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How To Decide Your Data Analysis Strategy? Top Strategies

by AndrewSmith

Data analysis strategy is the technique which lays the foundation of data practices required for research work. At the time of using data analysis strategy, it should be clear that it is not just a small patch but a long-term plan that guides for the research till the end. This strategy helps you define the targeted audience and rest of the detailed process. Furthermore, you can identify the technology to put for meeting the challenges of research. There are multiple strategies for data analysis, so you need to go for one which suits best to your research problem. The aim of this article is to decide the best suitable data analysis strategy.

How To Decide Data Analysis Strategy for Research?

One of the most important questions to address while working on a data analysis strategy is to make the right selection. From different strategies, you have to come up with one which can help you meet the designed ends of research.

To decide the type of strategy, you have to see which type of data you have to analyze. Also, you need to see the purpose of using the collected data. For example, if you have numeric data, it means you should go for a quantitative data analysis strategy. On the other hand, if the data is in subjective form, you are supposed to go for the qualitative data analysis technique. Similarly, the data type and goal of study will guide you if there should be other strategies of data analysis. If you find difficulty in deciding the strategy, you can ask professionals working on dissertation help online.

What Are Different Data Analysis Strategies?

Following are the different data analysis strategies:

Exploratory Data Analysis

A data set can work as a treasure if you explore it from different angles. As a researcher or data analyst, it is your responsibility to uncover the important aspects of data. Also, you need to highlight the facts and figures present in your data. Here, exploratory data analysis is an approach to get insights into data. In this analysis, you follow different steps in a standard order. Before analyzing the data, you must be clear about the aspects directly and indirectly linked with this strategy. One of the main aspects is openness which highlights the characteristic of data exploration. The openness should be of all prospects of data exploration.

Another important aspect of data exploration is about skepticism. Skepticism is an act that helps in data judgement. It works well to keep exploration systematically. It includes data evaluation and accurate description with the help of effective claims. It is skepticism that allows you to use a logical question in data evaluation. The main purpose behind this step is to get the best out of available data.

Quantitative data analysis

Having the right information about data analysis strategies makes it easy to get the best direction to find a solution to a particular problem. One of the best things about data analysis strategies is report generation. It helps in creating an analytical report for better understanding. Following are some methods of quantitative data analysis:

  • Cluster Analysis: this method of data analysis works for grouping based on similar data.
  • Cohort Analysis: this method works with the record of data and its comparison with information collected in different phases.
  • Regression Analysis: the change in one variable is observed based on the fluctuation in the second variable. In simple words, the identification of relationship between different variables is made with the help of regression analysis.
  • Conjoint Analysis: When the study aims to get insights into different facets, the conjoint analysis method is the best choice.

Qualitative data analysis

Qualitative data analysis is the inspection, identification and interpretation of several shapes, outlines and themes of the subjective data. The main aim of qualitative analysis is to find the answer to questions designed for research study. In this analysis method, you do not have to rely on factual aspects. In qualitative data analysis, you can find frequent use of content and thematic analysis methods. Let’s discuss these analyses in detail.

Content Analysis

The method to identify the pattern of text is called content analysis. This analysis technique works well for the exploration of scientific models. It includes historical, cultural and many other contents. Content analysis for research plays a major role in identifying the impact of content from a particular perspective. The most important thing related to content analysis for a dissertation is its evaluation approach. In this analysis technique, you can go for qualitative as well as quantitative evaluation.

Thematic Analysis

The thematic analysis involves breaking down the information you receive into themes or ideas repeated throughout your data. The thematic analysis provides insight into what’s happening in your study group and helps to prove or disprove your hypothesis or research questions.

Predictive Analysis

From several analysis methods, predictive data analysis strategy is one which plays a significant role in sensing the collected data and making decisions for future work. There are different categories of data which can help in predictive analysis. It includes raw data in the form of statistics, charts, graphs and figures from history. The condition for using the raw data is that it needs to be the latest one.

One of the most important things in this analysis is related to data segregation. In this way, you can better identify the useful and useless data. So, you can discard useless data to get benefits from reliable and valuable data.

Prescriptive Analysis

The prescriptive data analysis strategy works for aspects of future, but the approach is whole new, and it does not match a predictive one. In this analysis method, you can see that the major contribution is of smart techniques related to mathematics, machine learning, algorithm and many more advanced features. In prescriptive analysis, you are not only supposed to identify the problem or solution, but you have to evaluate the reason behind every happening and decision.

Final Thoughts:

By breading the above-mentioned points, you can evaluate the purpose of each data analysis strategy. The detail of each strategy can help you understand the process, and you can better meet the end results. Before using a particular strategy, you have to make a strong foundation by defining the right type of data. Even after reading the article, if something is still bothering in you, you can contact me.

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