Statistical Biases: Confounding, Causation & Correlation

by Raywat Deonandan, PhD

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    Hello and welcome to epidemiology. We're continuing our discussion of biases and things that resemble biases. And today we're talking about a particularly important kind of non-bias called a confounding, which quite excites me, because this is one of most common things that messes with studies, we'll get there though. First off, you're going to be able to identify Hawthorne and Rosenthal effects, which are common problems in randomized controlled trials, or RCT's. You're also going to be able to identify confounding variables, again that concept of confounding, which I think is so important and I think you're going to find it quite exciting as well because you are going to see it pop up in all matter of interactions you have with data and with people. And lastly, you're going to understand the concept of effect modification, which is similar to confounding, but quite distinct. So we've covered a variety of biases already, let's get into the Hawthorne and Rosenthal effects and they are combined to make something resembling placebo effect, we will talk more about placebo effect on the lecture about experiments. So Hawthorne effect is also called the observer effect and it is named after a case in the Hawthorne factory, where they discovered that when you turn the lights up in the factory the workers tended to work harder. In other words, individuals modify their behavior when they are being watched, that's the Hawthorne effect. So scientists tend to recruit patients who have a better adherence to drug therapies, who have a more likelihood of staying in a study, therefore less likelihood to have future loss to follow-up, they are special individuals, they're more likely to be good subjects. In other words, they're going to respond in a positive way more likely than other individuals not...

    About the Lecture

    The lecture Statistical Biases: Confounding, Causation & Correlation by Raywat Deonandan, PhD is from the course Epidemiology & Biostatistics: Advanced. It contains the following chapters:

    • Statistical Biases: Confounding, Causation & Correlation
    • Hawthorne and Rosenthal Effect
    • Confounding Bias
    • Effect Modification
    • Learning Outcomes

    Quiz for lecture

    Test your knowledge with our quiz for lecture Statistical Biases: Confounding, Causation & Correlation .

    1. Maternal age
    2. Smoking
    3. Gender
    4. SES
    5. Any of the traditional confounders
    1. Hawthorne effect
    2. Rosenthal effect

    Author of lecture Statistical Biases: Confounding, Causation & Correlation

     Raywat Deonandan, PhD

    Raywat Deonandan, PhD

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