Quantitative Methods Assignment Help

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Convocation of numerical data and generalizing it across groups of people or to explicate a particular phenomenon is the main focus of quantitative methods assignment help.

Introduction:

The preamble to a quantitative study is usually scribbled in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem — as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature — review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework — provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].
 
Quantitative methods assignment help pivots on numeric and steady data and detailed, convergent intellectualizing rather than divergent reasoning i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner. The quantitative methods assignment help determines the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population.

Among the specific strengths of using quantitative methods to study social science research problems:

• Permits for a broader study, necessitating a greater number of subjects, and enhancing the generalization of the sequels;
• Empowers greater objectivity and accuracy of outcomes. Generally, quantitative methods are depicted to render summaries of data that sustains generalizations about the phenomenon under study.
• Seeking well established standards processes that the research can be replicated, and then analysed and emulated with similar studies;
• You can encapsulate vast sources of information and mould comparisons across categories and over time; and,
• Personal bias can be eschewed by keeping a ‘distance’ from participating subjects and using acquired computational techniques.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is most productive and able to test hypotheses, but may bungle contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to “structural bias” and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behaviour, attitudes, and motivation;
• As results allocate numerical descriptions rather than detailed narrative and generally adopt less elaborate accounts of human perception so they are usually restricted;
• The research is frequently schlepped out in an unnatural, artificial environment so that a level of control can be solicited to the exercise, and,
• Pre-set answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Things to retain in mind when promulgating the results of a study using quantitative methods:

1. Interpret the data collected and their statistical treatment as well as all relevant results in correspondence to the research problem you are investigating. Interpretation of results is not appropriate in this section.
2. Report unanticipated events that occurred during your data collection. Explain how the genuine analysis contradicts from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
3. Elucidate the techniques you plied to “clean” your data set.
4. Specify a minimally abundant statistical procedure; dispense a rationale for its use and a reference for it. Specify any computer programs used.
5. Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
6. When using inferential statistics, provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
7. Avoid inferring causality, particularly in nonrandomized designs or without further experimentation.
8. Always tell the reader what to look for in tables and figures.
 
The overarching grail of a quantitative methods assignment help is to assort features, count them, and contrive statistical models in an endeavour to untangle what is observed in the most satisfactory manner.
 
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