Methodological Pitfalls in Social Network Analysis (Paperback)

~ Nicolas Marschall (Author)

Editorial Reviews

Product Description

Social Network Analysis comprises a toolbox of scientific methods for investigating relations between actors. The author of this book argues that even recognized scientists apply this popular research methodology without seriously considering its pitfalls. His study demonstrates how certain factors can interfere with the processes of data collection and data analysis, and therefore lead to invalid or unreliable results. The book begins with an introduction into the methods of social network analysis and then presents one of the most comprehensive discussions of literature on the issue of its validity. Afterwards it describes the author's study, which applies a simulation technique to investigate the influence of various factors on common measures in social network analysis. The results presented at the end give important hints concerning the proper use of network analysis. The book addresses users and students of social network analysis in sociology, political sciences and economics.

Product Details

• Paperback: 104 pages
• Publisher: VDM Verlag Dr. Mueller e.K. (November 19, 2007)
• Language: English
• ISBN-10: 3836430037
• ISBN-13: 978-3836430036
• Product Dimensions: 9.4 x 6.5 x 0.4 inches
• Shipping Weight: 6.4 ounces (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #1,561,810 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

4 of 4 people found the following review helpful:

2.0 out of 5 stars The price is much beyond the book`s value, April 5, 2009

By	R. F. Matheus (Brasil) - See all my reviews (REAL NAME)

It is a good first text introduction to network analysis reliability. Nevertheless, it you are a researcher in the field and need really comprehensive approach, search for Peter V. Marsden papers. This book is extremely expensive, for it 90 pages. Beyond, it is just a renamed, copy and paste printing of author`s thesis "Methodological Problems with Transformation and Size Reduction of Data Sets in Network Analysis".

2 of 3 people found the following review helpful:

4.0 out of 5 stars Methodological Pitfalls Exposed, October 2, 2008

By	G. A. Durant-law "Graham Durant-Law" (Canberra, Australia) - See all my reviews

I've just finished reading Methodological this book. The central theme is that current methods produce questionable results, which is precisely why I read the book. Running to 86 pages the book is an easy one-sitting read. For what it is it's also expensive. The book is a translation from German so in some places the English is - well unusual. Looking beyond this small problem, it appears to be a student or scientist research justification, or perhaps a short synopsis of a PhD, which means the style is very academic, but it is interesting!

Marschall quite rightly says data collection approaches colour results, and are full of implicit assumptions. He comes to the conclusion that size reduction and transformation processes, which are quite common in published studies, can significantly change the results of an analysis. By size reduction he means the loss of data resulting from sampling methods or even forgetting. Transformation is when the researcher manipulates data such as block modelling, dichotomisation, and symmetrisation.

The boundary specification or missing data problem is well known, and extensively discussed in other literature. Marschall highlights the problems this can cause. In particular he highlights mathematically the problems this may cause with small networks. Figure 4-1 on page 39 is most enlightening. Using five actors he shows how betweenness centrality can give very different and almost polar results simply by removing one actor. In his example an actor who was the most peripheral becomes the most central. This highlights why a deep understanding of the network and data matters. My own work confirms this, which is why I am reluctant to deal with anything other than whole very tightly defined populations.

I highly recommend this book to serious network analysis students, researchers and practitioners. Understanding the limitations and methodological pitfalls of network analysis matters!

Regards, Graham