DATA ANALYSIS

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Benefits of hiring us for your research analysis

• We help you submit your paper on time.
• Proper guidance on analysis and implemented statistical techniques.
• Rectify and avoid common errors in your paper that delays your paper and which can turn out to be costly later in the submission process.
• We help you understand and grasp the important statical and mathematical techniques which may be out of your area of expertise.
• We analyse your hypothesis testing and may suggest you ways to improve it and build more accurate statements.
• We help you justify your sample size, research methodology, analysis conducted and the interpretation.

What we offer

Tabular/ graphical representation: The primary data is analysed graphically by doing percentage analysis. It is the simples form of data representation, and gives your paper a neat and easy-to-read look.

Hypothesis testing: Based on the nature of your dissertation, hypothesis testing will be done to primary study using different statistical techniques like Chi Square Test, Anova Test, Factor Analysis, Correlation/ Regression, T-Test, etc.

 Data analysis with the help of:

• SPSS: Arguably the most commonly-used software in academic papers, SPSS analysis help is given by Project Guru for your dissertation.
• SAS: For studies of a slightly complicated nature, SAS help is offered.

Apart from SPSS and SAS, your dissertation can also make use of software like WINKS, SAS ODS, etc. Data Analysis of your dissertation is one by taking into consideration the statistical technique and software preferred by you.Undeniable edge to your research

• We analyse upto 10,000 respondents' questionnaires.
• Every individual's response is accounted for.
• The study will not only yield the desired result, but also make way for future research by revealing more details.

Research analysis help & tips

• Factor analysis using SPSS

Factor analysis is used to find factors among observed variables. In other words, if your data contains many variables, you can use factor analysis to reduce the number of variables.