Measurement – Data

by Raywat Deonandan, PhD

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    00:01 Hello and welcome to epidemiology. You know sometimes my clients or my research partners bring data they've already collected to me and ask me to analyze it and the problem is they've arranged the data or collected it in such a way that is not really useful or amenable to easy analysis. So today we're going to learn about data and data analysis and some of the concepts underlying data. So after today's lecture, you're going to understand the limitations of quantifying data. You're going to be able to identify the different types of measurement that a variable can embody and again variables make up our data. You're going to know why the normal curve is important in statistics. And you're going to understand the difference between type I and type II error, also a fundamentally important concept in statistics and data analysis.

    00:49 So let's begin by asking the question, what is measurement? What do you think measurement is? You measure things all the time, you measure your weight, you measure your height, maybe you measure certain qualities of a patient's blood sample. Measurement is when we assign a quantity to a quality, ultimately there is a quality we're trying to assess or learn about, and we quantify it in order to do math on it. So a value that may change within the scope of a problem is a variable. That's what a variable is, it is something that is changing all the time as opposed to a constant, there are constants in life, there are variables in life. Data analysis is all about processing the relationship between variables and constants between each other. So in the world of mathematics a variable can be written as X, it's a place keeper, it's just a registry that we later fill with a number and perform functions on.

    01:43 In research, a variable is a logical set of attributes like gender, age, something we want to learn about. And in computer science, a variable is just a symbolic name given to an unknown quantity. So the word variable is used in a variety of contexts depending upon the discipline that you come from. So when we're defining a variable, there are actually two components to consider, there is a conceptual component and an operational component. The concept is when we think about the thing we're trying to measure conceptually, the operational component is when we define the variable itself and it's that operational aspect on which the mathematics is performed. So consider this, consider if I'm trying to measure happiness. Now devise a scale for measuring happiness from 1 to 5 and if I ask you, on a scale of 1 to 5, how happy are you and you say 3. Okay, I can do math on that number now, but I've lost a kind of nuance about the thing I was trying to measure, which is the happiness. That always happens. The variables operational characteristics I do my statistics on, but we can't forget, there is a conceptual underlying philosophy that's important as well. So a variable is a kind bucket for containing quality or information.

    02:55 It isn't the quality or information itself is, it's just the container. I perform statistics or mathematics on the bucket, ultimately I want to derive meaning and philosophy and importance from the quality inside the bucket.

    03:09 There are two flavors of variables and we've covered this in a previous lecture, the two flavors are continuous and categorical. So a continuous variable is something like age or height or distance or temperature. It's a measurement that has meaning in between its values. I can be 25 years old. I can be 25.5 years old. I can be 25.51 years old.

    03:31 There's still meaning there. On the other hand, a categorical or discrete variable doesn't have meaning in between. Age group, gender, number of children, number of siblings, where I was born. There is nothing in between that gives me meaning. Those are the two general categories. I can create a dichotomous categorical variable that means it has two levels, dichotomous means having two levels, like sex, male or female, or disease presence, yes or no. I can dichotomize an existing continuous variable. In other words I can create a two level categorical variable from an existing continuous variable. For example, I can take age, which is a continuous flowing concept and create age group out of it, maybe under 18 versus 18 and over.

    About the Lecture

    The lecture Measurement – Data by Raywat Deonandan, PhD is from the course Data.

    Included Quiz Questions

    1. Measurement
    2. Logic
    3. S.I Unit
    4. Data
    5. Variable
    1. Independent variable
    2. Coherent variable
    3. Confounding variable
    4. Dependent variable
    5. Moderating variable
    1. Dependent variable
    2. Coherent variable
    3. Confounding variable
    4. Independent variable
    5. Moderating variable

    Author of lecture Measurement – Data

     Raywat Deonandan, PhD

    Raywat Deonandan, PhD

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