Comparison of the professional interests of students and IT professionals

Description This dataset supports the analysis of RIASEC (Holland Code) vocational interests across three distinct groups in the information and communication technology (ICT) sector: first-year ICT students, upper-year ICT students (2nd-4th year) of Zhytomyr State Polytechnic University, and professional IT workers in the Zhytomyr region of Ukraine. The study investigates how vocational interests develop and change through education and professional experience in the IT field. The RIASEC model, developed by John Holland (1997), organizes vocational interests into six types: Realistic (R), Investigative (I), Artistic (A), Social (S), Enterprising (E), and Conventional (C). The study uses the 18REST-2 questionnaire (Ambiel et al., 2018; Martins et al., 2024), an 18-item instrument measuring all six RIASEC dimensions with 3 items per dimension, rated on a 4-point scale. Data Collection First-year ICT students (N=83): Online survey conducted August 28-30, 2025, at Zhytomyr Polytechnic State University Upper-year ICT students (N=67): Online survey conducted October 21 - November 6, 2025, at Zhytomyr Polytechnic State University IT professionals (N=39): Survey of successful software developers from Zhytomyr companies, conducted November 4-5, 2025 All participation was voluntary and anonymous. File Contents Data Files (CSV format) (data)ICT_students_1st_year_2025.csv (N=83) Processed survey data from first-year ICT students entering in 2025 Contains calculated psychological scales including: HEXACO personality traits: Honesty-Humility (H), Emotionality (E), eXtraversion (X), Agreeableness (A), Conscientiousness (C), Openness to Experience (O) RIASEC vocational interests: Realistic (R), Investigative (I), Artistic (A), Social (S), Enterprising (E), Conventional (C) SCL-90-R psychological symptoms: 9 subscales measuring distress dimensions (Hostility, Interpersonal Sensitivity, Somatization, Depression, Paranoid Ideation, Anxiety, General Exhaustion) Q-method educational orientations: 8 ranked positions representing different motivations for university study (Edu_Pos_1 through Edu_Pos_8) Demographic variables: age, gender, previous education level, location of previous education, specialty All raw questionnaire responses have been processed into validated scale scores (data)ICT_students_2_4_years_2025.csv (N=67) Processed survey data from 2nd through 4th year ICT students Same structure and calculated scales as first-year student data (data)IT_companies_2025.csv (N=39) Represents successful professionals selected for demonstrated competence Processed survey data from professional IT workers Same structure and calculated scales as student data Analysis Scripts (R) (script)RIASEC_samples_comparison.R Statistical comparison of RIASEC interests across three samples Performs ANOVA with effect size calculations (eta-squared) Tests for homogeneity of variance (Levene's test) Generates descriptive statistics for each group Conducts post-hoc pairwise comparisons Creates comprehensive statistical summary tables in "Outputs" sub-directory (script)RIASEC_visualization.R Creates the following visualizations: Hexagonal radar chart (Holland's hexagon) showing mean RIASEC profiles for all three groups Trajectory plots showing development patterns across groups Violin plots highlighting the dramatic Investigative interest U-curve Follows color scheme: ICT First Year (blue), ICT 2-4 Years (purple), IT Professionals (orange) Generates PNG outputs to the same directory Reference Materials (text)questionnaire.txt Complete questionnaire in Ukrainian as administered to participants, including Informed consent information Sociodemographic questions HEXACO-60 personality items (18 items, short version) Q-method educational orientation ranking task (8 statements) 18REST-2 RIASEC interest items (18 items, 3 per dimension) SCL-9-NR symptom checklist items (9 items from standard scales) Technical Notes All scale scores are pre-calculated from validated psychometric instruments. RIASEC scores use the 18REST-2 scoring algorithm (sum of 3 items per dimension). HEXACO scores represent the mean of items per facet. SCL-9-NR scores follow the updated 2025 version with modified item wording and revised response scale to reduce social desirability bias (Dembitskyi et al., 2025). Data are completely anonymous with no personally identifying information.