Qualitative and psychometric data on self-compassion and psychology internship experience
Description
This dataset contains qualitative and psychometric data collected in a study investigating the relationship between self-compassion and the emotional experience of psychology internships. Participants were undergraduate psychology students engaged in supervised internships, who completed the Self-Compassion Scale (SCS) and answered five open-ended questions regarding their perceptions of internship-related experiences, including emotions, self-efficacy, stress, anxiety, learning, and time management. Psychometric data include total and subscale scores of the Self-Compassion Scale, allowing the classification of participants into low, medium, and high levels of self-compassion. For the qualitative component, participants’ discursive responses were analyzed using content analysis procedures, following Bardin’s framework, with thematic coding supported by NVivo software. The dataset includes anonymized raw responses, coded qualitative material, and processed quantitative variables used to define analytical groups. All data were collected online after informed consent and approval by a Brazilian Research Ethics Committee. Identifying information was removed to ensure participant confidentiality. This dataset is intended to support transparency, reproducibility, and secondary analyses related to self-compassion, emotional regulation, and professional training in psychology.
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Steps to reproduce
1. Recruit undergraduate psychology students currently engaged in supervised internships and obtain informed consent in accordance with ethical research guidelines. Collect data online using a structured questionnaire. 2. Administer the Self-Compassion Scale (SCS; Neff, 2003) along with five open-ended questions addressing participants’ emotional experiences during the psychology internship, including perceptions of self-efficacy, resilience, stress, anxiety, learning processes, and self-treatment after perceived errors. 3. Export the raw data to a statistical software package (e.g., IBM SPSS Statistics). Clean the dataset by removing incomplete or invalid responses. Reverse-score the negatively worded items of the SCS, compute the six subscales, and calculate the total self-compassion score following the original scoring guidelines. 4. Classify participants into low, medium, and high self-compassion levels based on established cut-off scores. Select participants at the extreme levels (low and high self-compassion) for qualitative comparison. 5. Export the anonymized responses to the open-ended questions into qualitative analysis software (e.g., NVivo). Conduct a content analysis following Bardin’s framework, including pre-analysis (floating reading and corpus definition), thematic coding, and iterative refinement of categories. 6. Define thematic axes based on emergent units of meaning rather than on the original questionnaire items. Code all textual segments manually according to these themes, ensuring mutual exclusivity and internal coherence. 7. Perform auxiliary analyses to support interpretation, including lexical frequency analysis for affective content, categorization of emotional valence (positive, negative, mixed, neutral), and analysis of enunciation focusing on narrative structure and expressive style. 8. Compare qualitative patterns between the low and high self-compassion groups, integrating thematic, lexical, and enunciative findings to support inferential interpretation. 9. Maintain anonymization throughout all stages and ensure that no identifying information is included in shared materials.