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Rank of the items of a survey based on Likert scale, in R programing. I've been able to go all the way to using the likert package in R. My difficulty is and convert the scale according to the "Psychological Distance Between Categories in the Likert Scale: Comparing Different Numbers of Options" (Wakita 2012) proposition, and assign the result of the converted scale to the argument "importance" of the linkert function. A good part of Wakita's work (2012) ([login to view URL]) has already been made by Jason M. Bryer ([login to view URL] ), Only missing the scale conversion according to Muraki's GPCM model. My work is being modeled on R, and I hope its results are also delivered in R. Usage of likert ([login to view URL]): likert(items, summary, grouping = NULL, factors = NULL, importance, nlevels = length(levels(items[, 1]))) "importance: a data frame of the same dimensions as items containing an importance rating for each item. The order of columns should match and the names from items will be used" All models IRT are now ready in R. The MIRT ([login to view URL]) package already does Muraki's GPCM calculations. All this I can do. Since I am neither a statistician nor a mathematician, I had difficulty transforming the scale (as proposed by Wakita), and that is what my job offer is focused on. Look at my steps: head(tdata[, substr(names(tdata), 1, 3) == "I2_"]) I2_1 I2_2 I2_3 I2_4 I2_5 I2_6 I2_7 I2_8 I2_9 I2_10 I2_11 I2_12 I2_13 I2_14 I2_15 I2_16 I2_17 I2_18 I2_19 I2_20 I2_21 1 5 4 4 2 4 2 5 4 5 5 2 2 5 3 4 4 4 4 4 4 5 2 5 2 4 5 5 5 5 4 3 3 2 2 3 3 5 4 5 4 3 5 3 3 5 5 5 4 5 4 5 4 5 3 3 2 4 4 5 4 3 5 3 4 4 4 2 4 3 4 4 4 4 4 3 2 3 3 4 3 4 4 4 3 3 4 3 5 4 4 3 4 5 3 3 4 4 3 3 3 5 4 5 3 4 3 5 5 4 6 5 3 3 4 4 5 4 3 2 2 3 5 3 3 2 4 2 3 3 3 4 I2_22 I2_23 I2_24 I2_25 I2_26 I2_27 I2_28 I2_29 I2_30 I2_31 I2_32 I2_33 I2_34 I2_35 1 4 2 2 4 5 4 4 4 4 4 4 4 4 5 2 2 5 3 4 4 4 4 5 4 3 2 4 4 5 3 5 5 2 3 3 4 4 3 2 3 3 3 5 5 4 5 3 3 3 3 4 3 2 3 5 3 4 3 5 5 4 4 3 5 3 5 3 4 5 4 5 3 4 5 6 5 3 4 3 5 2 2 2 2 5 2 4 3 5 [login to view URL] <- mirt(tdata[, substr(names(tdata), 1, 3) == "I2_"], 1, "gpcm", SE=T) coef([login to view URL], simplify = TRUE, IRTpars = TRUE) $items a b1 b2 b3 b4 I2_1 0.80 -4.9 -3.29 -1.693 -0.16 I2_2 1.12 -4.3 -1.69 0.500 2.24 I2_3 0.67 -3.7 -5.09 -2.018 -0.92 I2_4 0.86 -4.4 -3.79 -1.081 0.68 I2_5 0.99 -4.6 -2.08 -0.787 NA I2_6 0.66 -3.5 -2.49 -1.344 NA I2_7 0.73 -4.4 -4.09 -1.985 -0.32 I2_8 1.02 -3.9 -1.61 0.166 NA I2_9 0.74 -4.6 -2.80 -0.009 2.06 I2_10 0.71 -4.0 -1.90 0.550 2.28 I2_11 0.64 -3.2 -2.10 -0.220 1.44 I2_12 0.42 -4.1 -3.43 0.849 2.07 I2_13 0.83 -5.4 -3.06 -0.628 1.28 I2_14 0.79 -3.7 -2.86 -0.063 0.65 I2_15 1.10 -3.1 -3.89 -1.625 0.29 I2_16 0.96 -4.5 -3.16 -1.048 1.16 I2_17 0.85 -3.2 -3.12 -0.455 1.32 I2_18 0.85 -3.9 -3.14 -0.549 0.59 I2_19 0.83 -3.2 -3.49 -0.899 1.08 I2_20 0.93 -3.2 -3.44 -0.895 0.80 I2_21 0.75 -4.4 -2.70 0.189 2.48 I2_22 0.78 -3.4 -3.86 -1.796 -0.79 I2_23 0.86 -3.6 -4.55 -1.189 0.66 I2_24 0.72 -3.7 -1.80 0.745 2.22 I2_25 0.70 -4.0 -0.92 0.463 NA I2_26 0.87 -3.6 -2.54 -0.339 1.56 I2_27 0.99 -2.9 -1.37 0.392 NA I2_28 0.96 -4.5 -2.52 -0.196 1.53 I2_29 0.91 -3.0 -2.33 0.268 1.90 I2_30 0.66 -3.6 -1.64 0.791 2.90 I2_31 0.88 -4.6 -3.37 -1.204 0.38 I2_32 0.67 -3.6 -1.40 0.104 NA I2_33 0.66 -5.5 -4.14 -1.176 0.46 I2_34 0.83 -3.1 -2.88 -0.168 1.59 I2_35 1.29 -4.1 -1.86 -0.278 NA And I need to do the conversion of the items (like Wakita´s model)