Evidence map›Paper›PMID 30631716›Full record

ArticleInternational journal of STEM education2018

Finding the leaders: an examination of social network analysis and leadership identification in STEM education change.

Alexis V Knaub, Charles Henderson, Kathleen Quardokus Fisher

Open access · goldAbstract read
In one paragraph

Article in International journal of STEM education, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
12.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 36 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Alexis V Knaub1Western Michigan University, Kalamazoo, MI USA.
Charles Henderson1Western Michigan University, Kalamazoo, MI USA.
Kathleen Quardokus Fisher2Florida International University, Miami, FL USA.
Western Michigan University · USFlorida International University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSocial network analysis (SNA) literature suggests that leaders should be well connected and can be identified through network measurements. Other literature suggests that identifying leaders ideally involves multiple methods. However, it is unclear using SNA alone is sufficient for identifying leaders for higher education change initiatives. We used two sets of data, teaching discussion network data taken at three different times and respondent nominations for leaders, to determine whether these two methods identify the same individuals as leaders.

resultsRespondent-nominated leaders have more direct and indirect ties on average than non-leaders, which aligns with the SNA literature. However, when looking at individuals as leaders, many respondent-nominated leaders would not be identified using SNA because they are poorly connected. Also, many individuals who were not nominated would have been considered leaders because they are well connected. Further examining these results did not indicate why there is such a difference between the SNA-identified and respondent-nominated leaders.

conclusionsWhile these two methods identify some of the same individuals as leaders, there are many differences between the two methods. Using just one method may not be adequate for ensuring that suitable individuals are selected to lead these projects. We recommend multiple methods when selecting leaders.

Indexed as

Higher education changeHigher education leadershipSocial network analysis

Identifiers

PMID30631716
PMCPMC6310419
OpenAlexW2808805097

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.