BİR VERİ TOPLAMA PLATFORMU OLARAK CROWDSOURCING CS : MECHANICAL TURK ÖRNEĞİ ÜZERİNDEN BİR LİTERATÜR TARAMASI VE DEĞERLENDİRME

Pozitivist akademik çalışmalar teorilerin veya hipotezlerin test edilmesi temeline dayanır ve test sürecinde ise katılımcıların varlığı ve yapısı son derece önemlidir. Bu anlamda, geçmiş yıllarda öğrenciler, kolayda örnekleme yöntemi çerçevesinde, en yaygın kullanılan katılımcı grubu iken, son yıllarda Internet yeni katılımcı temini ve veri toplama aracı olarak yaygın olarak kullanılmaya başlamıştır. Bu bağlamda 2006 yılında Jeff Howe tarafından ortaya atılan Crowdsourcing CS kavramı son yıllarda giderek daha popular olmaya başlamıştır. Ancak beraberinde bir çok sorunu da gündeme taşımıştır. Basit ifadeyle crowdsourcing, bir araştırma bağlamında gerekli olan verilerin veya bilgi kaynaklarının çok sayıda kalabalık bir insan grubundan elde edilmesi şeklinde tanımlanabilir. Sosyal bilimler bağlamında ise gerekli olan verilerin internet ortamından çok sayıda katılımcısı bulunan belirli veri toplama platformları Amazon Mechanical Turk, Survey Monkey, TryMyUI, Facebook, Qualtricks vb. üzerinden toplanması şeklinde de değerlendirilebilir. Ancak sosyal bilimlerde bu platformlar aracılığıyla kolayda örnekleme yaklaşımıyla elde edilen verilerin örnekleme, güvenilirlik, geçerlilik, temsil kabiliyeti, istatistik analizler ve bulguların genellenebilirliği gibi çok sayıda sorunu öne çıkarmaktadır. Bu çalışmanın amacı, literatürde son yıllarda giderek yaygın kullanım bulan CS uygulamalarının artıları ve eksileri ile bu tür yollarla toplanmakta olan verilerin bilimsel çalışmalar bağlamındaki çıkmazları ve sorunları literatür taraması yaklaşımıyla eleştirel değerlendirmeler şeklinde ortaya konulmaya çalışılacaktır. Araştırma bağlamında, literatürde öne çıkan Amazon Mechanical Turk platformu temelinde tartışmalar ve değerlendirmeler yapılmaktadır.

Anahtar Kelimeler:

Crowdsourcing, Mechanical Turk, online örnekleme, veri toplama platformları

CROWDSOURCING CS AS A DATA COLLECTION PLATFORM: A LITERATURE REVIEW AND ASSESSMENT BASED ON THE CASE OF MECHANICAL TURK

Positivist academic studies are, generally, based on testing of theories and hypotheses; hence the testing process requires the existence of participants and their characteristics. Towards this end, in the past, students were most frequently used through convenience sampling method as participants in academic research, but in recent years, Internet has become popular as a source for supplying participants and a data collection method. In this context, the popularity of Crowdsourcing CS concept, which is coined by Jeff Howe in 2006, is increasing in recent years. Together with increasing popularity, several sampling, statistical and other scientific problems have been subjected to various debates across academic circles. In simple terms, crowdsourcing may be defined as the collection of data or information sources required within the context of a particular study from a big number of people or crowd. In the context of social sciences, crowdsourcing may be described as the collection of the needed data from various Internet data collection platforms such as Amazon Mechanical Turk, Survey Monkey, TryMyUI, Facebook, Qualtrics, etc. with huge number of participants or workers. But data collected through these platforms may pose many problems such as sampling issues, reliability and validity problems, representativeness, statistical analysis and the generalizability of findings. The purpose of this study is, through a critical literature review technique, to examine and discuss the pros and cons of data collected through CS approach and the appropriateness of this type of data in academic research purpose and its implications for the outcomes of scientific endeavor. In the context of this research, discussions and assessments are based on the data collection platform of Amazon’s Mechanical Turk platform which is discussed in the literature frequently during the last several years

Keywords:

Crowdsourcing, Mechanical Turk, online sampling, data collection platforms,

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