![]() ![]() Your sample is biased because some groups from your population are underrepresented. External validityĪttrition bias can skew your sample so that your final sample is significantly different from your original sample. Without complete sample data, you may not be able to form a valid conclusion about your population. You’re left with 30 participants in the treatment group and 65 participants in the control group.įor the participants who stay, the treatment is more successful than the control protocol in encouraging responsible alcohol use.īut it’s hard to form a conclusion, because you don’t know what the outcomes were for the participants who left the treatment group. Example: Attrition bias and internal validityIn your study, many more participants drop out of the treatment group than the control group. It can make variables appear to be correlated when they are not, or vice versa. This type of research bias can affect the relationship between your independent and dependent variables. In experiments, differential rates of attrition between treatment and control groups can skew results. Internal validityĪttrition bias is a threat to internal validity. But the type of attrition is important, because systematic bias can distort your findings.Īttrition bias can lead to inaccurate results, because it can affect internal and/or external validity. Some attrition is normal and to be expected in research. This means your study has attrition bias. You check the baseline survey data to compare those who leave against those who remain in the study.Īccording to the data, participants who leave consume significantly more alcohol than participants who stay. Example: Attrition biasDuring your study, a number of participants drop out and fail to complete the education program or the follow-up surveys. What matters is whether there’s a systematic difference between those who leave and those who stay. You can have attrition bias even if only a small number of participants leave your study. Attrition biasĪttrition bias is a systematic error: participants who leave differ in specific ways from those who remain. Without a sufficiently large sample, you may not be able to detect an effect if there is one in the population. Note that this type of attrition can still be harmful in large numbers because it reduces your statistical power. You find no statistically significant differences between those who leave and those who stay while checking your study data. Example: Random attritionRoughly equal numbers of participants drop out from your control and treatment groups by the end. Random attrition means that participants who stay are comparable to the participants who leave. When attrition is systematic, it’s called attrition bias. Types of attritionĪttrition can be random or systematic. In clinical studies, participants may also leave because of unwanted side effects, dissatisfaction with treatments, or death from other causes.Īlternatively, you may also need to exclude some participants after a study begins for not following study protocols, for identifying the study aim, or for failing to meet inclusion criteria. For example, they may not return after a bad experience, or they may not have the time, motivation, or resources to continue taking part in your study. Participants can drop out for any reason at all. More and more participants drop out at each wave after the pretest survey, leading to a smaller sample at each point in time. Example: AttritionYou’ll complete five waves of data collection to compare outcomes: a pretest survey, three surveys during the program, and a posttest survey. Most of the time, when there are multiple data collection points (waves), not all of your participants are included in the final sample. ![]() You provide a treatment group with short drug education sessions over a two-month period, while a control group attends sessions on an unrelated topic. Example: Longitudinal studyUsing a longitudinal design, you investigate whether an educational program can help college students manage their alcohol use. You can often combine longitudinal and experimental designs to repeatedly observe within-subject changes in participants over time. In experimental research, you manipulate an independent variable to test its effects on a dependent variable. Frequently asked questions about attrition bias. ![]()
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