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126 lines
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126 lines
9.0 KiB
Plaintext
{smcl}
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{* 2013}{...}
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{hline}
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help for {hi:valid}
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{hline}
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{title:Syntax}
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{p 8 14 2}{cmd:valid} {it:varlist}, {bf:partition}({it:numlist}) [{it:options}]
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{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.
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{p 4 4 2}{cmd:partition} permits to define in {it:numlist} the number of items in each dimension.
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{synoptset 20 tabbed}{...}
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{synopthdr}
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{synoptline}
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{syntab:Options}
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{synopt : {opt sco:rename(string)}}define the names of the dimensions{p_end}
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{synopt : {opt imp:ute}}impute missing item responses{p_end}
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{synopt : {help valid##impute_options:{it:impute_options}}}options for imputation of missing data {p_end}
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{synopt : {opt calc:method(method)}}define how scores are calculated{p_end}
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{synopt : {opt desc:items}}display a description of items and dimensions{p_end}
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{synopt : {opt graph:s}}display graphs for items description{p_end}
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{synopt : {opt cfa}}assess structural validity by performing a confirmatory factor analysis{p_end}
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{synopt : {help valid##cfa_options:{it:cfa_options}}}options for confirmatory factor analysis{p_end}
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{synopt : {opt conv:div}}assess convergent and divergent validity assessment{p_end}
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{synopt : {help valid##conv_div_options:{it:conv_div_options}}}options for convergent and divergent validity{p_end}
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{synopt : {help valid##reliability_options:{it:reliability_options}}}options for reliability assessment{p_end}
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{synopt : {opt rep:et(varlist)}}assess reproducibility of scores and items{p_end}
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{synopt : {help valid##repet_options:{it:repet_options}}}options for reproducibility{p_end}
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{synopt : {opt kgv(varlist)}}assess known-groups validity by using qualitative variable(s){p_end}
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{synopt : {help valid##kgv_options:{it:kgv_options}}}options for known-groups validity assessment{p_end}
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{synopt : {opt conc(varlist)}}assess concurrent validity{p_end}
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{synopt : {help valid##conc_options:{it:conc_options}}}options for concurrent validity assessment{p_end}
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{p2colreset}{...}
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{title:Description}
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{phang}{cmd:valid} assesses validity and reliability of a multidimensional scale. Specifically it evaluates
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structural validity, convergent and divergent validity, reproducibility, known-groups validity, internal consistency, scalability and sensitivity.
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{marker options}{...}
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{title:Options}
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{dlgtab:Options}
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{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},...
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{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
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missing values in the dimension is less than half the number of items in the dimension.
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{marker impute_options}{...}
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{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
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are not rounded. If {opt impute} is absent then {opt noround} is ignored.
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{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).
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{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
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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.
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{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.
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{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.
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{marker cfa_options}{...}
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{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
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likelihood), {bf:mlmv} ({bf:ml} with missing values) or {bf:adf} (asymptotic distribution free). The {opt cfas:tand} option displays standardized coefficients.
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{phang}{opt conv:div} assesses convergent and divergent validity. The option displays the matrix of correlations between items and dimensions.
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{marker convdiv_options}{...}
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{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
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default. Correlations between items and their own score are printed
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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
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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
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boxes. There is as many boxplots (graphs) as dimensions.
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{marker reliability_options}{...}
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{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
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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
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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
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0.3 by default. Loevinger's H coefficients less than # are printed in red. {opt hj:min(#)}
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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
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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.
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{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
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Correlation Coefficients (ICC) for scores and their 95% confidence interval are computed with Stata's {help icc} command.
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{marker repet_options}{...}
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{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
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confidence intervals for
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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
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coefficients. If the {opt kappa} option is absent then {opt ickappa(#)} is ignored.
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{phang}{opt kgv(varlist)} assesses known-groups validity according to the grouping variables precised in {it:varlist}.
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{marker kgv_options}{...}
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{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
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all boxplots in one graph. If {opt kgvboxplots} is absent then {opt kgvboxplotsgroup} is ignored.
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{phang}{opt conc(varlist)} assesses concurrent validity with variables precised in {it:varlist}. These variables are scores from one or several other scales.
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{marker conc_options}{...}
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{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
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coefficients greater than # are displayed in bold.
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{marker examples}{...}
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{title:Examples}
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{phang2}{cmd:. valid item1-item20, part(5 4 6 5)}{p_end}
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{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}
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{phang2}{cmd:. valid item1-item20, part(5 4 6 5) imp rep(item1bis-item20bis) kappa}{p_end}
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{marker alsosee}{...}
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{title:Also see}
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{p 4 13 2}help for {help alpha}, {help delta}, {help loevh}, {help icc}, {help kapci}.{p_end}
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