fundamental to spss
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8/12/2019 Fundamental to SPSS
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INTRODUCTION
DATA VIEW
VARIABLE VIEW
SCALING
OUTPUT WINDOW
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SPSS stands for “Statistical Package for the Social Science” which was latterchanged to “ Statistical Product and Service Solution” and now just SPSS
representing a Chicago based firm which owns this statistical package
This software was initially created by three Stanford graduate student in 1960’s
Key features of SPSS are
◦ More comprehensive then windows
◦ More user friendly then SAS
Key steps to open SPSS window
STARTALL
PROGRAMS
MENU
FOLDER
SPSS 14.0FOR
WINDOWS
OPEN NEWWINDOW
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Menu Commands
Toolbar icons
Subject or case
number
Click here to
name first variable
Empty data cells
Scroll bars
Data view, variable
view tabs
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File: Deals with different functions associated with files including opening, reading,
saving and exiting SPSS
Edit: A number of editing functions including copying, pasting, finding and replacing View: Several options that affect ways of screen appears, most frequently used is
Value label
Data: Operations related to defining, configuring, entering data also deals in sorting
cases, merging or aggregating files and selecting or weighting cases
Transform: Transformation of previously entered data include recording, computing,
new variables, reordering and dealing with missing values
Analyze: All forms of data analysis like tables, t-test, regression, reports, compare
means, etc.
Graphs: creation of graphs or charts with this option
Utilities: Utilities deals largely with fairly sophisticated ways of making complex data
operations easier
Add-ons: Option to buy advance software and add to basic software
Windows: Deals with the position, status and format of open windows
Helps: Include search capabilities, tutorials and statistics coach
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Specification of
each variable
Name
Type
Width
Decimal
Label
Value
Missing
Columns
Align
Measure orscale
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Three type of scale are prominently used in SPSS which are mentioned below
Scale: Scale have intrinsic numeric meaning that allow typical mathematical
manipulations. Key types are years, quizzes, final score, etc.◦ For Example, age is a scale variable: 16 is twice as much as 8
Ordinal: Measures have intrinsic order but mathematical manipulation are typically
meaningless
◦ Key example like male/female, true/ false, pass/fail
Nominal: Measures used for identification but have no intrinsic (lesser to greater)
order such as ethnicity, marital status, and alphabetical strings variables
◦ Nominal data is used for categorization but not used for analysis