<div dir="ltr">Yes, it does pass the design test (p-value of 0.9384607). I'm going to try the Bayes model. Any other thoughts about why this would be happening?<div><br></div><div>Thanks,</div><div><br></div><div>David</div>
</div><div class="gmail_extra"><br><br><div class="gmail_quote">On Wed, Feb 26, 2014 at 9:41 AM, Kosuke Imai <span dir="ltr"><<a href="mailto:kimai@princeton.edu" target="_blank">kimai@princeton.edu</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Does your data pass the design test we've developed? Sometimes, the skewed data leads to this type of problems. Another possibility is to use a Bayesian model, which is available through ictregBayes().</div>
<div class="gmail_extra"><br clear="all"><div><div dir="ltr"><div>Kosuke Imai</div><div>Department of Politics<br></div><div>Princeton University</div><div><a href="http://imai.princeton.edu" target="_blank">http://imai.princeton.edu</a></div>
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<br><br><div class="gmail_quote"><div><div class="h5">On Tue, Feb 25, 2014 at 6:44 PM, David Szakonyi <span dir="ltr"><<a href="mailto:ds2875@columbia.edu" target="_blank">ds2875@columbia.edu</a>></span> wrote:<br>
</div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5">
<div dir="ltr">Hello!<div><br></div><div>I've been analyzing a two treatment list experiment that has been properly randomly assigned. </div><div><br></div><div>I just ran the following regression with one simple explanatory bivariate variable (gender), but received the following error:</div>
<div><font face="courier new, monospace"><br></font></div><div><div><font face="courier new, monospace">> ml.resultsnp <- ictreg(outcome ~ male, data = true_subsetnp, treat = "treatstatnp", J=4, method = "ml")</font></div>
<div><font face="courier new, monospace"><br></font></div><div><font face="courier new, monospace">Error in solve.default(-MLEfit$hessian) : </font></div><div><font face="courier new, monospace"> system is computationally singular: reciprocal condition number = 7.02376e-18</font></div>
</div><div><br></div><div>I then added an additional covariate (logged age), and the model converges, but with exploding standard errors:</div><div><br></div><div><div><font face="courier new, monospace">Item Count Technique Regression </font></div>
<div><font face="courier new, monospace"><br></font></div><div><font face="courier new, monospace">Call: ictreg(formula = outcome ~ male + lage, data = true_subsetnp, </font></div><div><font face="courier new, monospace"> treat = "treatstatnp", J = 4, method = "ml")</font></div>
<div><font face="courier new, monospace"><br></font></div><div><font face="courier new, monospace">Sensitive item (1)</font></div><div><font face="courier new, monospace"> Est. S.E.</font></div><div><font face="courier new, monospace">(Intercept) 3.94811 4.15077</font></div>
<div><font face="courier new, monospace">male -1.43966 1.21671</font></div><div><font face="courier new, monospace">lage -1.78966 1.14337</font></div><div><font face="courier new, monospace"><br></font></div>
<div><font face="courier new, monospace">Sensitive item (2)</font></div><div><font face="courier new, monospace"> Est. S.E.</font></div><div><font face="courier new, monospace">(Intercept) -7.93707 10.31522</font></div>
<div><b><font face="courier new, monospace">male -16.06552 2965.82521</font></b></div><div><font face="courier new, monospace">lage 0.99201 2.63957</font></div><div><font face="courier new, monospace"><br>
</font></div><div><font face="courier new, monospace">Control items</font></div><div><font face="courier new, monospace"> Est. S.E.</font></div><div><font face="courier new, monospace">(Intercept) 0.21267 0.25312</font></div>
<div><font face="courier new, monospace">male 0.00017 0.05277</font></div><div><font face="courier new, monospace">lage -0.04225 0.06603</font></div><div><font face="courier new, monospace"><br></font></div>
<div><font face="courier new, monospace">Log-likelihood: -2039.366</font></div><div><font face="courier new, monospace"><br></font></div><div><font face="courier new, monospace">Number of control items J set to 4. Treatment groups were indicated by '1' and '2' and the control group by '0'.</font></div>
<div><br></div><div>A variety of other model specifications return very similar results: either failing to converge/compute or returning point estimates and very large standard errors (sometimes nearly exactly the same values for not at all correlated variables).</div>
<div><br></div><div>Does anyone have any suggestions about what might be going wrong? </div><div><br></div><div>Thanks,</div><div><br></div><div>David</div><span><font color="#888888"><div><br></div><div><br>
</div>-- <br><div dir="ltr">David Szakonyi<br>
Ph.D Candidate - Comparative Politics<br>Columbia University<br><a href="mailto:ds2875@columbia.edu" target="_blank">ds2875@columbia.edu</a><br></div></font></span></div></div>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div dir="ltr">David Szakonyi<br>Ph.D Candidate - Comparative Politics<br>Columbia University<br><a href="mailto:ds2875@columbia.edu" target="_blank">ds2875@columbia.edu</a><br>
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