Some instances of apparent duplicate publication from the Cornell Food and Brand Lab

Some concerns have been expressed recently (e.g., here) about a few of the research articles coming from the Cornell Food and Brand Lab.  While reading some past work from the same lab, I noticed some phrases that seemed to recur.  On doing some further comparisons, I found several examples of apparent duplicate publication.  I list five such examples here.

A: Two hundred words for almost any situation

Here are a couple of paragraphs from the same author that have been published at least five times over a 15-year period, with just a few very minor changes of wording each time. I have copied and pasted the relevant text here so that you can see all of the different versions.  (I hope the various publishers will allow this as "fair use".)

1. Wansink, B., & Ray, M. L. (1997).  Developing copy tests that estimate brand usage.  In W. Wells (Ed.), Measuring advertising effectiveness (pp. 359–370). Cambridge, MA: Lexington Books.

From page 361:

These two different measures of usage intent have different relative strengths. With infrequent users of a brand, volume estimates will be skewed toward 0 units (especially over a relatively short period of time). This is partially a drawback of numerical estimates that provide no gradation between 0 and 1 unit. In such cases, volume estimates would provide less variance and less information than an estimate of usage likelihood. As a result, usage likelihood estimates would allow a greater gradation in response and would be more sensitive in detecting any potentially different effects these ads might have on usage.

In contrast, with frequent or heavy users of a brand, a volume estimate is likely to be more accurate than a likelihood estimate. This is because the distribution of these volume estimates is more likely to be normally distributed (Pearl 1981). As a result, a volume estimate of one’s usage intent is likely to provide more variance and more information about the intended usage of heavy users than is a likelihood measure, which would undoubtedly be at or near 1.0 (100 percent probable). Under these circumstances, volume estimates would be a more accurate estimate of a heavy user’s usage volume of a brand.

2. Wansink, B., & Sudman, S. (2002).  Predicting the future of consumer panels.  Journal of Database Marketing, 9, 301–311. http://dx.doi.org/10.1057/palgrave.jdm.3240078

From page 309:

[T]hese two different measures of usage intent have different relative strengths. With infrequent users of a brand, volume estimates will be skewed toward 0 units (especially over a relatively short period of time). This is partially a drawback of numerical estimates that provide no gradation between 0 and 1 unit. In such cases, volume estimates would provide less variance and less information than an estimate of usage likelihood. As a result, usage likelihood estimates would allow a greater gradation in response and would be more sensitive in detecting any potentially different effects these adverts might have on usage.

In contrast, with frequent or heavy users of a brand, a volume estimate is likely to be more accurate than a likelihood estimate. This is because the distribution of these volume estimates is more likely to be normally distributed. As a result, a volume estimate of a person’s usage intent is likely to provide more variance and more information about the intended usage of heavy users than is a likelihood measure, which would undoubtedly be at or near 1.0 (100 per cent probable). Under these circumstances, volume estimates would be a more accurate estimate of a heavy user’s usage volume of a brand.

3. Wansink, B. (2003).  Response to ‘‘Measuring consumer response to food products’’.
Sensory tests that predict consumer acceptance.  Food Quality and Preference, 14, 23–26. http://dx.doi.org/10.1016/S0950-3293(02)00035-6

From page 25:

These two different measures of usage intent have different relative strengths. With infrequent users of a product, frequency estimates will be skewed toward 0 units (especially over a relatively short period of time). This is partially a drawback of numerical estimates that provide no gradation between 0 and 1 unit. In such cases, the frequency estimates provide less variance and less information than an estimate of consumption likelihood. With light users, consumption likelihood estimates will provide greater gradation in response and more sensitivity in detecting any potentially different effects a particular set of sensory qualities would have on consumption.

In contrast, with frequent or heavy users of a product, a frequency estimate is likely to be more accurate than a likelihood estimate. This is because the distribution of these frequency estimates is more likely to be normally distributed. As a result, a frequency estimate of one’s consumption intent is likely to provide more variance and more information about the intended consumption of heavy users than is a likelihood measure, which would undoubtedly be at or near 1.0 (100% probable). With heavy users, frequency estimates would be a more accurate estimate of a heavy user’s future consumption frequency of a product.

4. Bradburn, N. M., Sudman, S., & Wansink, B. (2004).  Asking questions: The definitive guide to questionnaire design—For market research, political polls, and social and health questionnaires (Revised ed.).  San Francisco, CA: Jossey-Bass.

From pages 134–135:

These two different measures of behavioral intent have different relative strengths. With infrequent behaviors, frequency estimates will be skewed toward 0 (especially over a relatively short period of time). This is partially a drawback of numerical estimates that provide no gradation between 0 and 1 unit. In such cases, frequency estimates would provide less variance and less information than an estimate of likelihood. As a result, likelihood estimates would allow a greater gradation in response and would be more sensitive.

In contrast, with frequent behaviors, a frequency estimate will be more accurate than a likelihood estimate. The reason is that frequency estimates are more likely to be normally distributed. As a result, a frequency estimate is likely to provide more variance and more information than is a likelihood measure, which would undoubtedly be at or near 1.0 (100 percent probable). Under these circumstances, frequency estimates more accurately correspond with actual behavior.

5. Wansink, B. (2012).  Measuring food intake in field studies.  In D. B. Allison and M. L. Baskin (Eds.), Handbook of assessment methods for eating behaviors and weight-related problems: Measures, theories, and research (2nd ed., pp. 327–345). Los Angeles, CA: SAGE.

From page 336:

These two different measures of intake intent have different relative strengths. With infrequent users of a food, frequency estimates will be skewed toward 0 units (especially over a relatively short period of time). This is partially a drawback of numerical estimates that provide no gradation between 0 and 1 unit. In such cases, the frequency estimates provide less variance and less information than an estimate of intake likelihood. With light users, intake likelihood estimates will provide greater gradation in response and more sensitivity in detecting any potentially different effects a particular set of sensory qualities would have on intake.

In contrast, with frequent or heavy users of a food, a frequency estimate is likely to be more accurate than a likelihood estimate. This is because the distribution of these frequency estimates is more likely to be normally distributed. As a result, a frequency estimate of one’s intake intent is likely to provide more variance and more information about the intended intake of heavy users than is a likelihood measure, which would undoubtedly be at or near 1.0 (100 percent probable). With heavy users, frequency estimates would be a more accurate estimate of a heavy user’s future intake frequency of a food.

You can check all of these here.  In addition to the draft PDF versions that I have annotated, the first example (from the 1997 book Measuring Advertising Effectiveness) is also available on Google Books here, the fourth example (from the 2004 book Asking Questions...) is available here, and the fifth example (from the 2012 book  Handbook of Assessment Methods for Eating Behaviors...) is available here.  Note that in the case of Asking Questions..., the PDF is an extract from a copy of the entire book that I found when searching on Google; I hope this possible violation of copyright on my own part will be forgiven.

B: Copying and pasting from multiple articles to make a new one

 

Consider this review article from 2015:

Wansink, B. (2015). Change their choice! Changing behavior using the CAN approach and activism research. Psychology & Marketing, 32, 486–500. http://dx.doi.org/10.1002/mar.20794

The image below shows the extent to which the 2015 article appears to consist of duplicated text from other publications.  Everything in yellow, plus three of the four figures (which I couldn't work out how to highlight in the PDF) has been published before, some of it twice; I estimate that this represents about 50% of the article.

Specifically, parts of this article appeared to have been copied without attribution from the following works (listed in approximate descending order of quantity of apparently duplicated text):

Wansink, B. (2011). Activism research: Designing transformative lab and field studies.  In D. G. Mick, S. Pettigrew, C. Pechmann, & J. L. Ozanne (Eds.), Transformative consumer research for personal and collective well-being (pp. 66–88). New York, NY: Routledge.

Wansink, B. (2013). Convenient, attractive, and normative: The CAN approach to making children slim by design. Childhood Obesity, 9, 277-278. http://dx.doi.org/10.1089/chi.2013.9405

Wansink, B. (2015). Slim by design: Moving from Can’t to CAN.  In C. Roberto (Ed.), Behavioral economics and public health (pp. 237–264). New York, NY: Oxford University Press.

Wansink, B. (2010). From mindless eating to mindlessly eating better. Physiology & Behavior, 100, 454–463. http://dx.doi.org/10.1016/j.physbeh.2010.05.003

Wansink, B., Just, D. R., Payne, C. R., & Klinger, M. Z. (2012). Attractive names sustain increased vegetable intake in schools. Preventive Medicine, 55, 330–332. http://dx.doi.org/10.1016/j.ypmed.2012.07.012

Annotated versions of all of these documents are can be found here. A Google Books preview of the 2015 chapter "Slim by Design" is available here to compare with the annotated document, which is a final draft version.

C: An article apparently recycled as the basis of a book chapter, without disclosure

The article:

Wansink, B., van Ittersum, K., & Werle, C. (2009).  How negative experiences shape long-term food preferences: Fifty years from the World War II combat front.  Appetite, 52, 750–752.  http://dx.doi.org/10.1016/j.appet.2009.01.001

The book chapter:

Wansink, B., van Ittersum, K., & Werle, C. (2011).  The lingering impact of negative food experiences: Which World War II veterans won’t eat Chinese food?  In V. R. Preedy, R. R. Watson, & C. R. Martin (Eds.), Handbook of behavior, food and nutrition (Vol. 1, pp. 1705-1714). New York, NY: Springer.

It appears that almost all of the 2009 research article—about 1,400 words—has been duplicated in the 2011 chapter, with only very minor changes and the omission of five sentences, which account for less than 100 words.  No disclosure of this re-use appears in the book chapter.  (In contrast, Chapter 87 in the same book contains, on pages 1357 and 1360, explicit acknowledgements that two passages in that chapter contain material adapted from two other previously published sources; each of these passages corresponds to about 120 words in the original  documents.)

You can examine annotated versions of the article and chapter here (note that the PDF file of the book chapter is an extract from a copy of the entire book that I found on Google).  The book chapter is also available on Google Books here (although three of the ten pages are missing from the preview).

Here is a snapshot of the 2009 article (left) and the 2011 book chapter (right).

D: Two almost-identical book chapters, published more or less simultaneously

There seems to be a very close resemblance between the following two book chapters:

Wansink, B. (2011).  Mindless eating: Environmental contributors to obesity.  In J. Cawley (Ed.), The Oxford handbook of the social science of obesity (pp. 385–414).  New York, NY: Oxford University Press.

Wansink, B. (2012).  Hidden persuaders: Environmental contributors to obesity.  In S. R. Akabas, S. A. Lederman, & B. J. Moore (Eds.), Textbook of obesity: Biological, psychological and cultural influences (pp. 108–122).  Chichester, England: Wiley-Blackwell.

Each chapter is around 7,000 words long.  The paragraph structures are identical.  Most of the sentences are identical, or differ only in trivial details; a typical example is:

(Mindless Eating, p. 388)

While this may appear to describe why many people eat what they are served, it does not explain why they do so or why they may overserve themselves to begin with. Figure 23.1 suggests two reasons that portion size may have a ubiquitous, almost automatic influence on how much we eat: First, portion sizes create our consumption norms; second, we underestimate the calories in large portion sizes.

(Hidden Persuaders, p. 109)

While this may describe why many people eat what they are served, it does not explain why they do so or why they may over-serve themselves to begin with. Figure 6-2 suggests two reasons why portion size may have a ubiquitous, almost automatic influence on how much we eat: First, portion sizes create our consumption norms; second, we underestimate the calories in large portions.

Overall, I estimate that about 85-90% of the text is duplicated, word-for-word, across both chapters.

It seems to be rather unusual to submit the same chapter for publication almost simultaneously to two different books in this way (the books were published less than six months apart, according to their respective pages on Amazon.com).  One occasionally sees a book chapter that is based on an updated version of a previous journal article, but in that case one would expect to find a note making clear that some part of the work had been published before.  I was unable to find any such disclosure in either of these books, whether on the first or last page of the chapters themselves, or in the front matter.  I also contacted the editors of both books, none of whom recalled receiving any indication from the author that any of the text in the chapter was not original and unique to their book.

I found final draft versions of each of these chapters here and here.  Each draft clearly states that it is intended for publication in the respective book in which it finally appeared, which would seem to rule out the possibility that this duplication arose by accident.  Interested readers can compare my annotated versions of these final drafts with each other here.  You can also check these drafts against the published chapters in the Google Books previews here and here (the first has the complete "Mindless Eating" chapter, but four pages of the "Hidden Persuaders" chapter are missing from the second). The degree of similarity is visible in this image, where yellow highlighting indicates text that it identical, word-for-word, between the two drafts ("Mindless Eating" is on the left, "Hidden Persuaders" is on the right).

E: Different studies, same introduction, same summary, different participants, same results

In 2003, when the current director of the Cornell Food and Brand Lab was still at the University of Illinois at Urbana-Champaign [PDF], he published a report of a pair of studies that had a certain number of theoretical aspects in common with another pair of studies that he had previously described in a co-authored article from 2001.  Here are the two references:

Wansink, B., & Seed, S. (2001).  Making brand loyalty programs succeed.  Brand Management, 8, 211–222. http://dx.doi.org/10.1057/palgrave.bm.2540021

Wansink, B. (2003).  Developing a cost-effective brand loyalty program.  Journal of Advertising Research, 43, 301–309. http://dx.doi.org/10.1017/S0021849903030290

I estimate that the introduction and summary sections from the two resulting articles are about 50% identical. You can judge for yourself from this image, in which the 2001 article is on the left, and the 2003 article is on the right.  The introduction is on the first six pages of the 2001 article and the first four pages of the 2003 article.  The summary section is near the end in each case.

Perhaps of greater interest here, though, is a comparison between Table 5 of the 2001 article and Table 2 of the 2003 article, which appear to be almost identical, despite purportedly reporting the results of two completely different studies.

Table 5 in the Wansink and Seed (2001) article apparently represents the results of the second study in that article, in which a questionnaire was administered to "153 members of the Brand Revitalisation Consumer Panel" (p. 216):

As shown in Table 5, this moderate benefit programme captured an average monthly incremental gain of $2.95 from the non-user and $3.10 from the heavy user. For light users, the most cost-effective programme was the one that offered the lowest benefit package. This programme level captured an average monthly incremental gain of $2.00 from the light user. (Wansink & Seed, 2001, p. 218)

Table 2 in the Wansink (2003) article appears to summarise the results of Study 2 of that (i.e., 2003) article, which involved "a nationwide survey of 2,500 adult consumers who had been randomly recruited based on addresses obtained from census records. . . . Of the 2,500 questionnaires, 643 were returned in time to be included in the study".

The second major finding of Study 2 was that, in contrast to the beliefs of the managers, the high reward program appears to be the least cost-effective program across all three segments. Given the simple two-period model noted earlier, Table 2 shows the low reward program is the most cost-effective across all three segments ($1.67), and the moderate reward program is the most cost-effective with the heavy user ($3.10). (Wansink, 2003, p. 307)

(For what it is worth, the average gain for the low reward program across all three segments in the 2003 article appears to be ($0.00 + $2.00 + $2.00 = $4.00) / 3 = $1.33, rather than the $1.67 reported in the text cited above.)


It seems remarkable that two studies that were apparently conducted on separate occasions and with different samples (unless "over 153" from the 2001 article is a very imprecise approximation of 643) should produce almost identical results.  Specifically, of the 45 numbers in each table that are not calculated from the others (i.e., the numbers in the first, third, fourth, and fifth columns, with the first column having two numbers per row), 39 are identical between the two tables (and of the six others, it seems likely that the three numbers corresponding to "Light user/Average Monthly Revenue before Start" were mistyped in the 2001 version of the table, where they are identical to the numbers to their immediate left).  These 39 identical "raw" numbers, plus the 17 out of 18 calculated numbers that are identical in columns 2 and 6, are highlighted in yellow in the two images above.

(*** BEGIN UPDATE 2017-03-12 20:53 UTC ***)
Jordan Anaya points out that the numbers in the third, fourth, and fifth columns of these tables were derived from the two numbers in the first column.  That is, the first number in the first column is multiplied by $3.00 to give the third column; the second number in the first column is multiplied by $3.00 to give the fifth column; and the second number is multiplied by either $1.00, $0.50, or $0.25 to give the value of the coupons used.

So the number of observed values that are exactly the same across these two independent studies is "only" 17 out of 18, identical to one decimal place. (In fact the 18th, "Nonuser/Low reward/After", appears to have been mistyped in the 2001 table, given the dollar amounts on the same line, so it looks like all 18 results were actually identical.)

None of this explains why the calculated value for "Average monthly revenue/After" for "Nonuser/Moderate reward" changes between 2001 and 2003.  See also the footnote under Table 4 from 2001 and Figure 3 from 2003, reproduced about six paragraphs below here.
(*** END UPDATE 2017-03-12 20:53 UTC ***)


The only point at which there appears to be a substantive difference between the two tables—namely, "Average Monthly Revenue after Program Start" and "Dollar Amount of Coupons Used" in the second line ("Nonuser", with a Program Reward Level of "Moderate")—is the explicit focus of discussion at two points in the 2001 article:

As shown in Table 5, this moderate benefit programme captured an average monthly incremental gain of $2.95 from the non-user and $3.10 from the heavy user. (Wansink & Seed, 2001, p. 218)

Across all products, the moderate reward programme was the only programme that motivated non-users to purchase. Perhaps, the high-reward programme required too much involvement on the part of the non-user and the low-reward programme did not offer enough benefit to induce trial. The moderate-reward programme might have struck the right balance. As non-users become more familiar with the product, however, a programme with more benefits might be required to sustain interest. (Wansink & Seed, 2001, p. 220)

In the 2003 article, however, the corresponding numbers for the moderate benefit programme for nonusers are much smaller, so that there is no effect to discuss.

I am at a loss to explain what might have happened here.  The fact that the two tables are not completely identical would seem to rule out the possibility that there was some kind of simple clerical error (e.g., including the wrong file from a folder) in the preparation of the later article.

It is also interesting to compare Table 4 of the 2001 article with Figure 3(a) of the 2003 article, the data for which appear to have been taken directly from the column entitled "Change in Purchases (in units)" in the tables just discussed.  Again, the different presentations of this information suggests that this is not simply a case of adding the wrong table to the second article.

 

(Note here that the value for "Change in Purchases (in units)" for the "Moderate" Program Reward Level for non-users appears to have been miscalculated in both tables (it should read 1.2, not 0.2, if the numbers reported for "Average Monthly Purchases Before/After Program Start (in units)" are correctly reported).  The (apparently) correct figure of 1.2 appears in Table 4 of the 2001 article, but not in Figure 3(a) of the 2003 article.  Also, the "Moderate Reward Program" / "Light User" value in Figure 3a of the 2003 article appears to have become 0.9 instead of 0.8, perhaps due to different rounding in the software being used to make the figure.)

In another twist, I found what appears to be a draft version of the 2003 article here.  In this draft, the table corresponding to Table 2 in the final version of the 2003 article (numbered 4 in the draft) contains the same numbers for "Nonuser" / "Moderate" as Table 5 in the 2001 article, and the effect that was noted in the 2001 article is still reported, as follows:

Based on the survey results of Table 4, the most cost-effective program for
nonusers and heavy users offered average benefits (moderate reward program). This
program level captures an average monthly incremental gain of $2.95 from the nonuser and $3.10 from the heavy user. (Draft, p. 13)

Across all products, the moderate reward program was the only program that motivated nonusers to purchase. Perhaps, the high reward program required too much involvement on the part of the nonuser and the low reward program did not offer enough benefit to induce trial. The moderate reward program might have struck the right balance. As nonusers become more familiar with the product, however, a program with more benefits might be required to sustain interest. (Draft, p. 17)

So apparently, at some stage between drafting and acceptance of the 2003 article, not only was the text describing this effect removed, but the numbers that support its existence were somehow replaced, in what became Table 2, by much smaller numbers that show no effect.

In summary: If the two studies in question are the same, then it would appear that there has been duplicate publication of the same results, which is not normally considered to be a good ethical practice.  This is compounded by the fact that the descriptions of the participants in these two studies diverge considerably; furthermore, there is a difference between the tables across the two articles that results in the disappearance of what appeared, in the 2001 article, to be a very substantial effect (that of the moderate-reward programme for non-users).  On the other hand, if the two studies are not the same, then the exact match between the vast majority of their results represents quite a surprising coincidence.

Readers who wish to explore these two articles further can find annotated versions of them here, as well as an annotated version of the draft of the 2003 article, which shows which parts of that draft did not make it into the final published article.

Conclusion

I have documented here what appear to be multiple varieties of duplicate publication:

• Re-use of the same paragraphs multiple times in multiple publications;
• Assembly of a new article from verbatim or near-verbatim extracts taken from other published work by the same author;
• Apparent recycling of a journal article in a later book chapter, without disclosure;
• Duplicate publication of an entire chapter almost simultaneously in two different books, without disclosure;
• Re-use of the same introduction section for two different empirical articles;
• Apparent republication of the same data, with slightly different conclusions, and different descriptions of the participants.