If you are a college student working on your first research project and have no idea how to write my paper, collect, and analyze data then you are in the right place. And if you are a researcher trying to improve your thesis then this article might help you improve your thesis.

Collection precedes analysis

Data can be collected using 2 methods: Quantitative and Qualitative. The most common technique in the quantitative method for collecting data is using questionnaires or surveys. Here information is collected face to face or by telephonic conversations with the respondents. Data is also collected through existing records in databases and archives or from certain documents e.g. census data. However, there are greater chances of getting biased results through this method, as it is not observed by the researcher himself.

In a qualitative method, the data is collected through observations wherein the researcher observes the sample’s behavior, movement instead of directly questioning the sample. Another method is in-depth interviews with individual respondents or key informants. This is very useful for paper writing service and the questions asked are open-ended and not fixed as that in questionnaires. Making use of a focus group is another method in a qualitative study. In this method subjects having similar traits or experiences related to the research conducted are selected (8-10 people) and the group is used to generate data and insights related to the research. The group helps solve problems related to the research while answering diverse unscripted questions.

Let’s move on to the analysis.


Below is the process of analyzing data. It won't explain how to do it for the reasons, but it should get you started on the steps that you need to know.

  1. Acquire the data: This is the process where after having a question you query the data to answer it. I call this data raw. It is not raw per se but it often is not exactly in the state I want it.
  2. Model: For most people, this is what data analysis is all about. Running statistical tests, creating models, overall, answering the question. This is where the magic happens.
  3. This means that you understand the task at hand. Do you run a t-test? Do you fit a random forest? Is it NLP? The options have filled thousands of books, blogs, podcasts, and Quora answers. :)
  4. This means that you understand the data. Does my procedure violate any assumption of the test or algorithm? What feature engineering is involved in?
  5. Interpret the results: Depending on the question, there's a way of evaluating the answer found (or not) in the modeling step. It can be validating the results of an algorithm, establishing confidence in a statistical test, or simply accepting that there's nothing to report.
  6. Report: Last but not least, reporting which can be many things. It can be an actual report, with text and visualizations, it can be an interactive data product that end users can interact with, and can be an automated data product (aka live machine learning product) interacting with other systems.

Data analysis takes years of learning and training, addressing multiple problems, etc. if you go through the dissertation written by essay writing service, you will realize data analysis can go from simple statistical summaries to complex machine learning solutions.

There are many caveats to each point. For instance, you can acquire multiple raw data which means multiple cleaning and at some point joining data sources. However, you can consider the assistance of professional researchers; and I’m sure their assistance will have a positive impact on your dissertation.