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Essential Concepts in SPSS: Understanding Variables and Data Analysis Techniques

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livana
51 diapositives · 705 Views · Jul 30, 2014

This study guide covers crucial concepts in SPSS, focusing on the operationalization of variables, types of data (nominal, ordinal, interval, ratio), and the importance of reliability and validity in research design. Students will learn how to create scales and indexes to simplify complex data, while also gaining competency in statistical analysis using SPSS. The guide outlines homework expectations, class participation, and the significance of understanding data types for effective research in social sciences.

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Essential Concepts in SPSS: Understanding Variables and Data Analysis Techniques
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  1. Variables 9/10/2013

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

  3. Homework: Due 9/12 • Chapter 1 • Question 1 Parts A &B • Question 2 • Exam 1 9/19….. (study guide for Thursday)

  4. 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:

  5. Opportunities to discuss course content

  6. Office Hours For the Week • When • Wednesday 10-12:00 • Thursday 8-12 • And by appointment

  7. Course Learning Objectives • students will achieve competency in conducting statistical data analysis using the SPSS software program. • Students will learn the basics of research design and be able to critically analyze the advantages and disadvantages of different types of design. 

  8. A way of getting content validity Indexes and Scales

  9. 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

  10. Likert Scale • A common way of creating a scale • Advantages • Disadvantages

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

  12. Guttman Scale

  13. SPSS Statistical Package for the Social Sciences

  14. What is a statistical package • Popular Versions • SPSS • SAS • R • Stata

  15. Getting SPSS Don’t Do Use it on the machines on campus- free! Consider purchasing a 6-month license (52.00 + 4.99 download fee) • 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).

  16. 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

  17. 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

  18. How SPSS Works

  19. 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

  20. Names and Labels Name Labels A longer definition of the variable These describe the actual variable • 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

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

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

  23. How Things are Displayed Edit • Options • Display names • Alphabetical

  24. Variables I Like Values and Labels

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

  26. Variables

  27. Variables • Measured Concepts • We need to operationalize concepts to test hypotheses • Concept- Conceptual Definition- Operational-Definition- Operationalization- Variable

  28. Four Categories of Variables

  29. Discrete variables

  30. Nominal Variables • Identify, label, and operationalize categories • Categories are • Exhaustive • Mutually Exclusive • Values are their for quantification only

  31. Nominal Examples

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

  33. Ordinal Examples

  34. Fun While it lasted

  35. More Ordinal Fun

  36. Health Care

  37. Nominal and Ordinal

  38. Continuous Variables

  39. 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.

  40. Interval Level Variables • The values matter at this level • The distances matter • The zero is arbitrary

  41. Examples of Interval Scales

  42. 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

  43. Examples

  44. Energy Use

  45. Nominal, Ordinal, Ratio

  46. Descriptive statistics

  47. Descriptive Statistics • These simply describe the attributes of a single variable. • You cannot test here (you need two variables) • Why do them?

  48. 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

  49. 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

  50. Lets Look at this one again