Cmv Limitations

Although most scholars have agreed that CMV pose a threat towards the field of study and social science theories (Kline et al, 2000; Reio, 2010; Williams et al, 1989) some scholars still argue that CMV is not a serious problem (e.g Crampton and Wagner, 1994; Spector, 1987, 2006). Spector (2006) in his statement stated that:

“The urban legend that there is universally shared variance inherent in our methods is both an exaggeration and oversimplification of the true state of affairs. CMV is often conceptualised maybe a legend, but biases are real and endemic to our research. Furthermore, this is not just a problem of survey and field research. Even laboratory experiments have problems with bias, such as experimenter expectancies and demand characteristics (Rosenthal and Rosnow, 1969)”.

Some scholars such as Baird et al, (2011), have used Crampton and Wagner (1994) and Spector (1987) justification for not checking for CMV. Some scholars

In their words, “We recognise of course, that standards for rigour in empirical work are continually rising. What were acceptable methodological practices even five years ago can easily and rapidly become unacceptable as social science scholars better understand the limitations of their empirical techniques and develop more rigorous methods for identifying and correcting for potential biases in their work”, . Additionally, they reported that for the articles published in JIBS between 2000 and the present only 65 of the 167 articles (about one third) mentioned or addressed common methods in their paper. Of this group, half the articles, (32) used Harman’s single factor test or something similar to test for CMV. Fifteen articles used another approach. Only nine articles used both Harman’s test and at least one other correction method to control for CMV. This statistics show that CMV problem still receive lack of attention from social science