{smcl}
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help for {hi:sheafcoef}
{hline}
{title:Title}
{p2colset 5 18 20 2}{...}
{p2col :{hi: sheafcoef} {hline 2}}Post-estimation command that displays
sheaf coefficients.{p_end}
{p2colreset}{...}
{title:Syntax}
{p 8 15 2}
{cmd:sheafcoef} {cmd:,}
{opt lat:ent(latend_spec)}
[
{cmdab:eq:uation(#}{it:#} | {it:name}{cmd:)}
{opt eform}
{opt beta}
{opt post}
{opt level(#)}
]
{title:Description}
{pstd}
{cmd:sheafcoef} is a post-estimation command that estimates sheaf coefficients
(Heise 1972). A sheaf coefficient assumes that a block of variables influence
the dependent variable through a latent variable. {cmd:shearcoef} displays
the effect of the latent variable and the effect of the observed variables on
the latent variable. The scale of the latent variable is identified by setting
the standard deviation equal to one. The origin of the latent variable is
identified by setting it to zero when all observed variables in its block are
equal to zero. This means that the mean of the latent variable is not
(necesarily) equal to zero. The final identifying assumption is that the effect
of the latent variable is always positive, so to give a substantive interpretation
of the direction of the effect, one needs to look at the effects of the observed
variables on the latent variable. Alternatively, one can specify one "key" variable
in each bloack of variables, which identifies the direction of a latent variable,
either by spedifying that the latent variable has a high value when the key variable
has a high value or that the latent variable has a low value when the key variable
has a high value.
{pstd}
The assumption that the effect of a block of variables occurs through a latent
variable is not a testable constraint; it is just a different way of presenting
the results from the original model. Its main usefulness is in comparing the
relative strength of the influence of several blocks of variables. For example,
say we want to know what determines the probability of working non-standard
hours and we have a block of variables representing characteristics of the job
and another block of variables representing the family situation of the
respondent, and we want to say something about the relative importance of job
characteristics versus family situation. In that case one could estimate a
{cmd:logit} model with both blocks of variables and optionally some other
control variables. After that one can use {cmd:sheafcoef} to display the
effects of two latent variables, family background and job characteristics,
which are both standardized to have a standard deviation of 1, and can thus be
more easily compared.
{pstd}
The output is divided into a number of equations. The top equation, labeled
"main", represents the effects of the latent variables and other control
variables (if any) on the dependent variable. The names of the latent variables
are as specified in the {cmd:latent()} option. If no names are specified, they
will be called "lvar1", "lvar2", etc. Below the main equation, one additional
equation for every latent variable is displayed, labelled "on_name1",
"on_name2", etc., where "name1" and "name2" are the names of the latent
variables. These are the effects of the observed variables on the latent variable.
{pstd}
The sheaf coeficients and the variance covariance matrix of all the coefficients
are estimated using {helpb nlcom}. {cmd:sheafcoef} can be used after any regular
estimation command (that is, a command that leaves its results behind in e(b)
and e(V)), The only constraint is that the observed variables that make up the
latent variable(s) must all come from the same equation.
{title:Options}
{phang}
{opt lat:ent(latent_spec)} specifies the blocks of variables that make up the
latent variabls. In summary, the syntax for {it:latent_spec} is:
{pmore}
[{it:name1}:]{it:varlist_1 }[{it:{help if}}]
[; [{it:name2}:]{it:varlist_2 }[{it:{help if}}]
[; [{it:name3}:]{it:varlist_3 }[{it:{help if}}][...]]]
{pmore}
It consists of blocks each such that each block is seperated by a semicolon (;).
Each block needs to constist of at least two variables. These variables must be
explanatory variables in the estimation command preceding {cmd:sheafcoef}, and
the same variable can only appear in one block. Optionally, each block of
variables can be preceded by a name for the latent variable followed by a colon
(:).
{pmore}
Moreover, one can identify one key variable in each block of variables, by
attaching a {cmd:+} or {cmd:-} (without a space) to a variable in a block. If one
of the observed variables has {cmd:+} attached to it, then the latent variable
will have a high value when that observed variable is high and a low value
when that observed variable is low. The opposite is true when one of the observed
variables has a {cmd:-} attached to it. If no observed variable in a block has a
{cmd:+} or a {cmd:-} attached to it, than the direction of that latent variable
is identified such that it's effect on the dependent variable is positive.
{pmore}
Finaly the {it:if} conditions determine which observations are used to identify the
scale of the latent variable. This can be useful when comparing effects across
groups. Consider the following example:
{cmd}
sysuse nlsw88, clear
gen ln_w = ln(wage)
drop if race == 3
gen byte black = race == 2
gen byte white = race == 1
gen blackXmarried = black*married
gen blackXnever_married = black*never_married
gen whiteXmarried = white*married
gen whiteXnever_married = white*never_married
reg ln_w black* white*, nocons
{txt}
{p 8 8 2}({stata "sheafcoef_ex 4":click to run}){p_end}
{pmore}
In this example we look at the effect of marital status on income for black and white
women. The interaction effect with {it:black} are the effects of the marital status
variables in the black sample, while the interaction effects with {it:white} are the
effects of the marital status variables in the white sample. To use {cmd:sheafcoef}
in this case we need to make sure that we identifiy the scale of the latent martial
status variable for the black sample using only the black respondents and identify
the scale of the latent variable for the white sample using only the white respondents.
This is what the {it:if} conditions are for:
{cmd}
sheafcoef, latent(black_marst: blackXmarried blackXnever_married if black ; ///
white_marst: whiteXmarried whiteXnever_married if white )
{txt}
{p 8 8 2}({stata "sheafcoef_ex 5":click to run}){p_end}
{phang}
{cmdab:eq:uation(#}{it:#} | {it:name}{cmd:)} specifies the equation from the
previous estimation command to be used when computing the sheaf coefficients.
This option is relevant when using {cmd:sheafcoef} after commands like {help mlogit}
or {help heckman} that return results in multiple equations. One can either
specify whether {cmd:sheafcoef} should consider the first, second, etc. equation
or one can type in the name of that equation. In the former case the number of the
equation should be preceded by a {cmd:#}.
{phang}
{opt eform} specifies that the effects of the latent variable and the control
variables are exponentiated. The effects of the observed variables in each
block on its latent variable are not exponentiated, because these represent
the effects of these variables on the standardized latent variable and not on
the dependent variable.
{phang}
{opt beta} asks that standardized beta coefficients be reported. The beta
coefficients are the regression coefficients obtained by first standardizing all
variables to have a mean of 0 and a standard deviation of 1. When specifying
the {cmd:beta} option after {help logit}, {help logistic} or {help probit} the
latent dependent variable (often refered to as y*) is standardized. {cmd:beta}
may only be specified after {help regress}, {help logit}, {help logistic}, or
{help probit}, and may not be specified when the previous estimation command
used clustered standard errors or the {cmd:svy} prefix.
{phang}
{opt post} causes {cmd:sheafcoef} to behave like a Stata estimation ({cmd:eclass})
command. When {opt post} is specified, {cmd:sheafcoef} will post the vector of
transformed estimators and its estimated variance-covariance matrix to
{cmd:e()}. This option, in essence, makes the transformation permanent. Thus
you could, after {opt post}ing, treat the transformed estimation results in
the same way as you would treat results from other Stata estimation commands.
For example, after posting, you could use {helpb test} to perform simultaneous
tests of hypotheses on linear combinations of the transformed estimators.
{pmore}
Specifying {opt post} clears out the previous estimation results,
which can be recovered only by refitting the original model or by storing the
estimation results before running {cmd:nlcom} and then restoring them; see
{manhelp estimates_store R:estimates store}.
{phang}
{opt level(#)} specifies the confidence level, as a percentage,
for confidence intervals. The default is {cmd:level(95)} or as set by
{helpb set level}.
{title:Examples}
{cmd}
sysuse nlsw88, clear
gen byte lower = inlist(occupation, 9, 10, 11, 12, 13) if occupation < .
gen byte middle = inlist(occupation, 3, 4, 5, 6, 7, 8) if occupation < .
glm wage lower middle married never_married union grade, link(log)
sheafcoef, latent(class: lower middle; ///
marital: married never_married) ///
post
test [main]_b[class]=[main]_b[marital]
{txt}
{p 4 4 2}({stata "sheafcoef_ex 1":click to run}){p_end}
{pstd}
Notice that in the example above the scale of class, as shown in the equation "on_class", runs from
lower (large negative value) to higher (0) indicating that the effect of class
is possitive: women with higher occupations receive more income.
{cmd}
sysuse nlsw88, clear
gen byte lower = inlist(occupation, 9, 10, 11, 12, 13) if occupation < .
gen byte middle = inlist(occupation, 3, 4, 5, 6, 7, 8) if occupation < .
logit union middle lower married never_married
sheafcoef, latent(class: lower middle; ///
marital: married never_married)
sheafcoef, latent(class: lower middle; ///
marital: married never_married) ///
eform
{txt}
{p 4 4 2}({stata "sheafcoef_ex 2":click to run}){p_end}
{pstd}
Notice that in the example above the scale of class, as shown in the equation "on_class", runs from
higher (0) to lower (large positive value) indicating that the effect of class
is negative: women with higher occupations are less likely to be a union member.
{cmd}
sysuse nlsw88, clear
gen byte lower = inlist(occupation, 9, 10, 11, 12, 13) if occupation < .
gen byte middle = inlist(occupation, 3, 4, 5, 6, 7, 8) if occupation < .
logit union middle lower married never_married
sheafcoef, latent(class: -lower middle; ///
marital: married never_married)
{txt}
{p 4 4 2}({stata "sheafcoef_ex 3":click to run}){p_end}
{pstd}
Notice that in the example above, by attaching a {cmd:-} to {it:lower}, we are saying that we want
the latent variable to have a low value when the observed variable {it:lower}
has a high value and {it:vice versa}. The consequence is that we changed the
direction of latent variable class such that the scale of class runs from
lower (large negative number) to higher (zero), and the effect of class now
becomes negative.
{title:Author}
{p 4 4}
Maarten L. Buis{break}
Universitaet Tuebingen{break}
Institut fuer Soziologie{break}
maarten.buis@uni-tuebingen.de
{p_end}
{title:References}
{phang}
Heise, David R. (1972). Employing nominal variables, induced
variables, and block variables in path analysis.
{it:Sociological Methods & Research}, {cmd:1}(2): 147--173.
{title:Also see}
{psee}
Online: {helpb nlcom}
{psee}
If installed: {helpb propcnsreg}
{p_end}