Aquantitative data analysis is one of the most preferred methods by an individual or a company in order to get simplified statistics. The quantified data makes iteasier to interpret and is hence a time-saving idea for formulating a profitable marketing strategy.
Quantitative datais preferable in special cases as it demonstrates the reports and conclusions in exact numerical terms giving the report a confident approach and making it more credible. Quantitative data analysis methods are very profitable to a researcher who is trying to evaluate huge data. It separates similar and confusing factors to make the report simple and easy to understand.
The most commonly used quantitative data analysis techniquesinclude surveys, observations or analyzing secondary data like financial statistics or accounts.
Analyzing Quantitative Data
There are several techniques in order toanalyzequantitative data including the classical and new age methods. With advancement in technology, the classical methods are modified to function online. Some of the most popularquantitative data analysis process includes the representation of quantitative variables to be structured into different themes and groups. These quantified data are then displayed in the form of graphical representations, such as bar graphs,boxplots, statistical charts, tables, stem and leaf displays, pie charts and histograms etc.
Some of the quantitative data analysis methods are
- Content Analysis
- Discourse Analysis
- Conversation Analysis
- Social Network Analysis
- Computer-Aided Analysis
Today numerous computer software has entered the research market making the analysis even easier. Technology isbeneficial, but it has its limits too. For instance, some human errors cannot be detected by a machine or corrected out of its capacity.
Listed below are some tips that you should keep in your mind while analyzing quantitative datain order to avoid complications:
- One should beat ease with the handling of primary data, because it is most commonly used to analyze the quantitative data.
- One should fully realize the data first and go through it thoroughly in order to avoid trivial mistakes and errors.
- One should check the validity of the data in different settings through samples.
- One should absolutely avoid using complicated graphical and statistical representations. The analysis should be demonstrated in a comprehensible manner while keeping in mind the main objectives of the research.
- It is advisable to take suggestions and remarks from fellow researchers to increase the quality and credibility of your research and point out any contradictions if any.
Impetus Research has been proudly helping organizations successfully in providing them useful statistics through quantitative data analysis for several years. Our expert professionals have pioneered in offering quality research evaluations to our clients for their successful business ventures.