1. 1 PEER Session 02/04/15

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3.  Multiple good data management software options exist – quantitative (e.g., SPSS), qualitative (e.g, atlas.ti), mixed (e.g., Excel)  Goal is to convert any research data from its ‘raw’ form to a form that can be readily analyzed 3

4.  You can fight Excel  By Formatting your spreadsheets in a traditional report style  You can work together with Excel  By Formatting your spreadsheets in a way Excel prefers

9. Data Analysis functions

10. One value per cell One type of data in each column No blank rows One row of column headings Be consistent Windhoek, WHK, WNDHK, Whoek, The Hoek… Avoid separating similar data across tabs

11.  Originally developed for the people in social science areas; no heavy programming background required  Designed as user-friendly and has pull-down menus to execute statistical commands  Ability to do data management & manipulations  Ability to store programs & produce reports/graphs 11

12. 12 Data Modification/ Transformation Pull-Down Menu SPSS Data File Outside Data Source Raw Data Data Analysis Importing Direct Entry Syntax Menu OR (Data Steps)(Analysis Steps)

13. 13 Title bar Tool bar Data View window Information barPull-down Menu bar Active cellAction bar Variable Names Help Menu

14. 14 64 Characters Max, No space Between Beg letter, @, #, or $ Variable Description Length Numeric, String, & Others Click here to see this view Value Code Description # of Decimals Missing value Description

15. 15 1. OK - results/action will be executed OKPaste VS. buttons Before we see Examples…

16. 16 1.Hit Paste to obtain Syntax Window 2. Run Syntax to obtain the results in the Output Window

17. 17 Raw Data  Subject 1  Subject #(1)  Female(1)  Intensive(1)  Reading (90)  Math (67)  Subject 2  Subject # (2)  Female(1)  Moderate(2)  Reading (72)  Math (46)  Subject 3  Subject # (3)  Male(0)  Basic(3)  Reading(41)  Math(73)

18. 18 Variable View Activated

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21. 21 2. Go to File Menu 3. Click “Read Text Data” 4. Click Files of type to Excel & choose Excel file 5. Hit Open 6. Check Worksheet #, Variable on the 1 st row, & Hit OK 1.Open the SPSS Data file

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23. 23 1.1 Introduction of SPSS 1.2 Data Entry 1.3 Data Cleaning using SPSS

24. 24 1.2 Data Entry into SPSS There are 2 ways to enter data into SPSS: 1. Directly enter in to SPSS by typing in Data View 2. Enter into other database software such as Excel then import into SPSS Let’s start with the second option, using data in Excel.

25. 25 Figure 1. Data from Hell

26. 26 Data from Heaven

27. 27 1. Give each variable a valid name (8 characters or less with no spaces or punctuation, beginning with a letter not a numeric number). Short, easy to remember word names. Avoid the following variable names: TEST, ALL, BY, EQ, GE, GT, LE, LT, NE, NOT, OR, TO, WITH. These are used in the SPSS syntax and if they were permitted, the software would not be able to distinguish between a command and a variable. Each variable name must be unique; duplication is not allowed. Variable names are not case sensitive. The names NEWVAR, NewVar, and newvar are all considered identical. General guidelines for data entry 2. Encode categorical variables. Convert letters and words to numbers. 3. Avoid mixing symbols with data. Convert them to numbers. 4. Give each case a unique, sequential case number (ID). Place this ID number in the first column on the left

28. 28 5. Each variable should be in its own column. Avoid this: Animal Control1 Control2 Experiment1 Experiment2 Change to: Animal Group 1 0 2 0 3 1 4 1 * It is recommended to use 0/1 for 2 groups with 0 as a reference group. * Do not combine variables in one column 6. All data for a project should be in one spreadsheet. Do not include graphs or summary statistics in the spreadsheet.

29. 29 8. However when data are repeatedly collected over a patient, it’s recommended to have patient-day observation on a simple line to ease data management. SPSS has a nice feature to convert from the longitudinal format to horizontal format. When the number of repeats are few 2 or 3, horizontal format may be preferred for simplicity. Date ID SYSBP 1/2/2005 1 130 1/3/2005 1 120 1/4/2005 1 120 3/1/2005 2 110 3/2/2005 2 140 Longitudinal data entry ID SYSBP1 SYSBP2 SYSBP3 1 130 120 120 2 110 140 Horizontal data entry 7. Each case should be entered on a single line or row. Do not copy a patient’s information to another row to perform subgroup analysis.

30. 30 9. For yes/no questions, enter “0” for no and “1” for yes. Do not leave blanks for no. Do not enter “?”, “*”, or “NA” for missing data because this indicates to the statistical program than the variable is a string variable. String variables cannot be used for any arithmetic computation. 10. Put ordinal variables into one column if they are mutually exclusive. Avoid: Pain Mild Moderate Severe 1 0 0 0 1 0 0 0 1 Preferred: Pain 1 2 3 11. Do not make columns wider then 8 characters, unless absolutely essential.

31. 31 Importing data from Excel spreadsheet into SPSS. In SPSS, go to: File, Open, Data Select Type of file (for example, Excel) you want to open Select File name you want to open

32. 32 Importing data from SPSS to Excel. In SPSS, go to: Data, Save as, Select Type of file (for example, Excel) you want to save into Give File name you want to save into

33. 33 1.3 Data Cleaning in SPSS 1. Re-coding existing variables 2. Creating new variables 3. Creating new variable from existing variables 4. Data labeling and formatting

34. 34 Data cleaning in SPSS (1): Recoding existing variables (1) Old New ID Group Group 1 A 0 2 A 0 3 B 1 4 B 1 We want to use numeric coding for group instead of A and B.

35. 35 Data cleaning in SPSS (2): Recoding existing variables (2) From SPSS dialog box, go to: Transform Recode Into Same variables

36. 36 1. Select Group from the variable box into String Variables box 2. Click on Old and new Values to proceed Data cleaning in SPSS (1): Recoding existing variables (3)

37. 37 1. Type the old value and the new value you want to convert into 2. Click on Add (To remove, or change, click on Change or Remove) 3. Type all values in the Old  New box, then click Continue 4. Click OK to execute the commands. Data cleaning in SPSS (1): Recoding existing variables (4)

38. 38 Computing patient’s age from birthday and date enrolled into the study. Data Cleaning in SPSS (3)

39. 39 Data Labeling Data Cleaning in SPSS (4): Data labeling and formatting (2)

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41.  Run frequencies and descriptives to get the ‘lay’ of the data  Ensure all values are in bounds and variables are valid  Conduct descriptive analyses Univariate Bivariate Multivariate  Conduct testing for differences (t- test, ANOVA, etc) 41