Dimension correlation analysis
Masculinity vs Femininity & Long-Term vs Short-Term Orientation: How These Dimensions Relate
This page analyzes the statistical relationship between the Masculinity and Long-Term Orientation dimensions across 188 countries with complete data. The analysis is based on the framework in Cultures and Organizations: Software of the Mind (Hofstede, Hofstede, & Minkov, 2010).
Statistical snapshot
Correlation Summary
Across 188 countries with complete data on both dimensions, the Pearson correlation between Masculinity vs Femininity and Long-Term vs Short-Term Orientation is r = 0.021. This is a very weak or negligible positive correlation, and it is Not significant. In practical terms, about 0.0% of the variation in one dimension can be predicted from the other.
The two dimensions are essentially independent in the live dataset. Knowing a country's score on Masculinity tells you almost nothing about its score on Long-Term Orientation.
Book reference: Masculinity and long-term orientation are statistically independent across national cultures.
Visualizing the relationship
Scatter Plot of Country Scores
Each point represents one country. The X-axis shows its score on Masculinity and the Y-axis shows its score on Long-Term Orientation. The dashed line is the least-squares regression line.
Close fits
Countries That Best Fit the Pattern
These are the countries whose positions on both dimensions most closely match the overall regression trend. They are the clearest examples of the relationship in the dataset.
| Country | MAS | LTO | Residual |
|---|---|---|---|
| Canada | 52 | 36 | -0.17 |
| Saudi Arabia | 53 | 36 | -0.21 |
| Denmark | 30 | 35 | -0.28 |
| Norway | 15 | 35 | 0.33 |
| Poland | 64 | 38 | 1.35 |
Notable exceptions
Outliers: Countries That Break the Pattern
These are the countries whose combination of scores differs most from what the overall trend would predict. They are often the most interesting cases for research and teaching, because they challenge the assumed relationship between the two dimensions.
| Country | MAS | LTO | Residual | Interpretation |
|---|---|---|---|---|
| North Korea | 60 | 100 | 63.51 | Scores higher on Long-Term Orientation than its Masculinity score would predict. |
| Taiwan | 45 | 93 | 57.12 | Scores higher on Long-Term Orientation than its Masculinity score would predict. |
| Ukraine | 27 | 86 | 50.84 | Scores higher on Long-Term Orientation than its Masculinity score would predict. |
| China | 66 | 87 | 50.27 | Scores higher on Long-Term Orientation than its Masculinity score would predict. |
| Japan | 95 | 88 | 50.10 | Scores higher on Long-Term Orientation than its Masculinity score would predict. |
Practical implications
Practical Implications
For Cross-Cultural Training
Because Masculinity and Long-Term Orientation are largely independent, cross-cultural training must cover them separately. A country can be high on one and low on the other, so each dimension needs its own explanation.
For Research Design
If your research question treats Masculinity as a predictor of Long-Term Orientation (or vice versa), the current r of 0.021 suggests you should also consider other dimensions, because 100.0% of variance remains unexplained by this pair alone.
For Business Strategy
Managers should not assume that a country's position on Masculinity implies anything about Long-Term Orientation. Each dimension needs to be assessed independently for market entry and team design.
Full matrix
Correlation Matrix: All Six Dimensions
The matrix below shows the Pearson correlation for every pair of dimensions in the dataset. Positive values indicate that the two dimensions tend to increase together; negative values indicate that they move in opposite directions.
| — | PDI | IDV | MAS | UAI | LTO | IVR |
|---|---|---|---|---|---|---|
| PDI | 1.00 | -0.60 | -0.02 | 0.07 | -0.23 | -0.25 |
| IDV | -0.60 | 1.00 | 0.20 | 0.10 | 0.29 | 0.01 |
| MAS | -0.02 | 0.20 | 1.00 | -0.03 | 0.02 | 0.07 |
| UAI | 0.07 | 0.10 | -0.03 | 1.00 | 0.30 | -0.25 |
| LTO | -0.23 | 0.29 | 0.02 | 0.30 | 1.00 | -0.51 |
| IVR | -0.25 | 0.01 | 0.07 | -0.25 | -0.51 | 1.00 |
Reading the matrix: Values close to +1 mean the two dimensions rise together. Values close to −1 mean one rises as the other falls. Values near 0 mean the two are statistically independent. For example, PDI and IDV typically show one of the strongest negative correlations in the dataset.
Frequently asked questions
Frequently Asked Questions
What does a positive or negative correlation between two Hofstede dimensions mean?
A positive correlation means countries that score high on one dimension also tend to score high on the other. A negative correlation means the opposite: high scores on one tend to go with low scores on the other. Correlation does not prove that one dimension causes the other.
Are these two dimensions the same thing?
No. In fact, they are close to independent in this dataset. Knowing one tells you little about the other.
Why do some countries deviate from the trend?
Countries that fall far from the regression line often have unique historical, economic, or geographic circumstances that push them away from the general pattern. These outliers are important because they reveal that cultural dimensions are not mechanically linked.
Should I use correlation to predict a country's score on one dimension from another?
You can use it as a rough check, but only if the correlation is strong. Even then, the residual table shows that many countries deviate substantially. Always verify with direct measurement before drawing conclusions.
How many countries are included in this analysis?
The current analysis is based on 188 countries that have valid scores on both dimensions. Countries missing a score on either dimension are excluded so that the correlation is computed on matched data.
Where do these dimensions come from?
The dimensions are from Geert Hofstede's research, later extended by Gert Jan Hofstede and Michael Minkov. See: Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and Organizations: Software of the Mind (3rd ed.). McGraw-Hill.
Explore further
Explore Further
- Compare two countries
- About the Hofstede model
- Glossary of key terms
- Explore the world map
- Analyze cultural clusters
Explore dimension pages
- Correlations involving Power Distance
- Correlations involving Individualism
- Correlations involving Masculinity
- Correlations involving Uncertainty Avoidance
- Correlations involving Long-Term Orientation
- Correlations involving Indulgence
Browse all analysis tools
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