Quantitative Analysis: Definition, Examples and Process
Data means pieces of facts or information collected from different sources, which is used for research or analysis, turning out into meaningful insights. Data that can be measured, counted, and represented in numbers is quantitative, whereas, data that is categorised based on features is qualitative. Quantitative analysis (QA) involves analysing quantitative data, whereas qualitative analysis consists in analysing qualitative data. In this article, we will shed light on quantitative analysis with examples.
Quantitative Analysis Definition
Quantitative Analysis is the method of piling up measurable and countable data, like revenue, wages, market share, etc., and evaluating them for determining the business's growth and performance. It is more effective in making faultless business decisions. In quantitative data analysis, hypothetical situations or real-world events are expressed in numbers for making predictions and assessing performance.
Both qualitative and quantitative analysis methods are meant for gathering data and extracting meaningful insights from it. The three quantitative data analysis methods for quantifying data are regression analysis, data mining, and linear programming. Quantitative analysis is used in multiple fields, like economics, business, social science, sports, finance, etc.
In the regression analysis, statistical equations are employed for estimating one variable's impact on the other. Linear programming approach involves finding out the optimal solution for any problem. Lastly, the data mining technique is a combination of regression analysis and linear programming. It is primarily used for evaluating a large data set.
Qualitative analysis generally requires in-depth research, as the data is descriptive and conceptual. Researchers need to look into the raw data, find differences or similarities, and develop categories depending upon characteristics or traits. Therefore, data analysis in qualitative research is quite challenging. In the quantitative content analysis, the research is carried out based on the textual and visual materials' features.
Examples of Quantitative Analysis
Quantitative analysis can be better understood by looking at examples. Before we look at the quantitative data analysis examples, we shall see some examples of quantitative data.
- A cup containing one gallon of milk.
- A boy standing behind a tree is five feet tall.
- More than 90% of students choose to go to college after their high school classes.
- A school has a strength of 500 students.
- The car has the capacity of four people.
- I got 100 emails today.
- There are 26 alphabets.
All of the above examples are quantitative. The result of all the above statements is expressed in numbers, which is countable.
You can now clearly discover the quantitative data after observing the above examples. We will discuss some instances that will help you to acknowledge what quantitative data analysis is.
1. Close-ended questionnaires
One of the best examples of quantitative data analysis is close-ended queries. It includes questions having yes/no type answers or multiple-choice questions. In other terms, close-ended questions offer choices for respondents to answer.
2. Machine data analytics
The data generated by the software from several sources is aggregated and visualised is called machine data analytics. The data is gathered from sources, like computers, networked devices, mobile phones, applications, financial transaction records, etc.
3. Structured data
Another example of quantitative data analysis is structured data. It is the data which is firmly edited. Hence, users can easily search for the data in the relational database. The structured data is generally quantitative data. Examples of structured data are emails, prices, dates, etc.
4. Random sampling
In the random sampling approach, every sample collected has an equal and same probability of being selected at any phase of the sampling process. Researchers use random sampling techniques to infer the population data depending on the outcomes of the subset of the population.
Quantitative Analysis Process
The quantitative data analysis process is categorised into three sub-processes, data validation, data editing, and data coding.
1. Data Validation
The data validation process is carried out to ensure whether the data required for analysis is gathered as per the pre-planned rules and standards. It involves four different steps to perform
- Fraud: This step verifies whether or not each respondent was personally interviewed.
- Screening: The screening process ensures that all respondents were selected according to the research guidelines.
- Procedure: It ensures whether all guidelines regarding data collection process are followed duly.
- Completeness: This step checks whether the interviewer has asked all the pre-decided questions to respondents.
The data validation process is quite time-consuming for a large number of respondents. For instance, consider the researcher required to gather the data from 300 respondents from three different cities. In such circumstances, the researcher can reach out to 30 respondents from each city. After that, the researcher can reach out to the remaining respondents through the phone or email.
2. Data Editing
After gathering data and validating it, the next step is data editing. The data collected from respondents may include errors. However, there may be some fields where respondents forget to fill-up, or they may fill the data incorrectly. For such purposes, the researcher needs to carry out necessary data checks, correct the incorrect data, and contact respondents for skipped data. The quantitative raw data must be correct and relevant to perform analysis.
Consider a situation where few respondents might have skipped some fields to fill. The researcher cannot use incorrect or incomplete data for analysis. Therefore, the researcher can follow one of the below four steps:
- The Listwise Deletion Method: The researcher can delete or remove all the rows whose one or more columns are unfilled or missing.
- Computing Mean / Median / Mode: For the missing value in a particular column, the researcher can compute mean, mode, or median of all other values available in that column, and use the resulting value as the missing value.
- Last Observation Carried Forward (LOFC): In this method, the missing value of the column is filled as the value present in its above row.
- Resurveying: The researcher can contact a particular respondent and ask for the missing value.
3. Data Coding
The data coding methods involve the grouping of responses and assigning them with respective values. For instance, grouping responses based on respondents age and assigning values. For the age group 10-20, 20-30, and 30-40, the values will be 0, 1, and 2, respectively.
Software for UX design
Wondershare Mockitt is one of the best pieces of software available for UX design. It is a highly robust online platform that can help users create prototypes and wireframes for several apps. The Wondershare Mockitt tool is used by more than 2,000,000 users and 15,000 organisations across the globe. It offers several features enabling enterprises to co-edit and co-manage their projects seamlessly.
Features of Wondershare Mockitt
The prototyping feature helps users to convert any design into an interactive animated prototype. The Wondershare Mockitt tool is best suited for novice users and helps them to work as efficiently as professionals.
This tool offers a single-click share option to transfer files with others. Multiple collaborators can view and edit a single project in real-time. In software development projects, this tool enables developers to handle multiple projects.
Wondershare Mockitt has a broad set of libraries and templates that help users to create user-friendly interfaces. You can also create your own design and reuse them later.
Quantitative data analysis involves gathering countable data, i.e., quantitative data and computing them for errorless business decisions. This analysis involves in-depth research with minimum bias and accurate results. On the other hand, it does not contain descriptive data. Hence, it has restricted information.