Types of Study Data and Summarizing Flashcards
Continuous Data
-Has a logical order with values that continuously increase or decrease by the same amount over time
-Ex: a HR of 120 BPM is twice as fast as a HR of 60 BPM
Interval Data
-Type of continuous data
-Interval data has no meaningful zero (zero does not equal none)
-Ex: Celsius temperature scale (0°C does not mean no temperature; it is the freezing point of water), also Fahrenheit
Ratio Data
-Type of continuous data
-Ratio data has a meaningful zero (zero equals none)
-Ex: Heart rate (a HR of 0 BPM is cardiac arrest, the heart is not beating), age, height, weight, time, blood pressure
Discrete (Categorical) Data
-Data fits into a limited number of categories
-2 types of discrete data, nominal and ordinal, have categories
Nominal Data
-With nominal data, subjects are sorted into arbitrary categories (names), such as male or female (0 = male, 1 = female or 0 = female, 1 = male)
-Sometimes described as “yes/no” data
-Ex: gender, ethnicity, marital status, mortality
Ordinal Data
-Ordinal data is ranked and has a logical order, such as a pain scale
-Ordinal scale categories do not increase by the same amount (unlike continuous data); a pain scale rating of 4 is worse than a pain scale rating of 2, but it does not mean that there is twice as much pain
-Ex: NYHA scale I-IV, 0-10 pain scale
Mean
-Average value
-Calculated by adding up the values and dividing the sum by the number of values
-Mean is preferred for continuous data that is normally distributed
Median
-Value in the middle when the values are arranged from lowest to highest
-When there are two center values (as with an even number of values), take the average of the two center values
-The median is preferred for ordinal data or continuous data that is skewed (not normally distributed)
Mode
-Value that occurs most frequently
-The mode is preferred for nominal data
MO NOM
Range
-The difference between the highest and lowest values
Standard Deviation (SD)
-Indicates how spread out the data is, and to what degree the data is dispersed away from the mean
(i.e, spread out over a smaller or larger range)
-A large number of data values close to the mean has a smaller SD
-Data that is highly dispersed has a larger SD
Gaussian (Normal) Distributions
-Large sample sets of continuous data tend to form a Gaussian, or “normal” (bell-shaped), distribution
-Ex: if a researcher collects 5,000 blood pressure measurements (continuous data) from Idaho residents and plots the values, the graph would form a normal distribution
Characteristics of Gaussian (Normal) Distribution
-Symmetrical curve, with most values closer to the middle
-Half of the values are on the left side of the curve, half on the right
-Small number of values on the tails
-When data is normally distributed: mean, mode, median are the same value and are at the center point of the curve
-68% of values fall within 1 SD of the mean and 95% fall within 2 SDs
Skewed Distributions
-Not symmetrical
-68% of the values DO NOT FALL within 1 SD from the mean, and the mean, median and mode are not the same value
-This happens when number of values is small (small sample size) or there are outliers
Outliers
-An extreme value, either very low or very high, compared to the norm
-When there are a small number of values (small sample size), an outlier has a LARGE impact on the mean and the data becomes skewed
-In this case, the median is a better measure of central tendency
-Distortion of outliers is decreased by collecting more values (as the number of values increases, the effect of outliers on the mean decreases)
Skew: Direction of Tails
-Data is skewed TOWARDS the outliers
-When there are more low values in a data set and the outliers are the high values, data is skewed to the right (positive skew)
-When there are more high values in the data set and the outliers are the low values, the data is skewed to the left (negative skew)
