A formal model accounting for measurement reliability shows attenuated effect of higher education on intelligence in longitudinal data

Author:

Eriksson Kimmo12ORCID,Sorjonen Kimmo3ORCID,Falkstedt Daniel4,Melin Bo3,Nilsonne Gustav35ORCID

Affiliation:

1. School of Education, Culture and Communication, Mälardalen University , Västerås, Sweden

2. Institute for Futures Studies , Stockholm, Sweden

3. Department of Clinical Neuroscience, Karolinska Institutet , Stockholm, Sweden

4. Institute of Environmental Medicine, Karolinska Institutet , Stockholm, Sweden

5. Department of Psychology, Stockholm University , Stockholm, Sweden

Abstract

The effect of higher education on intelligence has been examined using longitudinal data. Typically, these studies reveal a positive effect, approximately 1 IQ point per year of higher education, particularly when pre-education intelligence is considered as a covariate in the analyses. However, such covariate adjustment is known to yield positively biased results if the covariate has measurement errors and is correlated with the predictor. Simultaneously, a negative bias may emerge if the intelligence measure after higher education has non-classical measurement errors as in data from the 1970 British Cohort Study that were used in a previous study of the effect of higher education. In response, we have devised an estimation method that used iterated simulations to account for both classical measurement errors in the covariate and non-classical errors in the dependent variable. Upon applying this method in a reanalysis of the data from the 1970 British Cohort Study, we find that the estimated effect of higher education diminishes to 0.4 IQ points per year. Additionally, our findings suggest that the impact of higher education is somewhat more pronounced in the initial 2 years of higher education, aligning with the notion of diminishing marginal cognitive benefits.

Publisher

The Royal Society

Reference21 articles.

1. How Much Does Education Improve Intelligence? A Meta-Analysis

2. Intelligence and education: causal perceptions drive analytic processes and therefore conclusions

3. Campbell DT , Erlebacher A . 1970 How regression artifacts in quasi-experimental evaluations can mistakenly make compensatory education look harmful. In The disadvantaged child. compensatory education: a national debate (ed. J Hellmuth ), pp. 185–210, vol. 3. New York: Brunner/Mazel.

4. Lord’s Paradox in a Continuous Setting and a Regression Artifact in Numerical Cognition Research

5. Fuller WA . 2009 Measurement error models. New York, NY: John Wiley & Sons.

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