2013
DOI: 10.1093/iwc/iwt050
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Measuring Anxiety Towards Wiki Editing: Investigating the Dimensionality of the Wiki Anxiety Inventory-Editing

Abstract: Although wikis are common in both the workplace and in Higher Education, little research has studied the wiki user experience. Recent literature highlights that users may be anxious about editing wiki content; yet in most of this research this anxiety has not been measured quantitatively. Although computer anxiety metrics exist to measure anxiety towards technology, they lack specificity and relevance to the wiki editing context. This paper reports two studies used to research the validity and reliability of t… Show more

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Cited by 7 publications
(4 citation statements)
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References 47 publications
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“…We conducted a Principal Component Analysis (PCA) using the psych [109] and GPArotation [13] packages in R (Version 1.1.456) [107] so as to identify the dimensions present in the 51 word pairs. The primary purpose of PCA is to reduce the dimensionality of multivariate data, allowing for a large number of variables to be summarized within smaller subsets, or factors [24,41]. PCA was deemed most suitable as it does not require an a priori hypothesized or predetermined factor structure, making it ideal for exploratory analysis [24,41].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…We conducted a Principal Component Analysis (PCA) using the psych [109] and GPArotation [13] packages in R (Version 1.1.456) [107] so as to identify the dimensions present in the 51 word pairs. The primary purpose of PCA is to reduce the dimensionality of multivariate data, allowing for a large number of variables to be summarized within smaller subsets, or factors [24,41]. PCA was deemed most suitable as it does not require an a priori hypothesized or predetermined factor structure, making it ideal for exploratory analysis [24,41].…”
Section: Discussionmentioning
confidence: 99%
“…The primary purpose of PCA is to reduce the dimensionality of multivariate data, allowing for a large number of variables to be summarized within smaller subsets, or factors [24,41]. PCA was deemed most suitable as it does not require an a priori hypothesized or predetermined factor structure, making it ideal for exploratory analysis [24,41]. We note that various recommendations are made regarding what constitutes a suitable sample size for conducting reliable PCA.…”
Section: Discussionmentioning
confidence: 99%
“…Ravno na področju strani Wiki (npr. najbolj znana Wikipedia, hkrati pa obstajajo tudi številne rešitve za interno rabo podobnih strani) sta Cowan in Jack [5] pokazala, da lahko srečanje z urejevalnikom, ki uporablja nekoliko prilagojen, jeziku Markdown podoben, preprost označevalni jezik (wikitext), lahko povzroči tesnobo, saj jim je takšen način urejanja besedila popolnoma tuj. Morda je zanimivo izpostaviti, da kljub relativno širokem naboru popularnih aplikacij, ki uporabljajo označevalni jezik Markdown (Reddit, Discord, Google Docs, Facebook, Slack.…”
Section: Uporabniški In Pedagoški Vidikiunclassified
“…Ne glede na to, o katerem katerem označevalnem jeziku konkretno govorimo, so zunanji orisi njihovih prednosti in slabosti v večji meri skupni. Uporabnikom, ki se z njimi še nikoli niso srečali, lahko na prvi pogled deluje njihova uporaba težka, za nekatere celo zastrašujoča ali tesnobna, kot smo navedli v primeru raziskave za jezik wikitext [5], gre pa najbrž za strah pred drugačno obliko interakcije z računalnikom, podobno kot je to v primeru uporabe ukazne vrstice. Kljub temu imajo označevalni jeziki številne prednosti, zaradi katerih so lahko uporabni v marsikaterem poslovnem, akademskem ali zasebnem okolju: omogočajo lažje in bolj nadzorovano strukturiranje kompleksnih dokumentov, lažje so dosegljivi slepim in slabovidnim osebam, omogočajo različne transformacije in avtomatizacije.…”
Section: Uporabniški In Pedagoški Vidikiunclassified