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Measurement 9/11/2012. Readings Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (Pollock) (pp.48-58) Chapter 1 Introduction.

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Presentation on theme: "Measurement 9/11/2012. Readings Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (Pollock) (pp.48-58) Chapter 1 Introduction."— Presentation transcript:

1 Measurement 9/11/2012

2 Readings Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (Pollock) (pp.48-58) Chapter 1 Introduction to SPSS (Pollock Workbook)

3 OPPORTUNITIES TO DISCUSS COURSE CONTENT

4 Office Hours For the Week When – And appointment Not scientific knowledgeknowledge

5 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.

6 INDEXES AND SCALES A way of getting content validity

7 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

8 Examples GPA

9 Likert Scale A common way of creating a scale Advantages Disadvantages

10 Guttman Scaling Employs a series of items to produce a score for respondents Ordering questions that become harder to agree with Advantages and disadvantages

11 Guttman Scale

12 SPSS Statistical Package for the Social Sciences

13 What is a statistical package Popular Versions – SPSS – SAS – R – Stata

14 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)purchasing

15 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

16 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

17 HOW SPSS WORKS

18 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

19 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

20 Value Labels This shows how variables are operationalized Value= the numeric value given to a category Label= the attribute of the concept

21 In Data View You type in raw data It looks very much like Excel Rows= cases Columns= Variables

22 How Things are Displayed Edit Options Display names Alphabetical

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

24 Variables

25 Measured Concepts We need to operationalize concepts to test hypotheses

26 Four Categories of Variables

27 DISCRETE VARIABLES

28 Nominal Variables Identify, label, and operationalize categories Categories are – Exhaustive – Mutually Exclusive Values are their for quantification only

29 Nominal Examples

30 Ordinal Variables These identify, rank order, label, and operationalize categories The Numbers mean something here Operationalization denotes more or less of an attribute

31 Ordinal Examples

32 CONTINUOUS VARIABLES

33 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.

34 Interval Level Variables The values matter at this level The distances matter The zero is arbitrary

35 Examples of Interval Scales

36 Ratio Variables The Full properties of numbers A zero means the absence of a property Classify, order, set units of distance

37 Examples


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