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SPSS-Applications (Data Analysis) - Luchsinger Mathematics

Slide 1 CORTEX fellows training course, University of Zurich, October 2006 SPSS-Applications ( data analysis ) Dr. J rg Schwarz, Program 19. October 2006: Morning Lessons (0900-1200) spss Basics - Working with spss => parts of "The " - Special issues: Use of Syntax Editor, Select Cases & Split File data analysis with spss - spss -Methods (Description, Testing, Modeling) - Some notes (only "Type of Scales") Slide 2 CORTEX fellows training course, University of Zurich, October 2006 Program 19. October 2006: Afternoon Lessons (1330-1630) Extended Example: Multiple Regression - Key steps - Multiple Regression with spss Exercise - Multiple Regression Slide 3 Table of Contents spss Basics _____ 8 Sample Files (Page 9).

Slide 1 CORTEX fellows training course, University of Zurich, October 2006 SPSS-Applications (Data Analysis) Dr. Jürg Schwarz, juerg.schwarz @schwarzpartners.ch Program 19.

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Transcription of SPSS-Applications (Data Analysis) - Luchsinger Mathematics

1 Slide 1 CORTEX fellows training course, University of Zurich, October 2006 SPSS-Applications ( data analysis ) Dr. J rg Schwarz, Program 19. October 2006: Morning Lessons (0900-1200) spss Basics - Working with spss => parts of "The " - Special issues: Use of Syntax Editor, Select Cases & Split File data analysis with spss - spss -Methods (Description, Testing, Modeling) - Some notes (only "Type of Scales") Slide 2 CORTEX fellows training course, University of Zurich, October 2006 Program 19. October 2006: Afternoon Lessons (1330-1630) Extended Example: Multiple Regression - Key steps - Multiple Regression with spss Exercise - Multiple Regression Slide 3 Table of Contents spss Basics _____ 8 Sample Files (Page 9).

2 8 Starting spss (Page 10) & Opening a data File (Page 11)..10 Using the data Editor (Page 77) ..11 data Organization in spss (Page 36) ..12 Running an analysis (Page 14) & Viewing Results (Page 17) ..13 Using the Help System (Page 21)..16 Help menu .. 17 Dialog box Help buttons .. 17 Pivot table context menu 18 Choices in Entering data (Page 38) ..19 Reading data (Page 41)..20 Reading data from Spreadsheets .. 21 Reading data from a Text 23 Using the data Editor (Page 77 ff) ..25 Entering (new) Numeric data .. 26 Adding Variable Labels & Value Labels .. 28 Handling Missing data (Page 89).. 29 Special issue: Use of Syntax Editor.

3 31 Modifying data Values (Page 109 ff) ..39 Creating a Categorical Variable from a Scale Variable (Page 109) .. 40 Computing New Variables (Page 112) .. 41 Special issue: Select Cases & Split File ..42 Select 42 Split File .. 44 Creating and Editing Charts (Page 204) ..46 Slide 4 spss -Methods _____ 50 Description ..50 .. 50 52 .. 53 .. 54 Testing (Compare means) ..55 One-Sample T Test .. 55 Two-Sample T Test .. 57 Paired Samples 59 Some notes _____62 Type of Nominal 62 Ordinal Scale .. 63 Metric Scale .. 64 Summary: Type of Scales .. 65 Exercises: Scales ..66 Multiple Regression _____ 68 Introduction.

4 68 General purpose of (multiple) Key steps involved in using multiple 1. Formulation of the 72 2. Estimation of the model .. 72 3. Verification of the model .. 72 Regression in spss ..73 Simple Example (EXAMPLE01) .. 73 Default Elements .. 74 spss Output Regression analysis (EXAMPLE01) I .. 75 Slide 5 spss Output Regression analysis (EXAMPLE01) II .. 76 spss Output Regression analysis (EXAMPLE01) III .. 77 spss Output Regression analysis (EXAMPLE01) IV ..78 What about the requirements? .. 79 spss Output Residuals (EXAMPLE01) .. 80 Example with nonlinearity (EXAMPLE02) .. 81 spss Output Regression analysis (EXAMPLE02).

5 82 spss Output Residuals (EXAMPLE02) .. 83 spss Output Regression analysis (EXAMPLE02 quadratic term).. 84 spss Output Residuals (EXAMPLE02 quadratic term).. 85 Multiple regression ..86 Multicollinearity .. 86 Example of multiple regression (EXAMPLE03).. 87 spss Output Regression analysis (EXAMPLE03) I .. 88 spss Output Regression analysis (EXAMPLE03) II .. 89 spss Output Regression analysis (EXAMPLE03) III .. 90 Gender as dummy variable .. 91 Dummy coding of categorical variable .. 92 Exercises: Multiple Appendix _____94 Help Slide 7 Sources & Links ( ) Slide 8 spss Basics Sample Files (Page 9) Most of the examples presented here use the data file All sample files are located in the tutorial\sample files folder.

6 Find the data file On your computer \..\Program Files\ spss \tutorial\sample files\ Slide 9 The data file is a fictitious survey of several thousand people (n = 6400), containing basic demographic and consumer information. NameLabelageAge in yearsmaritalMarital statusaddressYears at current addressincomeHousehold income in thousandsinccatIncome category in thousandscarPrice of primary vehiclecarcatPrimary vehicle price categoryedLevel of educationemployYears with current employerretireRetiredempcatYears with current employerjobsatJob satisfactiongenderGenderresideNumber of people in householdwirelessWireless servicemultlineMultiple linesvoiceVoice mailpagerPaging serviceinternetInternetcallidCaller IDcallwaitCall waitingowntvOwns TVownvcrOwns VCRowncdOwns stereo/CD playerownpdaOwns PDAownpcOwns computerownfaxOwns fax

7 MachinenewsNewspaper subscriptionresponseResponse Age in in yearsFrequency120010008006004002000 Std. Dev = Mean = = of people in of people in householdFrequency3000200010000 Std. Dev = Mean = = 10 Starting spss (Page 10) & Opening a data File (Page 11) Other possibility: Double click on spss data file Slide 11 Using the data Editor (Page 77) The data Editor displays the contents of the active data file. data View Columns represent variables and rows represent cases (observations). Variable View Each row is a variable, each column is an attribute associated with that variable. Slide 12 data Organization in spss (Page 36) spss data is organized by cases (rows) and variables (columns).

8 Cases (rows) For a survey of individuals, each row would represent a respondent. In a scientific experiment, each row might correspond to a single recorded observa-tion. For sales summary data , each row might correspond to a time unit (for example, one month). Variables (columns) Each column in the data Editor corresponds to a specific measurement or type of recorded information. In many areas of research, these measurements are called variables, in some engineering fields they are called characteristics. Slide 13 Running an analysis (Page 14) & Viewing Results (Page 17) Slide 14 Slide 15 Slide 16 Using the Help System (Page 21) Slide 17 Help menu Dialog box Help buttons => => Slide 18 Pivot table context menu Help Slide 19 Choices in Entering data (Page 38) There are various methods of entering the data values.

9 spss data Editor Spreadsheet programs ( Excel) Database programs ( Access) For high-volume processing of surveys, you might consider scanning the survey responses. Slide 20 Reading data (Page 41) data can be imported from a number of different sources. Reading an spss data File spss data files, which have a *.sav file extension, containing your saved data . Reading data from Spreadsheets Rather than typing all your data directly into the data Editor, you can read data from applica-tions like Microsoft Excel. You can also read column headings as variable names. Reading data from a Text File Text files are common source of data .

10 Many spreadsheet programs and databases can save their contents in one of many text file formats. Comma or tab delimited files refer to rows of data that use commas or tabs to indicate each variable. Reading data from a Database (not shown in this course) data from database sources are easily imported using the Database Wizard. Slide 21 Reading data from Spreadsheets Find the Excel file " " on :Column headings are variable names. Slide 22 Open the Excel file from the File menu Slide 23 Reading data from a Text File Find the Text file " " on Slide 24 Slide 25 Using the data Editor (Page 77 ff) Remember data Editor Slide 26 Entering (new) Numeric data Open new data set (<File> <New> < data >) Click the Variable View tab at the bottom of the data Editor window.


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