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. 2013 Jul 25;8(7):e69172.
doi: 10.1371/journal.pone.0069172. Print 2013.

The critical period hypothesis in second language acquisition: a statistical critique and a reanalysis

Affiliations

The critical period hypothesis in second language acquisition: a statistical critique and a reanalysis

Jan Vanhove. PLoS One. .

Erratum in

  • PLoS One. 2014;9(7):e102922

Abstract

In second language acquisition research, the critical period hypothesis (cph) holds that the function between learners' age and their susceptibility to second language input is non-linear. This paper revisits the indistinctness found in the literature with regard to this hypothesis's scope and predictions. Even when its scope is clearly delineated and its predictions are spelt out, however, empirical studies-with few exceptions-use analytical (statistical) tools that are irrelevant with respect to the predictions made. This paper discusses statistical fallacies common in cph research and illustrates an alternative analytical method (piecewise regression) by means of a reanalysis of two datasets from a 2010 paper purporting to have found cross-linguistic evidence in favour of the cph. This reanalysis reveals that the specific age patterns predicted by the cph are not cross-linguistically robust. Applying the principle of parsimony, it is concluded that age patterns in second language acquisition are not governed by a critical period. To conclude, this paper highlights the role of confirmation bias in the scientific enterprise and appeals to second language acquisition researchers to reanalyse their old datasets using the methods discussed in this paper. The data and R commands that were used for the reanalysis are provided as supplementary materials.

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Conflict of interest statement

Competing Interests: The author has declared that no competing interests exist.

Figures

Figure 1
Figure 1. Three possible critical period effects.
The graphs are based on based on Figure 2 in .
Figure 2
Figure 2. Illustration of the difference between correlation coefficients and slopes.
Relationships on the same row were generated by the same underlying function (formula image and formula image, respectively) but are characterised by different correlation coefficients (formula image and formula image, respectively). The inverse is true for relationships in the same column.
Figure 3
Figure 3. Scatterplot of the AOA–GJT relationship in the North America study.
The trend line is a non-parametric scatterplot smoother. The scatterplot itself is a near-perfect replication of DK et al.'s Fig. 1.
Figure 4
Figure 4. Scatterplot of the AOA–GJT relationship in the Israel study.
The trend line is a non-parametric scatterplot smoother. The scatterplot itself is a near-perfect replication of DK et al.'s Fig. 5.
Figure 5
Figure 5. Regression lines for the North America data.
Solid: regression with breakpoint at aoa 18 (dashed lines represent its 95% confidence interval); dot-dash: regression without breakpoint.
Figure 6
Figure 6. Regression lines for the Israel data.
Solid: regression with breakpoint at aoa 18 (dashed lines represent its 95% confidence interval); dot-dash (hardly visible due to near-complete overlap): regression without breakpoint.
Figure 7
Figure 7. Deviances for regression models assuming breakpoints as a function of the position of the breakpoints.
Figure 8
Figure 8. Regression lines for the North America data.
Solid: regression with breakpoint at aoa 16 (dashed lines represent its 95% confidence interval); dot-dash: regression without breakpoint.
Figure 9
Figure 9. Regression lines for the Israel data.
Solid: regression with breakpoint at aoa 6 (dashed lines represent its 95% confidence interval); dot-dash (hardly visible due to near-complete overlap): regression without breakpoint.

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References

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No current external funding sources for this study.