24+ Sample Analysis Plans

What Is an Analysis Plan?

An analysis plan is an action plan of how to dissect and approach data. Data analysis is an important stage in any research. And having a blueprint or guide on how to analyze data benefits the researcher in many ways.

The field of big data is fast becoming one of the most relevant fields today. According to Search Business Analytics, the analytics of big data is the process of examining big data to gather important information including hidden trends, correlations, market patterns, and customer preferences. This is significant for businesses and organizations because utilizing big data analytics can help make sound and educated business decisions or even improve business outcomes. 

Having an analysis plan requires a broad understanding of key research methods. Before you can analyze anything, you need to familiarize yourself with the entire process in order to better execute your analysis plan. To achieve this, it is best to acquaint yourself with the following concepts first: 

Data Collection: Any research or pursuit of knowledge needs data. There will be nothing to analyze if you do not gather enough data first. Data collection involves several strategies and techniques. Depending on your preference or need, you can obtain data in several different ways. For qualitative research, typical data gathering techniques include interviews and focus group discussions. For quantitative research, some commonly used methods are survey forms, random sampling, observations, and experiments. Asking Questions: Analysis planning often supposes that there is a fundamental question that needs to be answered or at the very least, explored. Asking questions is a good way to kick start any pursuit of knowledge and information. You need to be able to ask the right questions because it will aid in planning out your analysis strategy. It is easy to get lost in all the data, so having a central question will help keep your objectives in full view. Data Interpretation: Interpreting data requires a solid approach. This may be relative to each person, but using a framework is usually the way to move forward. Having a set of previously established theories and concepts as a way to view the data is what interpretation entails. Seeing data through a specific lens does come with its own set of benefits and limitations. But for the purpose of analyzing data, it is important to stay focused and committed to a framework. For example, poverty as a phenomenon can be viewed from various perspectives. It can be interpreted through the eyes of those in power or it can be studied through the point of view of those struggling to survive. Whatever framework you decide to use, take note that critical thinking is absolutely essential if you want your data to mean anything. Data Validation: Once you have obtained enough data, you need to ensure that the data is verified and credible enough to be used in your research. Fact-checking is an important skill that all researchers need. Your sources need to be sound and credible. If you are unsure of a primary or secondary source, try checking more than one source. Checking it against another source with more authority will help you verify information that you are doubtful of. You always want to ensure that you validate your sources, especially for cases that have secondhand information or data that did not necessarily come from the main source (e.g., hearsay, stories that have been passed down orally). Data Cleaning or Sifting: As much as possible, you want to avoid including irrelevant, superfluous or redundant data as much as possible. To prevent this from happening, you need to be able to meticulously sift through your data. While it is good to gather a lot of data because it will give you more access and more material to work with; the downside of having too much data is you need to distinguish the relevant from the irrelevant. It is highly possible to consider all data important; so you need to decide which data will contribute or make more sense to your research question. Without filtering your data, you run the risk of ending up with a weak and disorganized output that lacks any real focus. This is why going back to the main question or hypothesis is key to conducting airtight research.

Tools for Analysis 

How do you begin to analyze? There are many different ways to dissect data. Some methods may work better than others in certain aspects. It is highly relative and would greatly depend on the objective of the research. However, there are several strategies that are commonly used in analyzing data:  

Comparisons: One basic way to analyze data is by comparing two or more things side by side. You normally get to see the obvious similarities and differences when you pit two things against each other. This makes it easier to pick out the important facets of the material you want to include in your research. You can choose to highlight a few comparisons and expound on each. Drawing conclusions will turn out to be relatively easier when you are able to see the stark differences. Origins and Background: To truly make sense of something, one effective way is to look into the past and understand its history or background. What events occurred that led to its current state? What were the significant details from the past that could have contributed to the present? Data can be analyzed by uncovering the past in order to make sense of its present and future. This is precisely the reason why providing a historical background is common in research. Trends and Patterns: One important way to analyze data is by recognizing trends and looking for patterns. What similarities can be established based on the data you gathered? What stands out and what is a common denominator in your findings? To illustrate, taking surveys from multiple people is interesting because you can discover new information, reinforce, or even contradict previous assumptions with the answers they provide. All you need is enough awareness and a keen attention to detail to notice the various trends and patterns based on the survey data collected from the respondents. Root Causes: Another tool for analyzing is getting to the root of a problem or phenomenon. This is where asking the right questions is key. Uncovering the reasons why something is the way it is or what has caused it to be that way can be helpful in understanding data. Further, using this root cause analysis can also serve as a springboard to venture into other related concepts that will add more dimension and depth to your study. Deep Dive: Another way to approach and analyze data is by doing a deep dive. It is through conducting an immersive and thorough study of something that one is able to see and appreciate all the different aspects of it. A deep dive may combine any of the previously stated techniques above, or it could open up a whole other dimension to a topic or issue. If you want to gain a full understanding and truly get to the core of a problem or issue, doing a deep dive is a good way to get started.

How to Create an Analysis Plan

An analysis plan can sometimes be quite complicated but if you want to simplify things, using an existing template will help make it a bit easier. Choose from the options above and simply edit it as needed. Follow the steps below to ensure you have a solid analysis plan:      

Step 1: Outline the Objectives 

To start, you need to establish your goals and objectives. This will serve as your guide when conducting not just the analysis phase of your research, but all facets of it. What do you want to gain from your analysis plan? What outcomes are you hoping to achieve from the analysis plan? Let the objectives serve as your ‘North Star’ to remain focused on your goals and to prevent unnecessary distractions.   

Step 2: Plan and Strategize 

The next step is to outline the methodologies you want to use in analyzing your data. How do you plan to analyze your data? What techniques will help you interpret the data you collected? What are the strategies best suited to draw conclusions and findings from your data? The tools and examples given above can help you with your ideas.   

Step 3: Organize the Information 

In research, the data collected is the material that needs to be analyzed. It is crucial to organize first and make sure all the data is clean and sorted out before you begin your analysis. It may also be helpful to divide the work into phases or sections. Handling data, especially if it is in large quantities, can be quite overwhelming. Thus, organizing it into parts or stages can make it seem more doable. 

Step 4: Implement the Plan 

Once you have organized your data and established your objectives and strategies, you can start applying your analysis plan. It is important to remember that with the absence of interpretation and analysis, data is just data. It is essentially passive information. What you choose to do with that information is what matters. You need to proactively make sense of the data by employing different tactics and methods of analysis. 

FAQs

How do you write a data analysis plan?

To write a data analysis plan, you need to establish your objectives and outline the strategies that will make up the bulk of your plan. List down your goals on what you want to gain from the analysis and findings. Make sure you have gathered enough data and sifted through it in preparation for analysis.

What should analysis include?

Your analysis should include a detailed examination of the topic and a thorough evaluation of the data. It is not enough to merely present the data. If you want to draw conclusions or make the right assumptions, a comprehensive analysis and interpretation should be conducted. You also need to ensure that your claims are supported by credible theories, facts, and evidence.

What is a good analysis?

A good analysis is an exercise in critical thinking. It is asking the right questions and using fact-based supporting ideas to back up claims and arguments. A weak or poor analysis is one that lacks depth, is insufficient in offering enough examples, and fails to connect relevant ideas.

What are the three steps of data analysis?

The data analysis process has three steps namely: evaluate, clean, then summarize.

Analysis plans are one way to ensure that data collection is not put to waste. The analysis and data interpretation portion is probably one of the most important parts of any research process. The way you analyze data is crucial and will determine if your research is impactful or not. Create your own plan today and let any of the sample templates above serve as your guide!