Dickinson, Hiscock and Agbayani

As a follow up to my previous post, Intelligence as Field, I would like to talk about two papers that essentially cover much of the same ground: Dickinson & Hiscock (2010) and Agbayani (2011). Unfortunately, I can't find a non-paywalled version of Dickinson & Hiscock (2010), and therefore I can't link to its full text, nor have I been able to read more than its title and abstract. [This is where I would normally begin a long rant about the ridiculously closed nature of science these days, but that's a worn-out subject, so let's move on.] To the rescue, Agbayani (2011) is available online and it provides all the essential information regarding the results and methods of Dickinson & Hiscock (2010). Agbayani is apparently a student of Hiscock, and Agbayani (2011) is a thesis paper that both outlines the approach of Dickinson & Hiscock (2010) and extends the range of its data and analysis.

The approach these authors take can be outlined as follows:

This approach of Dickinson, Hiscock and Agbayani is essentially equivalent to the approach I took with my idealized chart of data in Intelligence as Field. To see this, you can consider one of the charts of data with which that essay began and one of the comparative data sets that arises from it:

AgeRaw Intelligence Scores by Age and Year
95 40.8 41.6 42.0 42.5 43.3 44.2 45.1 46.0 46.9 47.9 48.8 49.8
85 45.6 46.5 47.0 47.5 48.4 49.4 50.4 51.4 52.4 53.5 54.6 55.7
75 50.4 51.4 51.9 52.5 53.5 54.6 55.7 56.8 58.0 59.1 60.3 61.5
65 54.0 55.1 55.6 56.2 57.3 58.5 59.7 60.9 62.1 63.4 64.6 65.9
55 55.8 56.9 57.5 58.1 59.2 60.4 61.7 62.9 64.2 65.5 66.8 68.1
45 57.6 58.8 59.3 59.9 61.2 62.4 63.7 64.9 66.2 67.6 68.9 70.3
35 58.8 60.0 60.6 61.2 62.4 63.7 65.0 66.3 67.6 69.0 70.4 71.8
25 60.0 61.2 61.8 62.4 63.7 65.0 66.3 67.6 69.0 70.4 71.8 73.3
15 51.0 52.0 52.5 53.1 54.2 55.2 56.4 57.5 58.7 59.8 61.0 62.3
5 15.0 15.3 15.4 15.6 15.9 16.2 16.6 16.9 17.3 17.6 18.0 18.3
105 115 120 125 135 145 155 165 175 185 195 205
Year

 


Age
Without
Flynn Effect
With
Flynn Effect

Difference
85 47.0 55.7 8.7
75 51.9 60.3 8.4
65 55.6 63.4 7.8
55 57.5 64.2 6.7
45 59.3 64.9 5.6
35 60.6 65.0 4.4
25 61.8 65.0 3.2
15 52.5 54.2 1.7
5 15.4 15.6 0.2

If you were to apply the approach of Dickinson, Hiscock and Agbayani to the original chart of data, you would arrive at exactly the same comparative data set, only with a different set of labels:


Age
Scores that
Reflect AGD
Scores that
Reflect TAE

FED
85 47.0 55.7 8.7
75 51.9 60.3 8.4
65 55.6 63.4 7.8
55 57.5 64.2 6.7
45 59.3 64.9 5.6
35 60.6 65.0 4.4
25 61.8 65.0 3.2
15 52.5 54.2 1.7
5 15.4 15.6 0.2

In the terminology of Dickinson, Hiscock and Agbayani, AGD stands for age group difference, reflecting the type of pattern that emerges from cross-sectional studies (that is, from reading up the chart at any given time). TAE stands for true aging effect and reflects the type of pattern that emerges from longitudinal studies (that is, from reading diagonally across the chart for any population cohort). FED is the Flynn effect difference.

In a certain sense, I'm quite pleased that Dickinson & Hiscock (2010) and Agbayani (2011) exist. They are the nearest thing I can find to a real-world analysis similar to what I outlined in Intelligence as Field, and of course it is gratifying to know that the real-world outcome turns out to be essentially the same as my idealized approach.

On the other hand, there is a major problem. Although it appears to me that Dickinson, Hiscock and Agbayani have done a creditable job in the gathering of their data, they have also managed to utterly mangle its interpretation.

In reading the conclusions these authors draw from their analysis (indeed, in reading through their entire approach to the problem), one quickly realizes that they are (mistakenly) saying the following:

That logic is completely backwards.

The only way to make logical sense of the data is to state it the other way. The cross-sectional studies (reading up the chart at any given time) are the true age-based differences—that is, the age-based differences that would show up in the absence of a Flynn effect. The longitudinal values (reading diagonally across the chart) represent the combination of age-based differences and the Flynn effect. The reason that the longitudinal raw scores remain fairly constant across adulthood is that the two competing influences (age-based decline and Flynn effect increase) are in rough equilibrium.

As far as I can tell, there is no reasonable way to make sense out of the Dickinson/Hiscock/Agbayani interpretation. To see this, consider what must happen to the data under and not under the influence of a Flynn effect. Let me use a version of my idealized chart of data, and let's assume there is no Flynn effect for the first fifty years. Under these conditions and under my interpretation, the chart of data would look something like this:

AgeRaw Intelligence Scores by Age and Year
95 40.8 40.8 40.8 40.8 40.8 40.8          
85 45.6 45.6 45.6 45.6 45.6 45.6          
75 50.4 50.4 50.4 50.4 50.4 50.4          
65 54.0 54.0 54.0 54.0 54.0 54.0          
55 55.8 55.8 55.8 55.8 55.8 55.8          
45 57.6 57.6 57.6 57.6 57.6 57.6          
35 58.8 58.8 58.8 58.8 58.8 58.8          
25 60.0 60.0 60.0 60.0 60.0 60.0          
15 51.0 51.0 51.0 51.0 51.0 51.0          
5 15.0 15.0 15.0 15.0 15.0 15.0          
105 115 125 135 145 155 165 175 185 195 205
Year

But by the account of Dickinson, Hiscock, and Agbayani, the chart would need to look much different. Since they are saying that the true aging effect scores reflect age-based differences sans a Flynn effect, then their chart of data (under no Flynn effect) would need to look more like this:

AgeRaw Intelligence Scores by Age and Year
95 48.0 48.0 48.0 48.0 48.0 48.0          
85 53.6 53.6 53.6 53.6 53.6 53.6          
75 58.0 58.0 58.0 58.0 58.0 58.0          
65 61.0 61.0 61.0 61.0 61.0 61.0          
55 61.7 61.7 61.7 61.7 61.7 61.7          
45 62.4 62.4 62.4 62.4 62.4 62.4          
35 62.5 62.5 62.5 62.5 62.5 62.5          
25 62.5 62.5 62.5 62.5 62.5 62.5          
15 52.1 52.1 52.1 52.1 52.1 52.1          
5 15.0 15.0 15.0 15.0 15.0 15.0          
105 115 125 135 145 155 165 175 185 195 205
Year

Now consider what would happen if a Flynn effect kicked in beginning at time 155.

In my chart and under my interpretation, the progression is quite natural. Raw scores begin to go up by say 2% every ten years for all age groups, and what results is a chart of data for years 165 to 205 that has all the same patterns we currently see in the empirical data for humans. Note that the pattern of age-based differences remains invariant under the changing Flynn effect assumptions:

AgeRaw Intelligence Scores by Age and Year
95 40.8 40.8 40.8 40.8 40.8 40.8 41.6 42.5 43.3 44.2 45.1
85 45.6 45.6 45.6 45.6 45.6 45.6 46.5 47.5 48.4 49.4 50.4
75 50.4 50.4 50.4 50.4 50.4 50.4 51.4 52.5 53.5 54.6 55.7
65 54.0 54.0 54.0 54.0 54.0 54.0 55.1 56.2 57.3 58.5 59.7
55 55.8 55.8 55.8 55.8 55.8 55.8 56.9 58.1 59.2 60.4 61.7
45 57.6 57.6 57.6 57.6 57.6 57.6 58.8 59.9 61.2 62.4 63.7
35 58.8 58.8 58.8 58.8 58.8 58.8 60.0 61.2 62.4 63.7 65.0
25 60.0 60.0 60.0 60.0 60.0 60.0 61.2 62.4 63.7 65.0 66.3
15 51.0 51.0 51.0 51.0 51.0 51.0 52.0 53.1 54.2 55.2 56.4
5 15.0 15.0 15.0 15.0 15.0 15.0 15.3 15.6 15.9 16.2 16.6
105 115 125 135 145 155 165 175 185 195 205
Year

But what are Dickinson, Hiscock and Agbayani going to do? How can they reasonably introduce a Flynn effect at time 155 and still remain true to the empirical data? For instance, they can't just begin to boost scores across all age groups, because then their chart would end up looking like this:

AgeRaw Intelligence Scores by Age and Year
95 48.0 48.0 48.0 48.0 48.0 48.0 49.0 50.0 51.0 52.0 53.0
85 53.6 53.6 53.6 53.6 53.6 53.6 54.6 55.7 56.9 58.0 59.2
75 58.0 58.0 58.0 58.0 58.0 58.0 59.2 60.3 61.6 62.8 64.1
65 61.0 61.0 61.0 61.0 61.0 61.0 62.2 63.4 64.7 66.0 67.4
55 61.7 61.7 61.7 61.7 61.7 61.7 63.0 64.2 65.5 66.9 68.2
45 62.4 62.4 62.4 62.4 62.4 62.4 63.7 64.9 66.3 67.6 69.0
35 62.5 62.5 62.5 62.5 62.5 62.5 63.8 65.0 66.4 67.7 69.1
25 62.5 62.5 62.5 62.5 62.5 62.5 63.8 65.0 66.4 67.7 69.1
15 52.1 52.1 52.1 52.1 52.1 52.1 53.2 54.2 55.3 56.5 57.6
5 15.0 15.0 15.0 15.0 15.0 15.0 15.3 15.6 15.9 16.2 16.6
105 115 125 135 145 155 165 175 185 195 205
Year

For years 165 and beyond, that chart does not match the current empirical data for humans, it is the chart of a completely different kind of population. So instead, let's let Dickinson, Hiscock and Agbayani try another approach, forcing a match to the empirical data. Then their chart might end up looking something like this:

AgeRaw Intelligence Scores by Age and Year
95 48.0 48.0 48.0 48.0 48.0 48.0 41.6 42.5 43.3 44.2 45.1
85 53.6 53.6 53.6 53.6 53.6 53.6 46.5 47.5 48.4 49.4 50.4
75 58.0 58.0 58.0 58.0 58.0 58.0 51.4 52.5 53.5 54.6 55.7
65 61.0 61.0 61.0 61.0 61.0 61.0 55.1 56.2 57.3 58.5 59.7
55 61.7 61.7 61.7 61.7 61.7 61.7 56.9 58.1 59.2 60.4 61.7
45 62.4 62.4 62.4 62.4 62.4 62.4 58.8 59.9 61.2 62.4 63.7
35 62.5 62.5 62.5 62.5 62.5 62.5 60.0 61.2 62.4 63.7 65.0
25 62.5 62.5 62.5 62.5 62.5 62.5 61.2 62.4 63.7 65.0 66.3
15 52.1 52.1 52.1 52.1 52.1 52.1 52.0 53.1 54.2 55.2 56.4
5 15.0 15.0 15.0 15.0 15.0 15.0 15.3 15.6 15.9 16.2 16.6
105 115 125 135 145 155 165 175 185 195 205
Year

That would be better if it weren't for the jarring discontinuity between the years 155 and 165. Why would the introduction of a Flynn effect cause such an immediate and ragged discontinuity across age groups and time? The answer of course is that it wouldn't.

There is only one logically correct interpretation:

As a consequence, the title of Dickinson & Hickson (2010) suggests a gross misinterpretation.

 

References

Agbayani, K. A. (2011). Patterns of age-related IQ changes from the WAIS to WAIS-III after adjusting for the Flynn effect. Retrieved online from http://repositories.tdl.org/uh-ir/handle/10657/236.

Dickinson, M. D. & Hiscock, M. (2010). Age-related IQ decline is reduced markedly after adjustment for the Flynn effect. Journal of Clinical and Experimental Neuropsychology, 32(8), 865-870.