Document Type : Research Article (Quantitative)

Authors

1 PhD student, educational psychology, Garmsar Branch, Islamic Azad University, Garmsar, Iran

2 Associate Professor, Department of Psychology, Garmsar Branch, Islamic Azad University, Garmsar, Iran

3 Assistant Professor, Department of Sociology, Garmsar Branch, Islamic Azad University, Garmsar, Iran

10.22034/ijes.2023.2004198.1420

Abstract

Purpose: The present study was conducted with the aim of Network data analysis application in drawing the communication network to measure evolution in middle childhood.
Methodology: The present horoscope was a descriptive-analytical psychometric study. The socio-statistics of the present study were all students of Tehran city in elementary school (6 to 12 years old) in the academic year of 2019-2020. The sample was 585 people who were selected by multi-stage cluster sampling method. Middle childhood development questionnaire was used to collect data. The data was analyzed using confirmatory factor analysis of the five-factor structure (flexibility, intimacy and friendship, separation times, physical health and empathy). The reliability of the questionnaire was calculated using Cronbach's alpha coefficient.
Findings: In this research, 585 people were selected as a sample group in the form of multi-stage clusters. The research tool was measuring the transformation in middle childhood. The methods of exploratory and confirmatory factor analysis, criterion validity, convergent-divergent validity were used to check the validity of the scale, and reliability was checked with two methods of internal consistency (Cronbach's alpha) and stability of results (retest) with a two-week interval. Exploratory factor analysis using principal component analysis and varimax rotation led to the extraction of 5 factors (empathy, flexibility, intimacy and friendship, physical health and leisure time).
 Conclusion: The results of confirmatory factor analysis confirmed the results of exploratory factor analysis. The reliability analysis of the test showed that the Cronbach's alpha coefficient of the subscales is higher than 0.7. Also, in all subscales, the Pearson correlation coefficient between the two implementations was higher than 0.85. The correlation between the subscales of measuring the development in the middle childhood period confirmed the convergent evidence of the questionnaire. Based on the results of the research, research evidence supports the simultaneous examination and consideration of all five subscales of the Persian version of the Middle Childhood Development Assessment.

Keywords

Main Subjects

Bigelow F J, Clark G M, Lum J A, Enticott P G. (2021). The development of neural responses to emotional faces: A review of evidence from event-related potentials during early and middle childhood. Developmental cognitive neuroscience, 51, 100992.
Borsboom D, Wijsen L. D. (2017). Psychology’s atomic bomb. Assessment in Education: Principles, Policy &Practice, 24: 440–446.
Engelhardt L E, Harden K P, Tucker-Drob E M, Church J A. (2019). The neural architecture of executive functions is established by middle childhood. Neuroimage, 185: 479-489. ‏
Greenberg M T, Abenavoli R. (2017). Universal interventions: Fully exploring their impacts and potential to produce population-level impacts. Journal of Research on Educational Effectiveness, 10: 40–67
Gregory T, Engelhardt D, Lewkowicz A, et al. (2019). Validity of the Middle Years Development Instrument for population monitoring of student wellbeing in australian school children. Child Indicators Research, 12(3): 873-899.
Jones D E, Greenberg M, Crowley M. (2015). Early social-emotional functioning and public health: The relationship between kindergarten social competence and future wellness. American Journal of Public Health, e1–e8.
luthar S S. (2006). Resilience in development and psychology, pp; (11):739-795
McKay A S, Karwowski M, Kaufman J. C. (2017). Measuring the muses: validating the Kaufman domains of creativity scale (K-DOCS). Psychology of Aesthetics, Creativity, and the Arts, 11(2): 216. ‏
Moffit J G, Kahle S, Hastings P D. (2011). Roots and benefits of costly giving: Children who are more altruistic have greater autonomic flexibility and less family wealth. Psychological Science, 26(7): 1038-1045. ‏
Olsson C A, McGee R, Nada-Raja S, Williams S. M. (2013). A 32-year longitudinal study of child and adolescent pathways to well-being in adulthood. Journal of happiness studies, 14: 1069-1083. ‏
Ryan R M, Deci E L. (2000). Self-determination theory. Basic psychological needs in motivation, development, and wellness. ‏
Schonert-Reichl K A, Guhn M, Gadermann A M, et al. (2013). Development and validation of the Middle Years Development Instrument (MDI): Assessing children’s well-being and assets across multiple contexts. Social indicators research, 114(2): 345-369.
Seligman M E, Csikszentmihalyi M. (2003). The motivational sources of creativity as viewed from the paradigm of positive psychology. ‏
Shonkoff, J P, Garner A S, Siegel B S, et al. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232-e246. doi: 10.1542/peds.2011-2663.
Tabachnick B G, Fidell L S. (2001). Using Multivariate Statistics, Allyn and Bacon, Boston, MA. Using Multivariate Statistics, 4th ed. Allyn and Bacon, Boston, MA. ‏
Theokas C, Lerner R M. (2006). Promoting positive development in adolescence: The role of ecological assets in families, schools, and neighborhoods. Applied Developmental Science, 10(2): 61-74. ‏
Wijsen l, Borsboom D, Alexandrova A. (2022). Values in Psychometrics, Perspectives on Psychological, Vol. 17(3): 788–804. ‏ Article reuse guidelines: sagepub.com /journals- permissions DOI: 10.1177/17456916211014183 www.psychologicalscience.org.