null
Loading... Please wait...
FREE SHIPPING on All Unbranded Items LEARN MORE
Print This Page

Network Analysis for Rating Datasets in R (a multi-disciplinary perspective)

List Price: $66.99
SKU:
9781041011699
Quantity:
Minimum Purchase
25 unit(s)
Expected release date is Oct 26th 2026
  • Availability: Confirm prior to ordering
  • Branding: minimum 50 pieces (add’l costs below)
  • Check Freight Rates (branded products only)

Branding Options (v), Availability & Lead Times

  • 1-Color Imprint: $2.00 ea.
  • Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
  • Belly-Band Wrap: $2.50 ea. (full-color printed)
  • Set-Up Charge: $45 per decoration
FULL DETAILS
  • Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
  • Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
  • Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
  • RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
  • Product Details

    Author:
    Iasonas Lamprianou
    Format:
    Paperback
    Pages:
    266
    Publisher:
    Taylor & Francis (October 26, 2026)
    Imprint:
    Routledge
    Release Date:
    October 26, 2026
    Language:
    English
    Audience:
    College/higher education
    ISBN-13:
    9781041011699
    Weight:
    16oz
    Dimensions:
    6.875" x 9.6875"
    File:
    TAYLORFRANCIS-TayFran_260501042650352-20260501.xml
    Folder:
    TAYLORFRANCIS
    List Price:
    $66.99
    Country of Origin:
    United States
    Pub Discount:
    30
    Series:
    Quantitative Methodology Series
    Case Pack:
    1
    As low as:
    $63.64
    Publisher Identifier:
    P-CRC
    Discount Code:
    H
  • Overview

    This is the first book to illustrate the use of Network Analysis for the analysis of rating datasets. It uses a multi-disciplinary approach to focus on the quantification and visualization of rater effects for exploration, diagnosis, reporting, decision-making, and planning.