help for pcmJean-Benoit Hardouin, Myriam Blanchin

Estimation of the parameters of a Partial Credit Model (PCM) or a Rating Scale Model (RSM) and specific graphs

Syntax for Rasch analysis

pcm varlist [if] [in]
[, rsm iterate(#) model difficulties(matrix) variance(real) minsize(#)
continuous(varlist) categorical(varlist)
Graphs nographitems dimname(string) nocorrected alpha(#)
filesave extension(string) dirsave(directory docx(string)
genlt(string) geninf(string) replace

Syntax for Rasch equating

pcm varlist [if] [in] [, rsm iterate(#) model difficulties(matrix) variance(real) minsize(#) Graphs dimname(string) eqset1(varlist) eqset2(varlist) eqset1name(string) eqset2name(string) EQGraph eqaddset1(#) eqaddset2(#) eqmultset1({it:#) eqmultset2(#) eqwithic eqgenscore(string) replace ]

varlist is a list of two existing categorical variables or more.

Description

pcm estimates the parameters of a Partial Credit Model (PCM) or of a Rating Scale Model (RSM). Parameters are estimated using Marginal Maximum Likelihood (MML). Several graphical representations can be easily obtained: comparison of the observed and theorical Item Characteristic Curves (ICC), Map difficulty parameters/Scores, results of the split tests, and information function (for the scale and by item). pcm allow equating two sets of items.

Options for a Rasch analysis

rsm estimates a Rating Scale Model instead of a Partial Credit Model.

iterate defines the maximal number of iterations of the maximisation algorithm.
By default, this number is fixed to 100.

model displays the outputs of the maximisation alogorithm .

difficulties(matrix) fixes the values of the difficulties parameters of the items (by default, these values are estimated).
The vector must be a matrix containing as many rows as items, and a column for each threshold parameter (number of answer categories minus 1).
A missing value (.) replaces unexisting difficulty paremeters in the matrix.

variance(#) fixes the value of the variance of the latent trait (by default, this value is estimated).

minsize(#) fixes the minimal value of the groups (30 individuals by default).

continuous(varlist) defines a list of continuous variables explaining the latent trait.

categorical(varlist) defines a list of categorical variables explaining the latent trait.

Graphs displays the graphs.

nographitems avoids displaying the graphs by items.

dimname(string) defines the name of the analysed dimension (this name appears on the graphs).

nocorrected avoids using corrected latent trait estimates on the graphs.

alpha(#) defines the level of confidence of the interval presented on the graphs of residuals.

filesave save the graph files.

extension(string) defines the extension of the graph files.

dirsave(directory defines the directory to save the graph files and the docx file.

docx(string) creates a docx file with the main results and graphs.

genlt(string) creates several variables. The new variables string contains Expected A Posteriori estimates of the latent trait and string_se their standard deviations. The new variable string_corr contains estimates of the latent traits allowing obtaining the closer estimations of the item-sum scores. The new variables string_opt contains estimated of the latent traits obtained by searching, for each observed item to maximize the probability to obtain the observed answer (the obtained value is the mean of all these estimates).

geninf(string) contains the information associated for each individual.

replace specifies that the existing files or variables will be replaced.

Options for a Rasch equating

rsm estimates a Rating Scale Model instead of a Partial Credit Model.

iterate defines the maximal number of iterations of the maximisation algorithm.
By default, this number is fixed to 100.

model displays the outputs of the maximisation alogorithm .

difficulties(matrix) fixes the values of the difficulties parameters of the items (by default, these values are estimated).
The vector must be a matrix containing as many rows as items, and a column for each threshold parameter (number of answer categories minus 1).
A missing value (.) replaces unexisting difficulty paremeters in the matrix.

variance(#) fixes the value of the variance of the latent trait (by default, this value is estimated).

minsize(#) fixes the minimal value of the groups (30 individuals by default).

Graphs displays the graphs.

dimname(string) defines the name of the analysed dimension (this name appears on the graphs).

eqset1(varlist) and eqset2(varlist) define the items containing in the two sets of items to equate (disjoint or not sets of items)

eqset1name(string) and eqset2name(string) define the names of the two sets of items (set1 and set2 by default)

EQGraph displays graphs

eqaddset1(#) and eqaddset2(#) allows adding a real to the row score to obtain a linear transformed score for each set of items (0 by default)

eqmultset1({it:#) and eqmultset2(#) allows multiplying the row score by a real to obtain a linear transformed score for each set of items (1 by default)

eqwithic allows representing the 95% confidence interval of the equated score on the graph

eqgenscore(string) create several variables containing the center (_mean_), the minimal (_min_) and the maximal (_max_) values of the 95% confidence interval and a random value (_random_) drawn in this interval

replace specifies that the existing files or variables will be replaced.

Outputs

e(): Output of the gsem procedure

r(ll): Marginal Log-likelihood

r(PSI): Personal Separation Index

r(difficulties): Estimates of the threshold parameters

r(matscorelt) : estimates of the latent trait associated to each complete item-sum score

r(matgroupscorelt) : estimates of the latent trait associated to each complete item-sum score and to each group of individuals used for graphical fit tests

r(covariates) : estimates of the variance of the latent trait and of the parameters associates to covariates

r(bestest): estimates of the latent trait obtained for each item by searching the value maximizing the response to each answer category

r(score2_to_1) : gives the results to equate the raw scores of the set2 to set1

r(score1_to_2): gives the results to equate the raw scores of the set1 to set2

Examples of Rasch analysis

. pcm item1-item9

. pcm item*, graph dirsave(c:\graphs) filesave genlt(lt) docx(report)

. matrix diff=(-1,-0.8,0.2\-.5,0,0.5\0,1,.\0.2,1,1.2\0.5,.,.)

. pcm item1-item5 , diff(diff) cat(sex) cont(age)

Example of Rasch equating

. pcm item1-item9, eqset1(item1-item5) eqset2(item6-item9) eqnameset1(First part) eqnameset2(Second part) eqaddset1(5) eqmultset1(-1) graph

Author

Jean-Benoit Hardouin, PhD-HDR, Associate professor - Hospital Practitioner

Myriam Blanchin, PhD, Research engineer

INSERM UMR 1246-SPHERE "Methods in Patients Centered Outcomes and Health Research"

Nantes University - Faculty of Medicine

Intitute of Research in Health 2

22 boulevard Bénoni-Goullin

44200 Nantes - FRANCE

Email:
jean-benoit.hardouin@univ-nantes.fr, myriam.blanchin@univ-nantes.fr

Also see

Online: help for raschtest and gsem if installed.