Quantitative research defined
Quantitative research attaches more importance to the use of numeric values when collecting, measuring, analyzing, and interpreting data (as opposed to qualitative analysis. Researching with quantitative methods can help find patterns and averages, test causal relationships, forecast future patterns/trends, and generalize sample results to larger populations. It relies heavily on the use of statistical methods and principles.
Data used in quantitative research can be primary or secondary in nature. Primary data are those generated by the researcher through the use of valid and reliable procedures and instruments such as questionnaire surveys, interviews, and others. In contrast, secondary data are data that the researcher obtains from external sources, e.g., government agencies, company records, etc.
There are different kinds of quantitative research. Among them are experimental research, correlational research, cross-sectional studies, longitudinal studies, surveys, etc.
What is data analysis?
The Cambridge Dictionary defines data analysis as the process of examining information, especially using a computer, in order to find something out or to help with making decisions.
Data analysis can also be defined as a process of inspecting, cleaning, summarizing, transforming, and modeling data with an intention of unraveling useful information that can help in arriving at logical conclusions and decisions.
Thus, the main purpose of data analysis is to interpret data meaningfully and use those interpretations to make informed decisions.
Quantitative analysis involves the organization, measurement, calculation, or manipulation of numeric data in order to identify meaningful patterns from which valid and reliable conclusions can be made.
Some tips on how to perform quantitative analysis
Have thorough knowledge of your data measurement instruments
You cannot analyze what you don’t understand. Therefore, it is imperative that you have a thorough understanding of all your data analysis techniques and procedures. This means that you have to delve deeply into statistics if you want to develop an ability to independently analyze quantitative data.
Strong knowledge of statistics will strengthen your data analysis enabling you to determine the statistical significance of your results, among others. It will also help your discussion of findings. Moreover, you need a sound knowledge of statistics to help see select the most suitable of the various measurement techniques available for your study.
Collect valid and reliable data
In quantitative research, the process of data collection follows rigid scientific protocol. Collected data must be trusted by all stakeholders before conclusions [and recommendations] arising from them are accepted. This is where validity and reliability come in.
Validity refers to the extent to which an instrument measures exactly what it purports to measure and nothing else. Validity is always specific to some particular use. An instrument may be valid for one purpose or context, but invalid for another. Hence, the researcher must ask himself or herself if the selected instrument is valid for the purpose within the context of the research methodology and design. For example, an instrument that has high validity in an economics thesis may be unsuitable for a psychology thesis methodology. Also, an instrument may be valid for one culture or geographical setting, but not valid for another.
Reliability refers to the extent of consistency with which an instrument measures what it is measuring. An instrument is regarded as reliable if it produces the same results repeatedly, or if similar results are consistently derived under the same or slightly different research conditions.
Learn how to correctly use statistical software
Having sound knowledge of statistical theories and principles is a necessary condition for analyzing quantitative data effectively [as noted above]. But in today’s computer-driven world, you need more than just theoretical knowledge. From SPSS to STATA and Eviews, several statistical software have been developed and continue to evolve to help students and researchers with computations that may not have been possible manually or with calculators. Therefore, not only should you have sound theoretical knowledge of statistics but you should also have the practical know-how to operate modern statistical software.
If you have opted for any software for your data measurement, needless to say that you must comprehensively study the operational mode of such software in relation to the statistical techniques the software will measure with the data fed into it. This will help you analyze [and interpret] authoritatively without any fear of contradictions.
Present your organized data and explain your measurement techniques
Before analysis, the quantitative data to be analyzed must be presented in a very legible format, usually in a table or any other format (according to the nature of the data). If it is secondary data in tabular format, be sure to indicate the source(s) of the data under the table. Presentation of your data makes it easy for other researchers to measure them with the same techniques so as to validate or dismiss your findings. Be sure to explain all the measurement techniques used to derive results.
Commence with descriptive statistics
The process of quantitative data analysis usually commences with descriptive statistics which can be used to summarize, organize and present the characteristics or properties of data. Measures of central tendency are very popular techniques for describing data sets. Examples are mean, median, and mode. Data sets can also be described using measures of variability or dispersion such as variance, standard deviation, kurtosis, etc.
After introducing the data via descriptive statistics you can now move to the more rigorous inferential statistics which uses sample data to make generalizations and predictions about the larger population of subjects. Some measurement techniques for inferential statistics include ANOVA, regression analysis, etc.
Add computer printouts for clarity and insight
It is very necessary to insert the printouts of all the computed outputs of the various measurement techniques used by the software to process collected data. These can include tables, graphs, charts, etc. Ensure that you accurately explain/interpret every computer output based on your knowledge of both the measurement technique and the software used. When in doubt about any aspect of the analysis, try and seek expert knowledge to clarify things further.
Be sure to indicate both the software and software version used under any diagram that analyzes data. If you have surplus data diagrams that you think will help offer more insight into your thesis, attach them in the appendix section.
Conclusion
Data analysis is an important component of every thesis because it is the major reason for collecting data for the research. It helps the researcher to identify and understand the patterns, trends, and themes in the data which can then help him or her when discussing the findings. Quantitative data analysis entails the systematic organization and manipulation of numeric data for meaningful purposes or uses.
