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This article is filled with many variables that are attempting to relate BMI with other social behaviors in children such as eating family meals. "Risk factors" are discussed as well, which are inherently confounding in their own right. A risk factor is not a correlation and is merely an unproven coincidence that is yet to be scientifically proven. In this study many of these confounding variables are included in the discussion such as snack food eating and physical activity. These terms are much too subjective to produce any general understanding and do not provide a clear and recognizable path to gaining further knowledge about these subjects.
Even though the study is rife with confounding variables, an acceptable argument is made by discussing some of these relationships....
The authors demonstrated a reasonable case for suggesting that some of these risk factors come into play, but that method of coming into play cannot be understood through qualitative analysis. Root causes of these issues were somewhat overlooked as well as the true purpose of this study is not exactly clear but proves that some intellectual thought has been given to some of these variables in an attempt to understand a larger, and more general picture.
References
Goldfield, G.S., Murray, M.A., Buchholz, a., Henderson, K., Obeid, N., Kukaswadia, a., & Flament, M.F. (2011). Family meals and body mass index among adolescents: effects of gender. Applied Physiology, Nutrition & Metabolism, 36(4), 539-546. Retrieved September 1, 2012 from EBSCO.
Postpartum Sexually Transmitted Disease Refining Our Understanding of the Population at Risk Yale University, UMI Dissertations, 2011, 1505365. The researchers developed a study to assess STD incidence during and after pregnancy and to determine risk factors associated with postpartum STD infections among mothers in their teens and early 20s. For this analysis, they included 848 women who were less than 24 weeks pregnant, ages 14 to 25. A total of
Quantitative vs. Qualitative Research According to Lopez-Alvarado (2017) and Muijs (n.d.), research design decisions are linked to ontology and epistemology. Ontology refers to the researcher’s beliefs about whether reality is absolute or contextual, universal or relative. Whether the researcher is a realist or a relativist determines research questions and designs, with an increased tendency for relativists to focus on phenomenological and qualitative methods and a realist to use quantitative methods. Muijs
Experimental, and Survey Research Social researchers have a wide array of qualitative and quantitative research methodologies available to them, including field, experimental and survey research. Each of these research methodologies has some strengths and weaknesses that make them better suited for some applications than others. The purpose of this paper is to provide a review of the relevant literature concerning field, experiment and survey research to identify their respective strengths
Difficulties Empirical research is necessarily designed to provide a workable framework through which a researcher may test a hypothesized explanation for observable phenomena, but the two primary branches of scientific inquiry differ greatly in terms of the analytical scope and style employed throughout an experiment. While quantitative research is capable of recording, sorting and analyzing voluminous amounts of numerical data, from credit card usage rates for various tax brackets to
E., contemporary or historical issues (Eisenhardt 1989; in Naslund, 2005); (3) the extent of control required over behavioral events in the research context (Yin 1994; as cited in Naslund, 2005); and (4) the researcher's philosophical stance, i.e., his/her understanding of the nature of social reality and how knowledge of that reality can be gained. (Naslund, 2005) Naslund (2005) states that qualitative research methods "primarily create meanings and explanations to research phenomena" and
Methods Section This research used a quasi-experimental design for third through fifth-grade students from 12 intact classrooms. There were 207 students in the study, which eliminated the possibility of the random assignments of participants. Students were examined in three groups: 68 students were in Group 1 from four noninclusion classrooms; 34 students were in Group 2 from two clustered inclusion classrooms; and 105 students were in Group 3 from six random