1. Introduction to Statistical Software Package
l Chapter 4 l
Introduction to Statistical Software Package
4.1 Data Input
4.2 Data Editor
4.3 Data Computation and
Transformation

2. Introduction to Statistical Software Package
The statistical software package used – SPSS (Statistical Package for the Social Sciences) v. 21/v. 22
Sophisticated software used by social scientists and related professional for statistical analysis.
Compatible with the Windows environment.
Sample average
Population average
“X-bar”
“mu”

3. 4.1 Data Input Defining variables (in Variable view): Variable names
Labels (Variable and value)
Missing values
Variable types
Column format
Measurement level 2 5 10 15 20
Defects per lot
Frequency (lots)
Average is 5.1
defects per lot

4. 4.1 Data Input Variable names 5+7 2
Must begin with letter. The remaining can be letter, digit, period or the #, _ or $.
Cannot end with period or underscore
Blanks and special characters cannot be used (E.g.: !, ?, ‘ and *)
Must be unique; Duplication is not allowed
The length in one variable cannot exceed 64 characters
Reserved keywords cannot be used as variable names (E.g.: ALL, AND, BY, EQ, GE, LE, LT, NE, NOT, OR, TO and WITH)
Are not case sensitive
5+7 2

5. 4.1 Data Input
Labels
Variable label is a full description of the variable name
Optional
It has a value label that can be in alphanumeric and numeric code
Alphanumeric code
Numeric code

6. 4.1 Data Input Missing values Variable types
How to dealt with missing data – Leave the cell blank or assign missing value codes
Rules for assigning missing value codes:
Must be the same data type as they represent (E.g.: Missing numeric data must use numeric missing value code)
Missing value code cannot occur as data in the data set
By default, the choice of digit is usually 9
Variable types
By default, SPSS assumes all new variables are numeric with two decimal places.
Can be changed to other type (date, currency, etc) and have different number of decimal
Data
Ranks
Rank of median
= (1+8)/2 = 4.5
6 4 9
Median
Average

7. 4.1 Data Input Column format Measurement level
It is possible to adjust the width of the Data Editor columns and change the alignment of data in the column (left, centre or right)
Measurement level
Can be specified as nominal, ordinal, interval or ratio 5
-20%
-10% 0%
Percent change at opening
Frequency
Average = -8.2%
Median = -8.6%

8. 4.2 Data Editor
Specifically, in editing and viewing data and result in SPSS, it has seven different types of windows:
Data editor (Our focus)
Viewer and draft viewer (Output)
Pivot table editor
Chart editor
Text output editor
Syntax editor
Script editor 10 20 30 40 50 $0
$100,000
$200,000
Income
Average = $38,710
Median = $27,216
Frequency

9. 4.2 Data Editor
Mode
Data view

10. 4.2 Data Editor Variable view Average, median, and mode are identical
in the case of a normal distribution

11. 4.2 Data Editor Menu-driven and has variety of pull-down menus.
The main menu bar contains 12 menu:
Graphs*
File
Utilities
Edit
Add-ons
View
Windows
Data
Help
Transform
*Available in all windows (Easier to generate new output)
Analyze*
Direct marketing
Average
Median
Mode

12. 4.2 Data Editor Toolbar Average Median Mode Variable File open
File print
Undo
Go to case
Find
Insert variable
Weight cases
Value labels
Show all variables
Average
Median
Mode
File save
Dialog recall
Redo
Go to variable
Insert cases
Split file
Select cases
Use sets
Spell check
Run descriptive statistics

13. 4.2 Data Editor Exercise Enter the following cases: Average Median
Mode

14. 4.3 Data Computation and Transformation
Data screening
Objectives:
Making sure that data have been entered correctly
Making sure that the distributions are normal
The importance of data screening
May affect the validity of the results that are produced
May assist in deciding; the need of data transformation, the usage of nonparametric techniques
This subtopic will be demonstrated using data of work3.sav
Average
Median
Mode

15. 4.3 Data Computation and Transformation
Errors in data entry
Error in data entry are common and therefore data files must be carefully screened
Use Frequencies or Descriptive command
Output example:
Average
Median
Mode

16. 4.3 Data Computation and Transformation
Assessing normality
The assumption of normality is prerequisite of many inferential statistical techniques
Normality can be explored using a combined assessment of graphic:
Histogram
Boxplot
Normal probability plot
Detrended normal plot
And normality test:
Kolmogorov-Smirnov and Shapiro-Wilk statistics
Skewness
Kurtosis
Average
Median
Mode

17. 4.3 Data Computation and Transformation
Assessing normality
Performed using the Explore command in Descriptive Statistics under Analyze
Histogram
Need to be Bell
shaped /symmetry
Average
Median
Mode

18. 4.3 Data Computation and Transformation
Assessing normality
Boxplot
Median should be
at the center of the
body (symmetric)
Average
Median
Mode

19. 4.3 Data Computation and Transformation
Assessing normality
Normal probability plot
Plot should fall more
or less in a straight line
Average
Median
Mode

20. 4.3 Data Computation and Transformation
Assessing normality
Detrended normal plot
There should be no
pattern to the
clustering of points
(The point should
assemble around
a horizontal line
through zero)
Average
Median
Mode

21. 4.3 Data Computation and Transformation
Assessing normality
Kolmogorov-Smirnov and Shapiro-Wilk statistics
Kolmogorov-Smirnov
Sample size
Shapiro-Wilk
Sample size < 100
If the significance level (Sig.) is greater than 0.05, normality is assumed
Average
Median
Mode

22. 4.3 Data Computation and Transformation
Assessing normality
Skewness and Kurtosis
Their value need to be close
to zero to be assumed normal
Basically, the data of age can be assumed normal
There is no need for transformation
Data can be analyzed using parametric test
Average
Median
Mode

23. 4.3 Data Computation and Transformation
Variable transformation
The decision to transform variables depends on the severity of the departure from normality
Most appropriate transformation method need to be selected*
*Suggested by Tabachnick and Fidell (2007) and Howell (2007)
C = A constant added to each score so that the smallest score is 1
K = A constant from which each score is subtracted so that the smallest score is 1; usually equal
to the largest score + 1
Data distribution
Transformation method [Num. Expression]
Moderately positive skewness
Square-Root [SQRT(X)]
Substantially positive skewness
Log 10 [LG10(X)]
(with zero values)
Log 10 [LG10 (X + C)
Moderately negative skewness
Square-Root [SQRT(K - X)]
Substantially negative skewness
Log 10 [LG10(K - X)]
Average
Median
Mode

24. 4.3 Data Computation and Transformation
To illustrate the process of transformation, the hours of exercise (hourex) will was examined using the preceding steps.
Variable transformation
The decision to transform variables depends on the severity of the departure from normality
Most appropriate transformation method need to be selected*
*Suggested by Tabachnick and Fidell (2007) and Howell (2007)
C = A constant added to each score so that the smallest score is 1
K = A constant from which each score is subtracted so that the smallest score is 1; usually equal
to the largest score + 1
Average
Median
Mode

25. 4.3 Data Computation and Transformation
It is obvious that variable hours of exercise (hourex) is not normal. Variable transformation need to be performed
Based on previous assessment, we know that variable hourex is not normally distributed but is significantly positively skewed and because of the is no zero value involved, Log 10 transformation method should be performed.
Variable transformation
The decision to transform variables depends on the severity of the departure from normality
Most appropriate transformation method need to be selected*
*Suggested by Tabachnick and Fidell (2007) and Howell (2007)
C = A constant added to each score so that the smallest score is 1
K = A constant from which each score is subtracted so that the smallest score is 1; usually equal
to the largest score + 1
Average
Median
Mode

26. 4.3 Data Computation and Transformation
Results
Variable transformation
The decision to transform variables depends on the severity of the departure from normality
Most appropriate transformation method need to be selected*
*Suggested by Tabachnick and Fidell (2007) and Howell (2007)
C = A constant added to each score so that the smallest score is 1
K = A constant from which each score is subtracted so that the smallest score is 1; usually equal
to the largest score + 1
Average
Median
Mode
It is apparent that Log 10 transformation was appropriate because the distribution of hourex is now relatively normal

27. 4.3 Data Computation and Transformation
Data transformation
Recoding
Collapsing continuous variables into categorical variables
Recoding negatively worded scale items
Replacing missing values and bringing outlying cases into the distribution
Computing
To obtain composite scores for items on a scale
Data selection
In the Select Cases option in the Data menu, data can be selected:
On specified cases using If option
Randomly using the Sample option
Based on time or case range using the Range option
Variable transformation
The decision to transform variables depends on the severity of the departure from normality
Most appropriate transformation method need to be selected*
*Suggested by Tabachnick and Fidell (2007) and Howell (2007)
C = A constant added to each score so that the smallest score is 1
K = A constant from which each score is subtracted so that the smallest score is 1; usually equal
to the largest score + 1
Average
Median
Mode