7 Types of Data Analysis in Research

Types of Data Analysis in Research

What is Data Analysis?

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. I imagine you can see why that’s important in research.

Note: An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings.

Data analysis is useful in drawing certain conclusions about the variables that are present in the research. The approach to analysis, however, depends on the research that is being carried out. Without using data analytics, it is difficult to determine the relationship between variables which would lead to a meaningful conclusion. Thus, data analysis is an important tool to arrive at any verifiable and logical conclusion.

With that being said, let’s look at the 7 types of data analysis in research.

7 Types of Data Analysis

Exploratory Data Analysis (EDA)

Exploratory Data Analysis refers to the critical process of performing initial investigations on data such as to discover patterns, spot anomalies, test hypothesis and confirm assumptions which would help to summarize statistics and graphical representations.

It is one of the types of analysis in research which is used to analyze data and establish relationships which were previously unknown. They are specifically used to discover and form new connections and also used for defining future studies or answering the questions pertaining to future studies.

The answers provided by exploratory analysis are not definitive in nature, but they provide little insight into what is coming. This approach of analyzing data utilizes visual methods.

Graphical techniques of representation are used primarily in exploratory data analysis and some of the most used graphical techniques are histogram, Pareto chart, stem and leaf plot, scatter plot, box plot, etc. The drawback of exploratory analysis is that it cannot be used for generalizing or predicting precisely about the upcoming events. The data provides a correlation which does not imply causation. Exploratory data analysis can be applied to study census along with a convenient sample data set.

Explanatory Data Analysis

Explanatory Data Analysis is also known as Causal Data Analysis. This type of data analysis determines the cause and effect relationship between variables. The analysis is primarily carried out to see what would happen to another variable if one variable changed. Causal model is said to be the gold standard amongst all other types of data analysis. It is considered to be very complex and the researcher cannot be certain that other variables influencing the causal relationship are constant especially when the research is dealing with the attitudes of customers in business.

Often, the researcher has to consider psychological impacts that even the respondent may not be aware of at any point in time and these unconsidered parameters impact the data that is analyzed and may affect the conclusions.

Predictive Data Analysis

I almost feel like I don’t have to explain this method. Much like the name suggests, Predictive Data Analysis involves using methods which analyze the current trends along with the historical facts to arrive at a conclusion that makes predictions about the future trends of future events.

The prediction and the success of the model depends on choosing and measuring the right variables. Predicting future trends is very difficult and requires technical expertise in the subject. Machine learning is a modern tool using interactive analysis for better results. Prediction analysis is used to predict the rising and changing trends in various industries.

Analytical customer relationship management, clinical decision support systems, collection analytics, fraud detection, and portfolio management are a few of the applications of Predictive Data Analysis. Forecasting about future financial trends is also a very important application of predictive data analysis.

Inferential Data Analysis

Inferential data analysis is amongst the different types of analysis in research that helps to test theories of different subjects based on the sample taken from the group of subjects. A small part of a population is studied, and the conclusions are extrapolated for the bigger chunk of the population. The goals of statistical models are to provide an inference, or a conclusion based on a study of the small amount of representative population. Since the process involves drawing conclusions or inferences, selecting a proper statistical model for the process is very important.

The success of inferential data analysis will depend on proper statistical models used for analysis. The results of inferential analysis depend on the population and the sampling technique. It is very crucial that a variety of representative subjects are studied to have better results.

This data analysis is applied to the cross-sectional study of time retrospective data set and observational data analysis. Inferential data analysis can determine and predict excellent results if and only if the proper sampling technique is followed and good tools for data analysis are properly utilized.

Decision Trees

This is classified as a modern classification algorithm in data mining and is a very popular type of analysis in research which requires machine learning. It is usually represented as a tree-shaped diagram of a figure that provides information about regression models or classification.

The decision tree may be subdivided into a smaller database that has similar values. The branches determine how the tree is built and shows where one goes with the current choices and where those choices would lead.

The primary advantage of a decision tree is that domain knowledge is not an essential requirement for analysis. Also, the classification of the decision tree is a very simple and fast process which consumes less time compared to other data analysis techniques.

Descriptive Data Analysis

Descriptive Data Analysis techniques often include developing tables of averages and quantiles, measures of distribution such as variance or standard deviation, and cross-tabulations or “CrossTabs” that can be used to examine many disparate hypotheses.

Those hypotheses are often about observed differences across subgroups. Specialized descriptive techniques are used to measure segregation, discrimination, and inequality. Discrimination is often measured using audit studies or decomposition methods. More segregation by type or inequality of outcomes need not be wholly good or bad in itself, but it is often considered a marker of unfair social processes; accurate measurement of the levels across time and space is a prerequisite to understanding these processes.

A table of means by subgroup can show important differences across subgroups, and this kind of descriptive analysis often invites causal inference. When we see a gap in earnings, for example, we naturally want to extrapolate reasons those patterns exist. But this enters the province of measuring impacts, and different techniques are needed. Often, means differ merely because of random variation, and statistical inference is needed to determine whether observed differences can stem merely from chance.

A CrossTab (or Contingency Table) or two-way tabulation shows the proportions of units with distinct values for each of two variables, or cell proportions. For example, we might ask what proportion of the population has a high school degree and receives food or cash assistance, which requires a CrossTab of education versus receipt of assistance. Then we might also examine row proportions or the fractions in each education group who receive assistance, perhaps seeing assistance levels sharply lower at higher education levels.

We could also look at column proportions, for the fraction of recipients with different levels of education, but this is the opposite direction from any causal effects. We might see a surprisingly high number or proportion of recipients with a college education, but this might be a result of larger numbers of college graduates than people with less than a high school degree (the column proportions of the total population without regard to receipt of assistance).

This method requires the least amount of effort amongst all other methods of data analysis. It describes the main features of the collection of data, quantitatively. This is usually the initial kind of data analysis that is performed on the available data set. Descriptive data analysis is usually applied to volumes of data such as census data. Descriptive data analysis has different steps for description and interpretation. There are two methods of statistical descriptive analysis that is univariate and bivariate. Both are types of analysis in research.

  • Univariate descriptive data analysis

    The analysis which involves the distribution of a single variable is called univariate analysis.

  • Bivariate and multivariate analysis

    When the data analysis involves a description of the distribution of more than one variable it is termed as bivariate and multivariate analysis. Descriptive statistics, in such cases, may be used to describe the relationship between the pair of variables.

Mechanistic Data Analysis

This method is the exact opposite of the descriptive data analysis, which required the least amount of effort, mechanistic data analysis requires a maximum amount of effort. The primary idea behind mechanistic data analysis is to understand the nature of exact changes in variables that affect other variables.

Mechanistic data analysis is exceptionally difficult to predict except when the situations are simpler. This analysis is used by physical and engineering science in cases of the deterministic sets of equations. The applications of this type of analysis are a randomized trial data set.

Conclusion

When carrying out research, be sure to understand the method of data analysis that would best suit the desired conclusions and data type being analyzed.

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