How do you ensure your quantitative data is accurate?

It's useful to map your variables, so you can picture in a diagram flow how they interact and/or affect each other. Such a map is also useful as an overview of the scope of the project. Categories and subcategories of data, related or unrelated, and where appropiate, linking those which are cause and effect.

In my experience, it is best to start by defining the Research Questions and if appropriate, specifying a set of hypotheses that are grounded in the learning sciences. These hypotheses will help define the variables to measure.

It might look a little burdensome initially but most definitely it is going to help us tremendously because it defines well the research process because it is very much needed and is an important part of the preliminary research process.

Before anyone starts collecting data, I always suggest reviewing (or creating) the logic model/theory of change for the organization as this will help you identify what data is important to measure.

Operationalization of the variables offers clarity and structure to the variables. It also facilitates the thought process to conduct the research with less sample biases.

Research methods literacy is highly important because a poorly designed research project that is fraught with potential confounds can render results that are completely uninterpretable.

It’s not just about choosing appropriate methods; how you implement these methods is critically important. For instance, a poorly crafted survey is at best recognized as useless and at worst, collecting data that is masquerading as useful.

My sense is that we underestimate the true worth of pilot data, the opportunity to test a method in a real-life scenario, just shy of the actual final design of the study. A pilot study not only tells us if there's any substance to a hypothesis but can tell us where our current study design is likely to be challenged.

Adding to the discussion, beside choosing the correct sample size, research method, the crucial point is selecting the correct sample, which is the accurate representation of the population to be studied.

One way to address limitations to internal and external validity is to triangulate the results of several research studies. Lab studies are often criticized for lacking external validity but are critical to establishing internal validity and establishing causality. Correlational studies conducted in the field can offer more external validity but can be fraught with potential confounds limiting internal validity. There are also ethical reasons that need to be considered in selecting one's research design. The use of waitlist control groups can be used in the case of an experimental research design with random assignment to an intervention predicted to yield benefits to research participants. Again, research design literacy is critical.

Building on Faby's response, you can also build-in a way to triangulate your data in your own study. For me, I include a small qualitative component, such as a focus group or interviews to "spot check" my findings, but also to ensure that my survey questions and data collection methods are interpreted in the way in which the study was intended. Often, this qualitative component can add more refined detail to the study, itself and it provides another validating aspect to your quantitative data.

Another important note to ensuring the integrity of your data is performing a thorough normalization process of the collection. With a clearly defined set of parameters for your data, this can easily be done automatically by running your data through a scrubbing tool (built-in, third party or even a simple self designed tool) to parse out the portions of the collected sample that will not give you a clear picture of your results.

Visualizations help to check the data quality, especially to see if the data is skewed. Boxplots, histograms are some of the visualization techniques that are implemented to have a comprehensive understanding of the data quality.

Data documentation is very important but a well structure data documentation is like powerful booster to our work and research. It looks like a burden to many and mostly delay it most of the time untill they realise that it is already a mess which is mostly impossible to sort.

I find using a program like Scrivener or a platform like Trello can assist in keeping all documentation and files together. Before starting the study, consider how you will manage data collection and documentation. I keep a sheet that lists where my data are stored, where my notes are, procedures, etc. It will save a lot of time later when you have to go back and write the report, thesis, or paper. You may use your data years later when you've forgotten the little details around data collection and storage. Having documentation now will help you find your data easily and efficiently in the future.

Plans for documenting data should be specified ahead of time in your research or evaluation plan and additionally follow guidelines for protecting privacy.

Even before conducting any analysis, eyeballing the dataset is important to check for any obvious errors in the data collection process.

In addition to asking your immediate faculty around you, I encourage researchers to join virtual groups to learn about how they collect and manage their data. Often, data management is regional - a university may adopt a certain way of doing things. By asking within your virtual professional groups, you can evaluate whether another method, procedure, etc. may be better suited to your needs. When I did data collection, my campus suggested one program. But through my virtual groups, I found another program that was far more useful for my research. I would not have found this program without asking outside of my university.