# Research

Topics Publications In progress publications Communications# Packages

AnaQol Project PRO-online R Packages Online R-package# Life of the unit

Projects Collaborations PhD thesis Traineeships Traineeships propositions Seminars# Next seminars

*21 septembre 2018*

*18 octobre 2018*

# Last publications

*06 juin 2018*__ Tessier P__*The European Journal of Health Economics*: .

*06 juin 2018*__ Tessier P__*BMJ Open*, **8**(2): .

*15 mai 2018*__ Maruani A__*Oral Dis*, **24**(4): 552-60.

*15 mai 2018*__ Sautenet B__*Am J Kidney Dis*, **71**(5): 690-700.

*02 mai 2018* __ Hardouin JB__*Trials*, **19**(1): 260.

# Updated

19 juillet 2018# The Stata module "Validscale"

# Description

**-validscale-** assesses validity and reliability of a multidimensional scale. This program computes elements to provide structural validity, convergent and divergent validity, reproducibility, known-groups validity, internal consistency, scalability and sensitivity.

# Download

Type "findit validscale" or "ssc install validscale" directly from your Stata browser.

# Syntax (version 1.2)

**validscale** *varlist* [, ** partition**(

*numlist*)

**(**

__score__name*string*)

**(**

__scores__*varlist*)

**(**

__mod__alities*numlist*)

__imp__ute

__no__round**(**

__comp__score*method*)

__desc__items

__graph__s

__cfa__**(**

__cfam__ethod*method*)

__cfas__tand

__cfac__ovs

__cfaa__uto

__conv__div**(**

__tconv__div*#*)

__convdivb__oxplots**(**

__a__lpha*#*)

**(**

__d__elta*#*)

**(**

__h__*#*)

**(**

__hj__min*#*)

**(**

__rep__et*varlist*)

__kap__pa**(**

__ickap__pa*#*)

**(**

__scores2__*varlist*)

**(**

__kgv__*varlist*)

__kgvb__oxplots

__kgvg__roupboxplots**(**

__conc__*varlist*)

**(**

__tc__onc*#*) ]

# Notes

This program requires an access to the following program(s):

**: ssc install delta**

__delta__**: ssc install detect**

__detect__**: ssc install imputeitems**

__imputeitems__**: ssc install kapci**

__kapci__**: ssc install loevh**

__loevh__**: ssc install lstrfun**

__lstrfun__# Options:

(__part__ition*numlist*): defines the number of items in each dimension. The number of elements of this list indicates the number of dimensions.(__score__name*string*): allows defining the names of the dimensions. If the option is not used, the dimensions are named Dim1, Dim2,... unless**scores**(*varlist*) is used.(__scores__*varlist*): allows selecting scores from the dataset.**scores**(*varlist*) and**scorename**(*string*) cannot be used together.(__mod__alities*numlist*): allows specifying the minimum and maximum possible values for items responses. If all the items have the same response categories, the user may specify these 2 values in*numlist*. If the items response categories differ from a dimension to another, the user must define the minimum and maximum values of items responses for each dimension. So the number of elements in*numlist*must be equal to the number of dimensions times 2. Eventually, the user may specify the minimum and maximum response categories for each item. In this case, the number of elements in*numlist*must be equal to the number of items times 2. By default, the minimum and maximum values are assumed to be the minimum and maximum for each item.: imputes missing items responses with Person Mean Substitution (pms) or Two-way imputation method applied in each dimension (mi). With PMS method, missing data are imputed only if the number of missing values in the dimension is less than half the number of items in the dimension.__imp__ute: By default, imputed values are rounded to the nearest whole number but with the__no__round**noround**option, imputed values are not rounded. If**impute**is absent then**noround**is ignored.(__comp__score*method*): defines the method used to compute the scores.*method*may be either**mean**(default),**sum**or**stand**(set scores from 0 to 100).**compscore**(*method*) is ignored if the**scores**(*varlist*) option is used.: displays a descriptive analysis of the items. This option displays missing data rate per item and distribution of item responses. It also computes for each item the Cronbach's alphas obtained by omitting each item in each dimension. Moreover, the option computes Loevinger's Hj coefficients and the number of non-significant Hjk. See__desc__items**loevh**for details about Loevinger's coefficients.: displays graphs for items and dimensions descriptive analyses. It provides histograms of scores, a biplot of the scores and a biplot of the items.__graph__s: performs a confirmatory factor analysis (CFA) using sem command. It displays estimations of parameters and several goodness-of-fit indices.__cfa__(__cfam__ethod*method*): specifies the method to estimate the parameters.*method*may be either**ml**(maximum likelihood),**mlmv**(ml with missing values) or**adf**(asymptotic distribution free).: displays standardized coefficients for the CFA.__cfas__tand: allows adding covariances between measurement errors. You can look at the examples to see the syntax of this option.__cfac__ovs: adds automatically the covariances of measurement errors found with the estat mindices command. The option only adds the covariances of measurement errors within a dimension.__cfaa__uto: assesses convergent and divergent validities. The option displays the matrix of correlations between items and rest-scores. If__conv__div**scores**(*varlist*) is used, then the correlations coefficients are computed between items and scores of**scores**(*varlist*).(__tconv__div*#*): defines a threshold for highlighting some values.*#*is a real number between 0 and 1 which is equal to 0.4 by default. Correlations between items and their own score are displayed in red if it is less than*#*. Moreover, if an item has a smaller correlation coefficient with the score of its own dimension than those computed with other scores, this coefficient is displayed in red.: displays boxplots for assessing convergent and divergent validities. The boxes represent the correlation coefficients between the items of a given dimension and all scores. So the box of correlation coefficients between items of a given dimension and the corresponding score must be higher than other boxes. There are as many boxplots as dimensions.__convdivb__oxplots(__a__lpha*#*): defines a threshold for Cronbach's alpha (see alpha).*#*is a real number between 0 and 1 which is equal to 0.7 by default. Cronbach's alpha coefficients less than*#*are printed in red.(__d__elta*#*): defines a threshold for Ferguson's delta coefficient (see delta). Delta coefficients are computed only if**compscore**(*sum*) is used and**scores**(*varlist*) is not used.*#*is a real number between 0 and 1 which is equal to 0.9 by default. Ferguson's delta coefficients less than*#*are printed in red.(__h__*#*): defines a threshold for Loevinger's H coefficient (see loevh).*#*is a real number between 0 and 1 which is equal to 0.3 by default. Loevinger's H coefficients less than*#*are printed in red.(__hj__min*#*): defines a threshold for Loevinger's Hj coefficients. The displayed value is the minimal Hj coefficient for a item in the dimension. (see loevh).*#*is a real number between 0 and 1 which is equal to 0.3 by default. If the minimal Loevinger's Hj coefficient is less than # then it is printed in red and the corresponding item is displayed.(__rep__et*varlist*): assesses reproducibility of scores by defining in*varlist*the variables corresponding to responses at time 2 (in the same order than for time 1). Scores are computed according to the partition() option. Intraclass Correlation Coefficients (ICC) for scores and their 95% confidence interval are computed with Stata's icc command.: computes kappa statistic for items with Stata's kap command.__kap__pa(__ickap__pa*#*): computes confidence intervals for kappa statistics using kapci.*#*is the number of replications for bootstrap used to estimate confidence intervals if items are polytomous. If they are dichotomous, an analytical method is used. See kapci for more details about calculation of confidence intervals for kappa's coefficients. If the kappa option is absent then the**ickappa**(*#*) option is ignored.(__scores2__*varlist*): allows selecting scores at time 2 from the dataset.(__kgv__*varlist*): assesses known-groups validity according to the grouping variables defined in*varlist*. The option performs an ANOVA which compares the scores between groups of individuals, constructed with variables in*varlist*.: draws boxplots of the scores split into groups of individuals.__kgvb__oxplots: groups all boxplots in one graph. If__kgvg__roupboxplots**kgvboxplots**is absent then the**kgvgroupboxplots**option is ignored.(__conc__*varlist*): assesses concurrent validity with variables precised in*varlist*. These variables are scores from one or several other scales.(__tc__onc*#*): defines a threshold for correlation coefficients between the computed scores and those of other scales defined in*varlist*. Correlation coefficients greater than*#*(0.4 by default) are displayed in bold.

# Examples:

**validscale item1-item20, part(5 4 6 5)**

**validscale item1-item20, part(5 4 6 5) imp(pms) graphs cfa cfastand cfacovs(item1*item3 item5*item7 item17*item18) convdiv convdivboxplots kgv(factor_variable) kgvboxplots conc(scoreA-scoreD)**

**validscale item1-item20, part(5 4 6 5) imp(pms) scores(s1-s4) rep(item1bis-item20bis) scores2(s1bis-s4bis) kappa**

# Outputs:

# Historic

- Original version of validscale