Unlike Qualitative data, quantitative data is objective and can be analysed through statistical tools and softwares because the data collected does not need any reasoning or subjective measure to make it numerically significant.
Due to the close-ended questions and data collection techniques, the raw quantitative data already comes with definite answers. Thus, the process of quantitative data analysis lies majorly on the software and other analysis techniques which are used for interpreting the data to answer the research questions.
Although the numerical nature of quantitative data makes it easily measurable and controllable, the data analysis techniques and softwares can vary depending on various things which require experts’ knowledge.
Before the process of data analysis, the data is considered raw even if it is sorted out in a readable manner. To make it meaningful and to understand it in relation to the research, the team of Five Vidya follows a step-by-step process.
To input the data in a software like Excel or SPSS, the data needs to be associated with one of the measurement scales (nominal, interval, ordinal, and ratio).
Once the data has been organized, then estimates are calculated which are used to draw a pattern and relation between variables. Mean, median, variance and standard deviation are few of the commonly used statistical estimates.
Based on the estimate requirement, the measurement scale is selected and vice-versa. In some research cases, descriptive statistics are not required, where the measurement scale needs to be selected first depending on the representation style.
After sorting out the data according to the measurement scale, it needs to be represented in a tabular form. Different techniques like cross-tabulation or TURF can be used for this based on which suits the data best and enhances the understanding.
Quantitative Analysis is used in many fields including analytical chemistry, financial analysis, social science, and organized sports.
Based on the size of data and solutions required, there are majorly two types of tests which are conducted for quantitative data analysis. These are named as parametric tests and non-parametric tests.
Parametric tests have a fixed distribution parameter of the population from which the sample data needs to be extracted. It is considered that the data and variables are evenly distributed among the population and certain assumptions are made as to what will be the direction of the result. Some of the common parametric tests which are used for quantitative data analysis are:
In non-parametric tests, there are no predetermined assumptions. Moreover, unlike the parametric tests, the sample size used for these tests are small as they do not provide accurate estimates for larger datasets. Due to the no-assumptions for distributions, this test is also known as the “distributions-free” test. Therefore, in case you have no or little information about the population used for analysis and it has a non-normal variable distribution, then you can use the non-parametric statistical tests like:
The more suitable test needs to be decided based on several criterias and the assumptions need to be carefully validated to avoid getting the wrong answer.
Mostly everyone talks about why you need to conduct a Quantitative data analysis. However, while trying to get the right answers, it is important to stay aware of some key points which can lead to the wrong conclusions.
Quantitative data is good at only covering one side of the questions like what, who, and when, questions which can be proved through evidence. It does not cover the areas of opinions and subjective scope of the questions like how and why, which provides a better understanding of the situation and responses. Therefore, quantitative data analysis techniques fall short in cases where the experiences and opinions of the participants need to be analysed.
Large datasets do not imply covering all the population, but it refers to a comparatively large number of participants in the sample selected. To represent a large population in a more accurate manner, it is important to consider participants who represent different sectors and areas with as much authenticity as possible. Therefore, quantitative data requires more time in building an appropriate sample size. Moreover, it requires using more than one data analysis technique to see which provides the closer to hypothesis answer.
In quantitative data analysis, the findings could become biased as well when it comes to the researcher as it can be manipulated easily to match the interests of the researcher themselves. It is also possible that the researcher might not even know but subconsciously they end up being biased by misinterpreting the responses due to the lack of explanation. Therefore, in these cases, the supervisor or research committee may suggest going with the qualitative data analysis or mixed data analysis approach.
To make our services worthy of their costing and to provide you with the outcomes which are suitable for your research, our team of experts provide you with the following benefits.
The process of data analysis turns out fruitful only when the data collected is reliable. Therefore, before moving on to the analysis process, our team does a quick assessment check to see whether the data is reliable or not and also, if it can be used to derive the results similar as expected through research hypotheses.
To provide service specific requirements, we have experts for different tasks. Similarly, for the quantitative data analysis services, we have statisticians and analysts who have experience of years working on different projects efficiently. This provides us the benefit of insights and expert’s guesses to provide solutions for complex problems easily.
We also create table graphs, charts, and other graphic representations for data analysis and related services. To present these graphical representations, we follow the university suggested guidelines of formatting and margins.
To make it easier for you to write the interpretation, results, discussions, and conclusions chapter, our experts also provide an interpretation report. This helps in understanding how the data analysis process took place and briefly tells whether the result meets the research hypothesis or not.
To know more about how we can help with the data analysis and interpretation services, you can also call us and ask out the questions directly from our experts through our contact us panel.
Five Vidya is a team of academic experts who joined hands to assist young scholars and researchers to successfully complete their PhD thesis. Our office and services are based out of Bangalore and Hyderabad (India). We provide academic services including PhD thesis writing, thesis editing, software implementation, journal manuscript writing, business case studies development, and so on
Disclaimer:" FiveVidya is a team of academic research consultants, writers and editors who provide ethical and comprehensive support for doctoral candidates for their research. FiveVidya does not provide any ready to submit work for their clients"
