Critical stages and tools for PhD Statistics

Apart from having ample amount of subject knowledge, researchers need to demonstrate expertise in statistical methods as well. It is very important to validate the research findings through apt statistical tools is being made compulsory by institutions globally.

A dissertation involves huge amount of data collection then its  tabulation followed by  analysis. Critical stages of PhD statistics are:

• Defining the research problem is the basic requirement: This is where you determine the kind of relationship that exists among variables,  specify the independent and dependent variable and then elaborate the strategy for putting the independent variables to use.

• Developing the Research Plan: Here you need to figure out your sample size and find the data that is missing that may impact the analysis of the result.

• Data collection: At this stage you need to be careful of choosing the right tools and applying them very carefully so that the results can be interpreted without any mistakes.

• Result interpretation: This is a large stage and here the researcher identifies the contribution of the variables to the results. It is important to write the interpretation in such a way that they can be understood easily.

• Conclusion: The conclusion helps to understand how much of the predetermined objectives were met through the rigorous statistical effort and finally what outcome has the research come on and what is its implication on existing research and the society in general.

There are four basic tools that can help you to run your statistical tests.

1. Microsoft Excel: MS Excel offers many tools for the purpose of visualisation and statistical analysis. The data that is imported from text files is quite simple for generating text metrics and customising the figures and graphics as well.
2. SPSS: It is the “Statistical Package for the Social Sciences.” It is a very popular tool because it is generic, comprehensive and incorporates descriptive statistics as well.  It offers tests for both, parametric as well as non parametric data. For these reasons it is very commonly used in academic papers and research reports.
3. MATLAB: It requires the researcher to have higher level of programming skills. It contains a huge database of analytic libraries. it also offers good options for customising our graphics and results. It can be sometimes quite challenging for beginners.
4. R: R is similar to MATLAB in features, but is available for free. It offers all necessary data transformations which helps the analysis of human behaviour in the most meticulous way. It has taken over MATLAB because of being popular for richer libraries comparatively.