Obesity

Obesity and Asthma Research Method Part III Obesity and Asthma Research Method Part III Statistical Analysis The review of the statistical analysis of the data Blue Cross Blue Shield (BCBS) received after performing their survey will enable them to have better information. Although they will ultimately use that data to determine if there should be increase in funding for programs that provide health care screening in children, it will also serve as another focus. A statistical analysis of our survey could also be useful for evaluating our current environment and making decisions that influence daily operations as well as the strategic position of BCBS. The examination of the association of one variable to another is a task that the administrators at BCBS must take. For example, if the funding for early childhood health care issues remains the same, it is possible that the statistical outcome of children suffering from illnesses will increase. We may communicate the expression by claiming that on average the outcome of children diagnosed with early childhood illnesses in the programs current state is equal to or less than that of the same program with additional funding. Administrators examine this claim, to decide if a difference is statistically significant. We will represent the null hypothesis by Ho, and Ha will represent the alternate hypothesis. When comparing the current program funding state as it is today with a similar program with more funding and resources, let CPF represent the current program funding. Let APF represent a program with additional funding and resources. As this is the case, we may express the null hypothesis as Ho: CPF < APF; Ha: CPF > APF will express the alternate hypothesis. By providing statistical analysis we expect to gain an insight that would better prepare BCBS to make decisions based on statistical data. Potential Challenges There are several vital confront exist to execute management research with an agreement through these ideologies of high-quality executive research. Through high opinion internal validity, BCBS’s management research is frequently accomplished within organizational settings; frequently it is not theoretically promising or practicable to employ investigational design with unsystematic, which is the utmost routine in technical research. There are variable interests to management researchers that cannot be integrated into the investigational research protocol as interference. For instance, upper management of the corporation may be unenthusiastic to agree to partake in a research in which the erratic concentration is decision-making essential if involvement destined the business would have to implement a scrupulous structure based on random assignment. Furthermore, organizations cannot repeatedly be randomized to interference and control groups because of geographic and other logistical barriers. As a result, administration research ought to be performed by way of non-experimental blueprint that can be more susceptible to perplexing issues. The projected study of complex variables such as Obesity and Asthma raises concerns about possible poor reliability and validity of measures. Initials data collection raises apprehensions about the prospect of stumpy retort rates of analysis. Additionally, management research is performed in organizational settings, the quantity of logistical issues in conducting a study that may not be entirely valued and addressed pending the study is essentially happening. Conversely, the research also presents a component of ambiguity, to include all research measures in its entirety before as component of the modus operandi. The projected employ of non-experimental blueprints, while justifiable and universal in social science research, will inescapably direct to discriminating apprehension amongst critics concerning the interior validity of the anticipated study. Minimizing the Challenges Power of statistical test, justification to BCBS to fund the research, ascertaining the research success, probability of results’ significance, refuting H0, prior funding, adequate sampling size and population, alpha value level, are concerns we must minimize because they undermine our experiment’s validity and reliability. In addressing reliability, our design factors in internal consistency of our measurement and repeatability, using test/retest, correlating two separate measurements, sensitivity to consistency (grouping same concept questions in a questionnaire) in the underlying experimental conditions, using computer program to solve for Cronbach's Alpha. Construct validity strengthens our conclusions, inferences or propositions, asking for causal relationship in our hypothesis and the observed outcome extrapolating our results for generalization. We will include a comparable control group in our research, to dampen history, testing, instrumentation, mortality and regression threats, mitigating single, multiple group and social interaction threats to internal validity. We will randomize our experiment ascertaining equivalency, which is the best way to strengthen the internal validity of our research. We will define concepts well before proceeding to the measurement phase of the study to mitigate social interaction threats to internal validity. For example; inadequate preoperational explication of constructs, implementing multiple versions variable survey questions questionnaire–to increase our study's utility, negating mono-operation bias. Implement multiple measures of key concepts; perform pilot studies to demonstrate validity of our measures. Mitigate Mono-method bias, and adequately define our Interaction of Testing and Treatment, generally use none restrictive, none-confounding constructs -- ascertaining that treatment level is good to infer cause and effect, using experienced researchers, negating Experimenter Expectancies pitfall, non- intimidating method design to eliminate Evaluator Apprehension, all of which are social threats to our construct validity. Data Presentation The report presents the data in no particular order and labels help identify each group. For example, when reporting the data from the survey, it makes no difference if the report has strongly disagree before the strongly agree and vice versa. The survey answers represent a balanced rating measurement scale. A balanced rating measurement scale has an equal number of categories above and below the midpoint. Generally, an equal number of favorable and unfavorable response choices balance a rating scale (Cooper & Schindler, 2008). In the choices that BCBS has included in the survey has as the midpoint a “Neutral” response choice with the favorable response such as “Strongly Agree.” and the unfavorable response type being “Strongly Disagree”. The first question of the survey is a multiple choice scale question in which the answer will allow BCBS to group the remanding survey question findings. The remaining questions of our survey ask the provider to rank their positions of satisfaction for different parts of their experience. such as Blue Cross Blue Shield provides the tools necessary for effective preventative screening, the provided tools for preventative screening are easily accessible to providers, the provided tools for preventative screening are overly utilized, you would like to see a new system in place for preventative health screening. BCBS has received the responses from 200 providers. Because the survey response types are of the rating scale, BCBS has grouped the data preliminary data set into a table. As a reference the questions on the survey are as follows: 1. Which preventative screening tools is your office currently utilizing' 2. Blue Cross Blue Shield provides the tools necessary for effective preventative screening. 3. The provided tools for preventative screening are easily accessible to providers. 4. The provided tools for preventative screening are overly utilized. 5. The provided tools for preventative screening are underutilized. 6. A revision to the provided tools for preventative screening is necessary. 7. You would like to see a new system in place for preventative health screening. |Providers using preventative screening tools for asthmatics (75) | |Question | |Question | Question |Strongly Disagree |Disagree |Neutral |Agree |Strongly Agree |Total | |#2 |40 |48 |48 |120 |120 |376 | |#3 |24 |32 |120 |60 |200 |436 | |#4 |60 |40 |90 |48 |96 |334 | |#5 |24 |40 |120 |96 |136 |416 | |#6 |34 |48 |108 |102 |88 |368 | |#7 |40 |48 |108 |84 |88 |368 | |Total |262 |280 |630 |538 |750 |2460 | | Power Classification An alternative approach that is used to determine the size or strength of a treatment effect is that of measuring the power of the statistical test. The power of a test the probability that the test will reject the null hypothesis if the treatment really has an effect (Gravetter & Wallnau, 2007, p .260). In ascertaining the statistical power relative to the management dilemma; the findings from the data will either support or reject the null hypothesis. Therefore, to ascertain if this research is likely to be successful, the associates involved on this research project should calculate the statistical power that should be done before a full-blown study is done. Being proactive will afford them the ability to determine the probability that the results will be significant, which is the rejection of H0, before the investment of corporate resources, for example, time, effort, and funding for the research. In addition, the calculation of the power will be dependent upon a variety of factors that will more than likely influence the power of the test. These factors would be ascertained from the sample size and population; demographics, diet, the size of the treatment effect, the value chosen for the alpha level and the conducting of a one or two-tailed tests. These are all primary drivers that can have an appropriate influence over the power of the hypothesis test. The logic surrounding statistical power demonstrates that power and size are related, therefore, the treatment effect on the sample means for this research can either lead to the acceptance or rejection of the established null hypothesis. Plan of Action In researching the benefits of increased funding for adolescent patients in preventative obesity and diabetes programs effectiveness, conclusions were presented, with validation. The research process presented validity and reliability in “estimating the precision needed by the researcher a larger sample will deliver a higher precision of estimate, a narrower or smaller error range and higher confidence level in the estimate” (Cooper & Schindler, 2008, p. 291). The recommendations based upon the quantitative analysis from the null statistical hypotheses, significance level, are to increase funding for the BCBS preventative children’s programs. The collection of data through a larger sample survey process, utilizing classification of adolescent patients with certain at risk conditions, and current preventative measured programs was used to build the hypotheses testing criteria. Comparisons involving the statistical hypothesis have shown that preventative health screening programs with additional funding and resources compared to be equal to or greater then moderate to low funded programs. Preventative programs developed will provide the ultimate resources for children who have or have tendencies to be affected by childhood obesity and diabetes. In conclusion The Blue Cross and Blue Shield Foundation in a pro-active role conducted a new research program to determine expanded health care programs. Blue Cross and Blue Shield are, “Looking beyond health care today for ideas that create healthier communities tomorrow (Blue Cross Blue Shield Association, 2009).” Reference Blue Cross Blue Shield Association (2009). Blue Cross Blue Shield Foundation. http://www.bcbsmnfoundaton.org. Cooper, D. R., & Schindler, P. S. (2008). Introduction of Business Research (10th ed.). New York, NY: McGraw-Hill Irwin. Gravetter, F.J. & Wallnau, L.B. (2007) Statistics for the Behavioral Sciences (7th ed): Belmont, CA; Thomson Higher Education Reliability and Validity: What is the Difference. (n.d.). http://www.socialresearchmethods.net/tutorial/Colosi/lcolosi2.htm