Bibliometric analysis across eight years 2008–2015 of Intelligence articles: An updating of

Pesta, Bryan J.

doi:10.1016/j.intell.2018.01.001

Cited by 13 publications

(18 citation statements)

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“…We did not code post 2016 articles because we did not think they had enough time to accumulate a relative, representative number of citations. One of the prior bibliometric reviews of Intelligence [11] did similarly. Moreover, we excluded some other articles published after 2000 (e.g., book reviews, obituaries, and some editorials), because they did not contain keywords.…”

Section: Methodsmentioning

confidence: 84%

“…We coded all keyword-containing articles in the journal, Intelligence , for the years 2000 to 2016. In line with other bibliometric studies on this journal [10,11], we included all articles except book reviews and obituaries. Note that the year 2000 was the first we found where the journal featured keywords, although only one such article appeared at this time (i.e., [12]).…”

Section: Methodsmentioning

confidence: 99%

“…On 11 July 2018, we next coded each article’s Web of Science (WOS) citation count. We did not also code Google Scholar (GS) counts, as Pesta [11] reported that these correlate 0.97 with WOS citation counts. In fact, GS counts are very close to double those reported by WOS.…”

Section: Methodsmentioning

confidence: 99%

“…In fact, GS counts are very close to double those reported by WOS. Note that these values come specifically from analyzing articles published in Intelligence [11].…”

Section: Methodsmentioning

confidence: 99%

“…Second, Pesta [11] examined articles published after Wicherts’ study. He analyzed 619 papers, written by 1897 authors, and published between the years 2008 and 2015.…”

Section: Introductionmentioning

confidence: 99%

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Bibliometric Keyword Analysis across Seventeen Years (2000–2016) of Intelligence Articles

Pesta

Fuerst²,

Kirkegaard³

2018

J. Intell.

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An article’s keywords are distinct because they represent what authors feel are the most important words in their papers. Combined, they can even shed light on which research topics in a field are popular (or less so). Here we conducted bibliometric keyword analyses of articles published in the journal, Intelligence (2000–2016). The article set comprised 916 keyword-containing papers. First, we analyzed frequencies to determine which keywords were most/least popular. Second, we analyzed Web of Science (WOS) citation counts for the articles listing each keyword and we ran regression analyses to examine the effect of keyword categories on citation counts. Third, we looked at how citation counts varied across time. For the frequency analysis, “g factor”, “psychometrics/statistics”, and “education” emerged as the keywords with the highest counts. Conversely, the WOS citation analysis showed that papers with the keywords “spatial ability”, “factor analysis”, and “executive function” had the highest mean citation values. We offer tentative explanations for the discrepant results across frequencies and citations. The analysis across time revealed several keywords that increased (or decreased) in frequency over 17 years. We end by discussing how bibliometric keyword analysis can detect research trends in the field, both now and in the past.

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Section: Methodsmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In fact, GS counts are very close to double those reported by WOS. Note that these values come specifically from analyzing articles published in Intelligence [11].…”

Section: Methodsmentioning

confidence: 99%

“…Second, Pesta [11] examined articles published after Wicherts’ study. He analyzed 619 papers, written by 1897 authors, and published between the years 2008 and 2015.…”

Section: Introductionmentioning

confidence: 99%

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