Mortality refers to losing participants from a study over time, threatening internal validity by introducing potential bias in the results.
Understanding Mortality as a Threat to Internal Validity
In social science research, maintaining a study’s internal validity is crucial for drawing accurate conclusions about causal relationships. One significant threat to internal validity is mortality, also referred to as participant attrition. Mortality occurs when participants drop out or are lost from the study before it is completed. This loss can happen for various reasons, such as lack of interest, moving away, illness, or other unforeseen circumstances.
Mortality is especially problematic in longitudinal studies, which track participants over an extended period. When participants drop out, it can create imbalances in the data, potentially leading to biased results. This bias undermines the study’s internal validity—the degree to which the observed outcomes can be confidently attributed to the independent variables rather than to external factors like participant dropout.
The Concept of Internal Validity
Before examining mortality as a threat, it’s important to understand the broader concept of internal validity. Internal validity refers to the extent to which a study accurately demonstrates a cause-and-effect relationship between the independent and dependent variables. A study with high internal validity minimizes confounding variables and other factors that might distort the relationship being examined.
Various threats can compromise internal validity, including history, maturation, instrumentation, and selection bias. Mortality, as one of these threats, poses unique challenges, particularly when the participants who drop out differ in important ways from those who remain in the study. This can lead to a distortion of the results, as the final sample may not accurately represent the initial population.
Causes of Mortality in Research
Mortality can occur for a wide range of reasons, and understanding these causes helps researchers design studies that minimize the risk of participant dropout. Common causes of mortality include:
1. Participant Fatigue
In longitudinal or experimental studies, participants may become fatigued or lose interest over time. This is particularly true in studies that require multiple waves of data collection, lengthy surveys, or extensive time commitments. As participants grow tired, they may choose to withdraw from the study, leaving researchers with incomplete data.
2. Illness or Personal Circumstances
Participants may face health issues or personal circumstances, such as moving to a different location or changes in family responsibilities, that prevent them from continuing in a study. These circumstances are often outside the control of the researcher but can lead to significant dropout, especially in long-term studies.
3. Perceived Lack of Benefit
If participants perceive that the study offers little personal benefit, they may choose to stop participating. This can happen in studies where participants feel that their time is not being well used or if they are unaware of the broader importance of the research.
4. Boredom or Frustration with Study Procedures
Participants may drop out if they find the study process boring, frustrating, or too demanding. For example, studies with complicated instructions or surveys that seem repetitive can lead to increased dropout rates. Similarly, if participants face technical issues with online data collection methods or feel that their privacy is compromised, they may decide to discontinue their involvement.
5. Adverse Reactions to Experimental Manipulations
In experimental studies, participants might experience discomfort or distress related to the experimental manipulation, causing them to withdraw. This is especially a concern in studies dealing with sensitive topics or involving physical or psychological interventions.
How Mortality Threatens Internal Validity
Mortality threatens internal validity primarily by introducing systematic bias into the study. When participants drop out, it is rarely at random. Instead, those who leave often differ from those who remain in ways that may affect the study’s outcome. This introduces bias because the final sample may no longer be representative of the population that was originally intended to be studied.
1. Non-Random Attrition
One of the biggest concerns with mortality is that it often results in non-random attrition. Non-random attrition means that the participants who drop out of the study are different from those who remain, and these differences may relate to the variables under investigation. For example, in a study on stress and academic performance, students who are more stressed may be more likely to drop out of the study. If the final analysis only includes students who completed the study and they happen to be less stressed on average, the results may underestimate the true impact of stress on academic performance.
Non-random attrition leads to selection bias because the remaining participants do not form a random, representative subset of the original sample. The characteristics of those who remain may skew the study’s findings, creating the illusion of a relationship between variables that is not truly there or, conversely, masking a relationship that does exist.
2. Reduced Statistical Power
Mortality reduces the overall sample size, which can diminish the statistical power of a study. Statistical power refers to the ability of a study to detect a true effect if one exists. When participants drop out, the sample size shrinks, making it more difficult to detect significant differences or relationships between variables.
For example, in a clinical trial comparing the effectiveness of two treatments, if too many participants drop out, the study may fail to detect differences between the treatments, even if such differences exist. This increases the risk of a Type II error, where researchers fail to reject the null hypothesis when it is, in fact, false.
3. Increased Variability
Participant dropout can also increase variability in the results, as the remaining sample may become more heterogeneous. This occurs when those who drop out share certain characteristics, leaving a more diverse group of participants who complete the study. Increased variability makes it harder to detect consistent patterns or effects, further complicating the analysis.
Examples of Mortality in Social Science Research
To illustrate how mortality affects internal validity, consider the following examples from social science research:
Example 1: Longitudinal Study on Academic Achievement
In a longitudinal study tracking students’ academic achievement over several years, some students may drop out of the study due to moving away, changing schools, or simply losing interest. If the students who drop out are disproportionately those with lower grades, the remaining sample will be biased toward higher achievers. As a result, the study may overestimate the general academic performance of the population, threatening its internal validity.
Example 2: Clinical Trial of a New Therapy
In a clinical trial comparing two psychological therapies for depression, participants may drop out because they feel that the therapy is not working for them. If more participants in the experimental group drop out than in the control group, the final results may indicate that the experimental therapy is more effective than it actually is. This happens because the control group may include participants who would have dropped out if they had been in the experimental group. The differential dropout creates an unbalanced comparison, compromising the study’s internal validity.
Example 3: Survey Research on Political Attitudes
In a survey study on political attitudes, participants may drop out if they feel uncomfortable with the questions or if they disagree with the overall tone of the survey. If the dropout rate is higher among participants with more extreme political views, the final sample will be skewed toward more moderate opinions, leading to biased conclusions about the distribution of political attitudes in the population.
Mitigating Mortality in Research
Researchers can take several steps to mitigate the effects of mortality and protect the internal validity of their studies. These strategies include:
1. Retaining Participants
One of the most effective ways to address mortality is to retain participants throughout the study. Researchers can enhance participant retention by:
- Building rapport: Establishing a positive relationship with participants and explaining the importance of their continued involvement.
- Incentivizing participation: Offering incentives, such as monetary compensation, gift cards, or other rewards for completing the study.
- Minimizing participant burden: Simplifying study procedures to reduce fatigue, boredom, or frustration. This might involve shortening surveys or limiting the number of follow-up sessions.
- Maintaining communication: Regularly communicating with participants to keep them engaged and informed about the study’s progress.
2. Conducting Sensitivity Analyses
If participant dropout is unavoidable, researchers can conduct sensitivity analyses to assess the impact of mortality on the study’s findings. Sensitivity analysis involves testing how the results change when different assumptions are made about the missing data. For example, researchers might assume that participants who dropped out had worse outcomes than those who remained and adjust their analyses accordingly.
3. Using Multiple Imputation
Multiple imputation is a statistical technique that allows researchers to estimate and replace missing data due to participant dropout. By using imputation, researchers can account for the data that would have been provided by participants who dropped out, helping to reduce the bias introduced by mortality. However, this method requires that the data are missing at random (MAR), meaning that the probability of missing data is related to observed data but not to unobserved data.
4. Intent-to-Treat Analysis
In clinical trials, researchers can use an intent-to-treat (ITT) approach, which includes all participants in the analysis, regardless of whether they completed the study. The ITT approach helps preserve the original randomization of participants and prevents biases from differential dropout between groups. However, it may still require strategies like imputation to handle missing data.
Conclusion
Mortality, or participant dropout, poses a serious threat to the internal validity of social science research. When participants drop out of a study, it can introduce systematic bias, reduce statistical power, and increase variability, leading to potentially flawed conclusions. Researchers must carefully consider the risk of mortality in their studies and employ strategies to mitigate its effects, such as retaining participants, conducting sensitivity analyses, and using statistical techniques like multiple imputation.