Manifest variable path analysis: potentially serious and misleading consequences due to uncorrected measurement error.
Despite clear evidence that manifest variable path analysis requires highly reliable measures, path analyses with fallible measures are commonplace even in premier journals. Using fallible measures in path analysis can cause several serious problems: (a) As measurement error pervades a given data set, many path coefficients may be either over- or underestimated. (b) Extensive measurement error diminishes power and can prevent invalid models from being rejected. (c) Even a little measurement error can cause valid models to appear invalid. (d) Differential measurement error in various parts of a model can change the substantive conclusions that derive from path analysis. (e) All of these problems become increasingly serious and intractable as models become more complex. Methods to prevent and correct these problems are reviewed. The conclusion is that researchers should use more reliable measures (or correct for measurement error in the measures they do use), obtain multiple measures for use in latent variable modeling, and test simpler models containing fewer variables.
Department of Psychology and Human Development, Vanderbilt University.
David A
DA
Cole
Department of Psychology and Human Development, Vanderbilt University.
Kristopher J
KJ
Preacher
D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical