Description Usage Arguments Details Value Author(s) References
Estimation of the rating scale model for continuous data by Mueller (1987).
1 2 3 4 5 6 7 |
data |
Data matrix or data frame; rows represent observations (persons), columns represent the items. |
low |
The minimum value of the response scale (on which the data are based). |
high |
The maximum value of the response scale (on which the data are based). |
start |
Starting values for parameter estimation. If missing, a vector of 0 is used as starting values. |
conv |
Convergence criterium for parameter estimation. |
x |
object of class |
... |
... |
object |
object of class |
P_{vi}(a ≤q X ≤q b) = \frac{\int_a^b exp[x μ + x(2c-x) θ] dx}{\int_{c-\frac{d}{2}}^{c+\frac{d}{2}} exp[t μ + t(2c-t) θ] dt}
Parameters are estimated by a pairwise conditional likelihood estimation (a pseudo-likelihood approach, described in Mueller, 1999).
The parameters of the continuous rating scale model are estimated by a pairwise cml approach using Newton-Raphson iterations for optimizing.
data |
data matrix according to the input |
data_p |
data matrix with data transformed to a response interval between 0 and 1 |
itempar |
estimated item parameters |
itempar_se_low |
estimated lower boundary for standard errors of estimated item parameters |
itempar_se_up |
estimated upper boundary for standard errors of estimated item parameters |
itempar_se |
estimated mean standard errors of estimated item parameters |
disppar |
estimated dispersion parameter |
disppar_se_low |
estimated lower boundary for standard errors of estimated dispersion parameter |
disppar_se_up |
estimated upper boundary for standard errors of estimated dispersion parameter |
itempar_se |
estimated mean standard errors of estimated item parameter |
disp_est |
estimated dispersion parameters for all item pairs |
iterations |
Number of Newton-Raphson iterations for each item pair |
low |
minimal data value entered in call |
high |
maximal data value entered in call |
call |
call of the CRSM function |
Christine Hohensinn
Mueller, H. (1987). A Rasch model for continuous ratings. Psychometrika, 52, 165-181.
Mueller, H. (1999). Probabilistische Testmodelle fuer diskrete und kontinuierliche Ratingskalen. [Probabilistic models for discrete and continuous rating scales]. Bern: Huber.
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