Measures of dispersion
1 / 40

Measures of Dispersion - PowerPoint PPT Presentation

  • Uploaded on

Measures of Dispersion. 9/25/2012. Readings. Chapter 2 Measuring and Describing Variables (Pollock) (pp.37-44) Chapter 6. Foundations of Statistical Inference (128-133) (Pollock) Chapter 3 Transforming Variables (Pollock Workbook) . Homework.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Measures of Dispersion' - lonna

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript


  • Chapter 2 Measuring and Describing Variables (Pollock) (pp.37-44)

  • Chapter 6. Foundations of Statistical Inference (128-133) (Pollock)

  • Chapter 3 Transforming Variables (Pollock Workbook)


  • Homework Due: Chapter 2 Pollock Workbook (10/2)

    • Question 1: A, B, C, D, E

    • Question 2: B, D, E (this requires a printout)

    • Question 3: A, B, D

    • Question 5: A, B, C, D

    • Question 7: A, B, C, D

    • Question 8: A, B, C

Office hours for the week
Office Hours For the Week

  • When

    • Friday and Wednesday 11-1

    • Thursday 8-12

    • And appointment

Course learning objectives
Course Learning Objectives

  • Students will learn the research methods commonly used in behavioral sciences and will be able to interpret and explain empirical data.

  • Students will achieve competency in conducting statistical data analysis using the SPSS software program.

Categories of descriptive statistics
Categories of Descriptive Statistics

Measures of Central Tendency

Measures of Dispersion

How wide is our range of data, how close to the middle are the values distributed

Range, Variance, Standard Deviation

  • The most common, the middle, the average

  • Mean, Median and Mode

Case summaries

Another SPSS Feature

Case Summaries

How to do it
How To Do it

Step 1

Step 2

Check off this box

What are they
What are They?

  • these measure the uniformity of the data

  • they measure how closely or widely cases are separated on a variable.

The range
The Range

  • The Simplest Measure of Dispersion

    • Maximum

    • Minimum

  • Range= max-min (only fun for ratio variables)

Back to the island
Back To the Island

  • What is the

    • Maximum

    • Minimum

    • Range

High vs low dispersion
High Vs. Low Dispersion

  • Polarized

  • Clustered

The standard deviation
The Standard Deviation

  • A More accurate and precise measure than dispersion and clustering

  • Is the average distance of values in a distribution from the mean

What it tells us
What it tells us

  • When the value of the standard deviation is small, values are clustered around the mean.

  • When the value of the standard deviation is high, values are spread far away from the mean.

From 2008
From 2008

Who was more divisive?

About the standard deviation
About the Standard Deviation

  • its based on the mean

  • the larger the standard deviation, the more spread out the values are and the more different they are

  • if the standard deviation =0 it means there is no variability in the scores. They are all identical.

Standard deviation in spss
Standard Deviation in SPSS

  • Open up the States.Sav dataset and use the union07 variable.

  • Analyze

    • Descriptive Statistics

      • Descriptives

        • Select your options

The standard deviation and outliers
The Standard Deviation and Outliers

  • Any case that is more than 2 standard deviations away from the mean

  • These cases often provide valuable insights about our distribution

How to determine the value of a standard deviation
How to determine the value of a standard deviation

  • The value of +/- 1 s.d. = mean + value of s.d

    • e.g. if the mean is 8 and the s.d is 2, the value of -1 s.d's is 6, and + 1 s.d.'s is 10

  • The value of +/- 2 s.d. = mean + (value of s.d. *2)

    • e.g. if the mean is 8 and the s.d is 2, the value of -2 s.d's is 4, and + 2 s.d.'s is 12

  • Any value in the distribution lower than 4 and higher than 12 is an outlier

An example from 2008
An Example from 2008

  • What is the Value of +/- 1 S.D?. (mean+ 1.s.d)

  • What is the Value of +/-2 S.D? (mean +/- 2 s.d)

Unwrapping the results
Unwrapping The Results

  • Which are Outliers

  • How would that shaper the 2012 campaign

Camel humps
Camel Humps

Dromedary (one hump)

Bactrian (bi-modal)

The normal bell shaped curve
The Normal/Bell Shaped curve

  • Symmetrical around the mean

  • It has 1 hump, it is located in the middle, so the mean, median, and mode are all the same!

Why we use the normal curve
Why we use the normal curve

  • To determine skewness

  • The Normal Distribution curve is the basis for hypothesis testing

What this tells us
What this Tells us

  • Roughly 68% of the scores in a sample fall within one standard deviation of the mean

  • Roughly 95% of the scores fall 2 standard deviations from the mean (the exact # is 1.96 s.d)

  • Roughly 99% of the scores in the sample fall within three standard deviations of the mean

A practice example
A Practice Example

  • Assuming a normal curve compute the age (value)

    • For someone who is +1 s.d, from the mean

    • what number is -1 s.d. from the mean

  • With this is assumption of normality, what % of cases should roughly fall within this range (+/-1 S.D.)

  • What about 2 Standard Deviations, what percent should fall in this range?

What is skewness
What is skewness?

  • an asymmetrical distribution.

  • Skewnessis also a measure of symmetry,

  • Most often, the median is used as a measure of central tendency when data sets are skewed.