simons, R. L., & Gibbons, F. X. (2015). 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, 609–618.

    Google Scholar 

  • Mace, R. (2000). Evolutionary ecology of human life history. Animal Behaviour, 59, 1–10.

    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, 466–473.

    Google Scholar 

  • Međedović, J. (2019). Life history in a postconflict society. Human Nature, 30(1), 59–70.

    Google Scholar 

  • Međedović, J. (2020a). Human life histories as dynamic networks: Using network analysis to conceptualize and analyze life history data. Evolutionary Psychological Science. https://doi.org/10.1007/s40806-020-00252-y.

  • Međedović, J. (2020b). Examining the link between religiousness and fitness in a behavioural ecological framework. Journal of Biosocial Science, 52, 756–767.

  • Međedović, J., & Bulut, T. (2019). A life-history perspective on body mass: Exploring the interplay between harsh environment, body mass, and mating success. Evolutionary Behavioral Sciences, 13(1), 84–92.

    Google Scholar 

  • Meij, J. J., Van Bodegom, D., Ziem, J. B., Amankwa, J., Polderman, A. M., Kirkwood, T. B. L., … and Westendorp, R. G. J. (2009). Quality–quantity trade-off of human offspring under adverse environmental conditions. Journal of evolutionary biology, 22, 1014–1023.

    Google Scholar 

  • Mell, H., Safra, L., Algan, Y., Baumard, N., & Chevallier, C. (2018). Childhood environmental harshness predicts coordinated health and reproductive strategies: a cross-sectional study of a nationally representative sample from France. Evolution and Human Behavior, 39, 1–8.

    Google Scholar 

  • Miller, W. B. (1995). Childbearing motivation and its measurement. Journal of Biosocial Science, 27, 473–487.

    Google Scholar 

  • Miller, W. B., Bard, D. E., Pasta, D. J., & Rodgers, J. L. (2010). Biodemographic modeling of the links between fertility motivation and fertility outcomes in the NLSY79. Demography, 47, 393–414.

    Google Scholar 

  • Nettle, D. (2011). Flexibility in reproductive timing in human females: integrating ultimate and proximate explanations. Philosophical Transactions of the Royal Society B: Biological Sciences, 366, 357–365.

    Google Scholar 

  • Nettle, D., & Frankenhuis, W. E. (2019). The evolution of life-history theory: a bibliometric analysis of an interdisciplinary research area. Proceedings of the Royal Society B, 286(1899), 20190040.

  • Nettle, D., & Frankenhuis, W. E. (2020). Life history theory in psychology and evolutionary biology: One research programme or two? Philosophical Transactions of the Royal Society B: Biological Sciences, 375. https://doi.org/10.1098/rstb.2019.0490.

  • Pearce, L. D., & Davis, S. N. (2016). How early life religious exposure relates to the timing of first birth. Journal of Marriage and Family, 78, 1422–1438.

    Google Scholar 

  • Promislow, D. E., & Harvey, P. H. (1990). Living fast and dying young: A comparative analysis of life-history variation among mammals. Journal of Zoology, 220(3), 417–437.

    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), 1474704916670165.

    Google Scholar 

  • Richardson, G. B., Sanning, B. K., Lai, M. H. C., Copping, L. T., Hardesty, P. H., & Kruger, D. J. (2017b). On the psychometric study of human life history strategies. Evolutionary Psychology, 15, 16666840.

    Google Scholar 

  • Roff, D. A. (2002). Life History Evolution. Sunderland: Sinauer.

    Google Scholar 

  • Rushton, J. P. (1985). Differential K theory: The sociobiology of individual and group differences. Personality and Individual Differences, 6, 441–452.

    Google Scholar 

  • Sanderson, S. K. (2008). Adaptation, evolution, and religion. Religion, 38, 141–156.

    Google Scholar 

  • Sear, R. (2020). Do human “life history strategies” exist? https://doi.org/10.31219/osf.io/hjezb.

  • Sheppard, P., Pearce, M. S., & Sear, R. (2016). How does childhood socioeconomic hardship affect reproductive strategy? Pathways of development. American Journal of Human Biology, 28, 356–363.

    Google Scholar 

  • Sherry, A., & Henson, R. K. (2005). Conducting and interpreting canonical correlation analysis in personality research: A user-friendly primer. Journal of Personality Assessment, 84, 37–48.

    Google Scholar 

  • Strayhorn, J. M., & Strayhorn, J. C. (2009). Religiosity and teen birth rate in the United States. Reproductive Health, 6, 14. https://doi.org/10.1186/1742-4755-6-14.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.

    Google Scholar 

  • Van Balen, F., & Trimbos-Kemper, T. C. (1995). Involuntarily childless couples: Their desire to have children and their motives. Journal of Psychosomatic Obstetrics and Gynecology, 16, 137–144.

    Google Scholar 

  • Webster, G. D., Graber, J. A., Gesselman, A. N., Crosier, B. S., & Schember, T. O. (2014). A life history theory of father absence and menarche: a meta-analysis. Evolutionary Psychology, 12(2), 147470491401200202.

    Google Scholar 

  • Woodley of Menie, M. A., de Baca, T. C., Fernandes, H. B. F., Madison, G., Figueredo, A. J., & Aguirre, M. P. (2017). Slow and steady wins the race: K positively predicts fertility in the USA and Sweden. Evolutionary Psychological Science, 3, 109–117.

    Google Scholar 

  • Xu, Y., Norton, S., & Rahman, Q. (2018). Early life conditions, reproductive and sexuality-related life history outcomes among human males: A systematic review and meta-analysis. Evolution and Human Behavior, 39, 40–51.

    Google Scholar 

  • Zietsch, B. P., & Sidari, M. J. (2019). A critique of life history approaches to human trait covariation. Evolution and Human Behavior. https://doi.org/10.1016/j.evolhumbehav.2019.05.007.

    Article  Google Scholar 

  • Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101, 1418–1429.

    Google Scholar 

Download references

Acknowledgments

The work on this manuscript was financed by the Serbian Ministry of Education, Science and Technological Development in the project 47011, realized by the Institute of Criminological and Sociological Research.

Author information

Authors and Affiliations

  1. Institute of Criminological and Sociological Research, Gračanička 18, 11000, Belgrade, Serbia

    Janko Međedović

Authors
  1. Janko Međedović
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Janko Međedović.

Additional information

Publisher's Note

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

Electronic Supplementary Material

ESM 1

(PDF 217 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Međedović, J. On the Incongruence between Psychometric and Psychosocial-Biodemographic Measures of Life History. Hum Nat 31, 341–360 (2020). https://doi.org/10.1007/s12110-020-09377-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12110-020-09377-2

Keywords

Search

Navigation