The Effect Of Nurses' Health Beliefs Behaviors On BMI And Obesity Research Proposal Examples

Research Design

Research Methodology and Design
Methodology
Determining the effect of nurses’ belief behavior towards body mass index and obesity requires a close examination of the specific variables such as Attitudes Toward Obese People (ATOP) and Beliefs About Obese People (BAOP). In this regard, the study will employ the use of quantitative method in the form of a quasi-experimental design to study these concepts. Among the most commonly used approach in designing a quantitative study is the quasi-experimental design, which in its simplest form will require a pretest and posttest of the comparison group (Levy & Ellis, 2012). The method shares the common characteristics of an experimental study, but was not truly defined as a true experiment. The only difference that sets the quasi-experimental from experimental method is randomization meaning the former does not use a randomly assigned group. When performing a study under the quasi-experimental setting, the researcher demonstrates how one variable influence the other. In relation to the theme of this study, the effects of the behavioral and belief perspectives of nurses towards obesity and BMI can be determined by investigating ATOP and BAOP from the internal consciousness emerging from experiences of the subjects that manifests in their responses.

Dependent Variable: Nurses’ perception of obesity or obese patients.

An open and closed-ended questionnaire will be employed to assess nurses’ perceptions of and beliefs towards obesity and obese patients.
Independent Variable: management of obese patients.
The overall nurses’ management of patients with obesity is determined by their perceptions of and beliefs towards obesity and obese patients. For that matter, SPSS software will be used to compute the correlation between nurses’ perceptions of and beliefs towards obesity and obese patients and their management of patients with obesity. The study design employed here is integral in the evaluation of the causal effect/intervention. Causal parameter here is the nurses’ perceptions of and beliefs towards obesity and obese patients.

Sample Population

The samples will be drawn from the professional nursing population working at the city hospital. The samples will be divided into three focus groups from three shifts consisting of 15 participants from each shift.

Sample Size

The quantitative study will employ forty-five (45) participants from each shift. This number was determined using quota analysis where the two initial strata analysis variables such as the ATOP and BAOP were multiplied by the number of participants, and three focus groups. Power analysis determined that 45 people would be recruited into this study. This number was determined using a two-tailed test, 5% significance level, medium, and effect size of 0.36 and 10% power level.

Sampling Techniques

This study will use non-probabilistic sampling techniques. A non-randomized sampling method was used in this study to gather participants. In the quasi-experimental setting, randomization would not be appropriate because the method requires a predetermined sample groups in an attempt to uncover the cause and effect of a pre-assigned condition (Aussems et al., 2009). For this study, the samples will be selected according to their familiarity to the topic of discussion where the invites will be sent only to selected professional nurses who are working closely with cases of obesity. As such, the invites will be sent through a nursing association where the association administrator is expected to forward the invites to the relevant and possible candidates.

Sample Inclusion Characteristics

The inclusion characteristics of the participants are
Critical care nurses
Stroke unit nurses
Sample Exclusion Characteristics
The exclusion characters of the participants are
Nurses who do not work in stroke units
Nurses who do not work in critical care units
The study also involves measuring subsequent effects of identified ATOP and BAOP variables, which can be determined by collecting data from the administered questionnaire. A maximum of one hundred (45) participants divided into three focus groups will be invited to participate in the study. However, obtaining participants for the study will require a signed consent from the association where the professional nurses are affiliated indicating that nurses’ response does not reflect the general opinion of the association or the institution that the nurses are connected with.

Data Collection

The data collection method for this study will consist of a survey questionnaire indicating three focus groups, semi-structural interviews, which will be the source of the pre and posttest data. The survey questionnaire will consist of 36 items that broadly covers the demographic information about the participants such as age, gender, income, and about the nurses’ experience in handling obese patient situation. Some of the questions will also employ the linkert scale as a response metric tool in research integrated into the questionnaire, which aims to determine the psychometric scale of the responses (Boone & Boone, 2012). The three focus groups will be formed in the hospital covering 15 nurses from three different shifts. The researcher on the other hand will be the moderator for the interview and administration of the survey. The data gathering process will only take about 60 minutes for each focus group.

Data Analysis

The quantitative method requires statistical analysis of the participant’s responses identifying obvious pattern indicating an effect of behavior or attitude of nurses towards BMI and obesity. The Likert Scale encompasses a series of five items with corresponding score/variable of 1= corresponds to strongly disagree, 2= disagree, 3= neutral, 4=agree, and 5=disagree. Analyzing the liker data encompasses the use of interval measurement scale created by calculating the mean of composite score. It is recommended to use descriptive statistics to accurately interpret the raw data to determine standard deviation for variability, and the mean score for central tendency. A T-test would be the appropriate approach for interpreting the results of the statistical data. The series of open-ended questions together with the descriptive questions makes up the variables for likert scale data analysis while the statements drawn from other items in the questioners will determine the variances on the subject’s responses. In terms of the demographic data, the research will also employ the use of descriptive statistics where frequencies, mode, median, and central tendencies will be measured. SPSS software will be key in the computation of the correlation between the independent and dependent variable.
On the other hand, the descriptive analysis of the variables also employs the function of Pearson’s R (correlational), bivariate, cross tabulation, and multivariate analysis to determine the adjusted differences of the responses indicating the scores for ATOP and BAOP. In addition, a multivariate regression analysis indicating the linear and logistic adjustments will be used with the help of available statistical software package such as StatPlus. The same tool will be used for analysis and comparison of the scores for the open-ended questions. However, the analysis will separate the scores for the Attitude Toward Obese Persons (ATOP) and the Beliefs about Obese Persons (BAOP) and determine the correlation of the two variables. In terms of validity, the ATOP and BAOP scores will be examined for both content validity and construct validity to ascertain accuracy of the results. Weighting on the other hand will address the systematic over or under representation of sampled population to account the systematic nonresponse and biases.

IRB

This section addresses the concerns of an institutional review board (IRB). The IRB section must address the rationale for research subject selection; the strategies and procedures for recruiting subjects, and the justification for inclusion of vulnerable populations.

References

Boone, Jr., H. N., & Boone, D. A. (2012). Analyzing Likert Data. Journal of Extension,50(2), 1-5. Retrieved from http://www.joe.org/joe/2012april/pdf/JOE_v50_2tt2.pdf
Aussems, M. E., Boomsma, A., & Snijders, T. A. (2009). The use of quasi-experiments in the social sciences: a content analysis. Qual Quant, 12, 1-22. Retrieved from DOI 10.1007/s11135-009-9281-4
Levy, Y., & Ellis, T. J. (2011). A Guide for Novice Researchers on Experimental and Quasi-Experimental Studies in Information Systems Research. Interdisciplinary Journal of Information, Knowledge, and Management, 6, 151-161. Retrieved from http://www.ijikm.org/Volume6/IJIKMv6p151-161Levy553.pdf