Descriptive Statistics: Part One

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# Descriptive Statistics: Part One - PowerPoint PPT Presentation

Descriptive Statistics: Part One. Farrokh Alemi Ph.D. Kashif Haqqi M.D. Objectives Definitions Sampling methods Types of variables. Reliability and validity Average Median Mode. Table of Content. Objectives.

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### Descriptive Statistics: Part One

Farrokh Alemi Ph.D.

Kashif Haqqi M.D.

Objectives

Definitions

Sampling methods

Types of variables

Reliability and validity

Average

Median

Mode

Table of Content
Objectives
• Define validity and reliability and explain the role of each in assessing the quality of data.
• Distinguish among nominal, ordinal, and numeric data, as well as discrete and continuous data.
• Given a set of numerical data, calculate the mean, median and mode, and state the relative advantages of each as a measure of central tendency.

Back to Table of Content

Definition of Variables
• A variable is an attribute of a person or an object that varies.
• Measurement are rules for assigning numbers to objects to represent quantities of attributes.

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What Is Statistics?
• Statistics is the science of describing or making inferences about the world from a sample of data.
• Descriptive statistics are numerical estimates that organize and sum up or present the data.
• Inferential statistics is the process of inferring from a sample to the population.
Definition
• Datum is one observation about the variable being measured.
• Data are a collection of observations.
• A population consists of all subjects about whom the study is being conducted.
• A sample is a sub-group of population being examined.
Sampling Methods
• Random sample: all subjects have equal chance of inclusion in the study.
• Systematic sampling: selecting the kth numbered subject.
• Stratified sample: random sampling within pre-defined groups of subjects.
• Staged sampling: A small random sample is made and if its results are ambiguous then another larger random sample is collected.

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Types of Variables
• A discrete variable has gaps between its values. For example, sex is a discrete variable. If male is 1 and female is 0, values in between have no meaning.
• A continuous variable has no gaps between its values. All values or fractions of values have meaning. Age is an example of continuous variable.

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Types of Variables (Continued)
• Nominal scale assign numbers to attribute to name the category. The numbers have no meaning by themselves, e.g. DRG code.
• Ordinal scale assign numbers so that more of an attribute has higher values, e.g. Severity.
• In an interval scale the interval between the numbers has meaning, e.g. Fahrenheit scale
• Ratio scale is an interval scale where zero has true meaning, e.g. Age.
Reliability and Validity

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### To Be Valid You Must Have a Reliable Measure. But You Can Have an Invalid Measure That Is Reliable.

Example of Reliability Calculation
• Next page shows a table from Hayward, RA, McMahon LF, Bernard AM. Evaluating the care of general medicine inpatients: how good is implicit review? Annals of Internal Medicine, volume 118(7), 1993, pp 550-556.
• Two reviewers rated the quality of health care delivered in the same case. The Table shows inter-rater reliability.
• 00000605-199304010-00010.
Average
• The mean, arithmetic average, is found by adding values of the data and dividing by the number of values. The mean of 3, and 4 is 3.5.
• The geometric average is found by multiplying the values of the data and taking the power of one divided by the number of values. The geometric average of 3 and 4 is square root of 3 times 4.
• Can you calculate the mean and geometric average for 3, 4, and 5?

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Example
• The mean of 3, 4 and 5 is the sum of these numbers divided by 3.
• The geometric average of 3, 4 and 5 is the cube root of 3 times 4 times 5. To calculate the cube root in excel you write a formula like: =(3*4*5)^0.33
• The answer is 3.86. Open Excel and verify that you can do this.
Difference Between Mean and Geometric Average
• A geometric average is used when averaging probabilities.
• A mean is used in most other context.
Median
• The median is the halfway point in a data set.
• To calculate median arrange data in order. Calculate half of the observations by dividing the number of values by 2 and rounding the value to the lower number. Count half the values and use the next value as median.

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Example
• The median for age of 7 patients (23, 45, 56, 23, 34, 65, 25) if given by:
• Order the list of values: 23, 23, 25, 34, 45, 56, 65.
• There are 7 observations. Divide 7 by two and round to lower number and you get 3.
• Skip the first 3 and the median is the next number. In this example, 34 is the median.
• Do this in Excel.
Mode
• The most frequent value observed is the mode.
• Mode is always an observed value in the data set.
• To calculate the mode, count the number of times each value is repeated. The value with most repetition is the mode.
• Do this in Excel.

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Example for Mode
• Age data: 23, 45, 56, 23, 34, 65, 25.
• 23 is repeated twice.
• All other values are repeated once.
• The mode is 23.
Differences in Measures of Central Tendency
• Mode, median and mean could be three different numbers in asymmetrical distributions of data.
• For any data set there is only one mean and median but there may be many modes.
• Median is less influenced by the extreme values than mean.
• Mean is almost never observed, median is observed in only odd numbered data sets and mode is always observed in the data set.