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a LMX agreement variable. The algebraic difference between supervisor mean LMX<br />

score and the subordinate mean LMX score for each dyad was calculated. These values<br />

were entered as the LMX agreement score of each dyad.<br />

3.8.2 Statistical Analysis Procedures<br />

First, frequency analyses were undertaken of the descriptive details gathered, to<br />

examine the demographic characteristics of the participants.<br />

Second, principal component factor analysis with varimax rotation (orthogonal rotation<br />

to maximise the variance of a factor on all variables) was used with the supervisor and<br />

subordinate LMX-7 measures (Graen & Uhl-Bien, 1995; Minsky, 2002) along with 5–<br />

item Organisational Commitment (Lee et. al., 2001), 3-item Turnover Intent measures<br />

(Hom and Griffeth, 1991) and 7-item supervisor LMX (Minsky, 2002). These<br />

measures were tested in this study, even though they have a history of acceptable<br />

reliability (e.g. Kim et al., 2010a; Minsky, 2002). Principal component factor analysis<br />

is a type of analysis that uses linear combination of the variance to extract maximum<br />

variance between observed variables (Bartholomew, Steele, Galbraith, & Moustaki,<br />

2008). Munro (2000) believed that factor analysis might be an important step for<br />

confirming or creating a measurement tool because factor analysis searches for joint<br />

variations in the observed variables and thereby establishes a common factor. As a<br />

result, a principal component factor analysis was performed to check a factor structure<br />

of all the research measures.<br />

Finally bivariate correlation analyses were used to examine the correlations between<br />

LMX agreement and the outcome variables. Bivariate correlation is a statistical<br />

procedure that explores the relationship between two variables, which are mutually<br />

51

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