Binary outcomes over time

[Grace Heymsfield]

I have a binary outcome (yes/no= 1/0) over 10 survey rounds of data; I'd like to display trends in proportion of a 'yes' outcome over time ... variables = COVID_knowledge_yn (binary) and surveyround (numeric, 1-10) ... would this be a Kruskal Wallis test? Advice on how to display this graphically? I'm striking out with a simple scatter plot.

[Jeff Wooldridge]

Grace: You have 10 outcomes for each individual? How many individuals?

[Jeff Wooldridge]

Or you have a different sample of individuals each time?

[Grace Heymsfield]

I have a different sample of individuals each time, but from the same market place (same primary sampling unit)

[Jeff Wooldridge]

You have data from one market? Then that’s your population, not PSU. The market would be the PSU if you first sample many markets and then sampled within each market.

[Jeff Wooldridge]

I would just run a regression of your outcome on the wave dummies and look at the pattern.

knowledge i.wave, vce(robust)

[Grace Heymsfield]

I have data from two markets, where we randomly sample individuals, and we revisit the same two markets every week

[Jeff Wooldridge]

Is there any reason to treat the markets separately? If not, do what I said and look at the F test.

[Grace Heymsfield]

No, I've tested assumptions that their results don't significantly vary. Do you have advice on how to display this graphically (change in proportions over time)? I'm interested in a line graph fitted model with x axis represented Round 1- XX, but I'm having a tough time getting proportion of responses to display.

[Joseph Coveney]

Do you have advice on how to display this graphically (change in proportions over time)?

You could try something like the following. (Begin at the "Begin here" comment.)

.ÿ
.ÿversionÿ16.1

.ÿ
.ÿclearÿ*

.ÿ
.ÿsetÿseedÿ`=strreverse("1588054")'

.ÿ
.ÿquietlyÿsetÿobsÿ10000

.ÿgenerateÿbyteÿCOVID_knowledge_ynÿ=ÿruniform()ÿ<ÿ0.5

.ÿgenerateÿbyteÿsurveyroundÿ=ÿmod(_n,ÿ10)ÿ+ÿ1

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.ÿ*
.ÿ*ÿBeginÿhere
.ÿ*
.ÿglmÿCOVID_knowledge_ynÿi.surveyround,ÿfamily(binomial)ÿlink(logit)ÿnolog

GeneralizedÿlinearÿmodelsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿNumberÿofÿobsÿÿÿ=ÿÿÿÿÿ10,000
Optimizationÿÿÿÿÿ:ÿMLÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿResidualÿdfÿÿÿÿÿ=ÿÿÿÿÿÿ9,990
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿScaleÿparameterÿ=ÿÿÿÿÿÿÿÿÿÿ1
Devianceÿÿÿÿÿÿÿÿÿ=ÿÿÿ13857.1625ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ(1/df)ÿDevianceÿ=ÿÿÿ1.387103
Pearsonÿÿÿÿÿÿÿÿÿÿ=ÿÿÿÿÿÿÿÿ10000ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ(1/df)ÿPearsonÿÿ=ÿÿÿ1.001001

Varianceÿfunction:ÿV(u)ÿ=ÿu*(1-u)ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ[Bernoulli]
Linkÿfunctionÿÿÿÿ:ÿg(u)ÿ=ÿln(u/(1-u))ÿÿÿÿÿÿÿÿÿÿÿÿÿ[Logit]

ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿAICÿÿÿÿÿÿÿÿÿÿÿÿÿ=ÿÿÿ1.387716
Logÿlikelihoodÿÿÿ=ÿ-6928.581252ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿBICÿÿÿÿÿÿÿÿÿÿÿÿÿ=ÿÿ-78154.14

------------------------------------------------------------------------------------
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COVID_knowledge_ynÿ|ÿÿÿÿÿÿCoef.ÿÿÿStd.ÿErr.ÿÿÿÿÿÿzÿÿÿÿP>|z|ÿÿÿÿÿ[95%ÿConf.ÿInterval]
-------------------+----------------------------------------------------------------
ÿÿÿÿÿÿÿsurveyroundÿ|
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ2ÿÿ|ÿÿ-.1040272ÿÿÿ.0894746ÿÿÿÿ-1.16ÿÿÿ0.245ÿÿÿÿ-.2793942ÿÿÿÿ.0713398
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ3ÿÿ|ÿÿ-.0120035ÿÿÿÿ.089456ÿÿÿÿ-0.13ÿÿÿ0.893ÿÿÿÿ-.1873341ÿÿÿÿ.1633271
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ4ÿÿ|ÿÿ-.0400053ÿÿÿ.0894517ÿÿÿÿ-0.45ÿÿÿ0.655ÿÿÿÿ-.2153274ÿÿÿÿ.1353167
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ5ÿÿ|ÿÿ-.0360053ÿÿÿ.0894518ÿÿÿÿ-0.40ÿÿÿ0.687ÿÿÿÿ-.2113275ÿÿÿÿ.1393169
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ6ÿÿ|ÿÿÿ.0120064ÿÿÿ.0894668ÿÿÿÿÿ0.13ÿÿÿ0.893ÿÿÿÿ-.1633453ÿÿÿÿ.1873581
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ7ÿÿ|ÿÿÿ.0240165ÿÿÿ.0894746ÿÿÿÿÿ0.27ÿÿÿ0.788ÿÿÿÿ-.1513504ÿÿÿÿ.1993835
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ8ÿÿ|ÿÿ-.1280622ÿÿÿÿ.089495ÿÿÿÿ-1.43ÿÿÿ0.152ÿÿÿÿ-.3034692ÿÿÿÿ.0473448
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ9ÿÿ|ÿÿ-.0120035ÿÿÿÿ.089456ÿÿÿÿ-0.13ÿÿÿ0.893ÿÿÿÿ-.1873341ÿÿÿÿ.1633271
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ10ÿÿ|ÿÿ-.0040014ÿÿÿ.0894589ÿÿÿÿ-0.04ÿÿÿ0.964ÿÿÿÿ-.1793377ÿÿÿÿ.1713348
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------------------------------------------------------------------------------------

.ÿtestparmÿi.surveyround

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ÿ(ÿ2)ÿÿ[COVID_knowledge_yn]3.surveyroundÿ=ÿ0
ÿ(ÿ3)ÿÿ[COVID_knowledge_yn]4.surveyroundÿ=ÿ0
ÿ(ÿ4)ÿÿ[COVID_knowledge_yn]5.surveyroundÿ=ÿ0
ÿ(ÿ5)ÿÿ[COVID_knowledge_yn]6.surveyroundÿ=ÿ0
ÿ(ÿ6)ÿÿ[COVID_knowledge_yn]7.surveyroundÿ=ÿ0
ÿ(ÿ7)ÿÿ[COVID_knowledge_yn]8.surveyroundÿ=ÿ0
ÿ(ÿ8)ÿÿ[COVID_knowledge_yn]9.surveyroundÿ=ÿ0
ÿ(ÿ9)ÿÿ[COVID_knowledge_yn]10.surveyroundÿ=ÿ0

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.

[Jeff Wooldridge]

Just to add to Joseph's helpful reply: You will get exactly the same fitted values as a linear regression. All estimates are simply the cell proportions of "Yes" by year. In a saturated model, all predicted values are the same: linear, logit, probit, and so on.

[Hafi Saad]

Hi there,

Merry Christmas and Happy New Year

I am new to STATA and I have population based prevalence rate (with 95% CI) for a disease for each year for 10 years (2005-2014) , what syntax in Stata I can use to assess whether there is a trend or not? Is it possible to make a graph for this trend with 95% CI?

Best regards