journal article Open Access Oct 15, 2018

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

View at Publisher Save 10.3390/jintelligence6040046
Abstract
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.
Topics

No keywords indexed for this article. Browse by subject →

References
22
[1]
Otlet, P. (1934). Traité de Documentation: Le Livre Sur le Livre, Théorie et Pratique, Editions Mundaneum.
[2]
Pritchard "Statistical bibliography or bibliometrics?" J. Doc. (1969)
[3]
Bornmann "Scientometrics in a changing research landscape" Sci. Soc. (2014)
[4]
Arvanitis, R. (2010). Bibliometrics and institutional evaluation. Science and Technology Policy, Eolss Publishers.
[5]
Cronin, B., and Sugimoto, C. (2014). Beyond Bibliometrics: Harnessing Multiple Indicators of Scholarly Impact, The MIT Press. 10.7551/mitpress/9445.001.0001
[6]
Engler "Bibliometrics and the study of religions" Religion (2014) 10.1080/0048721x.2014.893680
[7]
Rousseau, R., Egghe, L., and Guns, R. (2018). Becoming Metric-Wise: A Bibliometric Guide for Researchers, Elsevier.
[8]
Patra "Bibliometric study of literature on bibliometrics" J. Libr. Inf. Technol. (2006)
[9]
(2018, July 11). Web of Science 2018. Available online: https://login.webofknowledge.com/.
[10]
Wicherts "The impact of papers published in Intelligence 1977–2007 and an overview of the citation classics" Intelligence (2009) 10.1016/j.intell.2009.06.004
[11]
Pesta "Bibliometric analysis across eight years 2008–2015 of Intelligence articles: An updating of Wicherts (2009)" Intelligence (2018) 10.1016/j.intell.2018.01.001
[12]
Colom "Negligible sex differences in general intelligence" Intelligence (2000) 10.1016/s0160-2896(99)00035-5
[13]
Agresti, A. (2007). An Introduction to Categorical Data Analysis, Wiley. 10.1002/0470114754
[14]
Sharpe "Your chi-square test is statistically significant: Now what?" Pract. Assess. Res. Eval. (2015)
[15]
Intelligence and educational achievement

Ian J. Deary, Steve Strand, Pauline Smith et al.

Intelligence 2007 10.1016/j.intell.2006.02.001
[16]
Lynn "National IQs calculated and validated for 108 nations" Intelligence (2010) 10.1016/j.intell.2010.04.007
[17]
David "The relationship between working memory capacity and executive functioning: Evidence for a common executive attention construct" Neuropsychology (2011)
[18]
Conway "A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence" Intelligence (2002) 10.1016/s0160-2896(01)00096-4
[19]
Oberauer "The multiple faces of working memory: Storage, processing, supervision, and coordination" Intelligence (2003) 10.1016/s0160-2896(02)00115-0
[20]
Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate

Christopher R. Genovese, Nicole A. Lazar, Thomas Nichols

NeuroImage 2002 10.1006/nimg.2001.1037
[21]
Pike "Using false discovery rates for multiple comparisons in ecology and evolution" Methods Ecol. Evol. (2011) 10.1111/j.2041-210x.2010.00061.x
[22]
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg

Journal of the Royal Statistical Society Series B:... 1995 10.1111/j.2517-6161.1995.tb02031.x
Metrics
130
Citations
22
References
Details
Published
Oct 15, 2018
Vol/Issue
6(4)
Pages
46
License
View
Cite This Article
Bryan Pesta, John Fuerst, Emil Kirkegaard (2018). Bibliometric Keyword Analysis across Seventeen Years (2000–2016) of Intelligence Articles. Journal of Intelligence, 6(4), 46. https://doi.org/10.3390/jintelligence6040046