Tijana Zelenovic
Studies that are meant to determine the specific characteristics of a population are commonly subject to errors and bias due to the practical and ethical constraints of the participants. The difference between the actual value of a parameter and the measured quantity is the measurement error. It can be systematic is it occurs due to the use of a faulty instrument, or it could be random in case it naturally occurs (Althubaiti, 2016). Preventing or reducing measurement errors requires the use of several measures for the same construct and to do a pilot test for the measuring instrument. Also, one should double-check all measurements and formulas for accuracy.
Before carrying out research, the researcher should clearly understand the target population to avoid population specification error. In conjunction with recording inaccurate data, it is also possible to encounter the absence of data due to non-responsive error. This error can be addressed through follow-up surveys or the use of alternate data collection means.
Reference
Althubaiti, A. (2016). Information bias in health research: definition, pitfalls, and adjustment methods. Journal of multidisciplinary healthcare, 9, 211. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4862344/
Anna Kolilias
A researcher cannot carry out a careful study without clearly understanding the variables. As you have mentioned, there are various types of variables, including independent variables, dependent variables, extraneous variables, participant variables, and situational variables. Also, each can be identified due to specific characteristics. Most population studies are always carried out to examine a research problem. This examination process revolves around methods of analysis that contrast, compare, correlate, or integrate the relationship between/among variables (Bressan et al., 2018). Therefore, it is very crucial to have a better understanding of variables before attempting research.
Reference
Bressan, M., Rosseel, Y., & Lombardi, L. (2018). The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results. Frontiers in psychology, 9, 1876. Retrieved from: https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01876/full
Sheeja Mole Kolencherry Augustine
Indeed, the basic types of variables are the independent, dependent, and extraneous variables. Usually, the researcher has a higher degree of control over the independent variables and can manipulate or alternate them to observe the independent outcome variables. You have also highlighted that extraneous variables are a significant cause of bias in research. For this reason, it is essential to control them (McKay-Nesbitt & Bhatnagar, 2017). Personality variables, which refers to attitudes of the experimenter, can be controlled by ensuring consistency throughout the experiment. Elimination, balancing, and constancy of conditions are ways of controlling the extraneous physical variables. Controlling these variables is essential as it results in increased internal validity.
Reference
McKay-Nesbitt, J., & Bhatnagar, N. (2017). Experimental Methods. In Formative Research in Social Marketing (pp. 89-106). Springer, Singapore. Retrieved from: https://link.springer.com/chapter/10.1007/978-981-10-1829-9_6