The concepts of reliability and validity are the two fundamental approaches to conducting and analyzing experiments in the behavioral sciences. Reliability helps to set the clear distinction between particular notions. For example, if the researchers estimate the reaction speed, they should hold the test repeatably to develop the dependence of the reaction of the specific test subject (Goodwin, 2017). Only when the data collected is reliable can the psychotherapists evaluate the subject as having the speed or slow response. Therefore, the reliability of the experiments helps to build the consistency of the particular psychological reaction. As far as it allows the researchers to delineate the norm and deviation, it is an essential construct for conducting research in the behavioral sciences field. Moreover, through reliability, scientists can assess different indicators as inherent to mentally healthy people or patients with mental disorders.
The reliability also includes subcategories that are suitable for different research purposes. For example, the inter-rater reliability is a correlation of the independent observation, which helps to formulate the impartial regularity of particular events (Goodwin, 2017). Even though this tactic is helpful in some cases, sometimes, the different contextual conditions can affect the observators’ judgment, creating the fudge factor that can negatively affect the research results (Haradhan, 2017). Another subcategory of reliability is the test-retest, including the repeating of the measurements over time. It can be used in cases when the results are inconsistent and contain measurement errors. For example, when estimating the person’s reaction, the researcher might need to hold the retest in similar conditions to ensure no side factors affect the test subject (Goodwin, 2017). Assessing the disadvantages of this method, it is essential to mention that it involves the massive calculation and long data-collection period, which can sometimes significantly slow the research.
The validity of the behavioral research is another essential factor that should be analyzed before creating the tests. Validity ensures that test measurements are suitable for estimating particular behaviors. This is essential for any psychotherapy research because only through this factor can the results’ trustworthiness be proven. One of the divisions of validity is the face subcategory (Kenny, 2019). It includes the superficial estimation of the measurements conformity. For example, it can reflect the researchers’ observations about the test (Goodwin, 2017). The main disadvantage of face validity is that it is entirely subjective and can not be used as a reliable argument of the trustworthiness of the research measurements in the behavioral sciences.
The criterion validity is another subdivision of the second analyzed construct. It estimates the question of whether the measures can adequately predict future behaviors. For example, based on the test results, the person’s future performance can be foreseen (Goodwin, 2017). The main disadvantage of this tactic involves the enormous data volume processing, including the details of the particular situations. If it is applied without proper consideration of the specific test conditions, the results will be not-valid.
The example supposed by the peer empowering the comparison of the passers’ attitude towards the two people stealing the bike: the black guy and white woman. The different levels of measurement can allow estimating of the various data in this experiment (Williams, 2021). For example, the ordinal scales will be enough to collect the necessary for making conclusions data. The ranking system will help estimate the level of people’s aggression depending on the testing event. The interval and ratio scales are used in more complicated cases requiring profound calculation (Williams, 2021). It is vital to discern the level of measurement of the data before designing the research because it shapes the format of the data the researcher needs to collect (Williams, 2021). Statistical design should consider the methods of the data collection and interpretation. Moreover, the measurement level can help to set the limitations for data collection. Thus, identifying the level of measurement of data is an opening step for the statistical design.
References
Goodwin, C. J., & Goodwin, K. A. (2017). Research in psychology: Methods and designs (8th ed.). Hoboken, NJ: John Wiley & Sons.
Haradhan, M. (2017). Two criteria for good measurements in research: Validity and reliability. Annals of Spiru Haret University. Economic Series, 4, 59–82.
Kenny, D. (2019). Enhancing validity in psychological research. American Psychologist, 74(9), 1018–1028.
Williams, M. (2021). Levels of measurement and statistical analyses. Meta-Psychology, 5, 1–14.