Regardless of the purpose of research, researchers have to deal with either qualitative or quantitative data for making a scientific decision. The methods, tools and techniques used to analyse quantitative data differ greatly from the ones used in qualitative data analysis.
Thus, this article will primarily focus on the statistical methods to analyse quantitative data along with its examples. It will also discuss the types of quantitative research designs and create a list of tools that aims to bring precision to the data analysis process.
- How do you analyse quantitative data?
Quantitative data is the form of data that we can count in numbers and where each data set has unique numerical values. This type of data set aims to answer quantitative questions such as ‘how much’, ‘how often’, and ‘how many’. To analyse quantitative data, there are a number of methods, including cross-tabulation, trend analysis, conjoint analysis, MaxDiff analysis, SWOT analysis, Gap analysis, Turf analysis and other statistical analysis methods. Which method will be best to analyse quantitative data depends on the aims, objectives and purposed outcomes of a study. However, it also depends if you have hired a good dissertation writing service UK to work on the analysis.
- What are quantitative analysis examples?
Quantitative analysis is the process of interpreting data collected from either primary or secondary sources. In different disciplines, to analyse quantitative data, different analysis methods are used.
Examples of quantitative analysis include:
- In cyber security risk assessment, quantitative analysis is a systematic process of evaluating risk arising from threats that may be done by using the Fractional CISO-a quantitative method to measure anything in the cyber security risk framework.
- The statistical analysis of the data shows the relationship between the climate adaptations and mitigation funds for allocation collected through close-ended questionnaires.
- What are the four types of quantitative research?
Irrespective of the discipline, quantitative research is of four types, namely, descriptive, correlational, quasi-experimental and experimental research. The following is a brief description of each of these:
- Descriptive quantitative research design: It aims to describe the current status of the selected variables. It helps to provide systematic information about a phenomenon.
- Correlational quantitative research design: It tries to investigate the extent of the relationship between two and more variables involved in a study.
- Quasi-experimental quantitative research design: It aims to explore the cause and effect relationship between the variables involved in a particular study.
- Experimental quantitative research design: It mostly includes laboratory studies to find the cause and effect relationship between the variables involved in a study.
- List of tools and software to analyse quantitative data
Research students who want to achieve their research goals by using quantitative research design can speed up the process to analyse quantitative data through analytics software. These tools help researchers in organising the data and performing the basic to advanced level statistical analysis. They are also very important for interpreting data. They provide visualisations (graphs, charts, and tables) that you can present in front of anybody at any time. Some of these tools that are very useful to analyse quantitative data along with brief descriptions are as follows:
SPSS (statistical package for the social sciences) is the most sought after quantitative data analysis tool for social scientists. It was launched by IBM, and its popularity is due to its featured flexibility to deal with almost all types of data. Quantitative researchers can use it to generate charts, reports, plots of distributions, and descriptive statistics like means, medians, frequencies, modes, and other complex regression models.
It is also a good tool to analyse quantitative data. You can use it for performing all basic to advanced statistical data analyses. The point-and-click and command syntax interfaces make STATA user-friendly. Thus, generating graphs, charts and plots at STATA seems easy even to beginners.
SAS is a short form of Statistical Analysis System, and it is not only useful to analyse quantitative data for academic research, and several businesses also use it. It also facilitates the analyst in generating graphs, writing reports, forecasting, planning, project management and quality improvement tasks. Both intermediate and advanced level analysts can use SAS for dealing with large data sets and in performing complex as well as advanced analyses.
R is a platform for performing statistical computing and graphics related tasks. It provides a variety of highly extensible statistical and graphical functionalities. It helps in data manipulation, calculation and graphical representation by handling even large data sets with great care. High active community, strong graphic capabilities, a wide selection of packages, a comprehensive environment and the ability to perform even complex statistical calculations are amongst the few core features of R.
MATLAB is another important tool to analyse quantitative data. In fact, it is not only a statistical analysis tool, but it is also a numerical computing environment. It offers more flexibility than other software. It is a bit difficult to use this software as it does not offer drag-from-menu features; rather, the researcher must have full command of MATLAB programming language to perform statistical analysis.
Excel is strong statistical analysis software. It helps researchers in analysing the data present in a worksheet to assist analysis in making better business decisions. It generates high-quality PivotTable, which is a great thing to summarise, explore, analyse and interpret data. Moreover, it also facilitates the researchers in running some statistical tests like t-tests.
The above list contains all the top tools to analyse the quantitative data though there are many others as well. Which tool you want to use depends on the researcher’s convenience, nature of research and objectives.
Consequently, quantitative data analysis is a systematic process of organising and analysing information in numerical form to create a logical scientific story. To analyse quantitative data, there are many quantitative methods like trend analysis, conjoint analysis, MaxDiff analysis, and many more. Broadly, it has four types descriptive, correlational, quasi-experimental and experimental research. Moreover, you can use a number of statistical tools like SPSS, SAS, R, STATA, MATLAB, Excel and many more. Based on personal expertise and convenience in using, you can select any statistical tool to achieve your research goals. #livehyd #hydnews