In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process.
The same figure within data set can be interpreted in many different ways; therefore it is important to apply fair and careful judgement.
For example, questionnaire findings of a research titled “A study into the impacts of informal management-employee communication on the levels of employee motivation: a case study of Agro Bravo Enterprise” may indicate that the majority 52% of respondents assess communication skills of their immediate supervisors as inadequate.
This specific piece of primary data findings needs to be critically analyzed and objectively interpreted through comparing it to other findings within the framework of the same research. For example, organizational culture of Agro Bravo Enterprise, leadership style, the levels of frequency of management-employee communications need to be taken into account during the data analysis.
Moreover, literature review findings conducted at the earlier stages of the research process need to be referred to in order to reflect the viewpoints of other authors regarding the causes of employee dissatisfaction with management communication. Also, secondary data needs to be integrated in data analysis in a logical and unbiased manner.
Let’s take another example. You are writing a dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam using correlation quantitative data analysis method. You have specified FDI and GDP as variables for your research and correlation tests produced correlation coefficient of 0.9.
In this case simply stating that there is a strong positive correlation between FDI and GDP would not suffice; you have to provide explanation about the manners in which the growth on the levels of FDI may contribute to the growth of GDP by referring to the findings of the literature review and applying your own critical and rational reasoning skills.
A set of analytical software can be used to assist with analysis of quantitative data. The following table illustrates the advantages and disadvantages of three popular quantitative data analysis software: Microsoft Excel, Microsoft Access and SPSS.
|Excel Spreadsheet |
|Cost effective or Free of Charge|
Can be sent as e-mail attachments & viewed by most smartphones
All in one program
Excel files can be secured by a password
|Big Excel files may run slowly|
Numbers of rows and columns are limited
Advanced analysis functions are time consuming to be learned by beginners
Virus vulnerability through macros
|Microsoft Access||One of the cheapest amongst premium programs|
Flexible information retrieval
Ease of use
|Difficult in dealing with large database|
Low level of interactivity
Remote use requires installation of the same version of Microsoft Access
|SPSS||Broad coverage of formulas and statistical routines|
Data files can be imported through other programs
Annually updated to increase sophistication
Limited license duration
Confusion among the different versions due to regular update
Advantages and disadvantages of popular quantitative analytical software
Quantitative data analysis with the application of statistical software consists of the following stages:
- Preparing and checking the data. Input of data into computer.
- Selecting the most appropriate tables and diagrams to use according to your research objectives.
- Selecting the most appropriate statistics to describe your data.
- Selecting the most appropriate statistics to examine relationships and trends in your data.
It is important to note that while the application of various statistical software and programs are invaluable to avoid drawing charts by hand or undertake calculations manually, it is easy to use them incorrectly. In other words, quantitative data analysis is “a field where it is not at all difficult to carry out an analysis which is simply wrong, or inappropriate for your data or purposes. And the negative side of readily available specialist statistical software is that it becomes that much easier to generate elegantly presented rubbish”.
Therefore, it is important for you to seek advice from your dissertation supervisor regarding statistical analyses in general and the choice and application of statistical software in particular.
My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of quantitative data analysis methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy
 Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.
 Robson, C. (2011) Real World Research: A Resource for Users of Social Research Methods in Applied Settings (3rd edn). Chichester: John Wiley.