Statistical models beyond linear regression @beyond_LM

The MA stats class for political science students at @PolsciCph. Copenhagent Joined January 2023

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Statistical models beyond linear regression @beyond_LM

3 years ago

We see if the mis. obs. on Euroenthusiasm (EE) are conditional on Party’s size in nat. parl. using a log-reg. in which DV takes the value 1 when mis. The results indicate a negative relationship between Partys´ size in the nat. parl. and the presence of mis. val. in the EE var.

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Statistical models beyond linear regression @beyond_LM

3 years ago

The mis. pattern shows many mis. values in Party's Euroenthusiasm var, spread across multiple values. It overlaps with Party's size in nat. parl. We conducted missing_compare anal. to assess significant difference in Party's size between cases with mis. and non-mis.

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Statistical models beyond linear regression @beyond_LM

3 years ago

A missingness plot is performed to gain an overview of the variables that contain missing data and the extent of it. The plot indicates that the missing data is distributed across relatively few variables. This suggests the need for further investigation.

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Statistical models beyond linear regression @beyond_LM

3 years ago

The missing pattern shows data gaps per variable, revealing overlaps among variables. It's used for detecting patterns and addressing missing data. It can serve as a first step to concluding Non-MCAR #PatternDetection

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Statistical models beyond linear regression @beyond_LM

3 years ago

3a + b: Here we produce a table with both the orginal model (Model 1) and the one with with imputed values from the EU-group variable (EPGroup) on the variable indicating the parties position on EU-intergration using random interpcepts.

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Statistical models beyond linear regression @beyond_LM

3 years ago

Then we run a logistic model with the mutated binary NA variable as the dependent variable and SeatsNatPal.prop as the predictor. The results show that NAs are systematically correlated with the electoral system.

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Statistical models beyond linear regression @beyond_LM

3 years ago

This is a thread for portfolio 5: missing data As we will show a complete table for exercise 3 we will jump directly to exercise 2.

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Statistical models beyond linear regression @beyond_LM

3 years ago

In the summary statistics we see that the government wins 78% of the cases, making litigant win a rare phenomenon.

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Statistical models beyond linear regression @beyond_LM

4 years ago

First, we extract the summary statistics and plot it in a table using Stargazer.

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Statistical models beyond linear regression @beyond_LM

4 years ago

This thread is about the first portfolio assignment on binominal logistic regression. The script is uploaded in Pages on Absalon. Happy coding 👩‍💻

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Statistical models beyond linear regression @beyond_LM

3 years ago

Such as age, income, demography, education level, employment status etc. This would allow us to include all available data in our analysis and obtain more accurate results.

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Statistical models beyond linear regression @beyond_LM

3 years ago

This allows for valid statistical inference and accounts for the uncertainty introduced by the imputation process. This could be basing their likely voting pattern on other variables if they have not filled out which party they would vote for.

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Statistical models beyond linear regression @beyond_LM

3 years ago

Missing data can lead to biased and inefficient results, making it crucial to handle it appropriately in statistical analysis. One common approach to handling missing data is listwise deletion, but this can lead to biased results and loss of information.