How do you evaluate the validity and reliability of your research and address the limitations and challenges?

Reliability can be established by at least 3 independent experiments; giving the same results. And when you publish such results, each and every steps of materials and methods must be clearly stated so that other researchers can duplicate it.

This is fine - but anyone who already understands validity and reliability already knows it, and for someone who is coming to it for the first time, there's so much more groundwork and context that's necessary. If the goal is to provide a very basic level of understanding, an example would be best here.

Validity is what ensures your technique enables you to measure what you intend to measure, whereas reliability is your method's ability to measure consistently. Examples of these are: 1. Temporal validity, which tests the plausibility of research findings over a given time frame (eg, rise in cancer deaths due to untreated or undiagnosed cancers during the COVID-19 mandatory lockdown). 2. Interrater reliability, which measures the degree of agreement between different people observing or assessing the same thing. In medicine, it is often used when two or more specialists provide a diagnostic score based on imaging (pathological samples, tomographs, radiographs, magnetic ressonance).

To ensure the validity of the research, carefully frame the research questions and protocols with design and product teams and gather their input in deciding the study metrics. This helps to align expectations, surface assumptions early on and better scope the research study. I present preliminary findings to other members of the project team, to make room for critique and adjustments as necessary. In synthesis, I use data from multiple sources (web analytics, surveys, interviews, etc.) to identify patterns and build a coherent storyline.

The Flynn effect described a secular rise in IQ test scores. Does this mean we are all more intelligent than our grandparents? No. The test-retest reliability, which is the repeatability of a test over time, assesses the stability of the measurement. Scales require prior cultural adaptation and validation, so, in longitudinal studies, they may be used to infer temporal trends —such as the cultural changes that influenced a rise in need for abstract, logical, and critical thinking.

The means of data collection, representation, and transformation are key for reliability. It is not uncommon to transform qualitative data into quantitative data for analysis. For example, researchers often create a ranking system on a scale of 1-5, from least to worst (or vice-versa), when using interviews for data. In such cases, researchers can improve reliability by appropriately transforming their data. This begins with eliminating biases in the initial interview questions from which the original data is generated. A test-retest method can address this. Data must then be transformed appropriately (on a case-by-case basis). For example, researchers should ensure the differences between data figures 1 and 2, should be equal to 2 and 3.

Being aware of common biases is key. 1. Ascertainment bias is a form of systematic error that occurs during data collection, such as when study participants are not representative of the target population. The research design solution is random sampling, where participants are selected based on diagnostic and demographic inclusion criteria, rather than by convenience sampling. 2. Attrition bias is the selective dropout of some participants, who systematically differ from those who remain in the study. Performing intention-to-treat analysis, the investigators compare baseline data from participants who leave with those who remain at the end of the study.

To accurately diagnose a condition, physicians often resort to tests, whose performance can be measured using different characteristics: 1. Predictive value is the probability of correctly identifying a subject's condition given the test result. 2. Youden's J is the likelihood of a positive test result in subjects with the condition versus those without the condition —probability of an informed decision. 3. Sensitivity is the proportion of people who actually have a target disease that are tested positive (true positive or detection rate). 4. Specificity is the proportion of people who do not have a target disease that are tested negative (true negative rate). The false positive rate (1 ? specificity) is the probability of false alarm.

Discussing alternative interpretations of the data involves evaluating not only the possibility of occurrence of bias, but also which type of bias and the magnitude and direction of the error it may confer to the results. In clinical research, the generalisability of a study's findings is often limited by insufficient heterogeneity in population sampling. Epidemiology and drug effects may differ significantly between people of Asian or European descent, for instance, and this will need to be accounted for in policy recommendations.