The relationship between self-regulation, cognitive flexibility, and resilience among students: a structural equation modeling | BMC Psychology

The relationship between self-regulation, cognitive flexibility, and resilience among students: a structural equation modeling | BMC Psychology

Design and participants

This was a cross-sectional study carried out on samples of university students assessing the relationship between self-regulation, cognitive flexibility, and resilience. A total of 302 students participated in the study (146 men and 156 women). The mean age of students was 25.8 (SD = 4.05) years ranging from 18 to 35. Of these, 32.1% of the participants were undergraduate, the remaining students were postgraduate students (55.3% master, and 12.6% Ph.D. students). The characteristics of students are shown in Table 1.

Table 1 Distribution of Samples (n = 302)

Sampling and sample size

The study employed a convenient sampling method. The statistical population of the study included all university students from two metropolitans (Tehran, and Karaj), Iran during the academic year 2022–2023. To estimate the sample size, we followed the recommendation by Hair et al., which suggested a minimum of 200 individuals for conducting a structural equation modeling [22]. Due to time constrain and difficulty in traveling to collect data from several universities, we decided to collect data online. As such we invited the students via Telegram application targeting students’ groups. The message included a link to an Iranian platform (Porsline) where the students could sign the consent form and access the study questionnaires. The inclusion criteria consisted of the following conditions: (1) signing a written informed consent form, (2) being a student in the current semester of 2022–2023, and (3) aged 18 to 35 years.

Data collection

Participants were initially briefed about the study’s objectives. They retained the option to withdraw from the study at any point. The entire study, including data collection, adhered to the ethical standards established by our research committee. No financial incentives were offered to the participants for their involvement. They then proceeded to complete the online questionnaires. The study measures are described in the following section.


In addition to a demographic questionnaire collecting information on participants’ age, gender, and education, the following questionnaires were administered:

Connor–Davidson Resilience Scale (CD-RISC): The CD-RISC is a 25-item questionnaire that assesses the individual’s ability to cope with stress and adversity. Items are rated on a 5-point Likert scale ranging from 0 (not true at all) to 4 (‘true nearly all the time). According to exploratory factor analysis, the CD-RISC is a multidimensional instrument measuring five factors as follows: personal competence/tenacity, positive acceptance of change/secure relationships, trust in one’s instincts/tolerance of negative affect, spirituality, and control. The Preliminary research on the CD-RISC’s psychometric properties in the general population and clinical samples revealed sufficient internal consistency, convergent and divergent validity, and test-retest reliability [23]. psychometric properties of the Iranian version of CD-RISC are well documented. As such the internal consistency of the questionnaire as measured by Cronbach’s alpha was reported to be 0.89 [24]. The current study also obtained an alpha value of 0.91, which is well above the acceptable threshold.

Cognitive Flexibility Inventory (CFI): The CFI is a 20-item self-report questionnaire developed for aspects of cognitive flexibility that enable people to challenge and replace maladaptive thoughts with more adaptive ones. Items are rated on a 7-point Likert-type scale to define the respondent’s approach to challenging situations accurately. The CFI assesses three factors as follows: Alternatives, Control, and Alternatives to human behavior [25]. . Dennis and Vander Wall reported that CFI had good to excellent internal consistency, and test-retest reliability was high for the CFI and its subscales. The Iranian version of the CFI also showed desirable reliability and validity. The results obtained from factor analysis indicated three factors (Control, Alternatives, and Alternatives for Human Behaviors) that jointly explained 56.02% of the variance observed. The test-retest and Cronbach’s alpha coefficients for the Iranian version of CFI were 0.71 and 0.90, respectively [26]. In this study, the alpha coefficient for the CFI was 0.90.

Buford’s Self-Regulation Questionnaire: The 14-item self-regulation questionnaire was developed by Buford et al. was validated in Iran among a sample of university students standardized by Kadivar [27, 28]. The reliability coefficient of the questionnaire based on Cronbach’s alpha was calculated to be 0.71. The validities of the sub-scales of cognitive and metacognitive strategies were 0.70 and 0.68, respectively. Regarding the structure, the factor results showed that the correlation coefficient of the questions was acceptable, and the evaluation tool consisted of two factors. The value of the factors was acceptable, and the tool could determine 0.52 of the self-report variances. The structural validity was satisfactory. There were five possible answers for each question: “I totally agree,” “I agree,” “I’m not sure,” “I disagree,” and “I totally disagree.” Each question was scored from 1 to 5, except for questions 5, 13, and 14, which were scored in the reverse [28, 29].

Statistical analysis

Descriptive statistics were used to explore the data. To achieve the study objective we first assessed the correlation among self-regulation, cognitive flexibility and resilience. Then to examine the association between self-regulation and resilience with mediating variable (cognitive flexibility) structural equation modeling (SEM) was performed. In fact, we were interested to see to what extent cognitive flexibility could explain variance in self-regulation and resilience. The analysis served to assess the degree of alignment between the theoretical-causal model and the empirical data. The data were analysed using SPSS-27 and AMOS software.

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