Extraneous variables are factors that might influence the observed results of a study although they are not included in the research design as variables of interest (Cohen, Manion, & Morrison, 2013). In other words, something that is overlooked by a study or deliberately neglected in it can affect the correlation that is being examined, thus distorting the perception of outcomes. This is composed of external influences known as extraneous variables. In the study of childhood obesity and parent education, many factors can be regarded as extraneous variables because there are many contributors to childhood obesity identified in the academic literature that is not of interest in the given study. Most importantly, there may be different environmental and cultural conditions in participating families, and these conditions may act as extraneous variables. In order to control them, it is suggested to include background information in data analysis, which will allow a more reliable examination of correlations.
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The instruments employed by the given study can be divided into two categories: those used for the qualitative part and those used for the qualitative part. For the former, in-depth interviews will be conducted, the instrument for which is interview guidelines. Questionnaires will not be used for this part of the study because in-depth interviewing imply less formal, less structured, and more flexible information collection from participants than surveying. Another instrument in this category is coding which will be used for data analysis. For the quantitative part, questionnaires will be designed to assess the participants’ characteristics of interest on certain scales. Obtained data will be later analyzed through the SPSS software. The validity and reliability of the use of these instruments are confirmed by scholars who have used them for various similar studies. The research pursues to establish the correlation between parents’ education and children’s body mass index (BMI), which can be effectively achieved through the use of described instruments.
Description of the Intervention
Intervention is an integral part of experimental studies. When experiments are conducted, certain conditions are applied to a group of participants, and the results are compared to those coming from a group that has not received the intervention. In its present form, the given study does not imply interventions, at it pursues measuring existing characteristics (parents’ education and children’s BMI) for the purpose of establishing a correlation. However, an interventional component of such studies should not be disregarded (Cohen et al., 2013). By coming into contact with participants, researchers inevitably affect them, and the conduction of a study can lead to a change of practices among participants. In the given study, its context may increase parents’ awareness of the importance of their education for their children’s health.
Data Collection Procedures
Data collection procedures will include interviewing and surveying. The former will be conducted in the in-depth form in order to obtain qualitative data on parents’ knowledge, awareness, and attention to certain aspects of their children’s lifestyles and health. These data will be further coded for the purpose of structuring it and presenting it in an organized manner for addressing the research question. The surveying will be conducted by distributing questionnaires among participants to measure the key characteristics as well as additional ones, such as demographics. Special attention should be paid to designing the questionnaires, as the wording of questions should qualify for obtaining measurable quantitative results afterward. The questionnaires will be later processed with the SPSS statistical analysis software, which will help establish the correlation of interest.
Data Analysis for Demographic Variables
Demographic variables to be analyzed in the given study are parents’ and children’s age, the children’s gender, family structure (one or two parents), and cultural background. For the latter variable, six categories will be used: Asian, African American, Hispanic, White, mixed, and others. Learning the gender of participants is important for classifying their BMI correctly as per the underweight-normal-overweight-obese scale (males and females have different norms in terms of the weight-height ratio). Scoring will be performed with a standardized tool, such as a t-test. Statistical software will help detect complicated correlations among the variables and their connections to the variables that are the focus of the study (see Data Analysis of Study Variables). Obtained data will be of descriptive statistical nature.
Data Analysis of Study Variables
The given study pursues establishing a correlation between two certain variables: parents’ education and children’s BMI. The former will be measured based on the following scale: no formal education, 1-6 years of elementary education, 1-4 years of high school, college as attended, or degree awarded, such as AD, BS, BA, MS, MA, PhD, or other. The BMI will be calculated based on the children’s weight and height: weight divided by the square of height (the metric system will be applied, i.e. kilograms and meters for weight and height respectively). Analyzing the correlation through statistical analysis tools will allow making conclusions on a larger scale. With correctly designed research methods and correctly implemented data analysis, generalizability and reliability of findings will be achieved (Lowry, 2014). A t-test will serve as an inferential statistical test, and its results can be applied to other groups of the population outside of the selected sample.
Cohen, L., Manion, L., & Morrison, K. (2013). Research methods in education. New York, NY: Routledge.
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Lowry, R. (2014). Concepts and applications of inferential statistics. Web.