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By A. Tufail. Ferris State University.

When classifying variables by function we want to know what the variable does in the experiment buy generic kamagra effervescent 100 mg line. The independent variable is under the con- trol of or can be manipulated by the investigator buy kamagra effervescent 100mg free shipping. Generally this is the cause we 67 68 Essential Evidence-Based Medicine are interested in, such as a drug, a treatment, a risk factor, or a diagnostic test. The dependent variable changes as a result of or as an effect of the action of the independent variable. It is usually the outcome of exposure to the treatment or risk factor, or the presence of a particular diagnosis. We want to ﬁnd out if chang- ing the independent variable will produce a change in the dependent variable. The nature of each variable should be evident from the study design or there is a serious problem in the way the study was conducted. When classifying variables by their nature, we mean the hierarchy that describes the mathematical characteristics of the value generated for that vari- able. The choice of variables becomes very important in the application of statis- tical tests to the data. One can assign a number to each of these categories, but it would have no intrinsic signiﬁcance and cannot be used to compare one piece of the data set to another. Exam- ples of nominal data are classiﬁcation of physicians by specialty or of patients by the type of cancer from which they suffer. There is no relationship between the various types of specialty physicians except that they are all physicians and went to medical school. Ordinal data are nominal data for which the order of the variables has impor- tance and intrinsic meaning. Typical examples of ordinal data include certain pain scores that are measured by scales called Likert scales, severity of injury scores as reﬂected in a score such as the Trauma Score where lower numbers are pre- dictive of worse survival than higher ones, or the grading and staging of a tumor where higher number stages are worse than lower ones. Common questionnaires asking the participant to state whether they agree, are neutral, or disagree with a statement are also examples of an ordinal scale. Although there is a directional value to each of these answers, there is no numerical or mathematical relation- ship between them. Interval data are ordinal data for which the interval between each number is also a meaningful real number. However, interval data have only an arbitrary zero point and, therefore, there is no proportionality ratio relationship between two points. One example is temperature in degrees Celsius where 64◦Cis32 C hotter◦ than 32◦C but not twice as hot. This makes the results take on meaning for both absolute and relative changes in the vari- able. Examples of ratio variables are the temperature in degrees Kelvin where 100◦ Kelvin is 50◦K hotter than 50◦K and is twice as hot, age where a 10-year- old is twice as old as a 5-year-old, and common biological measurements such Instruments and measurements: precision and validity 69 as pulse, blood pressure, respiratory rate, blood chemistry measurements, and weight. This is called the number of signiﬁcant places, which is taught in high school and college, although it is often forgotten by students quickly thereafter. Height is an example of a continuous measure since a person can be 172 cm or 173 cm or 172. For exam- ple, a piano is an instrument with only discrete values in that there are only 88 keys, therefore, only 88 possible notes. Scoring systems like the Glasgow Coma Score for measuring neurological deﬁcits, the Likert scales mentioned above, and other ordinal scales contain only discrete variables and mathematically can have only integer values. We commonly use dichotomous data to describe binomial outcomes, which are those variables that can have only two possible values. Obvious examples are alive or dead, yes or no, normal or abnormal, and better or worse. This has the effect of dichotomizing the value of the serum sodium into either hypernatremic or not hypernatremic. Measurement in clinical research All natural phenomena can be measured, but it is important to realize that errors may occur in the process. Random error leads to a lack of precision due to the innate variability of the biological or sociological system being studied. For example, in a given popula- tion, there will be a more or less random variation in the pulse or blood pres- sure. Many of these random events can be described by the normal distribution, which we will discuss in Chapter 9. An imprecise instrument will get slightly different results each time the same event is measured. For example, serum sodium measured inside rat muscle cells will show less random error than the degree of depression in humans. There can also be innate variability in the way that 70 Essential Evidence-Based Medicine different researchers or practicing physicians interpret various data on certain patients. 