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. Author manuscript; available in PMC: 2014 Mar 17.
Published in final edited form as: Struct Equ Modeling. 2010 Oct 12;17(4):605–628. doi: 10.1080/10705511.2010.510050

TABLE 7.

Sensitivity

MD = 1.5
MD = 3.0
TAX FMM TAX FMM
π1 = .5
 LCA5 0.69 0.74 0.75 0.93
 LCA10 0.75 0.75 0.89 0.94
 1F 0.71 0.74 0.93 0.94
 2Fcl 0.74 0.79 0.93 0.93
 2Fss 0.75 0.79 0.93 0.93
π1 = .95
 LCA5 0.56 0.28 0.74 0.69
 LCA10 0.86 0.21 0.84 0.68
 1F 0.63 0.32 0.92 0.69
 2Fcl 0.65 0.20 0.93 0.69
 2Fss 0.69 0.30 0.95 0.70

Note. Average sensitivity of taxometric procedures (TAX) and the factor mixture model (FMM). Sensitivity is defined as the proportion of subjects in the second (minority) class correctly assigned to the second class. Table entries are conditional on the detection rates of the two-class structure as summarized in Table 6, which are close to zero for taxometric procedures for some conditions. Results based on fewer than six data sets are presented in italics. MD = Mahalanobis distance; LCA5 = latent class data with 5 indicators; LCA10 = latent class data with 10 indicators; 1F = one-factor data with 10 indicators; 2Fcl = two-factor data with cross-loadings with 10 indicators; 2Fss = two-factor data with simple structure with 10 indicators; π1 = proportion of subjects in the first class where π2 = 1 — π1.