Exploratory Data Analysis Research Paper

An exploratory data analysis research paper is a study task that is meant to provide the researcher with an in-depth understanding of some set of data. Before any conclusive statements can be made by the researcher, thorough analysis of the data must be performed so as to ensure that indeed conclusions can be reached.

The problem with statistical or quantitative research is that it requires complex and often expensive equipment such as computer software for analysis and interpretation. This presents a number of problems for researchers studying this type of subject matter because not everyone has access to these tools, therefore rendering them unable to perform full exploratory research on their own data sets.

How to write an exploratory data analysis research paper

Writing an exploratory data analysis research paper include some methodological approach for gathering data; such research may be observational, experimental, or in the form of surveys and questionnaires. In addition, they will focus on statistical analyses that are conducted on the datasets to give the researcher an idea as to what exactly is happening with his or her study and data set.

This sort of paper has many different uses throughout academia: it can often be used as part of a doctoral dissertation in order to test various hypothetical ideas through exploratory research before moving on to conclusive ones; it can also serve as the basis for any number of other quantitative projects that rely on statistical analysis methods or techniques.

The main focus of an exploratory data analysis research paper is the data itself and not necessarily the conclusions drawn from it; therefore, this sort of paper will explain how the data was gathered, what type of analysis was performed on it in order to understand what exactly is happening with that set of information. The most important part about an exploratory data analysis paper is that it must be able to establish a solid foundation for future studies and research efforts involving those same datasets or other similar ones.

This specific type of research paper relies heavily upon the researcher's ability to form hypotheses about their own sets of data based on thorough statistical analysis; such hypotheses may be used as focal points for any number of conclusive papers regarding those same dataset(s). This makes exploratory quantitative research very important for future researchers because it helps establish a baseline for further statistical studies that rely on the same data set(s).

The results of an exploratory data analysis research paper, much like all other sorts of quantitative research papers, should be presented in a clear and concise manner; they also must contain specific information regarding how the study was conducted as well as what types of data were analyzed using which methods. The conclusions resulting from this sort of research should always be clearly stated and justified by solid evidence provided by the researcher's findings.

This type of work is often overlooked because most researchers are more concerned with conclusive, definitive answers or theories rather than working to identify potential problems with their own data sets . Exploratory quantitative research is invaluable because it allows for the possibility of future conclusions being drawn from the same data sets; in addition to this, it allows for a much deeper understanding of any sort of data that is obtained from other sources.

Appropriate analytical procedures must be applied in order to find out what patterns exist within a set of given data. The purpose of exploratory analysis in a research paper is not only to determine whether or not such patterns exist, but also to discover the nature and structure of these hidden relationships. A researcher who uses exploratory analysis may compare samples based on different variables using methods like factor analysis or multidimensional scaling (MDS) . He or she might explore correlations between variables by examining scatterplots with best-fit lines, perhaps smoothing those plots using lowess regression curves . A researcher can also use exploratory analysis to uncover insights into the shape of a distribution by plotting histograms and density plots .

A researcher might also apply exploratory analysis when studying one or more specific variables. He or she may conduct many different types of statistical tests on the same dataset, perhaps looking for trends in mean values before applying factor analysis to identify underlying clusters . A researcher can even analyze data using descriptive statistics , examining its shape and form, without attempting any inferential statistics at all. In fact, some analysts choose this more qualitative approach when they wish to hold off from making generalizations based on their findings until absolutely sure that the data is truly representative of a larger population (as opposed to making unfounded generalizations). No matter what methods are used it is important to understand that exploratory analysis is simply the first step in a larger research project; it serves as a springboard for more conclusive and detailed studies.

Constructing a comprehensive picture of the underlying structure within a set of data requires careful consideration, perhaps even multiple rounds of analysis . Through exploratory quantitative research, researchers gain a better understanding of the intricacies involved with their own datasets and are able to produce far more extensive conclusions than would be possible had they not performed such an analysis before conducting much more detailed statistical work.

The main goal during this type of study is to use certain methods or procedures in order to visualize or make clear certain patterns that might exist within the given data ; these patterns may include clusters, outliers, missing values, etc. Different procedures are used by researchers based on the type of data that they have, and many statistical methods may be applied during exploratory analysis. A researcher might choose to graphically display a sample of his or her data using box plots , scatterplots , histograms , density plots, or other types of graphics in order to gain a better understanding of its characteristics .

The implications for exploratory quantitative research are extensive. Knowing which patterns exist within one's dataset allows him or her to make more informed decisions about the best method(s) for tackling additional questions related to that same set of data; it also enables the researcher to create much more thorough conclusions regarding what has actually been discovered as opposed to making unfounded generalizations based on only a portion of the that data.

Indeed, it is this type of review and analysis that allows a researcher to draw conclusions about his or her research project as a whole; it leads him or her to be able to provide answers based on patterns discovered within the given dataset rather than simply providing explanations based on what he or she would like to see. At the same time, however, exploratory quantitative research might lead a researcher in unexpected directions. He or she may discover new trends, clusters , outliers , etc., that were not originally expected ; such findings help researchers expand their understanding of their own data and provide them with new questions for future studies.

Steps in writing an exploratory data analysis research paper

To start writing an exploratory data analysis research paper, you will need to choose a research topic. You can either write a thesis on your own or be part of a group of researchers who explore the same area. However, it is important to do extensive research before you decide on your topic. There are two approaches in writing an exploratory data analysis paper: exploratory approach and confirmatory approach .

When writing an exploratory data analysis paper, there are several steps that you should keep in mind as these will help you produce your work easily and duly.

  • Choose research methodology

The first step in writing an exploratory data analysis paper is to choose a good journal. After that, you need to figure out what kind of article you are going to write; it ideally should be related to your research topic. The word count will also play a key role in determining the nature of your paper (whether it is a qualitative or quantitative paper).

  • Choose appropriate journal and section

So now that we have chosen our subject matter, it is time for us to think about whether this kind of analytical papers will suit a particular journal. Even if you have the perfect subject and idea for this type of analysis, your work might still get rejected because not all journals accept such papers. So before you start working on the analysis, it is best to choose the perfect journal that would publish your paper.

  • Get familiar with methodology before writing:

Now that you have selected a good journal and decided on what kind of article you are going to write, it is time for you to understand the process involved in experimental design or any other qualitative procedure. This will help while you are designing your experiment and framing hypothesis. The more comfortable you get with this methodology, the better research paper can be written. So at this stage, do read enough about the subject matter and get thoroughly familiar with it before starting out on actual data collection and analysis methodologies (at least up until chapter 3). You might miss even simple concepts but once these things get clear everything else looks so simple.

  • Figure out type of data and available tools.

Now it is time to figure out the kind of data you are going to work with (qualitative or quantitative). This will help us choose the right tool for data analysis. In short, quantitative analysis requires statistical software program such as STATA, SPSS, StataCorp LLC (with R-package), SAS Institute Inc., etc., whereas qualitative analysis requires graphic software for visualizing results, like MS Power Point , LaTeX , Word . The level of math needed to do these types of analyses also varies considerably; statistics involves a lot more complex set of computations than graphs. You can get much better in your quantitative skills by taking up 3 to 4 courses in statistics which will help you a lot even in reporting your quantitative findings.

  • Collect data:

Now that we are clear about the type of data analysis, it is time to actually do some collection of data for the analysis. This process requires setting up a protocol and carrying out actual experiments or field work, etc., depending on the nature of our research question. Typically this step involves a lot of moving around from place to place for research purposes; it can also be an online survey or any other kind of information gathering exercises. At this point you need to establish protocols and procedures as well as sample size calculation so that you don't get bogged down by complexities while analyzing your results later on.

  • Analyze Results:

Now we are all set with the data in hand. At this stage, analysis becomes the most important part of the research paper writing process. Here we need to choose from various available tools and methods of data analysis, depending on the nature of our study (e.g., experimental design). Analysis will also vary across different types of quantitative vs qualitative data; e.g., when analyzing qualitative results you might have to do coding or categorizing of responses before any statistical procedures can be applied. It is always advised to keep an open mind for new ideas at this point but exercise judgment as well, since no analytical tool is perfect; there is always some bias involved even if it goes unrecognized by users sometimes.

  • Writing a draft:

This is the most time consuming part of a research paper writing process. There are many drafts required while writing a good manuscript, like multiple review drafts with comments from peers and editor(s), etc. Make sure you have done enough editing in your draft before submitting it for peer-review.

  • Peer review:

At this point you will get to know three things; first whether your article is accepted or rejected (if it was rejected then you can try another journal as mentioned above). Secondly, if the journal accepts your article then how many revisions they want you to do? This also varies between journals but typically anywhere from 2 to 6 revisions!! Thirdly, what kind of response did the reviewers give after reviewing your article? This helps you to improve your paper writing skills in the next draft.

  • Publishing:

You get published! Congratulations! Now you will also need to promote yourself as a researcher at this point; it is good to send copies of your published article to various journals as well, so that they can take note of your work and call you for reviewing others' articles if necessary. You can also contact the press about your findings and publish more on media companies (like newspapers) or research websites like ResearchGate . This way you will be able to make a name for yourself in the field of research which is key if you want other people follow up on your work or conduct similar studies based on the same hypothesis.

EDA analysis In Research Paper Writing

A researcher might also apply exploratory analysis when studying one or more specific variables. He or she may conduct many different types of statistical tests on the same dataset, perhaps looking for trends in mean values before applying factor analysis to identify underlying clusters . A researcher can even analyze data using descriptive statistics , examining its shape and form, without attempting any inferential statistics at all. In fact, some analysts choose this more qualitative approach when they wish to hold off from making generalizations based on their findings until absolutely sure that the data is truly representative of a larger population. Indeed, this is the only way to avoid making unfounded generalizations.

In addition to looking at the shape and form of data, a researcher might also want to use exploratory analysis when attempting to identify possible outliers within that dataset; an outlier can be defined as any single piece of information within a set of data that does not match its general pattern. Outliers are common in large sets of data, but they can indicate something unusual or even problematic about the entire dataset if they are left unidentified.

No matter what methods are used it is important to remember that exploratory quantitative research is simply the first step in a larger research project; it serves as a springboard for more conclusive and detailed studies . Constructing a comprehensive picture of a data set is often the first step in a larger project, but it should not be confused with the conclusions that will eventually be drawn.

Thus, exploratory analysis can greatly benefit researchers by allowing them to make informed decisions about what methods and procedures are best suited for answering their research questions; indeed, without this type of preliminary study these decisions would likely be made on pure conjecture . The implications of such initial planning cannot be overstated and they include all aspects of an actual research project from its design all the way through to presenting findings. Without these important first steps a researcher might never even gather adequate information or find meaningful patterns within his or her given data ; because exploratory analysis allows him or her to determine which potential techniques might be appropriate for the task this will almost certainly never be a problem.

An initial investigation of the data may also involve calculating descriptive statistics and using graphical techniques, such as histograms, boxplots, scatter plots or stem-and-leaf displays, to summarize the data. This step in the exploration is where I've spent most of my time; learning different kinds of visualizations, getting familiar with ggplot2 's capabilities, playing around with dendrograms ... All these exploratory steps help me determine what might be interesting to look at more closely. If you're interested what I've done, please have a look at the code and the notebook where I explain my thought process and my approach.

Another important data visualization design principle is abstraction . A good visualization should be simple, clear and concise; it also needs to adhere to principles of good design. It shouldn't contain unnecessary clutter or noise. The goal is to turn data into information that can be easily digested by human brain willing to read the story shown in the chart or plot. This applies both for exploratory analysis as well as production-line type of investigations. Data with its patterns exposed thanks to proper visualizations might reveal answers we want, but not in a way we were expecting (and not necessarily desirable). And so the job of the data analyst is to make it possible for other people to read his or her mind.

The main principles that define a good visualization in research are:

  • good design: most importantly, the chart should not be cluttered with unnecessary elements (noise)
  • abstraction: we express information through charts only by showing what is relevant and hiding what is irrelevant; data reduction exercise
  • simplification: making complex data structures simple by visualizing their essence using different means.

Data visualization in its most basic form can be used as exploratory analysis by analyzing how well the given dataset fits into some predefined patterns. For example, you can plot time series to determine if there are any patterns trends in the data. Or you can try to find some kind of spatial correlation between your variables (like GDP per capita vs life expectancy). Sometimes, however, we don't know what kind of patterns might be present in our datasets and thus we need to explore what's available first. And so this is where exploratory analysis comes into play: looking for potential patterns and doing this iteratively until enough information has been mined from the dataset.

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Exploratory data analysis homework help

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Exploratory data analysis is analyzing your dataset in an attempt to understand it more completely and determine if there are any patterns or trends within your data. While EDA also produces artifacts such as graphs and tables, its main goal is understanding the structure of your problem and how the different variables correlate with each other.

Why use EDA analysis when writing a research paper?

There are two main reasons why one might want to perform EDA instead of simply using summary statistics on their data, answering questions about their dataset (by hand or by using software), and going on with their analyses:

  1. tests of statistical hypotheses and models are usually more powerful when the data has been inspected beforehand,
  2. Since EDA is a form of visual analytics it can be useful in an exploratory setting where only some aspects of your dataset may be known before looking at the data.

It should also be noted that while you could perform some amount of EDA after answering many questions about your data with summary statistics or using software such as Excel's PivotTables, SAS' PROC FREQ, or Stata's pairwise command, these techniques are generally less powerful than hypothesis testing (as they lack the ability to truly test whether two parameters come from different distributions) or much less flexible than performing EDA on the fly rather than constructing a fixed graph in advance.

There are also some benefits to doing EDA before, rather than after making hypotheses about your data:

It is easier and often safer to discover how well a hypothesis fits the data by exploring it visually rather than testing it.

You can be more efficient when using EDA since you do not yet have strong expectations for what your data should look like.

You need fewer statistical techniques when performing EDA because you often do not need to test whether two parameters come from different distributions (as is the case when performing hypothesis testing) or construct many different graphs to visualize the data.

Understand what your dataset looks like before going on with other analyses in order to: 

  • discover missing data and structure ,
  • have a better grasp of what you are analyzing,
  • be able to see possible trends during your exploratory phase and thus organize your set of hypotheses wisely. Make sure all appropriate information is covered by looking at it visually and that basic relationships between variables can be recognized. some of the most important and surprising results have come from EDA.
  • You can gain much information through EDA since there are numerous different methods for doing it, which makes it an interesting task often done at the very beginning of a research paper process when one has little prior information about their dataset (other than knowing that they will be analyzing its information).

Good examples of basic relationships to explore in your data:

  • the number of observations varying with the values or groups of a variable summary statistics: mean, median, variance/standard deviation specific patterns within each group of observations many potential patterns may emerge while performing exploratory analysis.

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