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  • Now, in this section I want to discuss the very exciting topic of cross-sectional

  • studies.

  • Now, these are really a snapshot in time.

  • I've mentioned one of the examples before and those are surveys.

  • We've all received a request to fill in a survey in our email inbox, and

  • the authors of that might of wanted us to be part of a cross-sectional study.

  • Another example,

  • a very good example of cross-sectional studies is epidemiological surveys.

  • We just want to know how many patients at a certain time has a certain disease.

  • More exciting things about the cross-sectional study though is,

  • it can be incorporated into other study designs.

  • And you'll see that quite often.

  • Now, let's consider an example, Lawrenson and colleague in 2013.

  • They really sent out a questionnaire to healthcare professionals.

  • Those who are optometrists and ophthalmologists.

  • And I want quote from the abstract that says,

  • conduct a cross-sectional survey of current practices.

  • So, what did they do?

  • They just wanted to know what advice patients were given, and

  • those were patients with age related macular degeneration.

  • So, that's indeed a snapshot of time.

  • They send out the survey and ask what advice are you giving right now.

  • Now, one thing you can do with these results is form many, many subgroups.

  • Say for instance, not done in this study.

  • But you could say let's divide the participants or

  • people who filled in the survey in to the different areas of the town,

  • that side of the river and that side of the river.

  • Once we've cut these two groups,

  • we can now compare what were the answers to the other questions.

  • And so, for almost all of those questions you can form two little groups

  • depending on what the answer, and evaluate the other variables.

  • And so, there's so many answers you can come up with in this sort of study.

  • I mentioned it's also very common to do

  • epidemiological studies in the form of a cross-sectional study.

  • We're really looking at prevalence.

  • Another example I wanted to tell you about is Sartorius and colleagues

  • they just looked, right here in South Africa at the determinants of obesity in South Africa.

  • And they did those from data, from very large surveys.

  • But again, it's a snapshot in time.

  • Now, unfortunately, there are problems with this kind of analysis.

  • First of all, there is a response bias.

  • Think of sending out a survey.

  • There's a certain subset of people who will respond to a survey and

  • those who won't, that might skew the data.

  • There might be something inherent about the subset who do decide to respond

  • versus those that don’t.

  • You've got to be very careful about that.

  • Also, cause and effect can't really be identified, or

  • separated from each other.

  • We might get from the different sides of town, different results.

  • But was living on different sides of town really the cause for

  • those differences in answers?

  • So, why do cross-sectional studies?

  • Well, number one is very quick.

  • If you think how quickly you can fill in the survey.

  • If you're the researcher, you ask some people to fill in the survey for

  • you, your data's collected right there, it can be very quick.

  • It also can be very cheap.

  • Imagine just sending it out via email, there's really very little cost to that.

  • So, you can get some powerful answers very quickly and

  • very cheaply from cross-sectional studies.

  • Now, in the next lecture, I'm going to talk about cohort series.

  • It'll be the last type of observation studies we're going to talk about.

  • Really interesting stuff.

Now, in this section I want to discuss the very exciting topic of cross-sectional

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A2 初級

在一個時間點上收集數據 橫斷面研究 (Collecting data at one point in time Cross sectional studies)

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    Jack 發佈於 2021 年 01 月 14 日
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