The researcher will make use of statistical and logical methodologies by drawing conclusions from the data that was obtained. The analysis will include assessments of demographic data as well as research variables. Comparatively, study variables comprise the examination of indicators of the dependent and independent variables, while demographic variables will contain assessments of core trends of numerical quantities like age. Examples of demographic variables include age. Variables related to demographics. As demographic variables, the researcher will take into consideration factors such as age, race, and gender in the situation of the proposed study. Measures of central tendencies, including mean, mode, and median, in combination with measures of dispersion that consider variance, kurtosis, skewness, and standard deviation, could be included in the study of these variables.
As data sources, it is possible to utilize MEDLINE, Embase, CINAHL, and Web of Science. The previous literature corpus is heavily based on these resources to analyze the data for the European region. Moreover, it is possible to conduct searches on health institutions’ websites to identify studies not published in scientific journals. The inclusion criteria for a reference study by Garrido-Miguel et al. (2019) were studies reporting the population-based prevalence of excess weight or obesity according to body mass index cutoffs proposed by the International Obesity Task Force. In addition, cross-sectional or follow-up studies and studies including populations aged 2 to 13 years were considered.
It is possible to base the research on existing methodologies. In the research of Garrido-Miguel et al. (2019), pooled prevalence means were estimated using data from cross-sectional studies and the beginning and endpoints of longitudinal investigations. Estimates of morbid obesity BMI were chosen as 35 at age 18 were rolled into the obesity category where available from studies in order to arrive at a single figure for obesity BMI was 30 at age 18 (Garrido-Miguel et al., 2019). In each analysis, they employed weighted pooling as a means of reducing the effect size. As a result, researchers averaged the prevalence estimates across studies, taking into account their respective sample sizes and number of people who were overweight (Garrido-Miguel et al., 2019). Consequently, the same principle could be used in the future study by considering a wider sample area.
Reference
Garrido-Miguel, M., Cavero-Redondo, I., Álvarez-Bueno, C., Rodríguez-Artalejo, F., Moreno, L. A., Ruiz, J. R.,… & Martínez-Vizcaíno, V. (2019). Prevalence and trends of overweight and obesity in European children from 1999 to 2016: a systematic review and meta-analysis. JAMA pediatrics, 173(10), e192430-e192430.