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 ed 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.

INDEXES AND SCALES A way of getting content validity

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 6- month license ( download fee)purchasing

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 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

Variables I Like Values and Labels

Exiting SPSS If you changed the actual dataset you must save it If you ran any statistics, you must save these as well

Variables

Measured Concepts We need to operationalize concepts to test hypotheses Concept- Conceptual Definition- Operational- Definition- 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 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 Mean, Median and Mode Measures of Dispersion How wide is our range of data, how close to the middle are the values distributed 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