Category: Methodology Consultation

Should you manipulate your primary data to accept the right hypothesis is an extension to the above question, where we could bring in ethics, beyond the technique. In the informal lingua of research, the practice of creating false data or creating selective reporting of data according to requirement is called as “data fudging”. One of the most common example of this, that we see commonly in research, involves choosing that section of the data that shows consistency in that direction of the hypothesis that is preferred by the researcher. The remaining data is ignored or eliminated.

In general, the norms of research say that the validity of those results is always under question whose results cannot be reproduced by the investigators. With the fear of getting caught over fudging of data, some scientists keep away from publishing their data and the methods. They only give out the results and the interpretation.

By now we know that this practice is prevalent in the field of research and you may see its presence in all disciplines. But data manipulation is quite a serious concern that challenges the honesty and integrity of ethics. The outliers, missing data and the non normality of the data adversely affects not just the validity but the reliability as well of the data.

Researchers should not confuse removing of outliers from the data before analysis as a practice of data manipulation. Rather that is more appropriate, to study the real problems through scatter diagrams and remove those points that appear far away or detached from the main cloud. These points are removed only for a cause.

It is agreeable in research to accept the null hypothesis, how much ever trivial it be, as long as you are able to justify the reasons for this acceptance. These could vary from finding error in the theory or the observation, your interpretation of the theory could be the reason or some external interference with the experiment faltered the results. As long as you are able to give an explanation for the failure to support your thesis, there is some learning and it can become the base to form an alternate hypothesis that can be created on the grounds of failure of the first experiment.

Always remember that the searches for trivial techniques to play around with data is far from science. A good scientist is the one who strives to disapprove the hypothesis so that its acceptance can be ensured. Statistical significance isn’t the only proof.

Are you looking for a way to simplify your methodology chapter and don’t know how to do it? I am sure looking at these tips you could get cues on how to deal better with the most difficult chapter of your thesis:

Problem: The base of the methodology chapter is derived from the previous chapter focussing on the Literature Review. Hence, at the onset, to regain focus, it is important that you di a quick recap of the research question of your dissertation. Have a very clear and precise definition of the problem that you are wanting to address through your research.

Approach: Next you need to have the approach in place for taking up the primary research. Not only would this help you but the reader also would be able to simplify the understanding by being able to contextualise the methodology. The approach should target to clear all ambiguity in the mind of the researcher, in terms of rationale, justification, sampling etc. so that both you as well as the reader has no confusions whatsoever about the methodology section of the chapter.

Description of technicalities: A good research opens up to the challenge of the research community to take up the same research and reproduce the results. A confident and authentic research would have all technicalities explained in detail to help those who wished to work on reproducing the same results.

Validation: It is necessary to give sound reasoning for choosing the methods that you have adopted to take up the research. More so if the methodology that you have chosen is novel or unique from the previous research that has been conducted in your area. In that case at every stage there should be a valid and strong justification for the methodology adopted by you. Also deal with all concerns linked to the reliability and validity of your research. Be particular about giving importance to small but important issues such as accuracy, precision, Scope of error, statistical significance etc. They add a lot of weight to your research.

Sampling technique adopted: Even I you have brought up sampling under validity and reliability issue, it is important enough to be given special attention. The sample size has aggregate role in defining the statistical significance of your results.

When we talk about dispersion and measures of central tendency, we talk about descriptive statistics. These measures are termed as descriptive statistics simply because you may use them to describe your data. While it may be easy to collect your research data, describing it in a meaningful manner can be a challenging task for many students. However, descriptive statistics is not terrifying at all, provided you understand its measures and learn to use them in an appropriate manner. In fact, descriptive statistics makes your complex data sets quite simple to comprehend.

Measures of central tendency include mean, median and mode. All these measures help you summarize your lists of scores and describe them through a single number. Is it not amazing to tell your readers about your entire data with a single number? Measures of central tendency really make your task easier by performing a few calculations. For example, you might need to describe the weights of people in your exercise group to an instructor. One way to do this is by telling about every single person and his/her weight. This would make the instructor do some relative calculations in their mind so they may figure out the general weight of people in your group.

Another method is to use the measures of central tendency and give a single average weight figure to your instructor. This is far easier to understand for the person requiring specific and relevant information. Some similar calculations can be done for cases where dispersion needs to be described. In such cases, measures of dispersion can be used. The measures of dispersion include variance, range and standard deviation. These help you understand the spread of scores when you have certain figures with you. Thus, all measures of descriptive statistics help in simplifying your task of describing your data. Overcome your fear and make use of these valuable tools for your research.

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You might be good at researching and writing, but a weak quantitative analysis might defeat the purpose of your project. Our experts are well versed with the statistical considerations like sample size, correlation and linear models.

At times, it is necessary to use the graphic representations like pie charts and tables in the written documents for an easy comprehension of the academic board. If you are not good with numbers or quantitative analyses, it is advisable to seek the help of trustworthy statisticians like us.

The crucial and most important academic task of dissertation requires a lot of planning and dedication in implementing it. It involves selecting a topic, collecting and analyzing data, presenting arguments and conclusions. Of all these stages the most important stage is the data collection and analysis stage. This is because most of the students spend major part of the time in data collection leaving them with less time for analysis and arguments. Before going to data analysis we need a good data to analyze it. The data must be collected keeping in mind the aim of the research and the target group. A perfectly collected data can lead to good analysis and proper outcome.

Depending on the nature of data and the research questions we can be able to analyze whether a hypothesis test is required or not. Also depending on the type of data collected we can make sure the use of statistical tools and techniques to analyze the data. Data analysis plan is most important and crucial as it provides the most important information for the researcher for their future research. The researcher needs to sort out the unwanted and unnecessary data for his/her research and go ahead with the other data. By doing so, it helps the researcher in saving time and eliminates confusion. Never a researcher must predict the conclusion based on the available data or even before collecting the data. This leads to bias in the research and hence leads to bad research.

Also not every research ends in number. Some research has quantitative data while some have qualitative data. Quantitative results can be presented n the form of tables and figures while qualitative information must be presented in a structured word format. A data analysis requires the researcher to choose the correct statistical tools, analyze the collected data and then present the interpreted data in results. As said earlier the data collection and analyzing is the most crucial part and so often time consuming. So it is better to take help of the guide or take assistance from a statistician to finish the analysis portion effectively and quickly.

Also there are many considerations that need to be looked into while analyzing the data. The researcher must study about which statistical tool is required to collect and analyze the data, arguments to defend the choice and use of statistical tools and techniques and potential problems while dealing with the analysis. Also he/she must think about the extent they wish to go in analyzing the data so as to save the time. A proper planning of analyzing data can save a lot of time for the researcher placing him on the safer side to prepare a beautiful presentation.

Data analysis plan can vary from one discipline to other and also based on requirements. And so data analysis plan is very important and requires a lot of care.