Related Experiment Video
Updated: Oct 6, 2025
08:33
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
Published on: September 4, 2019
7.2K
Outlier analysis: Natural resources and immigration policy
1Department of Political Science, University of Illinois, Chicago, Illinois, United States of America.
Plos One
|January 13, 2022
Summary
Replication analysis reveals that Shin's findings on resource wealth and immigration policy are statistical artifacts. Removing influential outliers, like Norway, negates the observed relationship, highlighting the importance of outlier diagnostics in regression analysis.
Area of Science:
- Political Science
- Economics
- Statistics
Background:
- Researchers have often overlooked influential observations in Ordinary Least Squares (OLS) regression analysis.
- Shin's article, "Primary Resources, Secondary Labor," posits a link between natural resource wealth and restrictive low-skill immigration policies in advanced democracies.
Purpose of the Study:
- To replicate Shin's findings on natural resource wealth and immigration policy.
- To investigate the impact of influential outliers on the relationship between oil and gas production and low-skill immigration policy.
Main Methods:
- Outlier diagnostics were performed on the original dataset.
- Robust regression analysis was employed to address potential outlier issues.
- The analysis involved excluding specific countries identified as outliers.
Main Results:
- Shin's findings are demonstrated to be a statistical artifact.
- Excluding Norway, an influential outlier, significantly weakens the negative relationship between oil wealth and immigration policy.
- Excluding two outliers completely eliminates the observed effect of oil wealth on immigration policy.
Conclusions:
- The relationship between natural resource wealth and restrictive low-skill immigration policies is highly sensitive to outliers.
- Outlier diagnostics are crucial for accurate regression analysis, particularly in political science and economics research.
- Robust regression methods confirm that the original findings were likely driven by a few influential observations.
Related Concept Videos
01:08Outliers and Influential Points
4.7K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.7K
01:12What Are Outliers?
4.4K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.4K
01:02Quantifying and Rejecting Outliers: The Grubbs Test
2.6K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.6K
01:00Detection of Gross Error: The Q Test
6.5K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.5K
01:16Unusual Results
3.4K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
3.4K
00:57Modified Boxplots
10.5K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
10.5K