Identifying Major Threats to Validity

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
Conclusion Validity
Violated Assumptions
  • Violated assumptions are threats to conclusion validity. They often occur when researchers fail to understand how assumptions regarding research variables affect their ability to draw conclusions from them (Centre for Social Research Methods, 2017b). In quantitative research, this threat is termed “violated assumptions of statistical tests” (Centre for Social Research Methods, 2017b). For example, it often affects statisticians who assume that their population sample is normally distributed when it is not. If they erroneously rely on such an assumption, they could make wrong conclusions from their research data (Yu, 2018).
  • Correctly identify the assumptions underlying the research process and account for their effects in the data analysis process.
Low statistical Power
  • This threat to conclusion validity often emerges when researchers do not have adequate resources to compute their findings. Therefore, they make wrong conclusions.
  • Use elaborate high power data analysis techniques, such as SPSS, and enlarge the sample size (Yu, 2018).
Construct Validity
Inadequate Preoperational Explication of Constructs
  • This threat to construct validity often occurs when a researcher does a poor job of defining the research constructs (Centre for Social Research Methods, 2017a).
  • Evaluate construct concept better
  • Use concept mapping method to define constructs
  • Involve experts when critiquing the operationalization of constructs
Mono-Operation Bias
  • The mono-operation bias occurs when researchers only use one assessment technique within a narrowly defined prism of analysis. The unintended consequence is the failure to fully grasp the breadth of the study (Centre for Social Research Methods, 2017a).
  • Implement multiple versions of assessments in the study
Mono-Method Bias
  • This threat occurs when researchers use one method of measurement to evaluate a specific research phenomenon. For example, a researcher cannot effectively claim to be measuring a person’s self-esteem because the concept has many layers to its conception.
  • It is important to use multiple tests to eliminate mono-method bias. Doing so would make it easier to analyze multiple layers of assessment in one study.
  • Conducting a pilot study to make sure the measures used assess the intended variables
Restricted Generalizability Across Constructs
  • This threat to construct validity refers to the ability of a specific study to achieve its intended goal without interfering with other aspects of its use. For example, when medical researchers determine that a treatment is effective, they may overlook the unintended consequence that the same drug could have negative side-effects on the target population.
  • Use a large sample size
External Validity
Reactive or interaction effect of testing
  • Pre-testing may desensitize some research subjects from responding effectively to the questions asked. This threat has been highlighted by Yu (2018) when he highlighted the effects of pre-tests to subsequent tests.
  • Use multiple structural models to account for the effects of different treatments on observational studies (Keiding & Clayton, 2014).
Interaction effects of selection biases and the experimental variable
  • Selection bias may occur when a researcher has established rapport with a specified group of respondents and chooses to engage with them as the desired sample, instead of selecting participants randomly.
  • Choose a random selection of respondents or research materials
Reactive effects of experimental arrangements
  • This threat to external validity occurs when researchers try to extrapolate experimental findings to contexts outside the purview of the experiment.
  • Expand sample size
Multiple treatment interferences
  • Exposing researchers to multiple treatments in research studies makes it difficult for them to control for the effect of prior treatments on their findings.
  • Use multiple structural models to account for the effects of different treatments on observational studies (Keiding & Clayton, 2014).


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.