Measurement 9/11/2012. Readings Chapter 3 Proposing Explanations, Framing Hypotheses, and Making...

Measurement

9/11/2012

Readings

• Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (Pollock) (pp.48-58)

• Chapter 1 Introduction to SPSS (Pollock Workbook)

OPPORTUNITIES TO DISCUSS COURSE CONTENT

Office Hours For the Week

• When– And appointment

• Not scientific knowledge

Course Learning Objectives

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

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 SCALESA 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

Examples

• GPA

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 (49.00 + 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

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

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

CONTINUOUS VARIABLES

What about em’

• 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

• A zero means the absence of a property

• Classify, order, set units of distance

Examples