Interests
| Purpose: | Provide general occupational interest (RIASEC) high-point codes and numeric profile data for each O*NET-SOC occupation. |
Structure and Description:
| Column | Type | Column Content |
|---|
| onetsoc_code | Character(10) | O*NET-SOC Code (see Occupation Data) |
| element_id | Character Varying(20) | Content Model Outline Position (see Content Model Reference) |
| scale_id | Character Varying(3) | Scale ID (see Scales Reference) |
| data_value | Decimal(5,2) | Rating associated with the O*NET-SOC occupation |
| date_updated | Date | Date when data was updated |
| domain_source | Character Varying(30) | Source of the data |
This file contains the general occupational interest (RIASEC) high-point codes and numeric profile data for each O*NET-SOC occupation.
Interest ratings are presented as two scales: OI reports the RIASEC level of each interest and IH presents “high-point codes”, the numbers of the RIASEC scales for the first, second and/or third highest ratings.
The high-point values represent the following elements:
| | 0.00 = No high point available | |
| | 1.00 = Realistic | |
| | 2.00 = Investigative | |
| | 3.00 = Artistic | |
| | 4.00 = Social | |
| | 5.00 = Enterprising | |
| | 6.00 = Conventional | |
The file is displayed in six tab delimited fields with the columns named O*NET-SOC Code, Element ID, Scale ID, Data Value, Date, and Domain Source. The six fields are represented by one row. There are a total of 8,307 rows of data in this file.
For more information, see:
File Structure Changes:
| Release Number | Description of Change |
|---|
| 5.0 | Date and Source columns added |
| 5.1 - 28.1 | No structure changes |
Data Example - interests:
| onetsoc_code | element_id | scale_id | data_value | date_updated | domain_source |
|---|
| 43-4041.00 | 1.B.1.a | OI | 1.00 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.b | OI | 1.85 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.c | OI | 1.00 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.d | OI | 3.39 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.e | OI | 4.47 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.f | OI | 7.00 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.g | IH | 6.00 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.h | IH | 5.00 | 2023-11-01 | Machine Learning |
| 43-4041.00 | 1.B.1.i | IH | 4.00 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.a | OI | 6.25 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.b | OI | 4.63 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.c | OI | 1.00 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.d | OI | 3.58 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.e | OI | 1.00 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.f | OI | 4.87 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.g | IH | 1.00 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.h | IH | 6.00 | 2023-11-01 | Machine Learning |
| 29-2034.00 | 1.B.1.i | IH | 2.00 | 2023-11-01 | Machine Learning |
