Developing a self-esteem measure requires a structured approach to ensure the instrument is valid and reliable. The process involves multiple steps, such as (1) item development, (2) pilot testing, (3) reliability and validity testing, and (4) cross-validation (Elangovan & Sundaravel, 2021; Sullivan, 2011).

Item Development

The first step involves creating a list of items that capture multiple aspects of self-esteem; this can be done through a literature review of existing self-esteem measures, qualitative methods (e.g., interviews), and consultation with experts (Boateng et al., 2018; Clark & Watson, 2019). Items should also be concise, clear, and free from biases; we should ensure that the response format (e.g., Likert scale) aligns with the measured construct (Boateng et al., 2018; Clark & Watson, 2019). After drafting the initial items, experts can review them to ensure content validity (Boateng et al., 2018; Clark & Watson, 2019).

Pilot Testing

Once an initial set of items is generated, a small-scale pilot study should be conducted with a sample representative of the targeted population (In, 2017). The goal is to identify problematic items by analyzing response patterns, item difficulty, and participant feedback (Knekta et al., 2019). Statistical techniques such as exploratory factor analysis (EFA) can determine which items contribute to a coherent factor structure (Knekta et al., 2019). Items that show weak factor loadings, low discriminations, and redundancy can be reviewed or removed to improve the measure (Knekta et al., 2019).

Reliability and Validity Testing

After refining the measure, a more extensive study should examine reliability and validity. Reliability can be assessed through internal consistency (Cronbach's alpha) and test-retest reliability over time (Matheson, 2019). Validity testing could include construct, criterion, convergent, and discriminant validity (Clark & Watson, 2019). The construct consists of testing the theoretical structure, and the criterion examines correlations with external outcomes related to self-esteem (Clark & Watson, 2019). Also, convergent validity compares scores with established self-esteem measures, and discriminant validity ensures the measure is distinct from unrelated constructs (Clark & Watson, 2019).

Cross-Validation

            The final step involves testing the measure with a new sample to confirm its validity and reliability in various populations; this ensures that the measure generalizes beyond the initial study sample (Boateng et al., 2018). Cross-validation can involve conducting additional factor analyses, comparing different demographic groups, and evaluating predictive validity (Boateng et al., 2018). If inconsistencies emerge, further revisions may be needed before finalizing the measure (Boateng et al., 2018).

References

Boateng, G. O., Neilands, T. B., Frongillo, E. A., R., H., & Young, S. L. (2018). Best practices for developing and validating health, social, and behavioral research scales: A primer. Frontiers in Public Health, 6, 366616. https://doi.org/10.3389/fpubh.2018.00149

Clark, L. A., & Watson, D. (2019). Constructing validity: New developments in creating objective measuring instruments. Psychological Assessment, 31(12), 1412–1427. https://doi.org/10.1037/pas0000626

Elangovan, N., & Sundaravel, E. (2021). Method of preparing a document for survey instrument validation by experts. MethodsX, 8, 101326. http