About mjkop56

mjkop56 · ‎03-14-2022

thank you!

mjkop56 · ‎03-14-2022

Thank you. The actual data in the table was made up, I was just trying to get feedback on the way of presenting the data. Does it make sense to show the CIs for both males and females here for each category of education? Is there anything wrong with this way of presenting the data? Is it better just to show the data for females only for example? (since you can deduce from that the data for males?)

mjkop56 · ‎03-13-2022

I'm conducting an analysis of survey data. The below tables (note - data is made up!!) shows, of each education category, the percentage of males and females. For example, of those with high school degree, 40% are male and 60% are female (40%+60% = 100%). The table also shows the standard errors and confidence intervals. The goal is really to just try to get at the reliability of the proportions. Two questions: I'm not very familiar with SEs and CIs around proportions. Does it make sense to show the SEs and CIs around the proportions of females and males each individually when the proportion of female+male = 100%? Is there anything inherently wrong with this table? Does it make sense to compare the CIs across any of these categories? I'm under the impression that one needs a statistical test to deduce statistical significance. Thanks in advance for any help. (Note - cross posted to statsexchange but no responses) Male Female Education Weighted proportion SE 95% CI Weighted proportion SE 95% CI Graduate or professional 70% 2.0% 64%-75% 30% 1.3% 25%-35% Bachelors 65% 1.0% 45%-70% 35% 1.0% 30%-40% Associate’s 55% 1.2% 50%-65% 45% 1.6% 40%-50% Some college, no degree 45% 2.0% 40%-50% 55% 1.7% 50%-60% HS graduate 40% 1.3% 30%-50% 60% 1.2% 55%-65%

mjkop56 · ‎02-05-2022

The point in including it is that providing gender needs to be optional, it can't be required. However, if there is a sizeable portion that did not want to answer, it affects the conclusions you can make.

mjkop56 · ‎02-05-2022

thanks for the explanation!

mjkop56 · ‎01-29-2022

thank you - your responses have been very helpful.

mjkop56 · ‎01-29-2022

thank you, this is helpful.

mjkop56 · ‎01-29-2022

"Prefer not to say is not missing, it's a category on it's own." => good point, as someone actually chose this option rather than skipping the question. But I'm wondering what the practical difference is between those two situations? In the case of "prefer not to answer", we don't know where they fit, so it seems to be the same thing as missing.

mjkop56 · ‎01-29-2022

thank you! I'm a bit unclear about how you came up with the extremes - could you please explain?

mjkop56 · ‎01-24-2022

I agree imputation is problematic, and who is in the PNA is unknown (I think it could go either way).

mjkop56 · ‎01-24-2022

Thank you! Hypothetical example. Here is the original data: Male Female PNA # Applied 900 1800 300 # Selected 330 570 100 Success Rate 37% 32% 33% 2 extremes: 1) all the PNA are male - success rate for male= 36%, female = 32% (note, F does not change) 2) all the PNA are female - success rates for male = 37% (M does not change), female = 32% I guess it's more that the data would be presented as a range: Male success rate ranges from 36% - 37% and Female success rate ranges from 32%-32% (Note this example is made up, real data would show more of a difference)

mjkop56 · ‎01-24-2022

Hi all, have data on the large number of people who were applying to receive an award. Everyone who applied must fill out a survey and specify their gender (although they can choose "prefer not to say"). My understanding it is NOT appropriate to put "error bars" on this data given that the survey was not a sample of the population, but rather the whole population. However, say there is missing data (some people did not report their gender) and one argues that error bars are needed to account for the missing data. Hypothetical example: Submissions are 40% female, 50% male, and 10% did not say. Or you could say that success rates for females are 25%, males 26%, unknown is 27% (so conclusion depends on who is in unknown); and one could conceivably create error bars to show what the result would be if ALL unknowns were all women v. ALL the unknowns are men. My understanding is that the error bars are used to reflect sampling error and NOT missing data. With missing data one would typically impute. Is this correct? Is there any value in making up error bars for missing data? (Note: cross posted to stack exchange but no responses)

mjkop56 · ‎11-17-2021

Thank you!!! Just so I understand, could you please explain why you would only include an interaction term and not the main effects? Also, I was planning to plot observed data v. the model's predicted probabilities. For the observed data on gender by year for everyone combined, I had planned to drop the duplicates in a given year, as the same person's gender would be counted multiple times. But if I don't drop duplicates before running the model, is that an issue when making comparisons between the observed data v. predicted?

mjkop56 · ‎11-14-2021

Hi, I posted previously (https://communities.sas.com/t5/Statistical-Procedures/longitudinal-multilevel-analysis-multiple-records-per-person/td-p/772039) and got some excellent advice about modeling a longitudinal data set. Summary: Dependent variable = gender. Independent variables = year and person’s position_department (e.g. Senior – Marketing, Principal-Marketing). The interest is in changes in gender over time. Received advice to: run a model of year, position_department, and their interaction which worked nicely. Now, I want to show predicted probabilities for: 1) ALL positions_departments combined (1 plot of gender over time), and 2) by department (vs. by position_department). Problem: there are duplicate people in a given year because the same person can be associated with more than one position_department. For example, the person below shows up in the data 4 times in a given year (therefore, their gender would be counted 4 times): person gender year Position-department department position 1 M 2017 Senior-Marketing Marketing Senior 1 M 2017 Principal-Marketing Marketing Principal 1 M 2017 Senior-Finance Finance Senior 1 M 2017 Principal-Finance Finance Principal This structure worked for my first question (analysis by position-department), however, if I just look at gender and year for all position-departments this person would be counted 4 times. Seems to be 2 options: 1) drop the duplicates (which would change the data and total N) and run 2 extra models. 1 extra model for all positions-departments combined after dropping duplicates (so data would have 1 row per person-year); and 1 extra model for analysis by department (data would have 1 row per person, year and department) 2) use the model previously estimated of year, position_department, and their interaction and output the predicted probabilities at department level and for all position-departments combined, which would give slightly different results since duplicates have not been dropped. Best option? Any thoughts would be much appreciated. thank you!

mjkop56 · ‎10-24-2021

Wonderful. thanks so much for the tips, Dave!

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Re: interpretation confidence intervals for proportions

Re: interpretation confidence intervals for proportions

interpretation confidence intervals for proportions

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Re: is appropriate to use error bars to account for missing data when ...

Re: is appropriate to use error bars to account for missing data when ...

Re: is appropriate to use error bars to account for missing data when ...

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Re: interpretation confidence intervals for proportions

Re: interpretation confidence intervals for proportions

Re: is appropriate to use error bars to account for missing data when ...

Re: is appropriate to use error bars to account for missing data when ...

Re: is appropriate to use error bars to account for missing data when ...

Re: interpretation confidence intervals for proportions

Re: interpretation confidence intervals for proportions

interpretation confidence intervals for proportions

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Re: is appropriate to use error bars to account for missing data when ...

Re: is appropriate to use error bars to account for missing data when ...

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is appropriate to use error bars to account for missing data when you ...

Re: longitudinal data - mutiple people per person/year

longitudinal data - mutiple people per person/year

Re: 95% CIs for proportions from nominal variable