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J. Jaccard and R. Turrisi, “Interaction Effects in Multiple Regression,” Sage University Papers Series on Quantitative Applications in the Social Sciences, 2nd Edition, Sage, Thousand Oaks, 2003.

has been cited by the following article:

• TITLE: Handling Observations with Low Interrater Agreement Values

  AUTHORS: Gensheng Liu, Mohammad M. Amini, Emin Babakus, Marla B. Royne Stafford

  KEYWORDS: Interrater Agreement, Aggregation, Survey Research

  JOURNAL NAME: Journal of Analytical Sciences, Methods and Instrumentation, Vol.1 No.2, December 16, 2011

  ABSTRACT: Considerable research has been conducted on how interrater agreement (IRA) should be established before data can be aggregated from the individual rater level to the organization level in survey research. However, little is known about how researchers should treat the observations with low IRA values fail to meet the suggested standard. We seek to answer this question by investigating the impact of two factors (the relationship strength and the overall IRA level of a sample) on the IRA decision. Using both real data from a service industry and simulated data, we find that both factors affect whether a researcher should include or exclude observations with low IRA values. Based on the results, practical guidelines on when to use the entire sample are offered.