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Old 05-31-2011
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Stats Types of scales and Stats tests?

So, there are 4 types of scales in stats.
a. Nominal (categorical)
b. Ordinal
c. Interval
d. Ratio

1. Can somebody explain me what's the difference between them. I mix up ordinal and interval scales as both of them can have different groups in sequence, or not? Any examples which can clear this out.

2. When to use t-test, chi square, one way ANOVA, matched pairs t-test and repeated measures ANOVA?
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Old 05-31-2011
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Great questions! I don't know the answer to them all, but here are a couple basic quick tricks that I use (of course it's more complicated but I've found i can answer most biostats questions this way):
1) t-test and ANOVA are basically the same in that they both compare the means. The big difference is that t-test compares 2 means, ANOVA compares the means of of 3 or more groups.
2) If you are not given means, then use a chi square test. This is used to compare categories (eg. number of people who responded yes/no, percentage of people who do xyz etc)

Those are the basics. A matched t-test is again just looking at the means between groups, except now you are matching variables. For instance, you want to look at the mean drug dose required to treat people who smoked for a mean number of years vs the mean dose in folks who smoked for a different mean number of years. So in this case drug dose and smoking is matched.
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Old 05-31-2011
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Quote:
Originally Posted by heights View Post
Great questions! I don't know the answer to them all, but here are a couple basic quick tricks that I use (of course it's more complicated but I've found i can answer most biostats questions this way):
1) t-test and ANOVA are basically the same in that they both compare the means. The big difference is that t-test compares 2 means, ANOVA compares the means of of 3 or more groups.
2) If you are not given means, then use a chi square test. This is used to compare categories (eg. number of people who responded yes/no, percentage of people who do xyz etc)

Those are the basics. A matched t-test is again just looking at the means between groups, except now you are matching variables. For instance, you want to look at the mean drug dose required to treat people who smoked for a mean number of years vs the mean dose in folks who smoked for a different mean number of years. So in this case drug dose and smoking is matched.
Nice rule of thumb!

I would like to add: a matched-pairs t-test is used to test whether there is a significant mean difference between two sets of paired data (correlated data).

for example:
- pair of twins
- where the same people are being measured in before-and-after comparison
- when the group is given two different tests at different times
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Old 05-31-2011
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Are we supposed to know how to use Chi square test?
I didnt get any details in the High yield Biostatistics..
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Old 06-01-2011
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Originally Posted by struggle View Post
Are we supposed to know how to use Chi square test?
I didnt get any details in the High yield Biostatistics..
No we don't have to use the chi square test or compute any other formula. We just have to know that WHEN we have to use them.
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Old 06-01-2011
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Star Thanks

Thanks a lot heights and bebix.
Now if any can explain the differences between the scales: nominal, ordinal, interval and ratio. Thanks

Last edited by mle guy; 06-01-2011 at 12:37 AM.
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Old 06-01-2011
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Smile Types of Numerical data

Types of Numerical data

As “data” we consider the result of an experiment. A rough classification is as follows:

• Nominal data Numbers or text representing unordered categories (e.g., 0=male, 1=female // 0=death, 1=alive // 0=north, 1=south, 2=west, 4=east)

• Ordinal data Numbers or text representing categories where order counts (e.g., 1=fatal injury, 2=severe injury, 3=moderate injury, etc.)

• Discrete data: This is numerical data where both ordering and magnitude are important but only whole number values are possible (e.g., Numbers of deaths caused by heart disease (765,156 in 1988) versus suicide (40,368 in 1988).

• Continuous data Numerical data where any conceivable value is, in theory, attainable (e.g., height, weight, etc.)

Hope this help
Ref: Principles of Biostatistics. Marcello Pagano
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Old 06-02-2011
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Thank you so much. It really helped
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Old 06-02-2011
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Originally Posted by mle guy View Post
Thank you so much. It really helped
Super!

now try to solve the Biostatistic questions!!!

I'm going to try to upload 2-3 per day
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