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How do you ensure the validity and reliability of your data analysis results?

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Data analysis is a crucial step in market research, as it helps you transform raw data into meaningful insights and actionable recommendations. However, to ensure that your data analysis results are valid and reliable, you need to follow some best practices and avoid common pitfalls. In this article, we will discuss how you can improve the quality and accuracy of your data analysis results by applying some simple techniques and tools.

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Bassem Y.

Board Director | Advisor - Harvard Business Review Advisory Council | Entrepreneur | 5G & Cloud - IoT | Service…
Eric S. Levy

Marketing Research Pro | Insights and Decision Support
Ashish Gambhir

Making India begging-free | Ex-EY, Deloitte, Serco

1 Define your research objectives and questions

Before you start analyzing your data, you need to have a clear idea of what you want to achieve and what questions you want to answer with your data. This will help you select the most appropriate data sources, methods, and tools for your analysis. It will also help you avoid irrelevant or redundant data that may confuse or bias your results. You should also define the criteria and indicators that you will use to measure your results and evaluate your performance.

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Eric S. Levy

Marketing Research Pro | Insights and Decision Support
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Before data analysis is started, the research objectives and questions should be established before the first datum is collected. It's often helpful to perform what's known as a "backwards research process," in which the report and findings are rough out in draft form, to provide colleagues and stakeholders with an understanding of what the study will yield. By doing so, you validate the objectives of the study as a whole by demonstrating how it will answer the business questions. Once that's done, and the research is designed, and data are collected, the analysis plan is driven by the backwards research "results."

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2 Choose the right data analysis tools

Depending on your research objectives and questions, you may need to use different types of data analysis tools, such as descriptive, inferential, or predictive tools. Descriptive tools help you summarize and visualize your data, such as charts, tables, or dashboards. Inferential tools help you test hypotheses and draw conclusions from your data, such as statistical tests, correlation, or regression. Predictive tools help you forecast future outcomes or trends based on your data, such as machine learning, simulation, or scenario analysis. You should choose the tools that best suit your data type, size, and complexity, as well as your analytical skills and resources.

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Eric S. Levy

Marketing Research Pro | Insights and Decision Support
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Choosing the right data analysis tools is a key process. Often, an exploratory study (e.g., cluster analysis) requires several analytical steps before answering the business questions. However, if the study is confirmatory, such as a customer satisfaction study, the tool often requires a hypothesis before beginning analysis. For instance, in the case of a customer satisfaction study, if this is an ongoing effort, the hypothesis is often the results are higher/lower/the same as the previous wave. In that case, the analytical tools chosen should be able to, with confidence, allow you to reject or accept your hypothesis and provide the details surround those findings.

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3 Clean and prepare your data

Before you apply any data analysis tools, you need to make sure that your data is clean and ready for analysis. This means that you need to check and correct any errors, inconsistencies, or missing values in your data, such as typos, duplicates, outliers, or gaps. You also need to standardize and format your data, such as using consistent units, labels, or categories. You may also need to transform or manipulate your data, such as aggregating, filtering, or sorting it. Cleaning and preparing your data will improve its quality and reliability, as well as make it easier and faster to analyze.

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4 Validate and verify your data analysis results

After you analyze your data, you need to validate and verify your results to ensure that they are valid and reliable. This means that you need to check and confirm that your results are consistent with your data, your assumptions, and your expectations. You also need to check and confirm that your results are robust and reproducible, meaning that they are not affected by random errors or variations, and that they can be replicated by other analysts or methods. You can use various techniques and tools to validate and verify your results, such as cross-validation, sensitivity analysis, or peer review.

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Ashish Gambhir

Making India begging-free | Ex-EY, Deloitte, Serco
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Beyond the techniques data validation also needs a certain mindset. You have to be open minded and avoid confirmation bias while triangulation of data and results. You have to be open to a completely different hypothesis revealing itself than the one you originally started with.

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5 Interpret and communicate your data analysis results

Finally, you need to interpret and communicate your data analysis results to your audience, such as your clients, stakeholders, or colleagues. This means that you need to explain and justify your results, as well as their implications and limitations. You also need to present and visualize your results in a clear and engaging way, such as using charts, graphs, or infographics. You should also provide actionable recommendations based on your results, as well as suggestions for further research or improvement. Interpreting and communicating your data analysis results will help you demonstrate their value and relevance, as well as persuade and influence your audience.

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Ashish Gambhir

Making India begging-free | Ex-EY, Deloitte, Serco
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Great, creative visualization can be the game changer. Powerful results can land flat if not presented well and even basic results can acquire meaning when visualization is given adequate effort and is aligned to requirements.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Bassem Y.

Board Director | Advisor - Harvard Business Review Advisory Council | Entrepreneur | 5G & Cloud - IoT | Service Delivery | Governance | Digital Platforms Engineering | PMO | SAFe Agilist SA
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The sample size is crucial to get reliable results. If you are working with a sample of data, make sure it is sufficiently large and representative of the population you are studying. A larger and more diverse sample generally leads to more reliable results

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How do you ensure the validity and reliability of your data analysis results?

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3

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1 Define your research objectives and questions

2 Choose the right data analysis tools

3 Clean and prepare your data

4 Validate and verify your data analysis results

5 Interpret and communicate your data analysis results

6 Here’s what else to consider

Market Research

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How do you ensure the validity and reliability of your data analysis results?

1

2

3

4

5

6

1 Define your research objectives and questions

2 Choose the right data analysis tools

3 Clean and prepare your data

4 Validate and verify your data analysis results

5 Interpret and communicate your data analysis results

6 Here’s what else to consider

Market Research

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