Preregistration - Urbanization and Its Relationship to Child Development in Bhutan
Creators: name: Nyström, Pär
name: Sen, Umay
name: Gredebäck, Gustaf
name: Dorji, Nidup
name: W, Wangchuk
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Child Psychology
subject: Health Psychology
subject: Developmental Psychology
subject: Psychology
subject: FOS: Psychology
subject: Social and Behavioral Sciences

description: [this description is also available in the attached .docx file] TITLE: Urbanization and Its Relationship to Child Development in Bhutan INTRODUCTION Today, 57% of the world population is living in the urban areas (World Bank, 2018). As the urban settings become more common across the world and also become the places where more children grow up, the role of urban context in child development gains more importance (UNICEF, 2018). Despite the significant role of urban environments on child development (Kağitçibaşi, 2007; Kagitcibasi &amp; Ataca, 2005), there has been very limited attention directed on how urbanization impact child development in developmental psychology. The aim of the present study is to fill this gap in the literature by particularly focusing on how urban context affects cognitive development. TIMELINE The data was collected between 16th of May and 31st of July, 2022. Visual inspection of data quality (e.g., histograms of variables) was assessed during August, 2022. Pre-registration was performed by the research team on 9th of September in Thimpu, Bhutan. No analysis or correlations were performed on the data up until this point. REFERENCE Numbers after variables indicate item numbers in document called Variable_list. VARIABLES OF INTEREST: Child Development: Cognitive development of children aged between 3 and 5 is measured by SON-R 2-8, which is a non-verbal intelligence test (Tellegen et al., 2018). Urbanization: Families were asked to report their migration history during the last 10 years (when and where). This migration history will be used to calculate two urbanization related variables: 1) urban exposure (UE) and 2) change in urbanization across years (UAY). Urban exposure is defined as the extent to which the family and/or children are exposed to urban environment. Urban exposure will be measured by calculating the area under the curve demonstrating the relationship between changing level of urbanicity in the places where the family lived (i.e., on the y-axis) across years (i.e., on the x-axis). Change in urbanization will be measured by extracting the  value from the regression line fitting to the same curve demonstrating the relationship between level of urbanicity and time. Poverty: Poverty is measured by calculating the household income divided by total number of household members [ b_1_21 / (b_1_18 + b_1_19)]. Additionally, Mental and Emotional Wellbeing questionnaire (h_3_i) is used to investigate the parents/caregivers’ perception of their financial status. It is measured by calculating the average score of Mental and Emotional Wellbeing questionnaire (h_3_i) by the following formula [( h_2_12 + h_2_13+ h_2_14+ h_2_15 + h_2_16)/5]. ANALYSIS PLAN A series of general linear models will be performed to answer research questions. For each step, predictors that have p-value higher than .1 in the prior analysis will be removed in order to facilitate simplicity and to avoid model overloading. Analysis will be performed in a stepwise manner, as explained below. First model will include the variables of urbanization (i.e., urban exposure and change in urbanization), poverty (i.e., income and perception of financial status), age and gender as factors (covariates) on child development (SON-R 2-8). In this model, urbanization measure will be based on the migration history of the last ten years (i.e., 2013-2022) which can be viewed as the migration history of the parents. Second model will be tested but this time the urbanization measure will depend on the migration history of children. In other words, time scale of migration history will begin from the year that the child was born (i.e., birth year of the child- 2022). It is important to note here that parents/caregivers and/or their spouses who are civil servants in Bhutan are excluded from the sample of the first two models. The main reason for such exclusion criteria is that the motivation of migration for these families who move because of a duty assignment differ from the families who move due to urbanization-related reasons (e.g., better economic and social opportunities). Third model will be run on the sample consisting of civil servants only by keeping the same variables that are included in the previous models. Whether the parents/caregivers (first model) or children’s migration history (second model) will be used as urbanization measure in the third model will be determined by comparing the explained variance in the first two models. The migration history that has more explained variance in the model will be chosen for the third model. The third model (i.e., sample only including the civil servants) is expected to function as a control group where the effects of urbanization will be washed out because in Bhutan, civil servants keep their social and economic benefits provided by the government regardless of where they live. At the final step, a fourth model will be run on 10 000 permutation samples of non-civil servant group (i.e., by using Monte Carlo method) equal in the size of the civil servant group. In doing so, it is aimed to demonstrate that the (expected) null effect in the civil servant group is not due to a lower sample size and/or lack of power in that sample. REFERENCES Kağitçibaşi, Ç. (2007). Family, self, and human development across cultures: Theories and applications, 2nd ed (pp. xxx, 463). Lawrence Erlbaum Associates Publishers. Kagitcibasi, C., &amp; Ataca, B. (2005). Value of Children and Family Change: A Three-Decade Portrait From Turkey. Applied Psychology, 54(3), 317–337. https://doi.org/10.1111/j.1464-0597.2005.00213.x UNICEF (Ed.). (2018). Shaping urbanization for children. UNICEF. Tellegen, P. J., Laros, J. A., &amp; Petermann, F. (2018). SON-R 2-8: Non-Verbaler Intelligenztest. Göttingen, Germany:: Hogrefe. World Bank (Ed.). (2018). World population prospects: The 2018 revision. World Bank.