Question

Using your critical thinking skills to understand why they are important; What does a researcher have to do to be ready for data analysis?

Answer 1

Critical thinking is the analysis of an issue or situation and the facts, data or evidence related to it. Critical thinking is a skill that allows you to make logical and informed decisions to the best of your ability. Critical Thinking is, in short, self-directed, self-disciplined, self-monitored, and self-corrective thinking.

Importance Of Critical Thinking:-

You can gain numerous benefits from mastering critical thinking skills, such as better control of your own learning and empathy for other points of view.
Critical Thinking skills teach a variety of skills that can be applied to any situation in life that calls for reflection, analysis and planning.
Critical Thinking is a domain-general thinking skill.
If you work in education, research, finance, management or the legal profession, then critical thinking is obviously important. But critical thinking skills are not restricted to a particular subject area.
Good critical thinking promotes such thinking skills, and is very important in the fast-changing workplace.
Critical Thinking promotes creativity.
Critical Thinking enhances language and presentation skills.
Good Critical Thinking is the foundation of science and a liberal democratic society.
A good critical thinker knows how to separate facts from opinions, how to examine an issue from all sides.
Critical thinkers are less likely to fall for scams or tricks because they approach everything with a healthy amount of skepticism (not easily convinced).

What does a researcher have to do to be ready for data analysis?

Data Analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
For performing Data Analysis Researcher have to be ready with the following steps:-

Step 1: Setting of goals:-

This is the first step in the data modeling procedure. It’s vital that understandable, simple, short, and measurable goals are defined before any data collection begins. These objectives might be set out in question format, for example, if your business is struggling to sell its products, some relevant questions may be, “Are we overpricing our goods?” and “How is the competition’s product different to ours?”

Step 2: Setting priorities for measurement :-

Once your goals have been defined, your next step is to decide what it is you’re going to be measuring, and what methods you’ll use to measure it.

a)Determine what you’re going to be measuring.

b)Choose a measurement.

Step 3: Data Gathering:-

The next phase of the data modeling procedure is the actual gathering of data. Now that you know your priorities and what it is that you’re going to be measuring, it’ll be much simpler to collect the information in an organized way.

There are a few things to bear in mind before gathering the data: Check if there already is any data available regarding the questions you have asked. There’s no point in duplicating work if there already is a record of, say, the number of employees the company has. You will also need to find a way of combining all the information you have.

Data preparation involves gathering the data in, checking it for accuracy, and entering it into a computer to develop your database. You’ll need to ensure that you set up a proper procedure for logging the data that’s going to be coming in and for keeping tabs on it before you can do the actual analysis.

Step 4: Data Scrubbing:-

Data scrubbing, or cleansing, is the process where you’ll find, then amend or remove any incorrect or superfluous data. Some of the information that you’ve gathered may have been duplicated, it may be incomplete, or it may be redundant.

Because computers cannot reason as humans can, the data input needs to be of a high quality. For instance, a human will pick up that a zip code on a customer survey is incorrect by one digit, but a computer will not.

The process involves identifying which data sources are not authoritative, measuring the quality of the data, checking for incompleteness or inconsistency, and cleaning up and formatting the data. The final stage in the process will be loading the cleaned information into the log or “data warehouse” as it’s sometimes called.

Step 5: Analysis of data:-

Now that you have collected the data you need, it is time to analyze it. There are several methods you can use for this, for instance, data mining, business intelligence, data visualization, or exploratory data analysis. The latter is a way in which sets of information are analyzed to determine their distinct characteristics. In this way, the data can finally be used to test your original hypothesis.

The data analysis part of the overall process is very labor intensive. Statistics need to be compared and contrasted, looking for similarities and differences. Different researchers prefer different methods. Some prefer to use software as the main way of analyzing the data, while others use software merely as a tool to organize and manage the information.

Step 6: Result interpretation:-

Once the data has been sorted and analyzed, it can be interpreted. You will now be able to see if what has been collected is helpful in answering your original question. Does it help you with any objections that may have been raised initially? Are any of the results limiting, or inconclusive? If this is the case, you may have to conduct further research. Have any new questions been revealed that weren’t obvious before? If all your questions are dealt with by the data currently available, then your research can be considered complete and the data final. It may now be utilized for the purpose for which it was gathered- to help you make good decisions.