Dataset Overview
Europass Survey
June - September 2019
Description
- Name: education_fields_indicator
- Number of observations: 1,180,896
- Number of features: 14
- Description: This dataset brings insights on the qualifications’ education fields appearing in collected CVs from the Europass CV online editor. More than 300K qualifications were observed among 350K respondents. The analysis provides information on the most reported education fields across respondents based on the established international classifications of ISCED FoET.
- Sources: Europass Survey: June - September 2019
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| Feature | Name | Description | Form1 | Stat2 | Text3 | |
|---|---|---|---|---|---|---|
| education_field | Education Field | ISCED FoET classification of a free-text qualification response. | ● |
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| eqf_granted | EQF Granted | EQF classification of a free-text qualification response. | ● | ● |
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| institution_country | Institution Country | Country of instutution where qualification was granted. | ● |
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| study_years | Study Years | Years of studying on a specific qualification. | ● |
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| enrollment_year | Enrollment Year | Year of enrollment. | ● |
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| graduation_year | Graduation Year | Year of graduation. | ● |
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| locale | CV Language | Language used to write CV. | ● |
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| country | Country | Country of residence. | ● |
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| age_group1 | Broad Age Group | Broad age groups with three categories. | ● |
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| age_group2 | Narrow Age Group | Narrow age groups with five categories. | ● |
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| gender | Gender | Female, male or missing value. | ● |
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| nationality | Nationality | Nationality. | ● |
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| mother_tongue | Mother Tongue | Native language. | ● |
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| responses | Responses | Number of responses with a particular combination of values for the above variables. | ● |
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| 1 Variable derives directly from a Europass CV; | ||||||
| 2 Variable is a statistical transformation of one or more Europass CV variables; | ||||||
| 3 Variable is a result of information retrieval using text mining |
Summary Statistics
education_field
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 161774 ( 13.70% ) |
| Number of unique values | 117 |
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* Missing features required for estimation, or unidentifi ed from classification algorithm. |
eqf_granted
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 506190 ( 42.86% ) |
| Number of unique values | 6 |
|
* Missing features required for estimation, or unidentifi ed from classification algorithm. |
institution_country
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 265799 ( 22.51% ) |
| Number of unique values | 186 |
| * Education.Organisation…Country.Code missing. |
study_years
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing values* | 109689 ( 9.29% ) |
| Number of unique values | 16 |
| Min. | 1 |
| 1st Qu. | 1 |
| Median | 2 |
| Mean | 3.11 |
| 3rd Qu. | 4 |
| Max. | 15 |
| NA’s | 109689 |
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* Education.Period.From.Year missing, or value less than 0 years, or value more than 15 years. |
enrollment_year
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 88051 ( 7.46% ) |
| Number of unique values | 76 |
| * Education.Period.From.Year missing. |
graduation_year
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 274806 ( 23.27% ) |
| Number of unique values | 72 |
| * Education.Period.To.Year missing. |
locale
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 29 |
| * SkillsPassport.Locale missing. |
country
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 68085 ( 5.77% ) |
| Number of unique values | 182 |
| * SkillsPassport…Country.Code missing. |
age_group1
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 399683 ( 33.85% ) |
| Number of unique values | 4 |
|
* SkillsPassport…Birthdate.Year missing, or over 65 yea rs old. |
age_group2
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 398438 ( 33.74% ) |
| Number of unique values | 6 |
| * SkillsPassport…Birthdate.Year missing. |
gender
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 514292 ( 43.55% ) |
| Number of unique values | 3 |
| * SkillsPassport…Gender.Code missing. |
nationality
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 557949 ( 47.25% ) |
| Number of unique values | 164 |
| * SkillsPassport…Nationality.Code missing. |
mother_tongue
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 248946 ( 21.08% ) |
| Number of unique values | 63 |
| * Skills.Linguistic.MotherTongue.Description missing. |
responses
| Feature | Result |
|---|---|
| Variable type | integer |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 199 |
| Min. | 1 |
| 1st Qu. | 1 |
| Median | 1 |
| Mean | 1.5 |
| 3rd Qu. | 1 |
| Max. | 926 |
| * No missing values permitted. |
