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98 lines
3.6 KiB
Plaintext
98 lines
3.6 KiB
Plaintext
9 months ago
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{smcl}
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{hline}
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{hline}
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help for {hi:mi_twoway}{right:Jean-François HAMEL}
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{hline}
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{title:Two-way imputations: estimating missing item responses in questionnaires for computing scores}
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{p 8 14 2}{cmd:mi_twoway} {it:varlist}, [{cmdab:sc:orename}({it:newvarname})
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{cmdab:rep:lace}
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{cmdab:add:}({it:#})
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{cmdab:st:yle}({it:keyword})
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{cmdab:clear}
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{cmdab:da}
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{cmdab:it:erate}({it:#})]
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{title:Description}
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{p 8 14 2}{cmd:mi_twoway} is an implementation of multiple imputation methods for tests and questionnaires
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based on a two-way ANOVA model of persons by items.{p_end}
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{p 14 14 2}Two methods are available: imputations based on a fixed effects two-way ANOVA, and imputations
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generated using data augmentation based on a mixed effect two-way ANOVA (with a random person effect
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assumed to follow a Norma distribution and a fixed item effect.{p_end}
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{p 14 14 2}{cmd:mi_twoway} is compatible with the Sata {help mi} procedures. The data is {help mi_set:set}
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and {help mi_impute:imputed} using {cmd:mi_twoway}, but all the estimations using multiple imputations are
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performed using the standard mi {help mi_estimate:estimate} procedures.
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{p 4 8 2}{it:varlist} is the list of the variables containing the item responses of the questionnaire
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(with possible missing data).
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{title:Options}
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{p 4 14 2}{cmd:scorename} specifies the name of the new variable containing, for each individual, the value of the
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score computed as the sum of the item responses. {it:scorename} is missing for each individual with at least one item
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missing response.
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{p 4 14 2}{cmd:replace} allows to replace individual scores in existing variables
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{p 4 14 2}{cmd:add} specifies the number of imputations to add; required with no imputations
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{p 4 14 2}{cmd:style} specifies in which style should be recorded the data: wide, mlong, flong,
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or flongsep; see {help mi_styles:[MI] styles}.
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{p 4 14 2}{cmd:clear} allows performing new imputations by remouving the previous one.
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{p 4 14 2}{cmd:da} generates imputations using data augmentation based on a mixed effect two-way
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ANOVA (with a random person effect and a fixed item effect. By default, imputations are generated
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using a fixed effects two-way ANOVA.
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{p 4 14 2}{cmd:iterate} defines the number of iterations of the data augmentation algorithm.
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By default, this number is fixed to 10.
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{marker example}{...}
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{title:Example}
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{pstd}
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Simulation of the data (using {help simirt}):
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{cmd:. simirt, nbobs(200) dim(5) group(0.5) deltagroup(0.4) clear}{right:(1) }
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{pstd}
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Creating the missing data, with a non-response rate of 10%:
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. {cmd:set more off }{right:(2) }
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. {cmd:forvalues i=1/200{c -(} }{right:(3) }
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2. {cmd:forvalues j=1/5{c -(}}{right:(4) }
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3. {cmd:replace item`j'=. if runiform()<0.1 & id==`i'}{right:(5) }
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4. {cmd:{c )-}}{right:(6) }
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5. {cmd:{c )-}}{right:(7) }
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{pstd}
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Generating 10 multiple imputations using a fixed effects two-way ANOVA:
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. {cmd:mi_twoway item*, scorename(score) add(10) style(wide)}{right:(8) }
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{pstd}
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Modeling score depending on {it:group} covariate using multiple imputations estimates ({help mi_estimate}):
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. {cmd:mi estimate: regress score i.group}{right:(9) }
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{pstd}
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Changing the way to impute data, using data augmentation based on a mixed effect two-way ANOVA:
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. {cmd:mi_twoway item*, scorename(score) replace add(10) style(wide) da clear}{right:(10) }
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{pstd}
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Changing the style of the data from {it:wide} to {it:mlong} ({help mi_convert}):
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. {cmd:mi convert mlong}{right:(11) }
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{pstd}
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Removal of the multiple imputations ({help mi_set##unset:mi_unset}):
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. {cmd:mi extract 0, clear}{right:(12) }
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