Data Analysis 7 Research Methodology Essay
Data Analysis 7 Research Methodology Essay
Consider the following problem: Astronauts on extended missions, such as those who may go to Mars, may have increased anxiety levels, which can be very problematic in enclosed areas or in space suits. You wish to determine which method of anxiety reduction is best without affecting safety or astronaut performance/health. The three options are medication, meditation practice, or both. Based upon this information, write the data analysis portion of a research proposal (as outlined in Chapter 5 of the textbook) to include the data needed, how the data would be collected, methodology, and treatment(s). Be sure to use a correlational, quasi-experimental, or experimental design as outlined in the readings. Identify issues related to Type 1 and Type 2 errors in the data analysis.
What Is Data Analysis?
Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. There are several methods and techniques to perform analysis depending on the industry and the aim of the analysis.
All these various methods for data analysis are largely based on two core areas: quantitative methods and qualitative methods in research.
Why Is Data Analysis Important?
Before we go into detail about the categories of data analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.
Let’s start with customers, arguably the most crucial element in any business. By using data analysis to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more.
In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.
From a management perspective, you can also benefit from analyzing your data as it helps you make business decisions based on facts and not simple intuition. For example, you can understand where to invest your capital, detect growth opportunities, predict your incomes, or tackle uncommon situations before they become problems.
Like this, you can extract relevant information from all areas in your organization, and with the help of a dashboard software, present the data in a professional and interactive way to different stakeholders.
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7 Essential Types Of Data Analysis Methods
Before diving into the seven essential types of data analysis methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive analysis up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.
a) Descriptive analysis – What happened.
The descriptive analysis method is the starting point to any analytic process, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights to your business.
Performing descriptive analysis is essential, as it allows us to present our data in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, but it will leave your data organized and ready to conduct further analysis.
b) Exploratory analysis – How to explore data relationships.
As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there’s still no notion of the relationship between the data and the variables. Once the data is investigated, the exploratory analysis enables you to find connections and generate hypotheses and solutions for specific problems. A typical area of application for exploratory analysis is data mining.
c) Diagnostic analysis – Why it happened.
One of the most powerful types of data analysis. Diagnostic data analytics empowers analysts and business executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.
Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics, e.g.
c) Predictive analysis – What will happen.
The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Like this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.
With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge on the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.
e) Prescriptive analysis – How will it happen.
Another of the most effective types of data analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.
By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key business areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics, and others.