simul_these/Modules/ado/personal/v/valid.hlp

{smcl}
{* 2013}{...}
{hline}
help for {hi:valid}
{hline}

{title:Syntax}

{p 8 14 2}{cmd:valid} {it:varlist}, {bf:partition}({it:numlist}) [{it:options}]

{p 4 4 2}{it:varlist} contains the variables (items) used to calculate the scores. The first items of {it:varlist} compose the first dimension, the following items define the second dimension, and so on.

{p 4 4 2}{cmd:partition} permits to define in {it:numlist} the number of items in each dimension.


{synoptset 20 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Options}
{synopt : {opt sco:rename(string)}}define the names of the dimensions{p_end}
{synopt : {opt imp:ute}}impute missing item responses{p_end}
{synopt : {help valid##impute_options:{it:impute_options}}}options for imputation of missing data {p_end}
{synopt : {opt calc:method(method)}}define how scores are calculated{p_end}
{synopt : {opt desc:items}}display a description of items and dimensions{p_end}
{synopt : {opt graph:s}}display graphs for items description{p_end}
{synopt : {opt cfa}}assess structural validity by performing a confirmatory factor analysis{p_end}
{synopt : {help valid##cfa_options:{it:cfa_options}}}options for confirmatory factor analysis{p_end}
{synopt : {opt conv:div}}assess convergent and divergent validity assessment{p_end}
{synopt : {help valid##conv_div_options:{it:conv_div_options}}}options for convergent and divergent validity{p_end}
{synopt : {help valid##reliability_options:{it:reliability_options}}}options for reliability assessment{p_end}
{synopt : {opt rep:et(varlist)}}assess reproducibility of scores and items{p_end}
{synopt : {help valid##repet_options:{it:repet_options}}}options for reproducibility{p_end}
{synopt : {opt kgv(varlist)}}assess known-groups validity by using qualitative variable(s){p_end}
{synopt : {help valid##kgv_options:{it:kgv_options}}}options for known-groups validity assessment{p_end}
{synopt : {opt conc(varlist)}}assess concurrent validity{p_end}
{synopt : {help valid##conc_options:{it:conc_options}}}options for concurrent validity assessment{p_end}

{p2colreset}{...}


{title:Description}

{phang}{cmd:valid} assesses validity and reliability of a multidimensional scale. Specifically it evaluates
structural validity, convergent and divergent validity, reproducibility, known-groups validity, internal consistency, scalability and sensitivity. 

{marker options}{...}
{title:Options}

{dlgtab:Options}

{phang}{opt sco:rename(string)} allows defining the names of the dimensions. If the option is not used then dimensions are named {it:Dim1}, {it:Dim2},...

{phang}{opt imp:ute} imputes missing items responses with Person Mean Substitution method applied in each dimension. 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. 

{marker impute_options}{...}
{phang}{it:impute_options} allow specifying options for imputation of missing. By default, imputed values are rounded to the nearest whole number. If {opt nor:ound} is precised then imputed values
are not rounded. If {opt impute} is absent then {opt noround} is ignored.

{phang}{opt calc:method(method)} defines the method used for calculating scores. {it:method} may be either {bf:mean} (default), {bf:sum} or {bf:stand}(set scores from 0 to 100).

{phang}{opt desc:items} displays a description of items. This option gives missing data rate per item and distribution of item responses. It also gives Cronbach's alpha for each item, which is the alpha statistic
calculated by removing the item from the dimension. Moreover, the option computes Loevinger's Hj coefficients and the number of non significant Hjk. See {help loevh} for details about Loevinger's coefficients.     

{phang}{opt graph:s} displays graphs for items and dimensions description. It provides histograms of scores, a biplot of dimensions and a biplot of items.

{phang}{opt cfa} performs a confirmatory factor analysis with Stata's {help sem} command. It displays estimations of coefficients and several goodness-of-fit indices.

{marker cfa_options}{...}
{phang}{it:cfa_options} allow specifying options for confirmatory factor analysis. {opt cfam:ethod}({it:method}) specifies the method of estimation of parameters. {it:method} may be either {bf:ml} (maximum
likelihood), {bf:mlmv} ({bf:ml} with missing values) or {bf:adf} (asymptotic distribution free). The {opt cfas:tand} option displays standardized coefficients. 

{phang}{opt conv:div} assesses convergent and divergent validity. The option displays the matrix of correlations between items and dimensions.  

{marker convdiv_options}{...}
{phang}{it:conv_div_options} allow specifying options for convergent and divergent validity. {opt 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 printed
in red if it is less than #. Moreover, if an item is less correlated with its own dimension than with another one the correlation is printed
in red. The {opt convdivb:oxplots} option displays boxplots for assessing convergent and divergent validity. The boxes represent the correlations between the items of a given dimension and all dimensions. So the box of correlations between items of a given dimension and its score must be higher than other
boxes. There is as many boxplots (graphs) as dimensions. 

{marker reliability_options}{...}
{phang}{it:reliability_options} allow defining the thresholds for reliability indices. {opt a:lpha(#)} defines a threshold for Cronbach's alpha (see {help 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. {opt d:elta(#)} defines a threshold for Ferguson's delta coefficient (see {help delta}). # 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. {opt h(#)} defines a threshold for Loevinger's H coefficient (see {help 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. {opt hj:min(#)}
defines a threshold for Loevinger's Hj coefficients. The displayed value is the minimum Hj coefficient for a item in the dimension. (see {help loevh}). # is a real number between 0 and 1 which is equal to
0.3 by default. If the minimum Loevinger's Hj coefficient is less than # then it is printed in red and the corresponding item is displayed.
																										                                                                                                                                                                                                            
{phang}{opt rep:et(varlist)} assesses reproducibility of scores by precising in {it:varlist} the variables corresponding to responses at time 2. Scores are calculated according to the {opt partition()} option. Intraclass
Correlation Coefficients (ICC) for scores and their 95% confidence interval are computed with Stata's {help icc} command.

{marker repet_options}{...}
{phang}{it:repet_options} display information about reproducibility of items. The {opt kap:pa} option computes kappa statistic for items with Stata's {help kap} command. The {opt ickap:pa(#)} calculates
confidence intervals for
kappa statistics. # is the number of replications for bootstrap used to estimate confidence intervals if items are polytomous. See {help kapci} for more details about calculation of confidence intervals for kappa's
coefficients. If the {opt kappa} option is absent then {opt ickappa(#)} is ignored. 

{phang}{opt kgv(varlist)} assesses known-groups validity according to the grouping variables precised in {it:varlist}.

{marker kgv_options}{...}
{phang}{it:kgv_options} allow to display graphs for known-groups validity. The {opt kgvb:oxplots} option draws boxplots of scores split into groups of individuals. The {opt kgvg:roupboxplots} option groups
all boxplots in one graph. If {opt kgvboxplots} is absent then {opt kgvboxplotsgroup} is ignored.  

{phang}{opt conc(varlist)} assesses concurrent validity with variables precised in {it:varlist}. These variables are scores from one or several other scales.  

{marker conc_options}{...}
{phang}{it:conc_options} allow to specify options for concurrent validity. The {opt tc:onc(#)} option defines a threshold for correlations between scores and those of other scales in {it:varlist}. Correlation
coefficients greater than # are displayed in bold.


{marker examples}{...}
{title:Examples}

{phang2}{cmd:. valid item1-item20, part(5 4 6 5)}{p_end}

{phang2}{cmd:. valid item1-item20, part(5 4 6 5) imp graphs cfa cfastand convdiv convdivboxplots kgv(factor_variable) kgvboxplots conc(scoreA-scoreD)}{p_end}

{phang2}{cmd:. valid item1-item20, part(5 4 6 5) imp rep(item1bis-item20bis) kappa}{p_end}


{marker alsosee}{...}
{title:Also see}

{p 4 13 2}help for {help alpha}, {help delta}, {help loevh}, {help icc}, {help kapci}.{p_end}