This document discusses various statistical methods for analyzing different data structures, including descriptive statistics, segmentation, dimension reduction, and measures of association. For each statistical method, it provides the appropriate data scale and examples applying the method to real world datasets. Statistical methods are matched to the type of research task and data structure, whether involving only variables, dependent and independent variables, or time series analysis. Pros and cons are outlined for several predictive analytic techniques.

1. Kaloyan Haralampiev
Angel Marchev 2.0

2. Relationship “Research tasks Data structure”
Research tasks Data structure
Descriptive statistics Variables only
Segmentation
Dimension reduction
Measures of associations Dependent variable(s) and independent
variable(s)Decision making under risk
Time series analysis Time as independent variable

3. Examples for different data structures
 Variables only  Dependent variable(s) and
independent variable(s)
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

4. Descriptive statistics
Scale Statistical method
Nominal Percent
Ordinal, rank or score + Cumulative frequencies
+ Cumulative percent
Interval + Central tendency (mean, median, mode)
+ Dispersion (variance, standard deviation, range)
+ Skewness
+ Kurtosis
+ Quartiles, deciles, percentiles

5. Example for nominal and ordinal scales
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

6. Example for interval scales
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

7. Segmentation
Scale Statistical method
Nominal
Binary
Ordinal
Rank
Score
Interval
TwoStep cluster
Binary
Score
Interval
+ Hierarchical cluster
+ K-means cluster
+ Multidimensional scaling

8. Clustering
8

9. Example for hierarchical cluster
Data: PISA (http://www.oecd.org/pisa/)
Software: Orange (https://orange.biolab.si/)

10. Example for K-means cluster
Data: PISA (http://www.oecd.org/pisa/)
Software: Orange (https://orange.biolab.si/)

11. Example for multidimensional scaling
Data: PISA (http://www.oecd.org/pisa/)
Software: Orange (https://orange.biolab.si/)

12. Dimension reduction
Scale Statistical method
Binary Likert scale + Cronbach’s Alpha
Factor analysisScore
Interval

13. Example for factor analysis
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

14. Measures of association and/or decision making under risk
Independent variable(s) Dependent variable(s)
Nominal
Ordinal
Interval
Nominal
Ordinal
Contingency tables
Logistic regression
Classification trees
ANOVA
Classification trees
Interval Logistic regression
Classification trees
Regression
Correlation
Classification trees
Mix of nominal and/or ordinal and/or
interval
Logistic regression
Classification trees
Classification trees

15. Example for contingency tables
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

16. Example for ANOVA
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

17. Advantages
 Quick method (relatively
short computing time)
 Coefficients easy to interpret
Disadvantages
 Uses only one possible function
 Don’t work with quantitative
target
 Ignores cases with missing
values
17
Applications
• Analysis of associations
• Classification
• Pattern recognition
• etc.

18. Example for regression and correlation
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

19. Advantages
 Quick method (relatively
short computing time)
 Coefficients easy to
interpret
 Works with different
functions
Disadvantages
 Don’t work with qualitative
target
 Ignores cases with missing
values
19
Applications
• Analysis of associations
• Pattern recognition
• etc.

20. Example for logistic regression
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

21. Advantages
 Quick method (relatively
short computing time)
 Coefficients easy to
interpret
Disadvantages
 Uses only one possible
function
 Don’t work with quantitative
target
 Ignores cases with missing
values
21
Applications
• Analysis of associations
• Classification
• Pattern recognition
• etc.

22. Example for classification trees
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

23. Advantages
 Non-parametric method
which fits data as they are
 Describes in detail the
positive and the negative
targets
 Works with qualitative and
quantitative targets
 Works with missing values
Disadvantages
 Slow method (relatively
large computing time)
 Generates very large trees
and pruning is needed
23
Applications
• Analysis of associations
• Classification
• Pattern recognition
• etc.

24. Another example for classification trees
Data: European social survey (ESS), Round 6, 2016, Bulgaria (http://www.europeansocialsurvey.org/)
Software: Orange (https://orange.biolab.si/)

25. Advantages
 Non-parametric method
which fits data as they are
 Describes in detail the
positive and the negative
targets
 Works with qualitative and
quantitative targets
 Works with missing values
Disadvantages
 Slow method (relatively large
computing time)
25
Applications
• Analysis of associations
• Classification
• Pattern recognition
• etc.