Quantitative Data Analysis

In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. 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 such as organizational culture of Agro Bravo Enterprise, leadership styles exercised, the levels of frequency of management-employee communications etc.

 

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 and integrating secondary data within discussions and analyses in a logical and unbiased manner.

 

Let’s take another example. A dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam for a specific period of time with the use of correlation method, where FDI and GDP have been specified as the two variables produces a 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 contains advantages and disadvantages of three popular quantitative data analysis software: Microsoft Excel, Microsoft Access and SPSS.

 

  Advantages Disadvantages
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

·         Expensive cost

·         Limited license duration

·         Confusion among the different versions due to regular update

Advantages and disadvantages of popular quantitative analytical software

Quantitative Data Analysis My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection of the research area to submitting the completed version of the work before the deadline.

 

John Dudovskiy