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¶ … Tobacco control and stress reduction
Data interpretation is a critical component of the research process, one which is necessary to ensure that the data has program significance -- i.e., can provide useful guidelines for changes in policy or to add to the existing research -- and also to ensure that the data simply meets basic statistical criteria for validity. In the case of the smoking cessation program profiled in the text by McKenzie, Neiger, & Thackeray, R. (2013), there are several obvious problems with the data -- only a limited number of the pooled workplaces (67%) actually completed the survey, 251 in total. 198 of the profiled workplaces (79%) had fewer than 51 employees. This means that the data may be incomplete regarding the full extent that such programs have been implemented (or the lack of extent to which they have been implemented) and most of the programs have a relatively limited number of involved persons. According to Fitzpatrick (2004 et al., cited by McKenzie, Neiger & Thackeray 2013) one method of data analysis is "comparing assessed...
For example, 52% of organizations did not allow smoking within the buildings at all while 37% allowed it only within designated areas (10% had no such policies). There was also an overrepresentation of large businesses within the sample, further complicating the results. Thus, the data presented is problematic for a variety of potential uses -- in terms of evaluating the utility of such a program in discouraging tobacco use, the surveyed entities are extremely varied in terms of the specifics of the programs they are using. The sampling is relatively incomplete and skewed in favor of particular types of businesses at the expense of others. Variance in program types for an already small…
Hospice Utilization: Survey Findings Survey Data Analysis Barriers to Hospice Care Utilization: Survey Findings Barriers to Hospice Care Utilization: Survey Findings Hospice care has been shown to improve patient quality of life, reduce depression, prolong life (reviewed by McGorty and Bornstein, 2003), and reduce the costs associated with end of life (EOL) care (Temel et al., 2010). As McGorty and Bornstein (2003) point out, however, hospice care in the United Kingdom is more
Sundborg et al. (2012) conducted a quantitative study, which examined the preparedness of nurses to provide care for women who are exposed to intimate partner violence (p.1). The study was carried out on the premise that intimate partner violence (IPV) has significant effect on women's health. Therefore, nurses need adequate preparations to identify such victims and provide suitable interventions. While the study provides significant insights relating to nurses' preparedness in
Rogers (2010) chapter on qualitative analysis described many useful and practical methods to help use this tool effectively and efficiently. The purpose of this essay is to examine this book chapter and address the important and key issues that are presented in the literature. This essay will also provide practical examples of the information presented in this argument from federal agencies to help contextualize the information and to demonstrate the
Leadership Effectiveness Inventory Data Reliability Analysis After collecting data from the Nordic questionnaires, a reliability test was carried out to ascertain whether the nine scales used could predict overall satisfaction with the manager as a leader. The analysis found the scales reliable and moreover, it was apparent that reducing the questions in the questionnaires could not compromise data reliability. In addition, communication, planning/execution and teamwork were proved to be the best predictors
SPSS Data Analysis Does the number of average study hours per week during the semester accurately predict final exam grades? Independent variable: average number of study hours per week. Hours is continuous data because it can take on any value below 168 hours, which is the number of hours in a week. Even though the data is reported in integer form the 'hours' data is continuous. Hours data is quantitative, since it can be
Important measures in data summarization include measures of central tendency (i.e. "averages" or the mean, median, and/or mode) and measures of dispersion or variability -- the range of the data and the standard deviation of the points within the data set (Shaughnessy et al. 2006). These statistical staring points can be used to derive a wealth of information form the data, including correlations to other related studies/data sets, reliability