Research sample and data collection
The research sample consisted of 585 individuals from the general population, aged 18 to 50 years (M = 36.25; SD = 8.08). Of these, 370 were women and 214 were men. Participants were reached through an online invitation that contained a link to register for the study. They participated voluntarily, without financial compensation, although receiving their results upon completion may have served as motivation. Data were collected in person from January to June 2024 using computer-assisted self-interviewing (CASI) on tablets and computers. The test battery was hosted on a secure and licensed online platform, REDCap22,23. It consisted of multiple instruments and required between 40 and 90 min to complete. However, this study focused solely on data obtained from the demographic questionnaire and the Psychological Immune Competence Inventory (PICI). Although these instruments were placed at the beginning of the battery, the study also aimed to detect potential careless response style using three specific indicators (response time per item, maximum longstring index and Mahalanobis distance), as described in detail in the Statistical analysis section. After considering the results of these analyzes, no participant was excluded. Given that psychological immunity is conceptualized as a trait-like construct, the long-term stability of the instrument was assessed through a test–retest procedure. Participants were re-contacted 6 to 8 months after the initial assessment and asked to complete only this one questionnaire, hosted on the REDCap platform22,23. A total of 261 participants, including 181 women, participated in the online retest.
The study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by multiple ethics committees: (a) the Ethics Council of the Faculty of Arts, Comenius University in Bratislava (ER/15/2023); (b) Ethical Committee of the Faculty of Medicine, Comenius University and University Hospital in Bratislava, Old Town Hospital (119/2023); (c) Ethical Committee of the Regional Public Health Authority with the seat in Banská Bystrica (approved on April 19, 2024); (d) Independent Ethical Committee of the Banská Bystrica Self-Governing Region (BBSK) for Biomedical Research (approved on May 21, 2024), and (e) the Ethics Committee of L. Pasteur University Hospital in Košice (approved on June 4, 2024). All participants were informed about the aims of the study, and written informed consent was obtained from each individual prior to participation.
Measures
A demographic questionnaire along with the Psychological Immune Competence Inventory (PICI) were used to meet the research objectives.
The psychological immune competence inventory
The PICI Inventory3 is an 80-item instrument designed to assess the global level of psychological immunity, but it also provides a detailed individual profile by examining the test scores in its three subsystems and 16 factors: (a) Approach-Belief Subsystem (Positive Thinking, Sense of Control, Sense of Coherence and Sense of Self-Growth), (b) Monitoring-Creating-Executing Subsystem (Creative Self Concept, Self-Efficacy, Goal Orientation, Problem Solving Capacity, Change and Challenge Orientation, Social Monitoring Capacity, Social Mobilizing Capacity and Social Creation Capacity) and (c) Self-Regulating Subsystem (Synchronicity, Impulse Control, Emotional Control and Irritability Control). Each item is answered by a four-point Likert scale, with 1 indicating “completely does not describe me” and 4 indicating “completely describes me”. The psychometric properties of the Slovak version of the PICI have to date been examined in a pilot study18using a version translated from English3. The pilot study indicated an acceptable fit of the theoretical model to the data. However, the three factors showed insufficient reliability (Sense of Control, Sense of Self- Growth and Impulse Control). Consequently, for the purpose of the present study, we decided to revise the Slovak version by comparing it with the Hungarian translation of the instrument. An expert in psychology, fluent in Hungarian and Slovak, performed the translation. Following this, four psychology specialists convened for a panel discussion to reach consensus on the final version. Subsequently, it was back-translated into English and forwarded to the inventory author for approval. In this study, Cronbach’s alphas for the test/retest are as follows: Global level of Psychological Immunity (0.95/0.96); Approach-Belief Subsystem (0.89/0.91); Monitoring-Creating-Executing Subsystem (0.91/0.93); Self-Regulating Subsystem (0.89/0.91); Positive Thinking (0.82/0.84), Sense of Control (0.60/0.69), Sense of Coherence (0.81/0.85), Sense of Self-Growth (0.73/0.78), Creative Self Concept (0.68/0.75), Self-Efficacy (0.69/0.75), Goal Orientation (0.74/0.80), Problem Solving Capacity (0.77/0.76), Change and Challenge Orientation (0.85/0.86), Social Monitoring Capacity (0.86/0.89), Social Mobilizing Capacity (0.76/0.78), Social Creation Capacity (0.84/0.85), Synchronicity (0.78/0.82), Impulse Control (0.74/0.79), Emotional Control (0.79/0.76) and Irritability Control (0.76/0.78).
Statistical analysis
Data analysis was performed using the statistical software JASP, version 0.18.324. To detect any potential careless response style, three indicators were employed: response time per item, the maximum longstring index and Mahalanobis distance. Thresholds were established at less than two seconds per item25 and a maximum of 20 consecutive identical responses in an 80-item questionnaire26,27.
Subsequently, the descriptive statistics and Cronbach’s alphas were obtained. In order to assess the normality of the data distribution, the Shapiro-Wilk test and Q–Q plot analysis were used. Furthermore, to gain a more comprehensive understanding of descriptive statistics, an analysis of gender differences was performed for each of the PICI factors, as well as a calculation of their correlations with age.
To verify the factor structure of the Slovak version, we replicated the research design used in the pilot study18. Confirmatory factor analysis was performed using structural equation modeling (SEM) and individual items were treated as ordinal variables. For parameter estimation, the diagonally weighted least squares method (DWLS) with robust standard error correction was used. To assess the fit of the model to the research data, the ratio of the chi-square to degrees of freedom was used, along with the following fit indices: RMSEA, SRMR, CFI, TLI and PNFI. Subsequently, the standardized factor loadings and the intercorrelations between the psychological immunity factors were examined. In assessing various aspects of the construct validity for each individual factor, the average variance extracted (AVE) and composite reliability (CR) analyzes were performed. To establish convergent validity, the AVE values have to be ≥ 0.5, while the CR must be equal to 0.7 or greater28.
The invariance testing aimed to determine whether observed differences in test scores could be meaningfully interpreted as reflecting true variations in psychological immunity levels, rather than measurement bias arising from differential functioning of the instrument across groups. The psychometric equivalence of the instrument across frequently examined sociodemographic groups (gender, age, and education) was evaluated using the four-step procedure recommended by Putnick and Bornstein29. This approach involves sequentially fitting models to evaluate configural, metric, scalar, and strict invariance. The process starts with configural invariance estimation using multi-group SEM analysis, with each subsequent model imposing additional equality constraints across the groups. The metric invariance is examined by constraining the factor loadings to be equal across the groups. For scalar invariance assessment, both factor loadings and intercepts are constrained to be equal. Finally, strict invariance measurement involves constraining factor loadings, intercepts, and residuals to be equal across the groups. As a foundational approach, structural equation modeling (SEM) was utilized using the DWLS method. Historically, the focus of the invariance evaluation was on the criterion of χ2 change significance. However, due to its very high sensitivity to even small model deviations in larger samples, some researchers have shifted to alternative fit indices like ΔCFI, ΔRMSEA, or ΔSRMR30,31. To evaluate model deterioration, Chen’s30 guidelines were followed; these recommend a maximum CFI decrease of 0.01, an RMSEA increase of 0.015, and an SRMR increase of 0.03 for metric invariance. For scalar and residual invariance, the same thresholds apply, except for the SRMR index, where the guideline indicates a maximum rise of 0.01.
To determine test–retest reliability, the stability and intraclass correlation coefficients between the first and second measurements were calculated. Due to the high attrition rate (55.38%), we also focused on detecting potential systematic error by using binary logistic regression. First, a dichotomous dependent variable was created based on participation in the retest, with 0 representing droppers and 1 representing stayers. Then, to predict attrition, independent variables were added to the regression model: age, gender, academic degree and test scores of each psychological immunity factor at the first measurement.