![]() ![]() You’re interested in how people think about risk and uncertainty.Examples of things that would count as history effects: Alternatively, it might be that you’re looking at an older study, which was perfectly valid for its time, but the world has changed enough since then that the conclusions are no longer trustworthy. Or, in between testing participant 23 and participant 24. For instance, something might happen in between a pre-test and a post-test. History effects refer to the possibility that specific events may occur during the study itself that might influence the outcomes. So let’s have a look at some of the most common examples… There’s a sense in which almost any threat to validity can be characterised as a confound or an artifact: they’re pretty vague concepts. But other times it’s unavoidable, simply because the researcher has ethics (e.g., see “differential attrition”). In practice, it’s really hard to think everything through ahead of time, and even very good researchers make mistakes. This can happen for all sorts of reasons, not least of which is researcher error. It’s absolutely possible to have confounds in an experiment, and to get artifactual results with non-experimental studies. Or, to put it another way, when you take psychology out of the wild and bring it into the lab (which we usually have to do to gain our experimental control), you always run the risk of accidentally studying something different than you wanted to study: which is more or less the definition of an artifact.īe warned though: the above is a rough guide only. By working in a more real-world context, you lose experimental control (making yourself vulnerable to confounds) but because you tend to be studying human psychology “in the wild” you reduce the chances of getting an artifactual result. To see this, it helps to realise that the reason that a lot of studies are non-experimental is precisely because what the researcher is trying to do is examine human behaviour in a more naturalistic context. For the most part, artifactual results tend to be a concern for experimental studies than for non-experimental studies. However, there’s always swings and roundabouts, and when we start thinking about artifacts rather than confounds, the shoe is very firmly on the other foot. Experimental research tends to be much less vulnerable to confounds: the more control you have over what happens during the study, the more you can prevent confounds from appearing. The possibility that your result is an artifact describes a threat to your external validity, because it raises the possibility that you can’t generalise your results to the actual population that you care about.Īs a general rule confounds are a bigger concern for non-experimental studies, precisely because they’re not proper experiments: by definition, you’re leaving lots of things uncontrolled, so there’s a lot of scope for confounds working their way into your study. Artifact: A result is said to be “artifactual” if it only holds in the special situation that you happened to test in your study.The existence of confounds threatens the internal validity of the study because you can’t tell whether the predictor causes the outcome, or if the confounding variable causes it, etc. Confound: A confound is an additional, often unmeasured variable 10 that turns out to be related to both the predictors and the outcomes.These two terms are defined in the following way: ![]() If we look at the issue of validity in the most general fashion, the two biggest worries that we have are confounds and artifact. ![]()
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