What are the best practices for designing valid and reliable data collection instruments? (2024)

Choose your research tool based on your research questions not vise versa! Design your research, decide what you want to research. Then use that to look for validated and reliable measures 😀

To ensure the accuracy and dependability of our findings, we employ a technique called triangulation. Let me illustrate this concept using my own doctoral research as an example. I'm delving into whether the soft skills my students acquire in college—like communication, teamwork, and problem-solving—truly serve them in the professional world. To do this, I'm using surveys for students, conducting interviews with employers, and analyzing work performance reviews to gauge whether these soft skills are evident in students' work performance. The primary advantage of triangulation lies in highlighting the trustworthiness and reliability of research outcomes through the use of multiple sources, methods, or viewpoints to fortify the results.

The best practices are to create a uniform data collection system that caters to the need of the project and also adds value to the project in hand. It is method of achieving truth and so sample size should be big enough to overshadowed the errors. The sample size should be heterogeneous and random. The valid questions should be answered by choosing good sample volume.

Data can be collected either from primary source or from secondary source. Primary source is the first hand information directly coming from respondents. On the other hand secondary source is data collected from books or already published research works. Data collection method also depends on type of research we undertake such as qualitative or quantitative research.

Success in surveys, questionnaires, interviews, and data collection hinges on data accuracy, diversity, sample size, and interpolation. By maintaining comparable diversity across samples, bias can be minimized. Even with a smaller sample size, overarching patterns within the complete dataset can be reliably reproduced using suitable interpolation methods.

The field of research is highly relevant to data collection methods. For example, in behavioral research, interviews conducted by expert interviewers can be the best method, although it is time-consuming. In non-behavioral fields where data can be collected by a mechanism (machines, networks and ...), ensuring the mechanism's efficiency is crucial.

Validity and Reliability are essential characteristic features of a perfect research. Validity refers to the maintenance of accuracy in research procedure. While doing so, make sure that internal validity must be maintained and external validity must be reduced. Internal validity establishes perfect relationship between independent variable and dependent variable via casual relationship. External validity refers to the effect of extraneous variables on the independent variable, however that must be reduced to establish relationship between cause & effect.Reliability in research is related to the maintenance of consistency in getting the research results mainly based on time factor.

It is extremely important that the results provided by each instrument is user-independent: the analysis of the same sample should provide the same results regardless who was using the instrument. In addition, the results should be easily explained and understandable by everyone. Finally, the advanced features and customizations of the analysis should be available from the general settings so that the user can specifically select the desired advanced features and unselect the others. All the instrument parameters involved in the measurements must be always saved and always available when analyzing the data that derive from the measurements.

When it comes to analyzing data, having a good understanding of the subject is key. Using math to draw logical conclusions works well but remember to address any limitations or exceptions separately. It's also important to test your ideas through experiments and to be clear about how errors might affect your analysis. And don't forget, the context in which you're analyzing data matters too.Just to illustrate, think about the "bandwagon effect." This is when people jump on a trend just because everyone else is doing it. If you're not careful with analyzing survey data, you might misinterpret the results and follow a trend without really understanding what's going on.

It is important to review any measure used and evaluate any strengths and limitations. This helps to inform futures research which will prompt the continued development of reliable and valid tools for research purposes.

One of the methods for checking the accuracy of data is to use established and recognized relationships with other factors. For example, suppose the goal is to investigate the relationship between two variables, 1 and 2. Data related to both variables has been collected. According to this approach, variables 3 and 4, whose relationship with variables 1 and 2 has been confirmed respectively, are also collected simultaneously. Upon reconfirmation of the same relationship in this case, the accuracy of the data related to variables 1 and 2 can also be accepted.

The precision, accuracy, statistic errors and reliability (reproducibility) of the instrument, have to be clearly stated in every research article that uses that instrument.