This paper focuses on an investigation of test fairness using bias analyses in the teacher employment examinations (TEEs). In order to become an English teacher in public schools, candidates are required to pass both the first and the second tests. This paper exclusively focuses on the second test, assessment of candidates' teaching performance via microteaching. A purpose of microteaching is to assist teachers in identifying their weakness and in improving their teaching skills (e.g., Cochran-Smith & Fries, 2008). However, Grossman (2005) and Monk (2008) show serious concern about the use of microteaching for assessment and selection purposes. Therefore, it would be worth investigating the extent to which scores given by raters (employers) would have an impact on candidates (i.e. employment decisions). For this purpose, bias analysis techniques were conducted to investigate interactions between raters and candidates. Four raters (two experienced English teachers and two local education board officers) rated 100 candidates. Data were analyzed using FACETS, a multi-faceted Rasch analysis program (Linacre, 2005). Results imply that scores given by the raters might have a direct impact on employment decisions. This paper discusses issues with the use of microteaching in the light of test fairness in TEEs.