What type of research uses numbers in interpreting and analyzing the data
Data Analysis is an important part of research as a weak analysis will produce an inaccurate report that will cause the findings to be faulty, invariably leading to wrong and poor decision-making. It is, therefore, necessary to choose an adequate data analysis method that will ensure you obtain reliable and actionable insights from your data. Show
Finding patterns, connections, and relationships from your data can be a daunting task but with the right data analysis method and tools in place, you can run through the chunk of data you have to come up with information regarding it. There are different data analysis methods available, this article is going to focus on quantitative data analysis and discuss the methods and techniques associated with it. You will learn about Quantitative Data Analysis in this article. You will also obtain a comprehensive understanding of Quantitative Data Analysis, including the methods and techniques involved. Continue reading to learn more about Quantitative Data Analysis. Table of Contents
What is Quantitative Data Analysis?Image SourceData Analysis can be explained as the process of discovering useful information by evaluating data whereas quantitative data analysis can be defined as the process of analyzing data that is number-based or data that can easily be converted into numbers. It is based on describing and interpreting objects statistically and with numbers as it aims to interpret the data collected through numeric variables and statistics. Quantitative data analysis techniques typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually gotten from avenues like surveys, questionnaires, polls, etc. data can also come from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. Replicate Data in Minutes Using Hevo’s No-Code Data PipelineHevo Data, an Automated No Code Data Pipeline a Fully-managed Data Pipeline platform, can help you automate, simplify & enrich your data replication process in a few clicks. With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 100+ Data Sources straight into your Data Warehouse or any Databases. To further streamline and prepare your data for analysis, you can process and enrich raw granular data using Hevo’s robust & built-in Transformation Layer without writing a single line of code! Get Started with Hevo for FreeHevo is the fastest, easiest, and most reliable data replication platform that will save your engineering bandwidth and time multifold. Try our 14-day full access free trial today to experience an entirely automated hassle-free Data Replication! Data Preparation Steps for Quantitative Data AnalysisImage SourceQuantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. This step is very important and has to be discussed before mentioning the methods and techniques involved because, if the data is not gathered correctly and cleaned, the analysis may not be carried out properly leading to wrong findings, wrong judgments on the hypothesis, and misinterpretation, therefore, leading to decisions made upon statistics that did not accurately represent the dataset. To prepare data for quantitative data analysis simply means to convert it to meaningful and readable formats, below are the steps to achieve this:
Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article which is the methods and techniques of quantitative data analysis. Methods and Techniques of Quantitative Data AnalysisQuantitative data analysis involves the use of computational and statistical methods that focuses on the statistical, mathematical, or numerical analysis of datasets. It starts with a descriptive statistical phase and is followed up with a closer analysis if needed to derive more insight such as correlation, and the production of classifications based on the descriptive statistical analysis. As can be deduced from the statement above, there are two main commonly used quantitative data analysis methods namely the descriptive statistics used to explain certain phenomena and inferential statistics used to make predictions. Both methods are used in different ways having techniques unique to them. An explanation of both methods is done below.
1) Descriptive StatisticsDescriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers gotten from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include:
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Sign Up here for a 14-day free trial and experience the feature-rich Hevo. 2) Inferential StatisticsIn quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values and descriptive statistics is all about explaining details of a specific dataset using numbers, but, it does not explain the motives behind the numbers hence, the need for further analysis using inferential statistics. Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences. They are various statistical analysis methods used within inferential statistics, a few are discussed below.
ConclusionThis write-up has talked about quantitative data analysis showing that it is all about analyzing number-based data or converting data into the numerical format by using various statistical techniques to deduce useful insights. It went further to show that there are two methods used in quantitative analysis, descriptive and inferential stating when and how each of these methods can be used by giving techniques associated with them. Finally, to carry out effective quantitative data analysis, one has to consider the type of data you are working with, the purpose of carrying out such analysis, and the hypothesis or outcome that may be gotten from the analysis. Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks. Visit our Website to Explore HevoHevo Data with its strong integration with 100+ Data Sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice but also transform & enrich your data to make it analysis-ready. You can then focus on your key business needs and perform insightful analysis using BI tools. Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements. Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts. |