共同方法偏差

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A narrow definition:
Method should be restricted to those measurement facets that represent "alternative approaches to assigning numbers to observations to represent an indivedual's standing on latent constructs".
Rti,tj = average correlation between trait i and trait j; tx = percent of trait variance in measure x; ty = percent of trait variance in measure y; true Rmk,ml = average correlation between method k and method l; mx= percent of method variance in measure x; and my = percent of method variance in measure y. This equation and the variance estimates from MTMM meta-analytic studies can be used to decompose the average observed correlation between measures of two different traits that share the same method into the proportion due to (a) the correlation between the traits they represent and (b) the common method they share.
Trait and method variance becomes confounded as the correlations among traits and among methods increase.
5/12/20Βιβλιοθήκη 52.2 Effects of General Method Bias on the Covariation Between Constructs
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1.2 What is Method Bias
There are mainly two detrimental effects produced by method factors have been recognized in the literature. Method factors can bias estimates of construct reliability and validity. Method factors can bias parameter estimates of the relationship between two different constructs.
2.2.1 Estimates based on MTMM meta-analytic studies Assuming that trait, method, and random error interacions do not exist, Cote&Buckley (1988) show that the observed correlation between two variables x and y is equal to:
1.1 What is Method
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A broad definition: Method encompasses potential influences at several levels of abstractio. These influences include the content of the items, the response format, the general instructions and other features of the test-task as a whole, the characteristics of the examiner, other features of the total setting, the reason why the subjects is taking the test
Overall, the weight of the evidence suggests that method factors are likely to have unequal effects on different measures--where they are different measures of the same construct or measures of different constructs.
I What is method bias II Empirical evidence of the effects of method biases III Ways to control for different sources of method bias IV Recommendations
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2.2.2 Estimates based on method-method pair meta-analytic technique.
This technique involves categorizing the meta-analytic correlations from previous studies on the basis of the susceptibility to method biases of the pair of methods used to measure the predictor and criterion. The argument is that some method-method pairs are more susceptible to method biases than others and that organizing the meta-analytic data on the basis of these pairings allows researchers to obtain an estimate of the effects that method biases have on the relationships of interest.
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2.3.2 The effects of response styles
The effect of the response styles on the magnitude of the correlations among the constructs depended upon whether the (a) true correlation between the constructs was positive or negative and (b) response style components affecting the scales were positively or negatively correlated. 2.3.3 The effects of proximity and reversed items
Sources of Method Bias in Social Science Research and Recommendations on How to Control It
Philip M. Podsakoff, Scott B. MacKenzie, Nathan P.Podsakoff
OUTLINE
Weijter et al. (2009,p. 7) concluded that up to a point, "correlations become weaker for nonreversed items and stronger for reversed items the further items are positioned from each other". 2.3.4 The effects of item wording 2.3.5 The effects of item context
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2.1 Effects of General Method Bias on Item Reliability or Validity
Some potential criticisms about the MTMM-based evidence of method bias.
The estimates of the proportion of item variance due to method are not completely independent because the MTMM matrices they analyze overlap to some extent.
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2.3 Estimates of Specific Types of Method Bias on the Covariation Between Constructs
2.3.1 The effects of same versus different sources.
Table 2 reports a summary of these types of meta-analytic studies. The results indicate that the average corrected correlation between leader baheviors and outcome variables when taken from the same source is 0.456, but only 0.191 when obtained from different sources. This means that the average corrected correlation between measures of leader behaviors and outcome variables is 239% larger when these measures are obtained from the same source than when they are obtained from different sources.