A Quantitative Study of Secondary School Students with Anorexia Nervosa

Background to the problem

            Eating disorders are characterized by abnormal feeding habits and extreme restrictive measures to control one’s weight, which often have negative implications on health. This is particularly true for people that have been diagnosed with anorexia nervosa. By definition, Anorexia is distinguished by radical attempts to lose weight and self-starvation. People with the condition are likely to restrict their calorie intake and may possess a strong desire to be thin (Harrington, Jimerson, Haxton& Jimerson, 2015). Therefore, the condition’s diagnosis has biological and psychological dimensions. There is a wide range of signs and symptoms that can exemplify the incidence of Anorexia in a patient. The most common are a relatively low Body Mass Index (BMI), fear of weight gain, rapid continuous weight loss, obsession for measuring and monitoring calories, food restrictions, excessive exercise, and purging. According to the DSM-5 diagnostic criteria, anorexia is diagnosed through three benchmarks: intense fear of gaining weight, restriction of energy intake relative to the body’s requirements, disturbance of body weight perception which leads to unhealthy view of body weight. Anorexia affects people of all ages, races, genders, and ethnicities. However, the inception of the disorder tends to happen in adolescence when perceptions about body weight begin to take shape (Peterson & Fuller, 2019).The prevalence of the disorder in the general population is about 0.4 percent for young women and 0.1 percent in young men.

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Anorexia has not been traced to any distinct cause. The general consensus in scholarship is that there is multiplicity of factors involved, including genetic and cultural factors (Hinney et al., 2017). The cultural factor is the most manifest in reality. Societies that highly regard thinness and a slim body type is likely to influence body weight perceptions, resulting in extreme attempts to control it (Hagman, et al., 2015). Academic literature shows that the onset of Anorexia follows a major stress-inducing event or a life-changing occurrence. While there is no direct cure for Anorexia, there is a broad set of interventions that are applicable in treatment and management. The general goal of treating and managing the disorders is to restore and promote healthy body weight and addressing the underlying issues. Hence, the treatment strategy often entails the combination of many approaches, including pharmacological and behavioral therapies.

This randomized control trial will assess the significance of treating the problem as opposed to treating the Symptom and the Underlying Issue. The researcher will recruit a cohort of secondary school students who will be assigned randomly to experimental and control groups. The intervention will include a hybrid therapy program involving nutritional and psychological mediations.

Study objectives & study significance

            The overall aim of the present study is to gauge the difference in the outcome of treating underlying issues and the outcomes of treating the problem. Underlying issues comprise individual needs while the problem itself entails the nutritional deficit resulting from self-restrictive eating habits. The findings will help illuminate on the consequence of focusing on the problem versus focusing on the underlying issues.

            The study has various implications to personal and public health, as well as the development of effective treatment solution for anorexia. Although anorexia seems like a mild and trivial health condition, it is linked to severe health effects in later stages. Effects of the disease are more visible in advanced cases than in earlier cases. Hence, the conception of anorexia in the general population is skewed towards advanced cases. This study will provide a more expansive understanding of the condition by differentiating the problem from the root of the problem. Specifically, the researcher will explore the outcome of treating symptoms and underlying issues before the inception of advanced stages.

            Notably, people with anorexia do not follow the same patterns of growth and development due to limited intake of energy and loss of weight. Indeed, anorexia affects various body processes and organs and could eventually lead to mortality if uncontrolled. This study aims to reverse the adverse effects of the disease by offering an effectual answer. Some of the main side effects that patient suffer include liver problems, gallstones, lean muscle loss, and reduced skin elasticity (Mehler & Brown, 2015). Liver damage occurs due to the rapid loss of weight and dramatic changes in an individual’s fatty acid levels. Gallstones materialize due to the deposit of cholesterol. Gallstones are known to be a source of pain and nausea and may eventually cause the destruction of the gall bladder. This study targets at reducing the occurrence of these late-stage health effects, which are not only damaging to patient health but also economically burdening.

Research Design & Sampling & Recruitment

            The study generally uses a quantitative approach and a randomised controlled trial (RCT) design. The choice of a quantitative approach is based on its utilization of statistical data as a tool for saving resources and time. By placing emphasis on numbers and figures, the present study will be more scientific in nature and will avoid the use of time-consuming procedures such as those used in qualitative methods. The use of a scientific method in data collection and analysis enhances the generalization of results. Additionally, the use of a quantitative method improves the ease of analysis. When the researcher collects quantitative data, the nature of the results will guide the researcher on which statistical tests to utilize. Hence the quantitative approach will reduce the potential for error and subjectivity.

            The application of a randomised controlled trial (RCT) means that the researcher will randomly assign subjects to an experimental group or a control group. The goal is to apply the intervention on the experimental group and deny it to the control group (Kennedy-Martin, Curtis, Faries, Robinson, & Johnston, 2015). The resulting outcome will be a difference in the two groups. The difference in outcomes will expose the effect of the intervention. The essence of randomization is to eliminate population bias and the facilitation of analysis through statistical tools.

            Study participants will be recruited from secondary schools in California using a random sampling approach. A random sample will offer the chance of performing data analysis with less likelihood of error. In addition, there will be an equal chance of selecting participants, meaning that accuracy will be improved and there will be inherent ‘fairness’ in the experiment.

Data Collection and Analysis

            Data will be principally collected from primary sources and will be statistical in nature. The central data points will include the frequency of anorexia signs and symptoms, including, BMI measurements, presence of fear of weight gain. Food restrictions, engagement in excessive exercise, depression and anxiety disorders, solitude, and continuity of weight loss, among others. These aspects will characterize the dependent variables. The independent variable will be intervention. Some data points will be self-reported while others will be measured directly through the help of psychologists and medical personnel. For instance, the BMI figures of each participants will be measured throughout the study period in weekly intervals both in the experimental and control group.

            The collected data will be analyzed through statistical software, specifically, using the Statistical Package for the Social Sciences. The software provides the required tools to perform descriptive and inferential statistics. Examples of descriptive statistics to be used as measures of central tendency, measures of variability, and frequency distributions, descriptive statistics will summarize the characteristics of collected datasets (Kaur, Stoltzfus & Yellapu, 2018). Some inferential statistics to be used are confidence intervals, t-tests, and regression (Sahu, Pal, & Das, 2015). Inferential statistics will be valuable in the eventual analysis and generation of inferences. The final results will integrate descriptive and inferential discussions.

Ethical considerations

Any study comprising human subjects must consider ethical concerns. Common ethical concerns in the current project include voluntary participation, anonymity, and confidentiality. The research will seek permission to conduct data collection by obtaining an introductory letter from secondary schools research and ethics committee. Moreover, informed written consent will be obtained from the research participants. Records will be color-coded, and students’ names will not be utilized, increasing confidentiality.

Rigor (Reliability & Validity)

            Reliability represents the consistency and replicability of research findings. In this study, reliability is the degree to which data interpretations obtained from several investigators are compatible and harmonious (Heale & Twycross, 2015). Essentially, any researcher that assesses the data obtained from this study should find similar findings. Since quantitative data is the primary source of information in this study, the researcher will have a consistent environment for participants, ensure subjects are familiar with the assessment, train human raters effectively, and conduct regular item analysis, and measure reliability. The measurement of reliability will be done through Cronbach’s alpha statistic, which gauges internal consistency (Vaske, Beaman, & Sponarski, 2017).. These methods of accounting for responsibility will be further strengthened through the use of rigorous methodology in accordance with the principles of design research. The application of rigorous methodology incorporates the use of standard recording procedures, including consistent field notes taking, the use of computer software, and the use of secondary empirical data in the validation of primary data.

            Reliability is critical to the element of validity, which represents the accuracy of the findings. The most instrumental threats to validity in the present study are reactivity, biases, and respondent biases. descriptibe in the experiment setting on the study outcome. Respondent biases have to do with the presentation of false information or withholding facts and information from the researcher due to the avoidance of negative personal image.  These validity threats will be minimized by considering the rewards of offering various answers and the social desirability of each response. Diverse data sources along with relevant guiding theory will be utilized to verify information. The use of a randomized controlled trial further bolsters validity. The measurement techniques to be used are suitable for the current experiment. Moreover, the researcher will control any possible confounding factors to increase accuracy and cogency.

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When Multivariate Analysis Is Appropriate For A Quantitative Study

Multivariate analysis deals with the observation and analysis of more than one variable at a time this technique is utilized in performing trade studies in design and analysis across a number of dimensions and at the same time taking into account the effect that the variable has on the responses of interest(Hair,2010).This type of analysis has several uses. These uses include; Capability-based design, inverse design, alternatives analysis, etc.

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Multivariate analysis can be used in quantitative studies in various different ways. These include:

Organizing and counting of the data that is surveyed.

All social researcher find the raw data as being invariable. This is because it is impossible for them to collect all the data from all the regions. Organization of the data is however very important for the detection of any unknown factors, verifications of the assumptions made and much more. For quantitative analysis, organization of data is very important especially for numerical processes that have to be done such as to simplify on the explanation of the phenomenon (Hair, Black, Babin, Anderson, & Tatham, 2006).

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The data thus has to be standardized before analysis is done. Open questions needs some criteria to be set for categorizing the answers. The data can be summarized by conducting some cross tabulation and some statistics.

Summarizing of data by multivariate analysis

Using the basic analysis, it might be quite hard to understand the tendency of what is being surveyed when the raw data contains a lot of information and questions. Basic analysis becomes problematic once someone has to deal with more than two variables. In this case, multivariate analysis can be used to analyze complicated information which the human mind cannot adequately comprehend. Its calculation is very intricate though this type of analysis has popularized as computers developed. (Hairet al 2006).Some of the major methods of this type of analysis include;

  • The principle component analysis- it summarizes multivariate information into simpler values.
  • The multiple linear regression analysis- it estimates other variables basing on some of the fixed variables.
  • Factor analysis- uses multivariate data to estimate the potential data
  • Discriminant analysis-it determines which group a certain data belongs basing on some fixed variables(Johnson, & Wichern, 1992)

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Multivariate regression works on deriving a formula that describes how some variables change in relation to change in other variables. General linearmodels can be used for the linear relations which makes used of different matrixes with the formula written as;


Y represents a matrix which contains a series of multivariate measurements, X represents a matrix which can be a design matrix, B is also a matrix with parameters which can be estimated and U represents a matrix which contains noise or errors(Morrison,1990). The general linear model can used a number of statistical models such as Analysis of Variance (ANOVA), ordinary linear regression, the T and F-test and many more. Multiple linear regression can also be used. According to (Morrison, 1990), is a generalized form of linear regression which considers more than one independent variable and restricts the dependent variable to one. These are used when the errors (matrix U), input in the equation do not follow a multivariate normal distribution. This type of multivariate statistical test may be useful in future research as it will aid in monitoring the changes of variables especially the numeric variable.

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