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144 lines
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Plaintext
144 lines
12 KiB
Plaintext
{smcl}
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{* 2013}{...}
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{hline}
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help for {hi:validscale}{right:Bastien Perrot}
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{hline}
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{title:Syntax}
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{p 8 14 2}{cmd:validscale} {it:varlist}, {opt part:ition}({it:numlist}) [{it:options}]
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{p 4 4 2}{it:varlist} contains the variables (items) used to compute 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} allows defining 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 scoren:ame(string)}}define the names of the dimensions{p_end}
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{synopt : {opt scores(varlist)}}use scores from the dataset{p_end}
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{synopt : {opt mod:alities(numlist)}}define minimum and maximum response categories for the items{p_end}
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{synopt : {opt imp:ute(method)}}impute missing item responses{p_end}
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{synopt : {help validscale##impute_options:{it:impute_options}}}options for imputation of missing data {p_end}
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{synopt : {opt comps:core(method)}}define how scores are computed{p_end}
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{synopt : {opt desc:items}}display a descriptive analysis 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 of the scale by performing a confirmatory factor analysis (CFA){p_end}
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{synopt : {help validscale##cfa_options:{it:cfa_options}}}options for confirmatory factor analysis (CFA){p_end}
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{synopt : {opt conv:div}}assess convergent and divergent validities assessment{p_end}
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{synopt : {help validscale##convdiv_options:{it:conv_div_options}}}options for convergent and divergent validities{p_end}
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{synopt : {help validscale##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 validscale##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 validscale##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 validscale##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:validscale} assesses validity and reliability of a multidimensional scale. Elements to provide
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structural validity, convergent and divergent validity, reproducibility, known-groups validity, internal consistency, scalability and sensitivity are computed.
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{marker options}{...}
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{title:Options}
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{dlgtab:Options}
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{phang}{opt scoren:ame(string)} allows defining the names of the dimensions. If the option is not used, the dimensions are named {it:Dim1}, {it:Dim2},... unless {opt scores(varlist)} is used.
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{phang}{opt scores(varlist)} allows selecting scores from the dataset. {opt scores(varlist)} and {opt scorename(string)} cannot be used together.
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{phang}{opt mod:alities(numlist)} allows specifying the minimum and maximum possible values for items responses. If all the items have the same response
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categories, the user may specify these 2 values in {it:numlist}. If the items response categories differ from a dimension to another, the user must define the possible minimum and maximum values of items responses for each
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dimension. So the number of elements in {it: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
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number of elements in {it: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.
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{phang}{opt imp:ute(method)} imputes missing items responses with Person Mean Substitution ({bf:pms}) or Two-way imputation method applied in each dimension ({bf:mi}). With PMS method, 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 values. By default, imputed values are rounded to the nearest whole number but with the {opt nor:ound} option, 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 comp:score(method)} defines the method used to compute the scores. {it:method} may be either {bf:mean} (default), {bf:sum} or {bf:stand}(set scores from 0 to 100). {opt comp:score(method)} is ignored
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if the {opt scores(varlist)} option is used.
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{phang}{opt desc:items} 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
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obtained by omitting each item in each 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 descriptive analyses. It provides histograms of scores, a biplot of the scores and a graph showing the correlations between the items.
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{phang}{opt cfa} performs a confirmatory factor analysis using {help sem} command. It displays estimations of parameters 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 (CFA). {opt cfam:ethod}({it:method}) specifies the method to estimate the 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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The {opt cfac:ovs} option allows adding covariances between measurement errors. You can look at the examples to see the syntax of this option. The {opt cfaa:uto} option adds automatically the covariances
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of measurement errors found with the {help estat mindices} command. The option only adds the covariances of measurement errors within a dimension.
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{phang}{opt conv:div} assesses convergent and divergent validities. The option displays the matrix of correlations between items and rest-scores. If {opt scores(varlist)} is used, then the correlations coefficients are computed between
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items and scores of {opt scores(varlist)}.
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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 displayed
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in red if it is less than #. Moreover, if an item has a smaller correlation coefficient with the score of its own dimension than the correlation coefficient computed with other scores, this coefficient is displayed
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in red. The {opt convdivb:oxplots} option displays boxplots for assessing convergent and divergent validities. The boxes represent the correlation coefficients between the items of a given dimension and all scores. Thus the
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box of correlation coefficients between items of a given dimension and the corresponding score must be higher than other
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boxes. There are as many boxplots 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}). Delta coefficients are computed only if {opt compscore}({it:sum}) is used
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and {opt scores}({it:varlist)} is not used. # 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 minimal 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 minimal 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 defining in {it:varlist} the variables corresponding to responses at time 2 (in the same order than for time 1). Scores are computed according to
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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(#)} option computes
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confidence intervals for
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kappa statistics using {help 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 {help kapci} for more details about
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calculation of
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confidence intervals for kappa's
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coefficients. If the {opt kappa} option is absent then the {opt ickappa(#)} option is ignored. {opt scores2}({it:varlist}) allows selecting scores at time 2 from the dataset.
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{phang}{opt kgv(varlist)} assesses known-groups validity according to the grouping variables defined in {it:varlist}. The option performs an ANOVA which compares the scores between groups of individuals, constructed with variables in {it:varlist}.
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{marker kgv_options}{...}
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{phang}{it:kgv_options} allow displaying graphs for known-groups validity. The {opt kgvb:oxplots} option draws boxplots of the 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 the {opt kgvgroupboxplots} option 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 specifying options for concurrent validity. The {opt tc:onc(#)} option defines a threshold for correlation coefficients between the computed scores and the scores of other scales defined in {it:varlist}. Correlation
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coefficients greater than # (0.4 by default) are displayed in bold.
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{marker examples}{...}
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{title:Examples}
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{phang2}{cmd:. validscale item1-item20, part(5 4 6 5)}{p_end}
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{phang2}{cmd:. validscale item1-item20, part(5 4 6 5) imp graphs cfa cfastand cfacovs(item1*item3 item5*item7 item17*item18) convdiv convdivboxplots kgv(factor_variable) kgvboxplots conc(scoreA-scoreD)}{p_end}
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{phang2}{cmd:. validscale item1-item20, part(5 4 6 5) imp scores(s1-s4) rep(item1bis-item20bis) scores2(s1bis-s4bis) 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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