How can you validate the reliability of survey data?

Data may be unreliable because those tasked with collecting it are under pressure to deliver data quantity in limited time or budget, and not data quality. Ask a bunch of surveyors to cover a 100 people, standing in a public place in the hot sun, for some small honorarium, and you can expect them to sit in the shade and fill out all the forms by themselves. But get a grip on how much time it takes, and the correct place to do it, and the correct time, and incentivice them for quality, and you will get a lot genuinely filled in forms, although the number of rrespondents will be less.

Some steps to help you validate the reliability of survey data: 1. Pretest the Survey 2. Use Established Scales and Questions 3. Random Sampling 4. Use Clear and Unbiased Language 5. Pilot Testing 6. Data Consistency 7. Data Cleaning 8. Internal Consistency 9. Test-Retest Reliability 10. Validity Checks 11. Peer Review 12. Statistical Analysis 13. Report Methodology: Be transparent in your survey reporting by detailing the methodology used, sample size, response rate, and any limitations. 14. Consideration of Non-Response Bias 15. Continuous Monitoring By following these steps, you can increase the reliability of your survey data and have greater confidence in the insights it provides.

Define your objectives in less than few sentences or maybe even a few words. These objectives then becomes the bible of the business. Always remember LESS is MORE.

The use of blockchain technology in the data supply chain ensures authenticity and immutability of the data. It also brings in traceability of the data aggregation process.

Influencer marketing allows brands to quickly adapt to changing market condition and trends. influencer are often early adopters and trendsetters, making them well suited to pivot and align with new consumer preference or emerging market dynamics.

For Validation of accuracy of Any survey data : Employ rigorous methodologies. Start with careful questionnaire design to avoid biased or unclear questions. Use random sampling to ensure representativeness and monitor response rates. Conduct pilot tests to identify issues. While collecting data, train interviewers for consistency and employ validation checks to catch inaccuracies. Analyze data for outliers or unusual patterns. Cross-check with external data or benchmarks if possible . Consider expert reviews and transparency in reporting methodology. Ethical conduct and respondent trust are essential. Finally, document the entire survey process meticulously to enhance data reliability, ensuring accurate report for the target population.

A well-designed sample, chosen with precision, is vital for the integrity of any research endeavor. Furthermore, the nuanced application of weightage cannot be overstated. Assigning appropriate weightage to different segments of the population is essential. This practice compensates for any disparities in group sizes within the sample, acknowledging the diversity inherent in most populations. Through judicious application of weightage, the study maintains its integrity, ensuring that each subgroup's contribution is accurately reflected in the final analysis.

Look, let's face it: your sample is not going to be a random representative sample of the population. Unless you have enormous resources, budget and social scientists on your team, your sample is going to be a sample of convenience. That's ok—as long as you recognize its limitations. For whatever pool of potential survey participants you have access to, go for stratified sampling according to the market categories that matter to you. It could be demographics like age and gender for B2C and firmographics like job title and function for B2B. Aim for equal or roughly equal representation of all.

Choose the sample of groups that resonate with the problem statement & the solution key. Mostly the problems are faced by the people with basic necessities but the urge to grow from lower middle class category to middle class & upper are the best samples. The upper middle class onwards usually buy out their problems or hire someone to take care of it.

Most surveys I have come across and designed rely on rating scales/ranking to facilitate quantitative analysis, which is straightforward to crunch and present. However, there is usually a tug-of-war between keeping the survey short to get responses by asking only what we know, potentially missing out on new trends, or asking so much detail that respondents may quit halfway through. Open-ended questions requiring qualitative analyses were sparse, particularly in large-scale surveys, due to limits in analytical firepower. With advances in NLP and AI, more open-ended questions can be used to derive meaningful details with less resources and shorter surveys. Early adopters who have simplified your surveys - what has been your experience?

When it comes to getting reliable data from surveys, the questions asked are like the secret sauce. Key things to note are: 1. Be Crystal Clear: Avoid jargon or complex language that could confuse respondents. For instance, instead of asking, "Do you understand the intricacies of patent filing?" ask, "Are you familiar with the patent application process?" 2. Avoid Leading Questions: Keep it neutral. Instead of saying, "Don't you think patent research is crucial?" ask, "What's your opinion on the importance of patent research?" 3. Use Open-Ended & Closed-Ended Questions: Mix it up. 4. Pilot Test: Before launching your survey, test with a small group to identify any confusing or irrelevant questions. Adjust as needed.

At a previous company, we evaluated our contract management using an ISO lens: For ISO readiness: "How often do you align with ISO in contract compliance?" Efficiency gauge: "Rate our contract management from 1-10." Satisfaction clarity: "Rate our renewal response time from 1-5." Clear questions on: "Rate our negotiation communication?" and "Efficiency post-agreement?" Feedback: "Suggest improvements for our contract process." NPS: "Recommend our services? (0-10)" Net Easy Score: "Ease navigating our contract process?" This structured survey highlighted strengths and revealed areas for growth, aligning us closer to ISO norms and boosting client satisfaction.

To reduce the risk of item and response scale wording distorting measurement properties, leverage well-established scales (e.g., a good source is the Handbook of Marketing Scales; Sage).

The WH's are the best processes of any business or survey. Some important triggers are very important like for example .. Asking difficult questions that usually have an answer in a YES or a NO are the best set of questions.

An anecdote I had heard from an ad veteran: Back in the 60s, India was primarily using tooth powder, neem, miswak etc. for dental hygiene. Companies introduced toothpaste, and promoted its use heavily, including recommending brushing multiple times a day. They did a survey regarding usage and the result was astounding, since the data indicated the toothpaste being used was several hundreds of tonnes of paste more than what was being sold in India. On probing a bit further, they understood- toothpaste and brushing were seen as refined, premium behaviour. So those who brushed once a day, said they brushed twice or thrice a day, whereas those who didn't use toothpaste replied that they brushed once a day!! Verify!!

I think this is a significant step that, like the preceding ones, requires thorough planning. Because it's the very step where one can understand the surveys' pitfalls before going live with a bigger audience in mind. However, just like in sampling, the test group should consist of a diverse set of individuals to avoid group-based biases.

Testing your survey basically only depends upon the kind of friends you have, I as a person i am friends with my Car Cleaner, Wing Security Guard, My Fav Street Food Vendor, My Neighbour, My Neighbours wife, The Bachelor Boy in the building, The rich kid in the building. BASICALLY, If you are able to be friends with different varieties of people you should be fine.

This is a critical step: 1. One must analyze the data and see if it is being compared to relevant benchmarks, is making logical sense and remove outliers. 2. Normal distribution and other statistical methods can be used depending on the type of data.

1) For reflective indicators, we consider the evaluation measures considered are indicator loadings> 0.7, Cronbach's alpha> 0.8, Composite Reliability between 0.8 to 0.9, Convergent validity (AVE) > 0.5, and discriminant validity (HTMT)<0.90. 2) For formative indicators, evaluation measures considered are; statistical significance of weights, collinearity (VIF)< 3, convergent validity (AVE) > 0.5, discriminant validity (HTMT)<0.90. Additionally, bootstrapping can be carried out to test the statistical significance of factor loadings and reliability and validity measures.

Beyond the questionnaire design, I always propose the following - if relevant and the information exists ensure that the final data is representative of the specific market by utilising quotas and weighting - distribute sampling over a longer period of time to capture a broader range of lifestyles - implement detailed data quality checks. Remove speeders, straight-liners, bot activity and respondents that input random verbatim from the final data set

You need to understand the minimum detectable effect in your survey and check confidence intervals. This is especially important if you are comparing responses of sub-surveys, requiring you to understand the correlations between these sub-surveys. In general, be wary of extraordinary insights or finely tuned tracking metrics. These are often backed by many simplifying assumptions, or are mistaking noise for signal.

Analysing your data precisely means asking some amazing questions, If you are an entrepreneur with a team, sit with your data analytics person & start crunching the data, ask very relevant information. Analysts are very cool people, even while the working hours are over you can ask some fun questions about the data & you ll be surprised to know some of the results will be amazing.

Beyond an insight report, provide you clients with access to the data to allow stakeholders to dive into the detail, if and when required. My clients always get a set of easy-to-navigate data tables and increasingly my clients look to me to provide interactive dashboards so that they can interrogate their own data. The dashboards are also a great way to broaden the use of the research throughout an organisation

Additional things that the report can include: Data Visualization: Use effective data visualization techniques to present results. Well-designed charts and graphs can make complex data more accessible and understandable for a broader audience. Comparative Analysis: If applicable, include comparative analyses. This could involve comparing survey results with previous surveys, industry benchmarks, or demographic data to provide context and insights. Future Research Directions: Suggest potential avenues for future research based on the gaps or areas of interest identified during your study.

This is an aspect that I always struggle with, a well done research usually has endless findings and inferences, then how does one stitch it all together to generate relevant insights? Start by noting down all your insights first, and read it out loud, this will eliminate a lot of the insights that we feel were really useful in our heads. It happens to the best of us, we get too attached to our own research. When stitching findings together, always relate them back to the broader research questions. Consider the "why" behind each pattern or trend – why does a particular demographic show a specific behavior? What could be the underlying reasons? Lastly, synthesize the findings into actionable insights. Voila! you have reportable insights

- Be careful with data cleaning (outliers, inattentive respondents). - Be careful with topics that may trigger social desirable responding. There are ways to mitigate this. - Be careful with common method bias. Not trivial to completely avoid, but also here there are techniques you can use both a priori (survey design) and a posteriori (analyses). Best to consider the risk during survey design already.

There’s loads of ways you can inspect your survey or data quality such as: - duplicate IP addresses - speed traps - duplicate text responses - gibberish words - all checkboxes answered - patterned answers - plagiarism detection

Check the internal consistency of questions loading onto the same factors. This step basically checks the correlation between questions loading onto the same factor. It is a measure of reliability in that it checks whether the responses are consistent. A standard test of internal consistency is Cronbach’s Alpha (CA). Cronbach Alpha values range from 0 – 1.0. In most cases the value should be at least 0.70 or higher although a value from 0.60 to 0.70 is acceptable. What should you do if you have a low value? A nice function in some programs is telling you the CA value after removing a question. IBM SPSS calls it “scale if item deleted.” You might consider deleting a question if doing so dramatically improves your CA.

To validate the reliability of survey data, one can check the composite reliability or Omega as well as Cronbach’s Alpha (CA). @Holger Steinmetz Deng, L., & Chan, W. (2017). Testing the difference between reliability coefficients alpha and omega. Educational and Psychological Measurement, 77(2), 185-203.

Cross-Validation: Compare the survey results with other reliable data sources if available. For instance, if your survey measures income levels, compare the results to government census data. It is really important to cross-validate your survey data if possible. Alternatively, you can also look at proxy data to compare the results. In consulting the rule remain that triangulating data is the best way to ensure it is accurate, robust, and takes into account multiple perspectives. Secondly do not forget ethical Considerations: Ensure that the survey is conducted in an ethical manner, respecting respondents' privacy and obtaining informed consent.