Table one is about the demographic characteristics of the targets of the workplace bullies, it displays the characteristics of social workers ranging by age, gender, and demographics Whitaker, T. Table two is about organizational settings and roles of target, it displayed supervisors, colleagues, subordinates, and clients were all identified as bullies. In addition, being treated with disrespect and having work de-valued as the hardest aspects of being bullied at the workplace.
Table four was the summary characteristics of bullies, the study showed characteristics were either passive or assertive by the coping scale. The passive behavior ranging score of 24 and assertive score of The median and mean scores were The last table, which was five was the classification of Reponses to the coping scale as passive or assertive coded in the same direction the table explained coping strategies and responses Whitaker, T.
In the article several questions were asked in a questionnaire. The more passive the response, the lower the value assigned to it, the more assertive the response, the higher the value was assigned to it. The data supported the research problem of workplace bullying. It was noteworthy and explained that three out of five social workers were recipients of rude, unpleasant, and unfavorable workplace environmental interactions.
However, the article did not provide inferential statistics. The data was collected through questionnaire and based on actual results to determine the statistics Whitaker, T.
In conclusion, this article explored the issues of workplace bullying of social workers and the coping responses, it included a number of concerned issues for the social working profession. Statistical analyses are grounded in the scientific method, and as such rely on experimental designs that are free of bias, reproducible, reliable, and valid. Statistical analysis is prevalent in the field of education research today, specifically in policy research and in studies of school management, funding, staffing, and student retention rates.
It is less common in studies of curriculum development and analysis, though in the early to mid-twentieth century it dominated this field as well. In the early twenty-first century, qualitative research, in the form of interpretive and critical methods, has been the most commonly used method in curriculum research. Statistical analysis is used in quantitative research to collect, organize, and describe empirical data.
All quantitative studies rely on statistical analyses because quantitative research is a method of approaching questions that is based on concrete, observable, "objective," and measurable data. Quantitative measures aim to explain causal relationships—though most often, in social science research, these methods can only determine correlations between the different factors studied, not make definite predictions about the universality of their application Creswell, A quantitative study in the social sciences follows the prescriptions of the scientific method: Reproducibility is foundational to quantitative experimentation; if a study cannot be reproduced by others, or by the same researcher at a later time, by definition it does not follow the constructs of the scientific method.
Because quantitative analysis applies mathematical equations to collected data to make the analysis objective, the social sciences researcher must be particularly attentive to the collection of non-biased data Sax, If the data is biased, statistical analyses might lead to false or inaccurate theories or predictions.
In the life or physical sciences, collecting neutrally biased information is less challenging than in educational research, as measurements of widths of cells and of electron transport, for example, have been standardized. In educational research, however, particular care must be paid to the way in which an investigator's biases may lead to the collection of a specific kind of information, or to a particular sampling method that might not fairly represent the population in question Creswell, Because all researchers hold values, qualitative education researchers have criticized quantitative studies since the late twentieth century for being only marginally useful in determining the "objective truth" of life in the classrooms or of methods of curriculum construction Pinar et al, In addition to concerns of reliability and bias, quantitative research must also be accurate and valid.
Accuracy refers to the extent to which the experimental results accord with theoretical models, or the extent to which empirical results measure the phenomenon in question. Validity in the social sciences can be assessed in three ways: Content validity refers the suitability of a study's data collection and analysis methods for the questions being investigated. Criterion validity refers to whether a study uses previously validated methods or, if it is novel in its approach, it has predictive value.
Quantitative research in curriculum construction and evaluation grew out of the common school movement in the nineteenth century and, though popular in the early to mid-twentieth century, drew increasing criticism into the late twentieth and early twenty-first centuries Pinar et al, However, at about the time it was losing popularity with educational researchers, quantitative research gained new importance in the newly developed field of education policy studies.
The movement toward accountability, toward assessing "learning gaps" between various groups, toward increasing the efficiency and effectiveness of policy implementations—along with the increasing role of the federal government and of national organizations in education—was founded upon quantitative analysis Heck, The quality of quantitative research in education has been harshly criticized, however, and it remains a matter of concern for policy analysts and quantitative researchers.
Some common critiques are that educational researchers are not adequately trained to approach social problems quantitatively, and that researchers often dismiss as irrelevant data that does not seem to match the expected results, offering explanations, after the fact, of why this data was not included in the final analysis Sax, ; Heck, Even quantitative researchers have posed these critiques.
They continue to advise policy makers, students of education, and professionals in the field of the importance of critically appraising a quantitative educational study before relying on its analyses. Quantitative research in education grew out of the common school movement in the nineteenth century. The common school, a term coined by educational advocate and reformer Horace Mann — , was a government-funded free school in which any child from any socioeconomic background or status could enroll.
The movement formed the foundation for modern day public education Travers,
I get asked this question fairly often so I thought I would do a few posts on it. The most common problem is that a student who is new to statistics has no idea where to even start. These examples use SAS but you could use any package you like. My recommendation to students beginning to learn.
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! 1! Paper HOW Statistical Analysis – The First Steps Jennifer L. Waller Medical College of Georgia, Augusta, Georgia ABSTRACT For both statisticians and non-statisticians, knowing what data look like before more. Free statistical analysis papers, essays, and research papers.
Statistical analysis encompasses the whole range of techniques used in quantitative studies, as all such studies are concerned with the examination of discrete data, with describing this data. Statistical Analysis in the Article The article incorporates tables, descriptive statistics that helps with the understanding of the data, it also provides five tables of illustrations. Table one is about the demographic characteristics of the targets of the workplace bullies, it displays the characteristics of social workers ranging by age.