Limitations and Delimitations Sample Clauses

Limitations and Delimitations. The limitations of this study should be considered alongside the findings. We have no way to compare the men who choose to participate in the study with those men who chose not to. Potentially, the men’s decision to participate or not might have some correlation with their social capital and social network size. Men with few social connections may have welcomed the opportunity to talk with someone new, or conversely, men who lack social connection may not have been agreeable to voluntary participation in the questionnaire. Either way, we cannot know, but we might assume that the reasons behind non-participation were universal and did not differ based on femicide, homicide, or other crime perpetration. It is also impossible to compare incarcerated femicide perpetrators with perpetrators who also committed suicide. These men may commit femicide fully understanding the consequences of their actions and choose not to live with these consequences. The social networks for these men may differ significantly from those included in our study. Some may argue that incarcerated men are inherently unreliable narrators, therefore any findings based on self-report are biased. Using the internal consistency between the scales as a proxy measure for ▇▇▇▇▇▇▇ response, the high coefficient alphas for the scales give us no reason to doubt the veracity of the respondents’ self-reports. Another limitation is that incarcerated men likely differ from men who have perpetrated crime but who have not been charged and incarcerated. This limitation has no bearing on the primary research question, which is focused on social capital before and following incarceration, but it may impact other aspects of the study and findings in ways that we cannot quantify. There are also a few delimitations, most notably, respondents’ Pre- and Post-Social Capital scores were informed by only a few questions each. We designed the questionnaire for this study to collect a wide range of information including specific details on the index relationship of femicide perpetrators and indicators of mental health and psychopathy of all respondents. Measures of social capital, social networks, community, and neighborhood factors all shaped the survey design, but time constraints limited how expansive each section could be. Also, because of the logistics of contacting inmates (permission and safety), the men we interviewed were all attending educational programs at their respective prisons. This may have biased th...

Limitations and Delimitations. The study took place at a small private, not for profit, Christian, liberal arts, four-year University in Southern California. First-time first-year college students participated in the study. Findings and recommendations based on the survey results are specific to this institution, which limits generalizability and implications of the results. Data collected for this study took place during the summer/fall 2016 and fall 2019 semester. The study will only report findings and conclusions on students attending these semesters and participating in the study. The Financial Education Proficiency developed for the study included questions adapted directly from ▇▇▇▇▇▇▇’▇ (2008) Jump$tart college student survey. The Financial Self-Efficacy Scale questions (▇▇▇▇, 2011) allows for the evaluation of a respondent’s awareness of their behavior and consequences associated with making specific financial decisions. Although these instruments have been validated with other samples, they were not created for college students, and this may impact the reliability and validity of the data. For each instrument, choice of wording and order were maintained and only slightly altered for a college student audience. In some instances, readability, word choice, and flow may have affected survey responses. Regardless of these limitations, the study sheds light on the much-needed topic, the importance of the relationship between college student financial literacy, self-efficacy, and financial planning.

Limitations and Delimitations. The generalization of the findings of this research to a broader population might be limited by the peculiarity of the composition of the student body of the school district under examination and by the social-economic changes that the school district has been undergoing in the past two decades. The findings do not represent all of the students in the class, but it does represent other students who are at the same risk. However, the value of learning about this specific population is of greater importance. Some of the risk factors of the target population might involve recent trauma a youth may have experienced, such as the death of a loved one or a severe upset of their typical life. Welfare dependence, family dysfunction, and parental substance abuse, single parenthood, and their parents having low-education levels themselves. Other risk factors might be at the community level because of a low-income neighborhood with a high crime rate, toxins in the environment, low language proficiency, or a low graduation rate at their school. The purpose of the study was to learn about the factors that contribute to the overrepresentation of African American students in the safety transfer. The outcome of the safety transfers was studied in two conditions with different levels, racial and special Education composition of the sending and receiving schools. This quantitative component of this mixed- methods study is to investigate the association between the students of color safety transfers and their disciplinary trajectory and academic proficiency (output). By better understanding the transitions in school environments, we could learn about the benefits of receiving a safety transfer as the impact on students’ behavioral (discipline referrals and attendance) and academic (GPA) change. The impact of a behavioral intervention (quality of implementation) was also investigated by looking at the correlation between change on students’ outcome and the quality of implementation in the receiving school. This chapter contains the results of the analysis conducted in, both, quantitative and qualitative data to answer the research questions:

Limitations and Delimitations. As with any modeling exercise, the necessary simplification of a complex reality implies limitations that must be considered in the application of the results. Since there is almost no surveillance data that would specify the etiology of diarrhea during a CHE we had to estimate the disease burden. We did not take into account the effects of herd immunity of rotavirus vaccination, which may lower disease burden. The model also did not consider possible vaccine side effects, including intussusception that might become apparent with large-scale implementation of the vaccine. However, they were not observed in the clinical trials of Rotarix.74,75 Given the probable high burden of rotavirus disease in CHE, even if these were to occur they are very unlikely to significantly alter the cost-effectiveness of the vaccine. The range of vaccine effectiveness was estimated from El Salvador and may not be a true estimate of the coverage that can be achieved in Somalia during a SIA. Given the high rates of diarrhea, the vaccine effectiveness may be on the lower end of the range. It has been observed with oral polio vaccine, another live vaccine, among populations with high burden of diarrhea, malnutrition, and other medical conditions such as TB and HIV,32 the vaccine effectiveness is lower. A decrease in vaccine effectiveness would increase the CEA.

Limitations and Delimitations. The short exposure time for the sophomore cohort may weaken the measurable effect of the program. Likewise, the lack of baseline and end-line data for the junior and senior cohorts, and the lack of a control group diminishes the robustness of the findings and makes them vulnerable to confounders. Finally, the study is limited to one specific mentoring program, therefore, the findings may only be applicable to this particular program, though themes from the research may be relevant to similar mentoring programs.

Limitations and Delimitations. Since the study was conducted within a pre-selected population that was already part of a cohort study, the sample used is not completely random and therefore decreases the generalizability of our findings. This study is also limited by the self-reported nature of cross-sectional surveys that can introduce information bias. Certain questions in the survey asked about past events, which can lead to recall bias. In addition, questions about hygiene and sanitary conditions are subject to bias since participants may not always feel comfortable sharing personal information. Furthermore, due to logistical and financial constraints, water quality data was only collected at one point in time and not longitudinally, which could have provided more insight into water quality variability, especially if data had been collected in more than one season.

Limitations and Delimitations. The study took place during the Fall and early Winter of 2021-2022 amid a global pandemic. Social distancing was required for safety measures. The pandemic has also added a significant extra workload to school leaders' and teachers' responsibilities. Thus, the researcher did not receive as many participants as initially desired. Additionally, the researcher was prohibited from recording any interviews because the corporate leadership was fearful of legal liabilities and time constructions. Therefore, the study included online surveys distributed via Google forms and open-ended questionnaires. The researcher also works for an educational organization that operates the school sites of the case study, which may inhibit the validity of the participants' responses because some participants may not feel secure providing honest feedback (▇▇▇▇▇▇▇▇, 2014). Conversely, delimitations of this study included specific boundary locations, age groups, and pandemic considerations; however, the researcher believes that the data collected was a cross-section of valid data in which to format a conclusion.

Limitations and Delimitations. 82 Conclusions ............................................................................................................... 83 REFERENCES ...................................................................................................................... 84 APPENDICES ....................................................................................................................... 93 Appendix A: Teacher and Curriculum Coach -Part Survey ...................................... 93 Appendix B: School Leader Part Survey................................................................... 95 Appendix C: School Leader Questionnaire ............................................................... 97 Appendix D: Teacher Questionnaire ......................................................................... 98 Appendix E: National Institute of Health-Dr. ▇▇▇▇▇▇▇ ▇▇▇▇▇▇▇ ................................ 99 Appendix F: National Institute of Health-▇▇▇▇ ▇▇▇▇▇ 101 Table 1. 1. Demographics of Participants........................................................................ 45 Table 2. 1. Evolving Themes ........................................................................................... 45 Table 3. 1. My Relationships with the School Leaders are Positive ............................... 46

Limitations and Delimitations. A noticeable limitation in this project is the lack of a “true” dataset. A “true” dataset is the complete dataset without any missing observations. This is impossible to acquire since missing data may be due to systematic issues, lack of response, miscoding, errors in imputation and a variety of other factors. Consequently, the mechanism of missing data needs to be investigated. Complete-case analysis assumes that the data is either MCAR or MAR. This assumption is robust to small amounts of missing data (<5%); however, large proportions of missing data are unlikely to be MCAR. This project assumes that the missing data mechanism was MAR. It is also possible that the missing data mechanism was NMAR. An assumption of Case study 1 is that MPR accurately reflects patient adherence to their statin therapy. There are limitations with this assumption. MPR does not directly measure patient consumption of their statin therapy; instead, it provides an indirect estimate of adherence based on pharmacy refill data.76 Other forms of adherence measurements are available which were not used in Case study 1. For example, Proportion of Days Covered (PDC) reflects the percentage of days the medication was available to the patient.64 PDC is calculated as the total days the complete medication regimen was available divided by the total number of days evaluated capped at 1.0.64 MPR can also be truncated or allowed to exceed a cap of 1.0. In Case study 1, MPR was truncated at 1.0. It is unclear whether using the PDC would impact whether a patient was adherent or not. ▇▇▇▇, et al. reported that differences between MPR and PDC were negligible and provided similar answers in terms of categorizing patients as adherent or non- adherent.64 Another limitation of this project is the small sample size of the liraglutide group relative to the exenatide group in Case study 2.18 The small sample is potentially sensitive to missing data which can result in inaccurate parameter estimates due to large uncertainties or variances. A larger sample size would mitigate this issue; however, there was not possible with the current design. In observational studies, unmeasured variables can be potential confounders despite controlling for all measurable variables. Propensity score matching may be considered in this situation, however it is highly sensitive to unmeasured confounders.77 It is not an absolute answer in the absence of a randomized controlled trial. Future investigation using propensity sco...