This assignment consists of two parts. The first one is a chart that highlights the major threats for conclusion, construct and external validities, while the second section explains the major threats to external validity and highlights possible ways of minimizing them. The first part appears below.
|Threats||Description of the Threat||Methods used to Minimize the Threats|
|Violated Assumptions|| || |
|Low statistical Power|| || |
|Inadequate Preoperational Explication of Constructs|| || |
|Mono-Operation Bias|| || |
|Mono-Method Bias|| || |
|Restricted Generalizability Across Constructs|| || |
|Reactive or interaction effect of testing|| || |
|Interaction effects of selection biases and the experimental variable|| || |
|Reactive effects of experimental arrangements|| || |
|Multiple treatment interferences|| || |
The major threats to validity in this study are analyzed within the prisms of how they influence external, conclusion, and construct validities. The three types of validity underscore the gist of this study because the focus of this review is centered on understanding their main threats (Chao, Chen, & Millstein, 2013). Although the above facts are integral to the development of high-quality social research studies, external validity requires special focus because it is reflective of the usability of research information. Indeed, as has been highlighted in the first section of the current study, external validity refers to the ability of users of research information to apply a study’s findings beyond the context of the review. In this essay, an emphasis is made to highlight the major threats to external validity and evaluate possible strategies for minimizing the same.
Major Threats to External Validity
In the context of this study, threats to validity refer to how ineffective people may be in making generalizations about the findings of a research. According to Schmidt and Hunter (2014), these threats to validity are often reported in cases where the independent variable is a construct of another variable. Based on this analysis, the threats to external validity generally draw a link with the independent variable. The aptitude-treatment interaction is the first threat to external validity. It refers to the presence of unique features that interact with the independent variable (Schmidt & Hunter, 2014). This threat has been primarily reported in psychotherapy studies where researchers conducted investigations on groups of people with common psychosocial symptoms that may be associated with other personal or lifestyle factors (Schmidt & Hunter, 2014). For example, studies that have used highly depressed people or volunteers as the sample population have suffered this problem (Yu, 2018). Using volunteers as an illustration of this threat, observers may question whether a researcher who uses volunteers and non-volunteers (as two groups of respondents) to conduct a study would arrive at the same conclusion. To understand this question, it is crucial to note that volunteering for a research and participating in the same experiment involuntarily implies the presence of a motivating factor that influences people’s willingness to participate in the study. This attribute may affect the external validity of the analysis.
Another threat to external validity is the situation or context that defines the research process. Several factors may be used as examples in this analysis, including lighting, culture, timing and location (just to mention a few). The specificity of these factors affects the ability of a researcher to generalize a study’s findings beyond the context defined by some of the features of the research environment discussed above (Chao et al., 2013).
The existence of pre-test and post-test events in research also poses a threat to the external validity of research studies because it limits people’s ability to extrapolate their findings if they do not carry out the two tests. For example, it is difficult to generalize a study that was developed by researchers who undertook pretest and posttest analyses to draw a link between two variables if both tests are not done. Finally, people’s expectations may also be a threat to external validity because numerous studies have pointed out that they influence performance (Chao et al., 2013; Schmidt & Hunter, 2014; Yu, 2018). This threat is known as the “Rosenthal effect” (Chao et al., 2013). To overcome it and the other threats discussed earlier, it is important to consider implementing the options discussed below.
How to Minimize Threats to External Validity
It is possible to minimize the threats to external validity by recalibrating the information received from one study into another one. Doing so addresses the minimal differences in application. Relative to the above suggestion, Yu (2018) points out two types of generalization problems that could be solved. One of them involves studies that could lend themselves to recalibration and the other one includes studies that cannot be generalized. Sampling bias which also poses a threat to external validity can also be addressed by using Barenboim’s (causal) graph to circumnavigate the sampling selection bias (Keiding & Clayton, 2014). This technique is notably known to develop a new unbiased estimate of the average causal effect of a given research sample and allows its findings to be replicated to a larger population.
This essay shows that external validity problems are associated with the inability of researchers to generalize a study beyond the context of their primary review. The major threats to external validity highlighted in this study include contextual limitations, the existence of pre-test and post-test events, and the aptitude-treatment interaction. These threats could be minimized by recalibrating the information received from one study into another one. The causal graph method is also another technique for minimizing the contextual limitations of a study when extrapolating its findings. These proposals show that the threats to external validity could be effectively addressed to produce high-quality findings in research.
Centre for Social Research Methods. (2017a). Threats to construct validity. Web.
Centre for Social Research Methods. (2017b). Threats to conclusion validity. Web.
Chao, H., Chen, S., & Millstein, R. (2013). Mechanism and causality in biology and economics. New York, NY: Springer Science & Business Media.
Keiding, N., & Clayton, D. (2014). Standardization and control for confounding in observational studies: a historical perspective. Statistical Science, 29(4), 529-558.
Schmidt, F., & Hunter, J. (2014). Methods of meta-analysis: correcting error and bias in research findings. London, UK: SAGE Publications.
Yu, C. (2018). Threats to validity of research design. Web.