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Organizing Your Data for Statistical Analysis in SPSS

Organizing Your Data for Statistical Analysis in SPSS. Edward A. Greenberg, PhD. ASU HEALTH SOLUTIONS DATA LAB. Revised January 4, 2013. SPSS Data Sets. SPSS Data Sets. SPSS Data Sets. Rows are cases or observations Columns are variables (measurements)

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Organizing Your Data for Statistical Analysis in SPSS

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  1. Organizing Your Data for Statistical Analysis in SPSS Edward A. Greenberg, PhD ASU HEALTH SOLUTIONS DATA LAB Revised January 4, 2013

  2. SPSS Data Sets

  3. SPSS Data Sets

  4. SPSS Data Sets Rows are cases or observations Columns are variables (measurements) Up to 231-1 columns (2,147,493,647) No limit on the number of cases

  5. Variable Types Numeric (40 character maximum length) Dates and times (various formats) Other variations of numeric (currency, comma, scientific notation, etc.) String (32,767 maximum length)

  6. Variable Names Variable names must be unique. Variable names may be up to 64 characters in length. Names can contain letters, numbers, or special characters. Names must start with a letter or @, #, or $.

  7. Unit of Analysis What constitutes a “case?” A person A household An organization An experimental trial

  8. Level of Measurement }Scale Nominal Ordinal Interval Ratio

  9. Labeling Data Variable names may be short and cryptic. Variable labels can be up to 255 characters. SPSS procedures display at least 40 characters of variable labels. Value labels can be up to 120 characters.

  10. Order of Variables The order of variables in the SPSS data file normally should be the same as the order of items in the questionnaire. Use variable names that help you identify the scale or instrument to which they apply.

  11. Case Numbers Each case in an SPSS file should include a case number. Often this will be the first variable in the file. The case number does not identify the subject but it links the data record to the subject’s questionnaire. Useful for correcting data entry errors

  12. Create a Codebook • When preparing to enter your data into SPSS, prepare a codebook for the data set. • The codebook documents all of the items to be entered in the data set: • Variable names and labels • Variable types and formats • Coded values for categorical items • Missing values

  13. Sample Codebook

  14. Missing Data Data may be missing for several reasons: Don’t know Refused to answer Not applicable Skipped a question Instrument problem Data entry omission

  15. Missing Values SPSS provides several ways of designating numeric data as “missing values.” A blank cell is treated as “system missing,” represented by a dot (“.”) in the SPSS Data Editor. Specific values can be declared as “user missing” values.

  16. Missing Values Up to three “user missing” values can be declared for a variable. Or, a range of values plus one additional value can be declared to be missing.

  17. Missing Values

  18. Missing Values In this example, variable AGEWED has three labeled values that are to be treated as missing

  19. Missing Values The three values are declared to be missing in the Missing Values dialog.

  20. Missing Values Expressions handle missing values in different ways. The result of (var1+var2+var3)/3 is missing if any of the three variables is missing. The result of MEAN(var1, var2, var3) is missing if all three of the variables are missing.

  21. Missing Values in Procedures The FREQUENCIES procedure excludes cases with missing values from computations.

  22. Multiple Responses • Multiple-response items are questions that can have more than one value for each case. • Two ways of coding: • For each response, a variable can have one of two values e.g., 1=Yes and 2=No (“multiple-dichotomy” method) • Create a series of variables for 1st choice, 2nd choice, etc. (“multiple categories” method)

  23. MULT RESPONSE Procedure In the MULT RESPONSE procedure, multiple response variables are combines into groups. The MULT RESPONSE procedure counts responses in multiple response groups in frequency or cross tabular tables. Total percentages of responses generally will exceed 100%.

  24. Repeated Measures Data that are recorded on more than one occasion for each subject Some procedures, such as GLM, require that all measurements for a case be on the same data record. Other procedures, such as the MIXED procedure, may expect one data record per occasion.

  25. Repeated Measures One data record per subject, one variable per occasion on which it is measured

  26. Repeated Measures One data record per occasion per subject

  27. Repeated Measures The good news is that SPSS allows you to easily restructure a data set Restructure selected variables into cases Restructure selected cases into variables Transpose all data

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