1. Introduction to SPSS
Asst. Prof. Dr. Emrah Oney

2. Topics we will cover today
SPSS at a glance
Basic Structure of SPSS
Cleaning your data
Descriptive Statistics
Charts
Other Resources

3. SPSS at a glance
SPSS stands for Statistical Package for the Social Sciences
SPSS was made to be easier to use then other statistical software like S-Plus, R, or SAS.
The newest version of SPSS is SPSS Today we will be working on SPSS 16.0.

4. How to open SPSS Go to START Click on PROGRAMS Click on SPSS INC
The computers in the lab typically have SPSS on the desktop. It is a red box that says SPSS on the top.

5. Opening a data file Click on FILE  OPEN  DATA
Click MY COMPUTER  LOCAL DISK C:/
Click PROGRAM FILES  SPSS
Click TUTORIAL  SAMPLE FILES
Select CATALOG.SAV

6. Basic structure of SPSS
There are two different windows in SPSS
1st – Data Editor Window - shows data in two forms
Data view
Variable view
2nd – Output viewer Window – shows results of data analysis
*You must save the data editor window and output viewer window separately. Make sure to save both if you want to save your changes in data or analysis.*

7. Data view vs. Variable view
Rows are cases
Columns are variables
Variable view
Rows define the variables
Name, Type, Width, Decimals, Label, Missing, etc.
Scale – age, weight, income
Nominal – categories that cannot be ranked (ID number)
Ordinal – categories that can be ranked (level of satisfaction)

8. Entering Data
Can either enter data by hand or import from other programs (i.e. Excel)
Hand entering
Insert a variable by:
Right clicking one of the rows in variable view and selecting “Insert Variable”
Entering a “Name” in variable view and pressing “Enter” or “Tab”
Right clicking on a column in the data view and selecting “Insert Variable”
Clicking on the “Insert Variable” icon in the Toolbar
Clicking on “Data”  “Insert Variable”

9. Entering Data Define variables in variable view
Name = Name of variable displayed in data view
Type = Numeric, Comma, Dot, Scientific notation, Date, Dollar, Custom currency, & String
Width = # of digits displayed in data view
Decimals = # of decimal places displayed in data view
Label = Name of variable displayed when running analyses
Values = Value Labels – i.e. 1 = Male, 0 = Female
Missing = Values that the system will recognize as missing
Columns = # of columns used to display variable in data view
Align = variable left, right, or center aligned
Measure = scale on which variable is measured – Nominal, Ordinal, or Scale (Interval or Ratio)

10. Entering Data Importing Data Click “File”  “Open”  “Data”
Select the file type in question
If Excel:
Make sure top row of excel file lists variable names & the variables all have different names
After selecting the file, click Enter – make sure the box “Read variable names from the first row of data” is clicked
Make sure you variable are defined properly in the variable view

11. Menus File & Edit Menus View Menu
Exactly the same as all Windows programs
View Menu
Allows you to customize the SPSS desktop
Status Bar – “Processor Area” at the very bottom of the screen
Toolbars
Fonts
Grid Lines
Value Labels – Make sure this is selected if you want to use them
Variables/Data view

12. Menus Data Menu Define Dates… = Inserts a Date variable
Insert Variable
Insert Case
Go to Case…
Sort Cases… = Ascending or descending order
Transpose… = Switches cases and variables (former in columns and latter in rows)
Merge Files – More on this later
Split Files – More on this later
Select Cases = If condition is satisfied, Random sample of cases, Based on time or case range, Use filter variable

13. Splitting and Merging Files
Click on “Organize output by groups” – grouping variable should be discrete (i.e. gender, hair color, etc.)
Click on grouping variable and move to “Groups Based on” box
Click “OK”
Merging
You can add either variables or cases
If adding variables:
Make sure both files share at least one variable that is identical, the key variable (i.e. SubID)
Make sure both files are sorted by this variable
Make sure, in both files, all cases have data for this variable and there are no duplicate cases
Click on “Merge Files”  “Add Variables”
Find the file you wish to merge with the one you have open
The variable in the “Excluded Variables” box should be the key variable, denoted by a (+) indicating its presence in both files
Click on “Match cases on key variables in sorted files”
Move the key variable to the “Key Variables” box

14. Menus Transform Menu Compute... Recode – Into Same/Different Variable
Name new variable in “Target Variable” box
Type equation in “Numeric Expression” box
Recode – Into Same/Different Variable
Select variable(s) to recode and move to the “Variables” box
Click “Old and New Values”
Click “OK”

15. Obtaining Descriptive Statistics
Click on “Analyze”  “Descriptive Statistics” 
Frequencies
Use to determine counts on values of variables
Cut scores and %iles

16. SPSS Output for Frequency Distribution

17. Relative Frequency Distribution
Relative Frequency Distribution of IQ for Two Classes
IQ Frequency Percent Valid Percent Cumulative Percent
Total

18. Grouped Relative Frequency Distribution
Relative Frequency Distribution of IQ for Two Classes
IQ Frequency Percent Cumulative Percent
80 –
90 –
100 –
110 –
120 –
130 –
140 –
150 and over
Total

19. Descriptives Click on “Analyze”  “Descriptive Statistics” 
Use to get descriptive statistics (central tendency, variability, etc.)
Use to convert variables to z-scores

20. Explore Click on “Analyze”  “Descriptive Statistics”  Explore
Use to examine descriptive statistics by grouping variable

21. Explore

22. Explore

23. Explore

24. Cleaning your data – missing data
There are two types of missing values in SPSS: system-missing and user-defined.
System-missing data is assigned by SPSS when a function cannot be performed.
For example, dividing a number by zero. SPSS indicates that a value is system-missing by one period in the data cell.

25. Cleaning your data – missing data
User-defined missing data are values that the researcher can tell SPSS to recognize as missing. For example, 9999 is a common user-defined missing value. To define a variable’s user-defined missing value…
Look at your variables in VARIABLE VIEW
Find the column labeled MISSING
Find the variable that you would like to work with.
Select that variable’s missing cell by clicking on the gray box in the right corner.
A range can also be used if you only want to use half of a scale.

26. Cleaning your data – missing data cont.
When you have missing data in your data set, you can fill in the missing data with surrounding information so it does not affect your analysis.
click TRANSFORM
click REPLACE MISSING VALUES
select the variable with missing values and move it to the right using the arrow
SPSS will rename and create a new variable with your filled in data.
click METHOD to select what type of method you would like SPSS to use when replacing missing values.
click OK and view your new data in data view

27. Graphing Data Click GRAPH Click CHART BUILDER Click HISTOGRAM
Put MEN on the X axis.
Click ELEMENT PROPERTIES. Check the box labeled DISPLAY NORMAL CURVE. This will impose a normal curve onto your graph. You can also change the style of your graph in this element properties window.
You can copy and paste these graphs into word and excel files.

28. Graphing Continued There are other ways to make graphs. Click ANALYZE
Click DESCRIPTIVE STATISTICS
Click FREQUENCIES
Click services
Click CHART
Click BAR CHART
Click PERCENTAGES

29. Data manipulation – select cases
By selecting cases, the researcher can select only certain cases for analysis
click DATA
click SELECT CASES
click RANDOM SAMPLE OF CASES
select your preferences

30. Data manipulation – compute new variable
Computing new variables – create a new variable from multiple variables
click TRANSFORM
click COMPUTE
fill in the new target variable TOTALSALES
fill in numeric expression = men+women+jewel
create an IF statement by clicking on the IF button
click INCLUDE IF CASE SATISFIES CONDITION
enter condition MAIL>10000
This new variable TOTALSALES tells us what the total sales are for catalogs which mailed over 10,000 catalogs.

31. Mean Class A--IQs of 13 Students 102 115 128 109 131 89 98 106 140 119
110
Class B--IQs of 13 Students
109
Σ Yi = Σ Yi = 1433
Y-barA = Σ Yi = 1437 = Y-barB = Σ Yi = 1433 =
n n

32. Mean The mean is the “balance point.”
Each person’s score is like 1 pound placed at the score’s position on a see-saw. Below, on a 200 cm see-saw, the mean equals 110, the place on the see-saw where a fulcrum finds balance:
1 lb at 93 cm
1 lb at 106 cm
1 lb at 131 cm
110 cm
units below
21 units above
4 units below
0 units
The scale is balanced because…
on the left = on the right

33. Mean Bill Gates All of Us Outlier Mean
Means can be badly affected by outliers (data points with extreme values unlike the rest)
Outliers can make the mean a bad measure of central tendency or common experience
Income in the U.S.
Bill Gates
All of Us
Outlier
Mean

34. Median
The middle value when a variable’s values are ranked in order; the point that divides a distribution into two equal halves.
When data are listed in order, the median is the point at which 50% of the cases are above and 50% below it.
The 50th percentile.

35. Median Median = 109 (six cases above, six below)
Class A--IQs of 13 Students 89 93 97 98
102
106
109
110
115
119
128
Median = 109
(six cases above, six below)

36. Median
If the first student were to drop out of Class A, there would be a new median: 89 93 97 98
102
106
109
110
115
119
128
131
140
Median = 109.5
= 219/2 = 109.5
(six cases above, six below)

37. Median All of Us Bill Gates outlier
The median is unaffected by outliers, making it a better measure of central tendency, better describing the “typical person” than the mean when data are skewed.
All of Us
Bill Gates
outlier

38. Median Mean Mean Median Median
If the recorded values for a variable form a symmetric distribution, the median and mean are identical.
In skewed data, the mean lies further toward the skew than the median.
Symmetric
Skewed
Mean
Mean
Median
Median

39. Median
The middle score or measurement in a set of ranked scores or measurements; the point that divides a distribution into two equal halves.
Data are listed in order—the median is the point at which 50% of the cases are above and 50% below.
The 50th percentile.

40. Mode A la mode!! The most common data point is called the mode.
The combined IQ scores for Classes A & B:
BTW, It is possible to have more than one mode!
A la mode!!

41. Mode It may mot be at the center of a distribution.
Data distribution on the right is “bimodal” (even statistics can be open-minded)

42. Mode Mean Median Mode Mode Median Mean
It may give you the most likely experience rather than the “typical” or “central” experience.
In symmetric distributions, the mean, median, and mode are the same.
In skewed data, the mean and median lie further toward the skew than the mode.
Symmetric
Skewed
Mean
Median
Mode
Mode
Median
Mean

43. Descriptive Statistics
Summarizing Data:
Central Tendency (or Groups’ “Middle Values”)
Mean
Median
Mode
Variation (or Summary of Differences Within Groups)
Range
Interquartile Range
Variance
Standard Deviation

44. Data manipulation – recode a variable
Recoding allows a researcher to create a new variable with a different set of parameters
click TRANSFORM
click RECODE INTO DIFFERENT VARIABLE
move mail over to the right
create a name for the new variable mailcategories
click OLD AND NEW VALUES

45. Other Resources
There are many resources online to help you learn SPSS (tutorials, blogs, etc.)
CSSCR has a Quicktime SPSS class on its website
CSSCR offers SPSS handouts which are also on its website
CSSCR offers classes on SPSS each quarter – come back for the SPSS Beyond the Basics class!