Research Writing

What is quantitative data in research/Characteristics/types

The quantitative data are measured with numerical values usually answer questions like How many? How often? Etc. Advantages of quantitative data

This type of data can also be defined as a group of quantifiable information that can be used for mathematical calculations and statistical analyzes that inform real-life decisions. 

Characteristics of quantitative data

The most important characteristics of quantitative data are the following:  

  • Numerical representation : Quantitative data takes numerical values ​​with numerical properties. Unlike categorical data that take numerical values ​​with descriptive characteristics, quantitative data exhibit numerical characteristics.
  • Order : There is a scale or order of quantitative data. For example, the numbers 1 through 3 can be written as 1,2,3 and 3,2,1 when sorted in ascending and descending order, respectively.
  • Arithmetic operation : You can perform arithmetic operations such as addition and subtraction of quantitative data. Almost all methods of statistical analysis can be carried out using quantitative data.
  • Standardized scale : Quantitative data have a standardized measurement scale.
  • Analysis : Quantitative data can be analyzed using descriptive and inferential statistical methods, depending on the objective of the investigation.
  • Data visualization : Some of the data visualization techniques adopted by quantitative data can be the scatter plot, dot plot, stacked dot plot, histograms, etc. 

Quantitative data types

Quantitative data types can be divided into discrete data and continuous data . Below we define each one of them: Advantages of quantitative data

Discrete data

Also known as attribute data, discrete data cannot be divided into smaller units. It is generally counted in whole numbers and there is nothing like half a value. 

Discrete data consists only of counting numbers and as such cannot be measured. It can be the number of students in a class, the number of days in a year, the age of an individual, etc. 

When trying to identify discrete data, we ask the following questions; You can count? Can it be divided into smaller parts? 

Discrete data can be said to be countable finite or countable infinite. An example of finite accounting data is an arbitrary set A = {1, 2, 3,…, n; where n is less than infinity} while that of an infinitely countable data is an arbitrary set B = {1,2,3,…}.

Continuous data

Another of the existing quantitative data types is continuous data, these consist of taking numerical values ​​that can be significantly divided into smaller units. Advantages of quantitative data

Unlike discrete data that cannot be measured, continuous data can be placed on a measurement scale such as weight, length, time, etc.

Continuous data can be said to be infinitely finite or infinitely infinite. For example, consider the cumulative grade point of students in a class, measured on a 5-point scale. 

A student can give any grade between 0 points and 5 points, including numbers like 1.573, 4.5, 2.6981, etc. We classify this as infinitely innumerable continuous data because it has an upper bound (5) and a lower bound (0). 

An example of an infinite number of data is the set of real numbers, R = {…, -1, 0, 1,…}. In this case, the data has no upper or lower bound. 

Continuous data can also be divided into two types: Advantages of quantitative data

  • Interval data : Defined as the type of data that is measured along a scale, in which each point is placed at the same distance from each other. It is an extension of ordinal data, with a standardized scale as opposed to the previous one.
  • Relationship data : It is an extension of interval data. It is the last when we talk about data measurement because it informs us about the order, the exact distance between the units on the scale and it has an absolute zero.

Examples of the types of quantitative data in an investigation

After knowing the types of quantitative data, we have for you some examples of how to apply them in research: 

Data projection: Researchers project future data using algorithms and mathematical analysis tools. 

For example, a company that is about to launch a new product to market will analyze quantitative data from previous research to predict an increase or decrease in sales. 

Census : It is used to acquire and record information about the members of a certain population. An example of this is the application of censuses by the government to predict which sector of the economy needs money and how much they need.  Advantages of quantitative data

Annual income : When establishing the selling price of a product, companies use quantitative data on the annual income of a person or household to determine their purchasing power. This exercise is part of the business research process and can be done before launching a new product or increasing the price of an existing product. 

Counter : Many e-commerce companies use this to determine the number of website visits they receive daily, the number of product downloads in the app store, the number of users, etc. The figures are generally generated automatically through pre-programmed codes. 

Customer satisfaction survey : This is a case of quantification of qualitative entities used by companies to improve their customer service. For example, telling a customer to give a rating ranging from 0 to 10 to evaluate the experience will help the restaurant decide whether or not it should make changes. 

Steps to perform quantitative data analysis and interpretation

Knowing the types of data is important. However, knowing how to analyze and interpret such information is necessary to make better decisions. Here we have for you the two steps to carry out this process: 

1.Data preparation

This is the first stage for the analysis and interpretation of quantitative data, where the raw information is converted into something meaningful and readable. There are four data preparation steps:

  1. Data validation : This is done to find out if the data collection was done without any bias. Researchers perform this step by choosing a random sample from a large population.
  2. Data editing: Large data sets can inevitably include errors, and that is why they need to be edited. During this process, the data is inspected to verify its integrity and consistency.
  3. Data entry and coding : This is the process of quantifying qualitative data to facilitate analysis. It involves grouping and assigning values ​​to survey responses.
  4. Data transformation : This is the process of changing the data to a new format. 

2. Quantitative data analysis methods

After completing the first stage, we are ready for data analysis. There are two main quantitative methods: descriptive statistics and inferential statistics.

  • Descriptive statistical method: Researchers make use of descriptive statistics to summarize quantitative data. It is often used when analyzing a single variable, and as such it is sometimes called univariate analysis. 
  • Inferential statistical method: This method measures the relationship between multiple variables to generate results and infer conclusions. Some examples of inferential statistics include; correlation, regression, ANOVA , etc. 

There are also other approaches to inferential statistics that are used to analyze real-life data or surveys. They include: Advantages of quantitative data

  • Cross Tabulation : Use basic tables to draw inferences between different data sets. These are placed in the same row or column. Inferences are drawn by studying the similarities and differences between the data in each row or column, as the case may be.
  • MaxDiff Analysis : Used to measure the respondent’s preference for a particular set of options. Select the one you prefer the most and the one you prefer the least. These data are compared for various samples to infer a conclusion.
  • TURF analysis : It is used mainly by companies to determine the effectiveness of their marketing strategies. For example, a business can run the same ad on different social media platforms and analyze the number of customers reached to determine the best place to run ads.
  • Gap Analysis : Measure performance and how to bridge this gap. Use a side-by-side matrix to measure the difference between these performances.
  • Text analysis : Used to extract useful information from a text document. Computers use this method to break up unstructured text documents into structured data. 

Advantages of quantitative data 

The main advantages of quantitative data are as follows:

  • Quantitative data are compatible with most statistical analysis methods, allowing for a larger study, using different statistical methods. Advantages of quantitative data
  • It summarizes the collected data and rationalizes it into relevant information. Quantitative data is numerical in nature and as such encodes qualitative data into numbers prior to analysis.
  • Facilitates the data collection process for research. Therefore, make comparisons between categories and over time. This is due to the fact that quantitative research uses a standardized procedure.
  • By encoding qualitative data in unknown numbers, researchers can eliminate the personal bias that can arise in handling information.

Disadvantages of quantitative data 

Some of the disadvantages of quantitative data are: 

  • They collect a set of data and may end up missing relevant information.
  • The results do not provide an elaborate explanation of human feelings and perceptions.
  • The research is carried out using standardized rules that give results that may not be applicable in the real world setting.
  • Uses preset responses that don’t capture enough details and human emotions.
  • Preset responses are a reflection of the researcher’s point of view rather than that of the respondent. 

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