Topic Review - Newest First (only newest 5 are displayed)

rqayyum

Re: variables

Thank you Dr. Khurshid for you clear and simpler explaination.

Congratualtions, on your second book.

I am trying to solve a problem and you may be able to help me. Problem is this:
When conducting a meta-analysis, can I combine effect sizes calculted from repeated-measure experiments to independent-randomized experiments. I do know that using repeated-measure's t-value will not give a proper estimate of effect size. I am trying to find that will I violate any statistical assumption by combining effect sizes from these two different experimental models to calcuate the final effect-size and its confidence interval.

[Edited by rqayyum on 05-10-2005 at 02:25 AM GMT]

anwer_khur

Re: variables

Broadly, data can be classified into two major groups: discrete or continuous. Discrete data are generally whole numbers and have finite values, and allows subjects to be grouped into mutually exclusive categories. Sex, genotype, and number of patients responding to treatment are examples of discrete data. On the other hand, continuous data can take an infinite number of possible values and no degree of precision will ever record them exactly. Height in mm, weight in kg and blood pressure are examples of continuous data.
One rule of thumb:
What you can count is Discrete.
What you can measure is Continuous.
There is some disagreement among statisticians about it but I think most of the time for our purposes it will work.

For details one can see:

Pocket Dictionary of Statistics (2002)
Hardeo Sahai & Anwer Khurshid
McGraw-Hill, New York.

This is available online at
http://www.mhhe.com/business/opsci/bstat/keyterm.mhtml
(No need to purchase)
[This is my second book published by international press. First one is
Statistics in Epidemiology: Methods, Techniques and Applications (1995)
Hardeo Sahai and Anwer Khurshid
CRC Press, Florida, USA]

rqayyum

Re: variables

A discrete variable is characterized by gaps or interruptions in the values that it can assume. These gaps or interruptions indicate the absence of values between particular values that the variable can assume. For example, the number of daily admissions to a hospital is a discrete variable since the number of admissions each day must be a whole number, such as 1, 2, 3, .... and not 2.358, 0.569, .....

On the other hand, a continuous variable does not possess the gaps and interruptions. A continous variable can assume any value within a specified relevant interval. Height and weight are examples of continous variables.

mehwish

variables

hi ppl,
what is the difference b/w continuous quantitative variable and discrete quantitative variable??