Variables
9/10/2013
Readings
• Chapter 3 Proposing Explanations, Framing
Hypotheses, and Making Comparisons
(Pollock) (pp.48-58)
• Chapter 1 Introduction to SPSS (Pollock
Workbook)
Homework: Due 9/12
• Chapter 1
– Question 1 Parts A &B
– Question 2
• Exam 1 9/19….. (study guide for Thursday)
About the Homework
• It must be turned in
during class.
• It cannot be emailed
• It must appear on the
workbook paper
(original or a
photocopy)
• You cannot:
OPPORTUNITIES TO DISCUSS
COURSE CONTENT
Office Hours For the Week
• When
– Wednesday 10-12:00
– Thursday 8-12
– And by appointment
Course Learning Objectives
1. students will achieve competency in
conducting statistical data analysis using the
SPSS software program.
2. Students will learn the basics of research
design and be able to critically analyze the
advantages and disadvantages of different
types of design.
A way of getting content validity
INDEXES AND SCALES
Why create a scale/index?
• To form a
composite measure
of a complex
phenomenon by
using two or more
items
• Get at all facets
• Simplify our data
Likert Scale
• A common way of
creating a scale
• Advantages
• Disadvantages
Guttman Scaling
• Employs a series of
items to produce a
score for respondents
• Ordering questions that
become harder to agree
with
• Advantages and
disadvantages
Guttman Scale
SPSS
Statistical Package for the Social
Sciences
What is a statistical package
• Popular Versions
–
–
–
–
SPSS
SAS
R
Stata
Getting SPSS
Don’t
• Purchase a student version
– Limited functions
– Limited variables
• Searching the internet for a
“free version”
– You might get a virus
– The Russians will steal your
identity (exception fallacy).
Do
• Use it on the machines on
campus- free!
• Consider purchasing a 6month license (52.00 + 4.99
download fee)
How to Open Data files
• Data Files on the Pollack CD
• GSS2008.SAV- the 2008 General
Social Survey Dataset
– n=2023
– 301 variables
• NES2008.SAV- the National
Election Study from 2008. n=2323
– 302 variables
• STATES.SAV- aggregate level data
for the 50 States. N=50
– 82 Variables
• WORLD.SAV- aggregate level data
for the nations of the world.
n=191
– 69 Variables
SPSS uses 2 windows
• Data Editor Window
– is used to define and enter your data and to perform
statistical procedures.
– very spread-sheet like
– .sav extension
• The Output Window
– this is where results of statistical tests appear
– This opens when you run your first test
– .spv extension
HOW SPSS WORKS
It is like a spreadsheet
• In Variable View
– You define your
parameters
– Give variables names
– Operationalize variables
• We will not do a lot of
this
Names and Labels
Name
• how the label appears at
the top of the column (like
the first row in excel)
• you cant use dashes, special
characters or start with
numbers
• These should represent the
variable
Labels
• A longer definition of the
variable
• These describe the actual
variable
Value Labels
• This shows how
variables are
operationalized
• Value= the numeric
value given to a
category
• Label= the attribute of
the concept
In Data View
• You type in raw data
• It looks very much like
Excel
• Rows= cases
• Columns= Variables
How Things are Displayed
Edit
• Options
• Display names
• Alphabetical
I Like Values
and Labels
Variables
Exiting SPSS
• If you changed the actual dataset you must
save it
• If you ran any statistics, you must save these
as well
Variables
Variables
• Measured Concepts
• We need to operationalize concepts to test
hypotheses
• Concept- Conceptual Definition- OperationalDefinition- Operationalization- Variable
Four Categories of Variables
DISCRETE VARIABLES
Nominal Variables
• Identify, label, and
operationalize
categories
• Categories are
– Exhaustive
– Mutually Exclusive
• Values are their for
quantification only
Nominal Examples
Ordinal Variables
• These identify, rank order, label, and
operationalize categories
• The Numbers mean something here
• Operationalization denotes more or less of an
attribute
Ordinal Examples
Fun While it lasted
More Ordinal Fun
Health Care
Nominal and Ordinal
CONTINUOUS VARIABLES
What makes them unique
• The values matter
• Your variable includes all possible values, not
just the one’s that you assign.
• Name, order, and the distances between
values matter.
Interval Level Variables
• The values matter at
this level
• The distances
matter
• The zero is arbitrary
Examples of Interval Scales
Ratio Variables
• The Full properties of
numbers.
• Its measurement on
Steroids
• A zero means the
absence of a property
• Classify, order, set
units of distance
Examples
Energy Use
Nominal, Ordinal, Ratio
DESCRIPTIVE STATISTICS
Descriptive Statistics
• These simply describe the attributes of a
single variable.
• You cannot test here (you need two variables)
• Why do them?
Categories of Descriptive Statistics
Measures of Central Tendency
• The most common, the
middle, the average
Measures of Dispersion
• How wide is our range of
data, how close to the
middle are the values
distributed
• Mean, Median and Mode
• Range, Variance, Standard
Deviation
Frequency Distributions
• This Provides counts and percentages (relative
frequencies) of the values for a given variable
• Computing a relative Frequency
• The Cumulative Percent is percentage of
observations less than or equal to the category
Lets Look at this one again
Examples
• St. Edward’s Data