Understanding Data – the variable
A big part of understanding the statistics in research is understanding what is behind the data. What the data actually is, and how you obtain the data determine the types of statistical tests you will use. If you have been reading research, you probably already have an idea about most of the terms we will talk about today. But just in case, today we will make sure everyone is on the same page.
The most basic element of data is the variable. A variable is a measurement that represents a characteristic. Some examples of variables would be age, gender, height, parity, estimated fetal weight, and fetal heart rate. Each of these characteristics will be different for different individuals. It is these differences that interest us. How are they different? Why are they different? Do the differences matter?
Not all variables can be treated the same statistically. Characteristics of the variable itself determine what types of statistics can be used. All variables will be either continuous or discrete.
A continuous variable can take any value between its minimum and maximum. Characteristics such as age, weight, and blood pressure are continuous variables. Continuous variables are measured with a number, and the differences between each number are proportional meaning the magnitude of difference between 1 and 2 is the same as the magnitude of difference between 3 and 4.
Some continuous variables are interval and others are ratio. The difference is that a ratio variable has a specific zero measurement which indicates there is none of that variable. A gravid 4 has had twice as many pregnancies as a gravid 2, and a gravid 0 has had zero pregnancies. This means gravidy is a ratio variable.
A discrete variable is any variable that is not continuous, meaning it is only able to take on specific values. Characteristics such as race, sex, and use of oxytocin in labor are discrete variables. There are several types of discrete variables that may be used in research. Discrete variables are also known as categorical variables.
Some discrete variables are dichotomous, meaning there are only two options. Usually the options are yes or no.
Some discrete variables are nominal, meaning the characteristic has multiple values but the values do not have a mathematical relationship. For example, state of residence is a categorical value. Each individual may have a different state of residence, but there is no inherent ranking of the states. Other categorical variables include race and ethnicity, marital status, and place of birth.
Other discrete variables are ordinal, meaning the characteristic has multiple values that have a mathematical order without a defined magnitude of difference. For example, the pain scale asks women to rate the pain they feel as a number between 0 and 10. In this scale an answer of 10 is more pain than an answer of 9, but the difference between 9 and 10 may not be the same as the difference between 2 and 3. Similarly, the measurement of a “9” may not be the same from woman to woman.
Birth Worker Survey
The Birth Worker Survey included all types of variables. Age is a continuous ratio variable. Gender is dichotomous. Race is nominal. The questions about beliefs about childbirth are ordinal. This matters, because you report information about the variables differently.
50% of the respondents indicated they provide childbirth education services (a dichotomous variable). Childbirth educators were asked about the hours per session taught, number sessions in a typical course, and the number of courses taught per year. These are all continuous variables. Researchers using continuous variables will report the maximum value, minimum value and the mean value.
Hours per Session: Minimum 1.5; Maximum 10; Mean 3.1
Sessions per Course: Minimum 1; Maximum 12; Mean 6.4
Courses per Year: Minimum 2; Maximum 20; Mean 6.4
Childbirth Educators were also asked about the organizations they trained or certified with. This data is discrete, so it should be reported as a frequency. Frequency is reported as both the number of responses, percentage of the total. In research, these values would also be listed with measures of spread (confidence interval), but we will talk about those in a future post. Here is a partial list of the most frequently cited organizations.
American Academy of Husband Coached Childbirth n=4 (16%)
International Childbirth Education Association n=3 (12%)
Lamaze International n=3 (12%)
Childbirth and Postpartum Professionals Association n=2 (8%)
Childbirth International n=2 (8%)
Hypnobirthing n=2 (8%)
Spinning Babies n=2 (8%)
In the next post we will learn about descriptive statistics.